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A series of compounds based on the dipeptidyl nitrile scaffold were synthesized and assayed for their inhibitory activity against the T . cruzi cysteine protease cruzain . Structure activity relationships ( SARs ) were established using three , eleven and twelve variations respectively at the P1 , P2 and P3 positions . A Ki value of 16 nM was observed for the most potent of these inhibitors which reflects a degree of non-additivity in the SAR . An X-ray crystal structure was determined for the ligand-protein complex for the structural prototype for the series . Twenty three inhibitors were also evaluated for their anti-trypanosomal effects and an EC50 value of 28 μM was observed for the most potent of these . Although there remains scope for further optimization , the knowledge gained from this study is also transferable to the design of cruzain inhibitors based on warheads other than nitrile as well as alternative scaffolds .
Chagas disease is caused by the protozoan parasite Trypanosoma cruzi , which is transmitted by blood-sucking reduviid bugs of the subfamily Triatominae [1–3] . Also known as American trypanosomiasis , Chagas disease remains a serious public health problem in Latin America [4 , 5] and its spread from non-endemic countries represents an emerging worldwide challenge[6] . While some acute cases may be treated with benznidazole or nifurtimox , both of which show poor side effect profiles [7 , 8] , there is currently no effective therapy for chronic cases [9 , 10] . This has led to the study of many new macromolecular targets and collaborative efforts worldwide , including initiatives such as Drugs for Neglected Diseases ( DNDi ) [11] . Detection of the parasite remains challenging in the chronic phase of the disease although highly sensitive in vivo imaging that allows parasite burden to be monitored in real time in murine disease models has been reported recently[12] . The cysteine protease cruzain is considered to be an attractive target for therapeutic intervention in the treatment of Chagas disease [10 , 13–17] and the vinyl sulfone K777 ( 1; Fig 1 ) has been developed as an irreversible inhibitor of this enzyme [15] . The human enzymes most closely related to cruzain are the cathepsins , in particular cathepsin L , and these have been targeted ( Fig 1 ) using the nitrile ‘warhead’ to form a covalent bond with the catalytic cysteine in a reversible manner [18–23] . The cathepsin K inhibitor Odanacatib ( 2; Fig 1 ) [20] has been evaluated extensively as a treatment for osteoporosis [24 , 25] , demonstrating that the presence of the electrophilic nitrile [26] in a molecular structure is not necessarily incompatible with achieving pharmacokinetic and toxicological profiles that are compatible with dosing in humans . Structural analogs of 2 have been shown [27] to be potent cruzain inhibitors and in vivo activity has been reported for 3 and 4 ( Fig 1 ) in a murine model of acute T . cruzi infection [28] . Nitrile-based cysteine protease inhibitors targeted at rhodesain have also been shown to inhibit cruzain [29] and a library of cathepsin inhibitors has been used as a source of antiparasitic leads [30] . In the molecular design context , formation of a covalent bond between ligand and target can enable relatively flat or even convex regions on the protein surface to be exploited [31] . The molecular surface of cruzain at the catalytic cysteine is saddle-shaped ( i . e . concave in one direction and convex in another ) and would not be considered ideal for forming non-covalent interactions . Although it is sometimes assumed that covalently-bound inhibitors are necessarily non-specific , it is important to remember that these ligands also form non-covalent interactions with their target proteins that may modulate affinity . A number of marketed drugs form covalent bonds with their targets and in many cases binding appears to be irreversible [31 , 32] . Binding that is irreversible is likely to be advantageous when long residence times [33] are required or if the therapeutic effect depends on inhibition of more than a single target . Potential for immunogenicity [34] is frequently a concern for covalently-bound drugs although a compound that binds irreversibly to intact target may still readily dissociate once the protein has been degraded prior to fragment peptides being presented on MHC class II molecules . The design objectives of this study were to explore and map structure-activity relationships ( SARs ) for dipeptidyl nitrile inhibitors of cruzain and to evaluate compounds based on this scaffold for their trypanocidal activity . The dipeptidyl nitrile scaffold , as exemplified by 5 ( Fig 1 ) , was adopted for this study since it is an established [22] starting point for synthesis of Papain-like cysteine protease inhibitors and the P2 and P3 substituents can be varied easily using readily available synthetic building blocks . This study builds on previous work by the NEQUIMED team [35] and the molecular design in this study may be regarded as being more hypothesis-driven [36 , 37] than prediction-driven in that compounds were selected for synthesis on the basis of their potential to provide information because they had been predicted to be active using quantitative models . It is important to stress that medicinal chemistry design is not just about making predictions and the first step in building a predictive model is to assemble relevant and informative data with which to train the model .
The Ethics Committee on Animal Experimentation of the Faculty of Pharmacy of Ribeirao Preto–University of Sao Paulo , approved the cytotoxicity assays ( approval no . 010263/2014 ) . This Committee adheres to Conselho Nacional de Controle de Experimentação Animal–CONCEA , created by Brazilian Law number 11794 of 8 October 2008 . Assays were run according to the guidelines of the Ministry of Science , Technology and Innovation of Brazil . The Biosafety Committee of the Faculty of Pharmacy of Ribeirao Preto–University of Sao Paulo , also approved the use of genetic modified organisms ( approval no . 0019–17 ) . 1H and 13C NMR spectra were recorded on HP – 400 and 500 MHz instruments in CDCl3 or ( CD3 ) 2SO with TMS as internal standard . High resolution mass spectra ( HRMS ) were conducted on a QqTOF Bruker Daltonics spectrometer under the conditions of electrospray ionization ( ES ) , using positive ionization . Infrared spectra were obtained from FT-IR Bomen Hartman & Braun mod MB-102 . Melting points were determined on a Quimica Micro MQAPF-302 apparatus and are uncorrected . Thin layer chromatography was performed on Fluka Analytical Sigma-Aldrich silica gel matrix , pre-coated plates with fluorescent indicator 254 nm and/or iodine vapors for detection of amines . Flash column chromatography was performed on silica gel ( pore size 60 Å , 70−230 mesh ) and eluent hexane/ethyl acetate . Method development for the characterization and separation of compounds was carried out with a HPLC system consisting of a Shimadzu CBM-20A , degasser DGU-20A5 , LC-20AT pump , a SIL-20A HT autosampler using a 2000 μL sample loop , a CTO-20A column oven , SPD-M20A detector , and a FRC 10A fraction collector . The detector was set at 200–800 nm . The system was controlled and data analyses were performed using the LC solutions software . Two HPLC protocols were used in this study and , in each case , solvents were filtered through a 0 . 45 μm Merck-Millipore filter before use and degassed in an ultrasonic bath . In Protocol A , chiral analysis and separation was carried out at 35°C ( column oven ) , using a part of analytical and semi-preparative cellulose-2 Phenomenex column ( Analytical: 5 μm , 250 mm x 4 . 6 mm I . D , semi-preparative: 5 μm , 250 mm x 10 mm I . D ) , by isocratic elution with a flow rate of 0 . 5 ( analytical ) and 2 . 36 mL min-1 ( semi-preparative ) . The mobile phase composition was acetonitrile-water ( 60:40 ) ( v/v ) . Volumes of 10 μL ( analytical ) and 1000 μL ( semi-preparative ) were injected . Quantification was carried out at 200–800 nm and the chromatographic run time was 20 min . In Protocol B , purity analysis and separation were carried out at 35°C ( column oven ) using an analytical column Luna c8 ( 10 μm , 150 mm x 4 . 6 mm I . D ) , by isocratic elution with a flow rate of 0 . 5 mL min-1 . The mobile aqueous phase composition was 50 to 70% of methanol . All solvents were filtered through a 0 . 45 μm Merck-Millipore filter before use and degassed in an ultrasonic bath . Volumes of 10 μL to 100 μL were injected . Quantification was performed by measuring sample absorbance at 200–800 nm . The chromatographic run time was 20 min . The cruzain inhibitors described in this study were prepared using one of the two routes summarized in Fig 2 . Structural variations at the P1 , P2 and P3 positions are shown in Fig 3 and the structures of the inhibitors are defined in Table 1 . Route A ( Fig 2 ) is exemplified by the synthesis of 5 . Compounds 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 20 , 21 , 22 , 28 , 29 , 30 , 32 , 34 , and 37 were prepared in a similar manner . Specific rotations ( [α]T = α/lc , in deg mL g−1 dm−1 , but reported herein in degrees ) were observed at the wavelength 589 nm , the D line of a sodium lamp . T was set to be 24°C . Samples were weighed from 0 . 2 to 7 . 5 mg using a precision balance ( Sartorius , Model CPA26P ) and were fully dissolved in methanol ( HPLC grade , Panreac ) or dimethyl sulfoxide ( Sigma Aldrich ) for those that were not completely soluble in methanol . The rotations were measured using a Digital Polarimeter ( P2000 , Jasco ) : α = observed rotation in degrees; l = cell path length of 1 decimeter; a standard polarimeter tube of 1 . 0 dm in length; c = concentration in g mL-1 . Values were calculated using 5 measurements for each compound . The recombinant cruzain was expressed and initially purified as recently described by Lee et al . [39] . The 0 . 5 mg mL-1 solution of procruzain [in 100 mM sodium acetate buffer ( pH 5 . 2 ) , 300 mM NaCl] obtained after affinity chromatography on HisTrapFF crude nickel columns ( GE Healthcare LifeSciences ) , was activated by incubation with 5 mM DTT at 37°C water bath during 2 hours . Active cruzain was inhibited with molar excess of compound 5 ( dissolved in DMSO ) to prevent degradation due self-proteolysis . The lack of proteolytic activity was confirmed via fluorometric assay against the substrate Z-Phe-Arg-AMC ( Bachem , Km = 1 μM ) . The protein was dialyzed overnight at 4°C in 50 mM Tris ( pH 7 . 5 ) 300 mM NaCl buffer and concentrated to 3 mg mL-1 for purification using superdex 200 ( 10/300 ) size exclusion chromatography column ( GE Healthcare LifeSciences ) . The fractions with pure cruzain inhibited with compound 5 ( highest peak at 34 min . ) eluted in 50 mM Tris ( pH 7 . 5 ) 300 mM NaCl buffer , were collected , concentrated to 10 mg mL-1 , buffer exchanged in 2 mM Bis-Tris pH 5 . 8 , followed by the overnight incubation with 2 . 5 mM of compound 5 ( at least 5 times more inhibitor than protein in molar ratio , and 3 . 5% of DMSO on final protein solution ) . Hanging drops encompassing 576 crystallographic conditions [Joint Center for Structural Genomics ( JCSG ) screens , I to IV , anions suite and cations suite] were configured using Mosquito Nanoliter Dropsetter ( TTP Labtech ) . Each condition was screened in 1:1 and 2:1 ration between protein solution and mother liquor . Crystals of maximum size were obtained after one month from a precipitating agent of 0 . 1 M HEPES , 1 . 2 M K/Na tartrate at pH 7 . 5 . Crystals were flash-cooled in liquid nitrogen after soak in 25% ethylene glycol . Diffraction was measured at beamline 8 . 3 . 1 of the Advanced Light Source ( ALS , Lawrence Berkeley Lab , CA ) under low temperature conditions ( 100 K ) , using Elves[40] to determine the data collection strategy . The crystals obtained were fragile with lower diffraction power and very sensitive to radiation damage . 230 frames were collected with 2 seconds of exposition time and 0 . 4° of oscillation between frames . Reflections were indexed , integrated and scaled using XDS package [41] . The initial phasing model without water , ligand and heteroatoms used for molecular replacement in Phaser [42] was prepared from the model ( PDB entry 3KKU ) . 5 chains by Asymmetric Unit ( ASU ) related by non-crystallographic symmetry ( NCS ) were identified in Phaser with top LLG of 5021 and top TFZ of 44 . Phenix Refine [43] and Coot [44] were used for all steps of structure refinement and interactive model building . The model was positioned initially by rigid body refinement and subjected to torsional NCS , secondary structure restraints followed by multiple cycles of individual coordinate and B-factors refinement . The inhibitor molecule ( compound 5 ) was manually placed and fit to electron density of chain A using Coot . Clear and representative density for the entirety inhibitor was observed at better than 1 . 5 σ above the noise level . Chain B did not have clear electron density for the inhibitor at the region of the covalent attachment with the catalytic Cys25 , and to avoid over-interpretation the inhibitor it was not built in this chain . For chains C , D and E that are less exposed to solvent region , weak and no clear electron density was observed for the inhibitor . B-factors were initially refined isotropically and latter subject to TLS . The geometry and the structure were assessed using Molprobity [45] . There was no outlier in the Ramachandran statistics , with 97 . 3% of all residues on favored regions . The data collection and refinement statistics are provided in Table 2 . Structural coordinates and observed structure factor amplitudes were deposited in the Protein Data Bank under accession code 4QH6 . Enzyme kinetic assays were carried out at 37°C in 200 L of a solution containing 100 mM acetate buffer pH 5 . 5 , 300 mM NaCl , 5 mM DTT ( dithiothreitol ) , 5% v/v DMSO ( dimethyl sulfoxide ) , 0 . 01% v/v Triton X-100 and 0 . 15 nM cruzain , using Corning® 96-well black flat bottom microplates . The rate of the reaction was monitored using a Biotek Synergy HT plate reader through the fluorescence emission at 460 nm ( excitation at 355 nm ) due to the hydrolysis of the substrate Z-Phe-Arg-7-amido-4-methylcoumarin ( Z-FR-AMC , Sigma-Aldrich ) . The enzyme stock aliquot was rapidly thawed at 37°C and kept on ice until activation , in which it was incubated for 20 min . in the assay buffer ( 100 mM acetate pH 5 . 5 and 5 mM DTT ) followed by additional 2 min . incubation with inhibitors before the reaction was started by the addition of the substrate . Visual inspection and a pre-reading of plate wells were performed to check for possible precipitation and background fluorescence , respectively . Fluorescence emission spectra were also recorded for all inhibitors , using the same excitation wavelength ( 355 nm ) as for the fluorometric assay . None of the compounds displayed a significant fluorescence signal around 460 nm , the emission wavelength used to monitor the reaction kinetic . Thus , potential inner-filter effects did not have to be taken into account in our experiments . The reaction was started by the addition of varying concentrations of the substrate and the wells monitored for a total of 5 minutes of reaction . The initial velocities of the substrate hydrolysis under first-order reaction were calculated by Gen5 Biotek software based on the linear-regression coefficient from the fit to data of Relative Fluorescence Unit ( mRFU ) as a function of time ( min ) . Each experiment was performed in duplicates for each compound being tested . A control measurement in absence of inhibitor ( KM ) was carried out for each setup plate and the kinetic affinity constants ( Ki ) were calculated from non-linear fit of Michaelis-Menten curves to the data of initial velocities as a function of eight different concentrations of the substrate Z-FR-AMC from 30 . 0–0 . 8 μM . All inhibitors were evaluated in two different concentrations , which were chosen based on a previous screening ( percentage of inhibition ) at 1 . 7 μM substrate ( ~KM ) . Additionally , the affinity constant for compound 5 and for the three most potent inhibitors of the series were calculated using three different concentrations . SigmaPlot ( v . 10 . 0 ) was employed for the non-linear fit and the kinetic parameters determination . Reversibility of compound 5 was tested by measuring the recovery of enzymatic activity after a rapid and large dilution of the enzyme–inhibitor complex . Compound was incubated at 4 . 5 μM ( 10-fold the IC50 ) with 15 nM cruzain ( 100-fold the concentration required for the activity assay ) for 30 min and this mixture is diluted 100-fold into the reaction buffer containing the enzyme substrate to initiate reaction in same conditions as used for Ki determination with 1 . 7 μM of Z-FR-AMC . The progress curve for this sample was then measured and compared to that of a similar sample of enzyme incubated and diluted in the absence of inhibitor . The irreversible inhibitor E-64 ( CAS 66701-25-5 , Sigma Aldrich ) was used as reference . The assays against amastigote forms were performed in LLCMK2 cells . Previously , the cells were cultivated in 96-wells microplates ( 103 cells/well ) . After 2 hours the CL Brener ( B5 clone ) or Tulahuen trypomastigotes were added in a 1:10 ratio and incubated for another 2 hours . Then , the wells were washed with PBS to remove the extracellular parasites and the tested compounds were added in the final concentrations of 0 . 031 , 0 . 125 , 0 . 5 , 2 . 0 , 8 . 0 , 32 . 0 , 128 and 512 μM . After this , the microplates were incubated at 37°C , in CO2 atmosphere , for 5 days . Then , 10 μL of FluoReporter lacZ/Galactosidase Quantitative Kit ( Life Technologies ) were added and after 30 minutes the fluorometric reaction was read in a microplate reader ( Synergy H1 , Biotek ) at 386 nm excitation and 448 nm emission . For both assays the percentage of parasite lysis was determined from the following formula: %lysis = 100 –{[ ( X-PC ) / ( PC-NC ) ]x100} , where the optical density values of the samples ( X ) , the positive controls ( PC ) and negative controls ( NC ) were used . Culture medium was used as positive control ( PC ) and medium with 0 . 6% DMSO ( v/v ) as negative control ( NC ) . All assays were performed in triplicate with two independent experiments . All compounds were subjected to the MTT colorimetric assay using Balb-c 3T3 clone A31 cells ( mouse fibroblast cells ) acquired from the Rio de Janeiro Cell Bank ( BCRJ code 0047 ) . Inhibition values were determined in the cell-based assay as previously described [46] . Briefly , cells were cultured at 37°C in an atmosphere of 5% CO2 using DMEM medium ( Cultilab , Campinas-SP , Brazil ) supplemented with 3 . 5 g glucose ( Sigma-Aldrich ) , 1% penicillin/streptomycin solution and 10% FBS ( Cultilab ) . Cells were plated at a concentration of 105 cell/mL in 96-well plates and incubated for 24 h . All compounds were freshly diluted from 50 mM DMSO stock solutions to obtain the final concentration of 250 μM and added to each well by replacing the medium . The cell viability was assessed during 24 , 48 and 72 h using the MTT ( Sigma-Aldrich ) reagent , with an incubation time of 3 h . Formazan crystals were dissolved using a solubility reagent composed of DMSO , glacial acetic acid and extran for 1 h . The readout was obtained using Biotek Synergy HT plate reader at 570 nm . Benznidazole was used as control . This assay was done in quatruplicate in two independent experiments . Statistical analyses were made in GraphPad Prism 5 . Dunnett’s multiple comparison tests were performed using benznidazole as the standard compound for the ordinary ANOVA analysis using 95% confidence interval .
The cruzain inhibitors described in this study were prepared using two routes as summarized in Fig 2 . Some racemization of the P2 amino acid was observed when using route A and resolution of enantiomers by chiral HPLC became necessary . For this reason , the alternative route B was developed and it is particularly suitable for varying the P3 substituent . The structural variations at the P1 , P2 and P3 are shown in Fig 3 . Enzyme inhibition results are presented for the inhibitors in Table 1 . Protein crystal structures and SAR for cruzain [47 , 48] and structurally-related targets such as cathepsin L [18 , 49] provide a framework for the design described in this study . Compound 5 ( pKi = 6 . 3 ) is the structural prototype for this series and has been previously reported to inhibit a number of cysteine proteases [22] . Replacement of the methylene link between amide and nitrile with cyclopropane ( 7; pKi = 6 . 6 ) resulted in a two-fold increase in potency and a small but consistent potency difference of 0 . 3 to 0 . 4 log units was observed for five pairs of inhibitors in which the P2 substituent was either phenylalanine or leucine . The P1 cyclopropane is common to a number of cysteine protease inhibitors [19 , 20] and may confer metabolic stability by virtue of the relatively high carbon-hydrogen bond dissociation energy of cyclopropane [36] . This structural transformation has also been observed to result in a four to forty-fold increase in aqueous solubility for cathepsin K inhibitors based on a cyclohexane dicarboxamide scaffold [19 , 50] . In contrast , geminal dimethyl substitution of 5 at P1 resulted in a six-fold decrease in potency ( 8; pKi = 5 . 5 ) , which may reflect geometric differences between the two linkers . While the R-enantiomer of 5 ( 6; pKi = 5 . 2 ) is , unsurprisingly , a less potent inhibitor of the enzyme , introduction of a 3-chloro substituent to phenylalanine is associated with a two-fold increase in potency ( 9; pKi = 6 . 6 ) . Compound 36 ( pKi = 5 . 5 ) with a cyano substituent at the meta position of phenylalanine , synthesized in order to probe the backbone amide hydrogen bond donors of Met68 and Asn69 , was an order of magnitude less potent than 7 ( pKi = 6 . 6 ) . These molecular recognition elements have exploited in the design of cathepsin inhibitors [49] . Compounds with tyrosine ( 10; pKi = 6 . 7 ) , tryptophan ( 12; pKi = 6 . 4 ) and arginine ( 13; pKi = 3 . 9 ) as the P2 substituent were synthesized on the basis of their potential for interaction with Glu208 since analogous interactions have been observed in crystal structures of protein-ligand complexes [47] . Substitution of leucine for phenylalanine at P2 resulted in increases in pKi ranging from 0 . 3 to 0 . 4 units although this replacement was not actually made for the structural prototype 5 . In contrast , this substitution has been reported to lead to decreases of 0 . 3 and 0 . 7 log units respectively in pKi for 24 and 25 against Bovine cathepsin L [22] . Using 4-methyl-L-leucine as the P2 amino acid led to 32 ( pKi = 4 . 6 ) and two compounds ( 14; pKi = 5 . 1 ) and ( 37; pKi = 4 . 9 ) with cycloleucine as the P2 amino acid were 1 . 2 and 1 . 7 log units less potent respectively than their phenylalanine analogs 5 and 7 . A number of structural variations for the P3 substituent ( benzoyl in the structural prototype 5 ) were explored . The trifluoracetyl derivative 15 ( pKi = 5 . 4 ) is an order of magnitude less potent than 5 despite the greater hydrogen bond acidity of the P3 amide NH . Replacement of the P3 phenyl ring of 5 with simple heteroaromatic rings typically results in loss of potency although 16 is equipotent with the structural prototype . Even double aza substitutions ( 18; pKi = 5 . 9; 19; pKi = 5 . 8 ) are relatively well-tolerated and these observations are consistent with contact between hydrophobic protein surface and the face , rather than edge , of P3 aromatic substituent . Amino-protected synthetic intermediates 24 and 25 were , at best , equipotent in comparison with the series prototype 5 . The P3 phenyl of 5 was replaced with 3-bromopyridine to explore the possibility of halogen bond formation [21] with the backbone carbonyl of Thr59 , although the potency of 20 ( pKi = 6 . 6 ) is comparable with that of the reference , which may be interpreted as circumstantial evidence that the halogen bond does not form . Replacing the P3 phenyl of 5 with 1-methyl , 3-t-butylpyrazol-5-yl resulted in a 0 . 9 unit increase in pIC50 ( 21; pKi = 7 . 2 ) and this structural change had been observed to potency enhancement of 1 . 3 log units against cathepsin L which , like cruzain , has a leucine residue in contact with the P3 substituent [18] . A degree of non-additivity was observed in the SAR and this was quantified using differences ( ΔpKi ) in pKi values . It is important to draw a distinction between a ΔpKi value and the contribution to affinity of an intermolecular contact in a protein-ligand complex which is not , in general , an experimental observable[51] . Non-additive SAR of this nature should be anticipated whenever variable parts of the molecular structure are relatively rigid or are able to influence each other’s orientations with respect to the conserved part of the molecular structure . Fig 4 illustrates how the effects on pKi of replacing the P3 phenyl of 5 with 1-methyl , 3-t-butylpyrazol-5-yl and substituting phenylalanine at C3 with chloro are mutually reinforcing ( 28; pKi = 7 . 8 ) . Although the effect is small , it is a feature of the SAR that might be exploited , and even enhanced during further optimization of the series . The X-ray crystal structure ( PDB: 4QH6 ) determined for the complex of 5 with cruzain reveals a binding mode similar to those observed for structural analogues bound to cathepsin L [18 , 49] ( Fig 5 ) . The side chain carboxylic acid of Glu208 , which corresponds to alanine in cathepsin L , is exposed to solvent . The contact between ligand and Gln19 side chain highlights the need to account for interactions other than the covalent bond when interpreting binding affinity of ligands that bind covalently and reversibly . The ligand phenylalanine group penetrates less deeply into the S2 site than does the corresponding 3-methyphenyl group of an analog bound to cathepsin L ( Fig 6 ) . These binding modes hint at a rationale for the non-additivity observed for the effects of the 1-methyl , 3-t-butylpyrazol-5-yl and 3-chlorophenylalanine groups ( Fig 4 ) in that both groups may need to be present for effective penetration of the S2 site . The competitive nature and reversibility of inhibition was established for 5 ( Fig 7 ) and a number of the other inhibitors . A number of compounds were evaluated for their trypanocidal activity against amastigote forms of Tulahuen and CL-Brener T . cruzi strains and the results are presented in Table 3 . No clear relationship emerged between potency of inhibition of the enzyme and trypanocidal activity . A pEC50 value of 4 . 6 was observed for the most potent of these ( 12; pKi = 6 . 4 ) indicating that it is an order of magnitude less potent against the Tulahuen strain amastigote than benznidazole ( pEC50 = 5 . 8 ) . A pEC50 value of 4 . 3 was observed for 6 which is a relatively weak inhibitor of cruzain ( pKi = 5 . 20 ) and the possibility must be considered that the weak trypanocidal activity of this compound may be the result of acting on targets other than cruzain . A number of the compounds show % cell lysis at 32 μM comparable to benznidazole against the more resistant CL-Brener strain amastigotes , although this should be seen in the context of the lower activity of benznidazole against this strain . Potential cytotoxicity of inhibitors was assessed with the Balb-c 3T3 cell-based assay and compounds were evaluated over three days using benznidazole as a control . Cytotoxicity at the highest concentration tested that did not lead to precipitation ( 250 μM ) was low for most compounds . The most potent inhibitor of the amastigote T . cruzi Tulahuen strain ( 12 ) showed the same range of cytotoxicity when compared to benznidazole ( Fig 8 ) . There are a number of reasons that potency in an enzyme kinetic assay may not translate to activity in a cell-based assays . The enzyme inhibitors may be insufficiently permeable or transporter substrates and therefore subject to efflux . Even when an inhibitor accesses the appropriate intracellular compartment , it may still need to compete with high affinity substrates . It is noteworthy that inhibitors with IC50 values in the 1–2 nM range ( i . e . at least an order of magnitude more potent than the dipeptidyl nitriles described in our study ) show activity against an amastigote form of T . cruzi , that is only in the 5 to 10 micromolar range [28] . Even when the free intracellular concentration of inhibitor is sufficient to engage the proposed therapeutic target , it may still be necessary to perturb other cellular processes in order to achieve the desired phenotypic response . For example , the therapeutic benefits of sorafenib and imatinib appear to derive from the engagement with more than a single target [53] . K777 ( 1 ) has also shown efficacy in a murine schistosomiasis model [54] , demonstrating that it engages targets other than cruzain and the possibility should be considered that its anti-trypanosomal effects may be partly due to inhibition of other cysteine proteases in addition to cruzain . In conclusion , we have mapped SAR for cruzain inhibition using three , eleven and twelve variations respectively at the P1 , P2 and P3 positions of a dipeptidyl nitrile scaffold , determined the binding mode of one of the inhibitors by X-ray crystallography and demonstrated that inhibition is both competitive and reversible . The most potent compound against the parasite was 12 , for which an EC50 value of 28 μM was observed and this activity was demonstrated not to be due to general cytotoxicity of the compound . No clear relationship emerged between potency against the enzyme and trypanocidal activity and we have discussed the implications of this for the design of anti-trypanosomal agents . We have shown that the scaffold is of practical value for developing SAR for cruzain inhibitors and there remains scope for further optimization . The knowledge gained from this investigation is also transferable to the design of cruzain inhibitors based on warheads other than nitrile as well as alternative scaffolds . | Chagas disease is a parasitic infection with high morbidity and mortality that is endemic in much of Latin America where it remains a serious public health problem . With increased migration , Chagas disease represents an emerging worldwide challenge and there is an urgent , unmet need for safe and effective medication . The available drugs to treat Chagas disease may be effective in the acute phase of the disease , but efficacy in the chronic phase remains controversial . They can cause serious side effects that lead sufferers to abandon treatment . Using a hypothesis-driven approach to molecular design and drawing on cysteine protease cruzain structural information , we have mapped structure-activity relationships for a dipeptidyl nitrile scaffold and demonstrated that compounds are competitive inhibitors , bind reversibly and bear trypanocidal activity . The binding mode revealed by the crystal structure of the protein-ligand complex for one of the inhibitors shows that binding involves the formation of a covalent bond between the catalytic cysteine and the nitrile carbon . As such , we believe that our study represents a valuable step in the search for new drugs for the treatment of a neglected disease that continues to affect the lives of millions of people . | [
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The bacterial H-NS protein silences expression from sequences with higher AT-content than the host genome and is believed to buffer the fitness consequences associated with foreign gene acquisition . Loss of H-NS results in severe growth defects in Salmonella , but the underlying reasons were unclear . An experimental evolution approach was employed to determine which secondary mutations could compensate for the loss of H-NS in Salmonella . Six independently derived S . Typhimurium hns mutant strains were serially passaged for 300 generations prior to whole genome sequencing . Growth rates of all lineages dramatically improved during the course of the experiment . Each of the hns mutant lineages acquired missense mutations in the gene encoding the H-NS paralog StpA encoding a poorly understood H-NS paralog , while 5 of the mutant lineages acquired deletions in the genes encoding the Salmonella Pathogenicity Island-1 ( SPI-1 ) Type 3 secretion system critical to invoke inflammation . We further demonstrate that SPI-1 misregulation is a primary contributor to the decreased fitness in Salmonella hns mutants . Three of the lineages acquired additional loss of function mutations in the PhoPQ virulence regulatory system . Similarly passaged wild type Salmonella lineages did not acquire these mutations . The stpA missense mutations arose in the oligomerization domain and generated proteins that could compensate for the loss of H-NS to varying degrees . StpA variants most able to functionally substitute for H-NS displayed altered DNA binding and oligomerization properties that resembled those of H-NS . These findings indicate that H-NS was central to the evolution of the Salmonellae by buffering the negative fitness consequences caused by the secretion system that is the defining characteristic of the species .
Horizontal gene transfer ( HGT ) has profoundly shaped the course of bacterial speciation and diversification . The uptake of ‘pre-assembled’ genetic loci involved in antibiotic resistance , virulence , phage resistance or novel modes of metabolism can instantly confer beneficial phenotypes to the recipient cell . HGT events have been critical in the evolution of almost all bacterial pathogens from their non-pathogenic progenitors [1]–[5] . Two of the critical events when the Salmonellae diverged from their last common ancestor with E . coli were the acquisition of the Salmonella Pathogenicity Island-1 ( SPI-1 ) and the tetrathionate reductase ttr gene clusters [6]–[8] . SPI-1 is a 40 kb genomic island encoding a Type 3 Secretion System ( TTSS ) required for triggering inflammation and for invasion of cells lining the intestinal mucosa [9]–[12] . Together these systems enable Salmonella to outcompete other microbes in the mammalian gut where SPI-1 induces a potent oxidative inflammation that generates tetrathionate , which then serves as a terminal electron acceptor for anaerobic respiration that is available solely to Salmonella but not other gut microbes [7] . Despite its overall importance to bacterial evolution , any individual HGT event is more likely to reduce bacterial fitness than to improve it . Even potentially beneficial genes can disrupt regulatory networks or drain metabolic resources away from the production of energy or biomass if they are not properly regulated [13] . Indeed , studies examining the barriers to new gene acquisition found that genes expressed at high levels are much more likely to be selected against in the new host [14] , [15] . Virulence-associated genes , including those that encode secretion systems like the TTSS , can be particularly costly and are often lost in the absence of purifying selection ( e . g . virulence attenuation by laboratory passage ) [16]–[19] . For example , triggering TTSS activation from the Shigella virulence plasmid in liquid media causes the destabilization and eventual loss of the plasmid from the population [20] . The nucleoid associated protein H-NS was proposed to buffer the fitness costs associated with HGT by silencing genes with a %GC content significantly lower than the host genome average and are therefore likely to have been acquired from a foreign source [21]–[25] . H-NS confers this benefit both by counteracting transcription at standard promoters and by preventing spurious transcription within an adenine and thymine-rich ( AT-rich ) open reading frame at sequences that can adventitiously resemble a bacterial promoter [26] . H-NS exhibits low sequence specificity and targets DNA by recognizing specific structural features in the minor grove of AT-rich DNA [27] , [28] . H-NS polymerizes along target AT-rich sequences by virtue of two independent dimerization domains , leading to the formation of extended nucleoprotein filaments [29]–[32] . As a result of its activity , H-NS regulates the majority of horizontally acquired sequences in species such as E . coli , Yersinia , Shigella and Salmonella [1] , [33]–[35] . Members of the H-NS protein family are distributed between the alpha , beta and gamma proteobacteria . Functional analogues that bear minimal sequence or structural resemblance to H-NS have been identified in Pseudomonas sp . ( MvaT and MvaU ) and Mycobacteria sp . ( Lsr2 ) [36] , [37] . While global gene expression data sets from Escherchia coli ( E . coli ) , Yersinia enterolitica ( Y . enterolitica ) , Salmonella enterica Sv . Typhimurium ( S . Typhimurium ) , Pseudomonas aeruginosa ( P . aeruginosa ) and Mycobacteria smegmatis ( M . smegmatis ) point to a common role for the H-NS/MvaT/Lsr2 proteins as silencers of foreign AT-rich sequences , the fitness consequences of mutating the xenogeneic silencers among these species differs significantly [21]–[24] , [38]–[40] . In P . aeruginosa , MvaT and MvaU together are essential and depletion of both of these proteins results in the activation of the Pf4 prophage , which kills the bacterial cell [41] . In most strains of E . coli , mutations in hns mildly impede growth rates whereas failed attempts at constructing hns mutants in Y . enterolitica and Y . pseudotuberculosis strains strongly suggest hns is an essential gene in Yersinia sp . [42] , [43] . S . Typhimurium strain 14028s hns mutants are only viable if additional mutations are present in either the PhoP-PhoQ two component signaling system or the stationary phase sigma factor RpoS [22] . What remains unclear is why global H-NS mediated gene silencing is so critical for the fitness of S . Typhimurium and Y . enterolitica , but is largely dispensable to other closely related species such as E . coli . Several members of the Enterobacteriaceae including E . coli , S . Typhimurium and Shigella flexneri ( S . flexneri ) encode a second H-NS-like protein , StpA . StpA shares 53% sequence identity with H-NS as well as several functional properties , such as the ability to self-associate and bind AT-rich DNA [44]–[48] . H-NS and StpA also share a similar domain architecture exemplified by the detection of StpA/H-NS heterodimers in vivo and in vitro [49]–[52] . Global transcript analysis and ChIP-on-chip data sets indicate StpA and H-NS co-localize in E . coli and S . Typhimurium , but the loss of stpA only affects the transcript levels of a subset of these loci [47] , [48] . In fact , loss of StpA alone does not generate observable phenotypes but will further impair the fitness of strains lacking H-NS [45] , [53] , [54] . The mild effects of stpA depletion may be attributed to low intracellular StpA concentrations [46] , [55] . StpA is a substrate of the Lon protease and a StpA point mutation , F21C , that imparts resistance to proteolytic cleavage also restored stationary phase viability to an E . coli hns mutant strain [56] . Other reports , however , suggest H-NS and StpA exhibit similar expression levels with the StpA protein reaching 25 000 copies per cell at mid-exponential phase and H-NS reaching 20 000 copies [57] . Despite significant sequence homology between H-NS and StpA , the basis for their functional dissimilarities remains unknown . In this study , we employed an experimental evolution strategy to select for mutations that compensate for the strong fitness defects of S . Typhimurium hns mutants . Using whole genome sequencing we identified parallel adaptations in many of the hns mutant lineages including genomic deletions in the pathogenicity locus SPI-1 and non-synonomous changes in the gene encoding StpA . The stpA mutations altered residues in the oligomerization domain and several enhanced the ability of StpA to silence hns regulated genes without having an effect on StpA expression levels . Much of the fitness defect in the hns mutants could be attributed to overexpression of SPI-1 . This work provides compelling evidence that H-NS potentiates bacterial speciation by improving bacterial tolerance for horizontally acquired sequences . These findings also suggest that fitness-cost buffering by xenogeneic silencing proteins contributes to the observed tendency for genomic islands to be AT-rich .
Disruption of the hns gene in the wild type S . Typhimurium 14028s strain background severely restricts its growth rate to the point where cultivation is difficult [22] . However , we previously demonstrated that hns mutations can be achieved in strains that harbor additional mutations in the gene encoding the alternative sigma factor RpoS ( σS or σ38 ) . Alleles that reduce σS activity frequently arise during laboratory passage and are present in another commonly used Salmonella laboratory strain , LT2 . The alleviating effect of rpoS mutations in the hns mutants may be due to the fact that loss of H-NS dramatically improves the stability of RpoS [58] , which may cause the inappropriate overexpression of stationary-phase genes and interfere with the expression of housekeeping genes controlled by RpoD . To facilitate this study the hns gene from S . Typhimurium 14028s was replaced with a kanamycin resistance cassette in a background harboring a 5 amino acid in frame deletion within the coding region of rpoS that reduces RpoS activity ( referred to as rpoS* ) [22] . Although this additional mutation improved the tolerance of 14028s for hns mutations , Δhns/rpoS* strains continue to display severe growth defects including dramatically reduced colony size . In the course of an earlier microarray study of a S . Typhimurium Δhns/rpoS* strain we noted one isolate appeared to lose a large cluster of genes at some point during laboratory passage [22] . To identify the nature of this deletion the isolate was further analyzed by whole genome sequencing where reads were assembled against the S . Typhimurium 14028s reference genome ( Genbank ID CP001363 . 1 ) using Geneious Pro 5 . 5 . 6 software . This analysis revealed that the isolate incurred a 10 kb genomic deletion spanning nucleotides 1 , 334 , 560 to 1 , 344 , 664 ( Figure 1A ) . The deleted region is highly AT-rich ( GC% = 40% as compared to the genome average of %GC = 52 ) and encodes several putative envelope proteins including the PhoP activated genes pagC , pagD , pliC , envE , envF and msgA [59] . Multiple studies have shown that expression of pagC is strongly repressed by H-NS , and the spontaneous loss of these genes from the Δhns isolate suggested that hns mutants are genetically unstable and may shed horizontally acquired sequences during passage [21] , [22] , [60] . We sought to experimentally determine if the loss of horizontally acquired sequences is a reproducible outcome of deleting hns from S . Typhimurium , as well as to identify novel compensatory mutations that may alleviate the fitness defects associated with the loss of H-NS . Toward this end an in vitro evolution screen was performed where six independently derived freshly constructed Δhns/rpoS* mutant lineages were serially passaged alongside six lineages of the isogenic rpoS* background ( the “wild type” strain ) in Luria-Bertani broth for 30 days , or approximately 300 generations ( Figure 1B ) . The lineages were designated WT or Δhns , “A” through to “F” . Each day during the experiment , aliquots from the cultures were stocked and stored at −80°C to enable the retrospective analysis of genomic changes in each lineage over time . At the end of the evolution period , the growth rates of the passaged wild type and the passaged Δhns lineages were monitored alongside their unpassaged ( day 0 ) counterparts ( Figure 1C ) . All six lineages lacking H-NS displayed significant increases in their growth rates compared to their respective day 0 clone , while the wild type lineages displayed modest improvements in growth ( Figure 1C ) . Notably , by day 30 the Δhns lineages all exhibited growth rates similar to that of the wild type strains at day 30 . To identify mutations that arose throughout the evolution period , genomic DNA from the passaged WT and Δhns lineages and their progenitor lines was analyzed by Illumina whole genome sequencing . In total , the six Δhns lineages acquired 15 missense mutations , 2 small deletions , 2 small insertions and 5 chromosomal deletions larger than 10 kb ( Table 1 ) . Most striking was the high degree of similarity in these mutations . Five of six Δhns lineages incurred unique 10–50 kb deletions within the Salmonella Pathogenicity Island 1 ( SPI-1 ) and all six Δhns lineages accumulated missense mutations within the stpA gene encoding the H-NS paralogue StpA ( Figure 2 , Table 1 ) . In agreement with our earlier observations [22] , three Δhns lineages acquired mutations in the genes encoding the PhoP/PhoQ two component system that activates many H-NS repressed genes involved in virulence , acid stress , resistance to antimicrobial peptides and intramacrophage survival [59] , [61]–[64] . Specifically , lineages A and E acquired frameshift mutations in PhoP and PhoQ respectively while Δhns lineage B acquired a missense mutation ( Y320D ) in the cytoplasmic sensor kinase domain of PhoQ . Throughout the experiment each Δhns lineage acquired a total of three to four mutations with the exception of Δhns lineage D , which acquired eight . It is notable that Δhns lineage D incurred the largest chromosomal deletion that extended beyond SPI-1 into the locus encoding mutS and mutL , essential components of the methyl-directed mismatch repair pathway [65] . The loss of either mutS or mutL from E . coli has been shown to result in a mutator phenotype and may explain the accumulation of other missense mutations specific to the Δhns lineage D , namely idnK ( E62G ) , mutY ( D316N ) , yecS ( P169S ) , yhfC ( M255V ) and stm1881 ( V321A ) [66] . Analysis of the SPI-1 deletion junction regions revealed that 3 of the 5 deletions occurred without any homology in the sequences flanking the deleted segment . The other 2 SPI-1 deletions occurred between segments homologous in only 4 nucleotides . This suggests that RecA mediated recombination did not play a role in the loss of this island in the Δhns mutants ( Figure S1 ) . Analysis of the wild type lineages revealed that comparatively fewer genetic changes arose during the course of the experiment . 3 of the 6 wild type lineages acquired large chromosomal deletions that extended from 10 kb to 58 kb downstream of the uvrC locus ( Table 2 ) . Common to all three deleted fragments were components of the uvrABC nucleotide excision pathway and constituents of the flagellar apparatus . Under the laboratory growth conditions used in this study , expression of the uvrABC and flagellar genes likely resulted in a disadvantageous use of cellular resources . Apart from these deletions no mutations common among the wild type lineages were observed . To determine the timeline of the genetic changes that took place , genes of interest were PCR amplified from the frozen daily stocks of the hns mutant lineages and the PCR products were submitted for Sanger sequencing . This assay enabled the detection of mutant alleles soon after they arose in a given lineage and the relative proportion of the wild type and mutant alleles in the population at each day could be estimated from the sequencing chromatograms by analyzing the dual fluorescence peaks at a particular nucleotide . The relative signal strength of wild type vs . mutated nucleotides was used to approximate the emergence and dominance of each mutation in each population over time . To determine when the large chromosomal SPI-1 deletions arose a PCR assay was employed; amplifying a region bridging the deleted segment . This detection method did not enable us to estimate the relative proportion of SPI-1 deletion strains in the population . We found the mutations in the PhoP/PhoQ regulatory system and the SPI-1 deletions were acquired by the hns mutant lineages in the early stages of the passaging period , prior to the missense mutations in stpA ( Figure 3 ) . The PhoP/PhoQ and SPI-1 mutations were detected as early as day 2 of the evolution period in Δhns lineages A , B and D , suggesting these mutations confer the greatest growth advantages and/or are most easily acquired . Of particular interest is the Δhns lineage C , which did not obtain inactivating mutations in either the PhoP/PhoQ or SPI-1 but displayed a comparable increase in fitness as Δhns lineages A , B , D , E and F in liquid growth assays ( Figure 1C ) . Δhns lineage C acquired a stpA missense mutation ( M4T ) by day 5 that persisted at low frequency until it also acquired a second mutation in the housekeeping sigma factor RpoD ( G471D ) , at which point the Δhns/stpA/rpoD mutant rapidly outcompeted both the Δhns and Δhns/stpA mutant strains in the population by day 13 . To address the concern that lineage C acquired SPI-1 inactivating mutations that were not detected with the Geneious Pro software we performed a reference alignment of the raw Δhns lineage C paired end reads to the S . Typhimurium 14028s reference genome using the Bowtie software package and also preformed a de novo genomic assembly of the evolved Δhns lineage C with Velvet [67] , [68] . A list of variants from both the Bowtie and Velvet assemblies was generated with Samtools and no other mutations besides for the StpAM4T and RpoDG471D variants were identified [69] . The fact that five out of six Δhns lineages rapidly and independently incurred deletions within the SPI-1 locus suggested that SPI-1 misregulation is a major contributor to fitness defects in S . Typhimurium Δhns mutants . SPI-1 expression is repressed by hns and activated by a complex positive feedback loop where the production of the HilD regulatory protein induces the expression of HilA , a transcription factor that directly activates expression of the TTSS and effector proteins [70] . To determine the degree to which SPI-1 impairs growth of the S . Typhimurium Δhns mutant , we deleted the 40 kb genomic island from a wild type strain prior to introducing the hns deletion by transduction . The SPI-1 deletion significantly improved the growth of the Δhns strain and also provided a mild improvement in growth of the wild type strain ( Figure 4A ) . The region of SPI-1 lost in all Δhns lineages included the promoter upstream of hilD . Introduction of a hilD mutation into the Δhns background conferred a growth benefit similar to that of the 40 kb SPI-1 deletion ( Figure 4B ) . These results indicate that in the absence of H-NS , SPI-1 is activated through a hilD dependent pathway and that the uncontrolled expression of SPI-1 encoded virulence determinants significantly impairs Salmonella growth . Salmonella enterica harbors a second pathogenicity island , SPI-2 , that encodes a type-3 secretion system distinct from the one encoded on SPI-1 . Lucchini et al . , previously reported that construction of a Salmonella ΔssrA/Δhns double mutant unable to express the genes encoded in SPI-2 significantly increased the growth rate of the Δhns strain ( grown in LB media and using strain LT2 ) [21] . To determine if inactivation of SPI-2 encoded TTSS would offer the same fitness benefit as deletion of SPI-1 from a Δhns background , we introduced a 25 kb SPI-2 genomic deletion into Δhns and Δhns/ΔSPI-1 strains ( Figure S2 ) . Inactivation of SPI-2 did not significantly improve growth of either the Δhns or Δhns/ΔSPI-1 14028s strain to the same extent as loss of SPI-1 . A similar experiment was conducted in LPM ( low pH , low Mg2+ and low phosphate ) media known to activate SPI-2 to determine if fitness of the Δhns mutant would be adversely affected in a manner dependent on SPI-2 . The Δhns mutant failed to grow in this media but this growth defect was not alleviated in the ΔssrA/Δhns double mutant indicating that other factors , not SPI-2 , impact fitness in our strain under these particular conditions . The only gene that acquired mutations in all six passaged Δhns lineages encodes the H-NS paralogue StpA . All of the acquired stpA mutations resulted in single amino acid substitutions or in-frame insertions that map to the predicted N-terminal and central dimerization domains of the protein . Because disruption of stpA in a Δhns background is known to exacerbate hns mutant phenotypes , we found it unlikely that these substitutions impaired stpA function . Intriguingly , the StpA mutations arise exclusively at sites where the unchanged amino acid is not conserved with H-NS , and the residue changes appear to render StpA more “H-NS-like” ( Figure 5A ) . We hypothesized that the stpA mutations impart H-NS-like silencing properties to StpA and therefore partially compensated for the loss of hns at loci outside of SPI-1 in the serial passaging experiment . To test the ability of the StpA variants to complement hns mutant phenotypes , we cloned the stpA locus from each passaged Δhns lineage and wild type stpA into a low copy vector with the native stpA promoter . The resulting plasmids were pStpAWT , pStpAT37I cloned from Δhns lineage A , pStpAT37I/E42ins from Δhns lineages B and D which both acquired the T37I substitution and an E42 insertion , pStpAM4T from Δhns lineage C , pStpAA77D from Δhns lineage E and pStpAK38Q/F76L from Δhns lineage F . The StpA plasmids were transformed into a Δhns/ΔstpA S . Typhiumurim background in order to determine whether or not the isolated StpA variants could ameliorate bacterial fitness in the absence of hns . Introducing either pStpAWT or StpAT37I did not significantly improve growth of the Δhns/ΔstpA mutant ( Figure 5B ) . On the other hand expression of the StpAM4T mutant significantly improved bacterial fitness in the liquid growth assay . Likewise , the StpAT37I/E42ins variant also offered an observable growth advantage . The strains expressing StpAA77D and StpAK38Q/F76L initially displayed a slight growth advantage and then plateaued at a similar final optical density as the StpAWT expressing strain . Given that expression of the StpA variants identified in the serial passaging experiment enhanced bacterial fitness to varying degrees , we next tested the ability of the modified StpA proteins to complement the impaired motility phenotype of hns mutants . H-NS is required for both the expression and assembly of a functional flagellum [71]–[73] . H-NS indirectly stimulates flagellar gene expression by repressing hdfR , a known repressor of the flhDC regulatory locus and , in addition , H-NS directly binds to the flagellar protein FliG and helps organize rotor subunit assembly [22] , [74] . StpA has also been shown to bind FliG , but does not promote motility in the absence of H-NS unless cellular StpA levels are artificially elevated [74] . To determine if the StpA variants stimulate motility to a greater extent than wild type StpA , we employed the same strains used in the liquid growth assays and measured their radial swarming diameters on soft agar motility plates . After a 16 hr incubation period , wild type S . Typhimurium displayed a swarming diameter of 62 mm ( Figure 5C ) . Similar to the hns mutant strain , the S . Typhimurium Δhns/ΔstpA strains harboring pStpAWT and pStpAT37I did not migrate beyond the original inoculation zone . Remarkably , the StpA variants StpAM4T , StpAA77D and StpAK38Q/F76L restored motility to the Δhns/ΔstpA strain by 30% , 44% and 34% that of the wild type strain respectively ( Figure 5C ) . StpAT37I/E42ins provided a small yet significant increase in swarming diameter to 16% the wild type diameter . One possibility by which the StpA variants could restore motility to the Δhns mutant would be if the single amino acid substitutions increase StpA protein stability . Intracellular StpA pools are reportedly subject to proteolysis by the Lon protease in strains lacking hns [56] . In this study a mutation in the N-terminal dimerization domain of StpA , F21C , was shown to impart resistance to proteolysis and increase intracellular StpA concentrations . To determine if any of the StpA mutations identified in our laboratory passage screen influenced protein levels , the amount of intracellular StpA was quantified by western blot analysis . Δhns strains harboring epitope tagged StpA or its variants was probed with an α-FLAG antibody . DnaK levels were analyzed on the same blot as a loading control . Similar to the StpAF21C variant , StpAT37I and StpAT37I/E42 accumulated to higher intracellular levels than StpAWT ( Figure 6 ) . In contrast the variants StpAM4T , StpAA77D and StpAK38Q/F76L were detected at similar levels to that of StpAWT . This suggests that the StpA variants identified in this study fall into one of two categories , mutations that increase intracellular StpA levels similar to the previously identified StpAF21C variant , and a novel class of mutations that do not significantly alter intracellular StpA levels . Notably , it was the latter class of variants that provided partial complementation for the loss of hns in the growth and motility assays suggesting that the amino acid substitutions M4T , A77D and K38Q/F76L alter the functional properties of StpA and not its stability . Much like H-NS , StpA has also been implicated in silencing AT-rich regions of the genome . Although the set of genes under control of StpA shares significant overlap with the set of genes regulated by H-NS , in the absence of H-NS , the silencing activity of StpA alone does not provide sufficient repression of H-NS regulated loci [46] , [48] , [75] . To determine if the missense mutations acquired throughout the evolution of the Δhns lineages enhanced StpA's silencing activity , we measured the steady state transcript levels of four model H-NS and StpA regulated loci from a Δhns/ΔstpA strain harboring pStpAWT , pStpAM4T , pStpAA77D and pStpAF21C . The StpAM4T and StpAA77D variants were chosen for transcript analysis because they provided the greatest restoration of the Δhns growth and motility defects without altering protein stability , while the StpAF21C variant was included to determine the regulatory consequences of increased intracellular StpA levels . Also included in the analysis were a Δhns complemented strain ( Δhns+pHNS ) and a Δhns strain , which served as reference points for repressed and derepressed transcript levels . cDNA from mid-log cultures was analyzed by Q-PCR with primers specific to proV , hilA , ssrA and yciG . proV is a well studied H-NS regulated gene target that resides outside the Salmonella pathogenicity islands , while hilA and ssrA are transcriptional activators encoded within SPI-1 and SPI-2 respectively . yciG is part of the rpoS regulon and was previously shown to be highly induced in a Salmonella SL1344 strain lacking stpA [48] . Relative to the Δhns complemented strain , the transcript levels of proV , hilA , ssrA and yciG increased by 20-fold or greater in the Δhns strain ( Figure 7 ) . The expression of yciG is highly repressed in the Δhns+pHNS strain , its transcript levels were lower than the detection limit of the Q-PCR cycler and could not be reported with confidence . The Δhns/ΔstpA strain harboring pStpAWT displayed a greater increase in the transcripts levels of proV , ssrA and yciG compared to the Δhns strain , while hilA transcript levels were reduced by 4 . 5-fold in the presence of pStpAWT . Substituting StpAWT with StpAM4T significantly reduced the expression levels of proV and ssrA by approximately 2-fold and 10-fold respectively . The StpAA77D variant provided even greater repression of proV and ssrA by reducing their transcript levels by 4-fold and 20-fold relative to StpAWT . Similar to the Δhns+pHNS strain , both StpAM4T and StpAA77D maintained yciG expression levels close to the detection limit of the sensor . In contrast , the StpAF21C variant that accumulates to higher intracellular levels than StpAWT did not maintain significantly lower expression levels of any of the four genes tested relative to the pStpAWT strain . This further establishes that the StpAM4T and StpAA77D variants as a novel set of mutations that enhance StpA silencing activity without affecting protein stability . While the two single point mutations , M4T and A77D , significantly enhanced StpA's silencing activity at the proV , ssrA and yciG promoters regions these substitutions did not provide increased repression of hilA , encoding the SPI-1 transcriptional activator HilA . hilA expression is induced by three transcriptional activators , HilC , HilD and RtsA [70] . In the absence of H-NS it is possible that silencing complexes generated by StpAM4T and StpAA77D , although more effective than StpAWT , were unable to impede the combined HilC and HilD-mediated activation of hilA . We repeated our in vitro evolution on an expanded number of freshly constructed hns deletion mutants to determine if loss of hns invariably led to mutations in stpA and , if so , to use this technique as a novel method of mapping functional residues in stpA . Toward this end hns deletion mutations were introduced by transduction into the rpoS-low strain to generate 12 independent lineages . To assess the impact SPI-1 may have on the evolution of stpA another 12 linages were generated by introducing the hns mutation into a strain already lacking SPI-1 . Each of the 24 lineages were serially passaged in LB media over the course of 21 days and the stpA genes of each lineage were amplified by PCR and sequenced . Sequencing of the stpA genes revealed missense mutations in 10/12 of the hns mutants in the rpoS background and 12/12 of the rpoS*/SPI-1 mutant background ( Table 3 ) . Remarkably the two hns mutant strains that did not acquire misssense mutations in stpA did acquire silent mutations , suggesting that either that stpA is prone to mutation in the absence of hns or that the presumably silent mutations actually affect StpA levels or function by increasing mRNA stability or by altering codon usage . As before all missense mutations mapped to the oligomerization domain between residues 2 and 80 of the stpA protein . Furthermore some lineages acquired as many as 4 different nucleotide substitutions . The fact that 30 independent lineages ( 24 in this experiment and 6 in the initial experiment ) acquired mutations in stpA and that none of these were nonsense mutations confirms that there is strong selective pressure to acquire mutations in stpA in the absence of H-NS . Notably there were some differences observed in the specific mutations acquired between the two lineages ( those with or without SPI-1 ) . In the presence of SPI-1 the StpA protein was altered at several different residues but a cluster of mutations occurred at or near codon 38 ( nucleotides 112–114 ) encoding lysine including a silent mutation at nucleotide 111 . Strains that evolved in the absence of SPI-1 acquired a notably different set of mutations where all but one lineage acquired a mutation at nucleotide 110 resulting in the StpA ( T37I ) variant . Additional mutations changed the asparagine at positions 2 or 7 to an aspartic acid ( N2D or N7D ) . This suggests that the pressures that select for mutations in StpA may differ in the absence of SPI-1 . The results of the evolution experiment provided an opportunity to map what single or double residue changes in StpA would be sufficient to engender it with H-NS-like functionality . This functionality of each StpA variant was assessed by their ability to restore motility ( Figure 8A ) when expressed in the hns mutant background . This assay was chosen because our data with the earlier StpA variants indicated motility restoration correlates closely with their to silence H-NS regulated loci . These assays uncovered functional changes in single amino acids that cluster to two discrete regions of the StpA protein ( Figure 8 ) . The functional variants StpAN2D , StpAM4T , and StpAN7D map to the short helix 1 that lies within the N-terminal dimerization domain while the variants StpAF76V , StpAF76L , StpAA77D and StpAM78K all map to helix 4 which is contained in the central dimerization domain . Other single residue StpA variants , where changes mapped to helix 3 or the short linker segments that connect helix 3 to the other helices , failed to restore significant motility to the hns mutant . Modeling these changes on the previously published H-NS oligomer structure show that the individual changes that confer H-NS-like function to StpA are buried within the dimerization interfaces or present on the outer , convex , surface of the H-NS filament while the residues that do not lie predominantly on the concave surface of the filament , and are largely predicted to have surface exposed side chains ( Figure 8B ) . It is important to note the StpA residues were mostly assessed individually ( only two double-mutants were assessed ) and that some residues that appear to have no gain of function in our assays may have a more dramatic impact in combination with other changes . Electrophoretic mobility shift assays were used to determine if changes in the StpA variants that led to increased “H-NS-like” function manifest as differences in their ability to form nucleoprotein complexes on DNA . Like H-NS , StpAWT displays cooperative binding to a model 289 bp AT-rich sequence ( %GC = 34 ) but forms nucleoprotein complexes are consistently observed to have significantly lower mobility than those formed by H-NS on the same DNA target ( Figure 9 ) . Remarkably the nucleoprotein complexes formed by the StpAM4T and StpAA77D variants formed complexes with motility more similar to H-NS than wild type StpA . StpAM4T formed two complexes on DNA , one that migrated with the top band of the DNA ladder like StpA and one that migrated further into the gel at the same position as the H-NS complex . StpAA77D almost exclusively formed a single H-NS like complex . StpAT37I , which had enhanced protein levels in vivo , but failed to complement for H-NS for either motility or silencing , formed a lower mobility nucleoprotein complex identical to that of the wild type StpA protein . Notably , there were no differences in overall affinity for DNA between the different variants . This data indicates that subtle changes in the dimerization domains of StpA can generate large and quantifiable differences in properties of the nucleoprotein complex and that the functional differences observed between StpA variants manifest as differences in their effects on nucleoprotein structure . At high protein concentrations both StpA and H-NS have the ability to spontaneously oligomerize into higher order structures in the absence of DNA , a phenomenon that can be measured by changes by analytical gel filtration chromatography . We assessed the gel filtration profiles of StpA and its variants ( Figure 10 ) to determine if any changes in their oligomerization states could be observed . StpAWT and the StpAT37I , which do not effectively substitute for H-NS , displayed two prominent peaks with calculated molecular weights of approximately 450 and 150 kDa ( StpA monomer is ∼15 kDa ) . The chromatographic profiles of the StpAM4T and StpAA77D proteins indicate that these proteins have a dramatically reduced propensity to form the oligomeric species that elutes early during chromatography . We note that the asymmetrical rod-like structure of the StpA and H-NS oligomers prevent an accurate determination of molecular weight based on mobility through the column when compared to a set of globular standards . Differences in shape or flexibility would also manifest as different elution profiles by gel filtration . Nevertheless these findings when taken as a whole indicate that the functional differences between the StpA variants ( and also the functional differences between H-NS and StpA ) are primarily due to differences in manner of their oligomerization and not in the specificity of their DNA binding domains .
The xenogeneic silencing model predicts that the selective silencing of foreign DNA accelerates bacterial evolution by reducing the fitness cost associated with HGT . While multiple studies have established a role for the H-NS , MvaT and Lsr2 protein families in regulating newly acquired sequences , the evolutionary advantage of foreign gene repression by H-NS and its impact on genome content had not been assessed by experimental evolution [21] , [22] , [39] , [40] . The genetic adaptations we identify that improve growth in strains lacking H-NS indicate that xenogeneic silencing played a major role in the evolution of the Salmonellae by buffering the fitness consequences caused by the SPI-1 encoded TTSS , a defining characteristic of the species . Indeed a recent study on the evolution of Salmonella revealed that , while many sequences acquired by HGT will adopt the %GC of their host over time , the major pathogenicity islands have selectively retained their AT-richness , presumably to maintain their silencing by H-NS [6] . The fact that we observed large deletions in SPI-1 , rather than inactivating point mutations or small indels , is somewhat surprising and suggests that this region may be naturally unstable and prone to gene loss . The 5 deletions independently occurred at different sequences , each with limited no flanking homology , suggesting that replication errors and not homologous or site-specific recombination likely caused the loss of these regions from the genome . Multiple lines of evidence suggest that maintaining a TTSS represents a costly investment of cellular resources . Induction of the Yersinia TTSS by low calcium essentially halts bacterial growth and the plasmid-encoded Shigella TTSS is readily lost during laboratory passage [20] . An association between impaired bacterial growth and SPI-1 expression in wild type cells was recently reported in a study conducted by Sturm et al [76] . This study tracked the spontaneous induction of the SPI-1 encoded TTSS at a single cell level using time-lapse microscopy imaging . Sturm et al . correlated the expression of the TTSS with retarded growth rates that were alleviated by mutations in the SPI-1 activator hilA . Under the conditions employed in this study the hns mutant strains only incurred genomic deletions within SPI-1 . However , a targeted disruption of SPI-2 was previously shown to partly improve growth in an hns mutant background [21] . We believe the loss of SPI-1 and not SPI-2 from our hns mutant lineages likely reflects that the conditions we employed in this study favored SPI-1 expression . It is important to note that the conditions employed during the experimental evolution experiment were arbitrarily chosen and it is entirely likely that subtle changes in environment will significantly impact which loci will impinge on fitness in the absence of H-NS . While the wild type lineages passaged in parallel to our hns mutants did not acquire mutations in the SPI-1 locus , genomic deletions encompassing components of the flagellar apparatus were noted in 3 out of 6 of the wild type lineages . Flagella and TTSS are evolutionarily related and highly homologous in both primary sequence and structure [77] . The fact that the flagellar loci of the hns mutant lineages did not acquire mutations is consistent with the fact that hns mutants fail to express flagella to begin with . Pathogens like Salmonella spend a substantial amount of time outside of the host environment and our studies suggest that H-NS is essential for enteric bacteria to retain virulence in the absence of selective pressures . Naturally occurring SPI-I deletions have occasionally been identified among environmental Salmonella isolates that have consequentially lost the ability to invade host cells [78] . Spontaneous SPI-1 mutations are thought to arise throughout host infection generating a subpopulation of “avirulent defectors” that propagate much faster than their TTSS-positive predecessors [79] . Diard et al demonstrated that Salmonella infection with a constitutively active SPI-1 TTSS strain resulted in a sharp rise of the genetically avirulent subpopulation and consequently premature clearing of the infection [79] . The only hns mutant lineage that did not incur a SPI-1 deletion , Δhns lineage C , acquired a missense mutation in rpoD ( RpoDG471D ) . The recent crystal structure of the E . coli RNAP/σD holoenzyme shows RpoD residue G471 is located in an exposed loop region , enriched in highly conserved aromatic and positively charged residues [80] . An alignment of the E . coli RNAP/σD structure with the RNAP/σD initiation complex from Thermus thermophilus reveals the conserved loop region harboring residue G471 is in close proximity the template strand during transcriptional initiation [81] . It is possible that introduction of the negatively charged aspartic acid residue at position 471 could hinder transcriptional initiation and result in reduced expression of the SPI-1 locus , however it is currently unclear how this mutation would affect SPI-1 but not impede the expression of many other important σD targets . Another central and important outcome of this study was the identification StpA oligomerization variants that partially compensate for several H-NS dependent phenotypes . Many of these mutations do not increase cellular StpA protein concentrations , as has been observed previously [46] , [54] . Notably a recent study on a spontaneous mutant that improved fitness of an E . coli strain lacking Hha and YdgT , two molecules that collaborate with H-NS to facilitate gene silencing , identified a promoter mutation that dramatically enhanced H-NS levels [82] . StpA in S . Typhimurium was recently proposed to repress the rpoS regulon during exponential growth and the major caveat of our study is that we started with strain encoding a defective RpoS , which would alleviate the selective pressure to maintain a wild type copy of StpA [48] . H-NS also represses numerous genes activated by rpoS in response to cellular stress [83]–[85] . A study of the hdeAB promoter region suggested H-NS repression was overcome by the RNAP•σS complex , while RNAP associated with the house keeping sigma factor σD was more effectively inhibited by the presence of H-NS [85] . A similar finding was also reported for the dps promoter [86] . One model that could be extrapolated from these observations is that StpA restricts transcription of the RNAP complexed with σS while H-NS more efficiently represses RNAP bound to σD . Complicating this model is the fact that H-NS and StpA can heterodimerize and that each may individually regulate cellular σS concentrations [48]–[50] , [87] , [88] . The story that is emerging from this and other recent studies is that subtle changes in local nucleoid architecture , directed by the structure of the oligomerized protein , underlies the diverse functions ascribed to the H-NS like molecules . Several findings indicate that changes in DNA shape and tension are the relevant outputs of this class of transcriptional modulators; a mode of gene regulation that is particularly challenging to study using conventional assays like EMSA and footprinting . Our results indicate that StpA and H-NS differ primarily not in their ability to bind AT-rich DNA per se , in fact StpA binds DNA with an apparent affinity higher than that of H-NS , but by some physical property that manifests as a change in promoter architecture once bound by the protein . Due to its apparent higher affinity for DNA and elevated propensity to form higher-order oligomers in conventional assays one would predict that StpA would be a more effective silencer than H-NS in most situations . We note that there are significant qualitative differences in the shifted DNA complexes between the StpA variants that can complement for the loss of H-NS and those variants that cannot . This supposition is further supported by recent studies on the H-NS-like transcriptional activator Ler and H-NS paralogs encoded on plasmids demonstrating that the central linking domain , not the DNA binding domain , is the primary determinant in how these molecules functionally differ from H-NS [89] , [90] . Evidence that H-NS , StpA , and the “H-NS-like” Ler proteins each form characteristically distinct higher order protein/DNA complexes has been more directly provided by recent atomic force microscopy imaging studies and single molecule “DNA stretching” experiments [91]–[94] . Lim et al reported that StpA-induced DNA/protein filaments were significantly more rigid than those produced by H-NS , and that the StpA filaments were insensitive to changes in pH , temperature , and osmolarity; conditions known to disrupt H-NS-DNA binding [92] . Another observation that might support divergent oligomerization properties of StpA and H-NS is that StpA can silence the E . coli bglG operon , but only in the presence of H-NS molecules deficient in DNA binding [50] , [95] . This observation was used to suggest that the H-NS proteins can heterodimerize with StpA to facilitate silencing of bglG . However , based on our new findings , we cannot exclude the possibility that the hns mutant strain used in that study acquired mutation ( s ) in stpA during routine lab passaging that enabled it to act like H-NS . The fact that compensatory stpA mutations arise rapidly and reliably in the absence of H-NS is a worrying outcome of this study . Complicating matters further is the apparent functional heterogeneity in the various stpA mutations we uncovered , i . e . the different compensatory mutations do not share the exact same properties . It is unclear how much care has been taken in the maintenance of the various hns mutant strains employed in many prior studies and in all but one case it is clear that the stpA locus was not sequenced to check for mutations . Regrettably this leaves some doubt regarding the validity of earlier studies on the phenotypes of strains lacking H-NS . Given their genetic instability , all future work on hns mutants in either E . coli or Salmonella should be performed on multiple freshly constructed ( transduced ) isolates and laboratory passaging of such strains should be kept to a minimum . Whenever possible the genomes of hns mutants should be re-sequenced to verify that phenotypes ascribed to H-NS are not , in fact , due to a mutation in a different gene .
The plasmids and strains employed in this study are listed in Table 4 and a complete list of oligonucleotides sequences is provided in Table 5 . In a previous study , a FLAG-epitope tag was incorporated into the XhoI and BamHI sites of the low copy vector pHSG576 to generate pWN425 [22] . The stpA coding sequence and 206 bp upstream region ( comprising nucleotides 2976460 to 2968067 in the S . Typhimurium 14028s genome Genbank ID CP001363 . 1 ) was PCR-amplified from S . Typhimurium 14028s genomic DNA with primers ALO115 and ALO116 . The amplified fragment was ligated into the PstI and BamHI sites of pHSG576 backbone for expression of StpA harboring a C-terminal FLAG epitope tag . The StpA coding sequence and promoter region were incorporated into pHSG576 in the opposite orientation of the lac promoter , such that stpA expression levels were controlled by the native stpA promoter . The resulting plasmid pStpAWT was used for complementation studies . Similarly , plasmids harboring the StpA variants identified in the experimental evolution screen were constructed using the pHSG576 backbone with a C-terminal FLAG epitope tag . The mutated StpA alleles were PCR amplified from the genomic DNA of their respective hns mutant lineages that had been passaged for 30 days . The same primer pair used to amplify the wild type stpA coding sequence and 5′ promoter region was employed . The mutant stpA allele PCR fragments were inserted into the PstI and BamHI sites of vector pHSG576 harboring the FLAG epitope sequence 3′ of the BamHI site . The plasmids generated and the corresponding hns mutant lineage that the stpA alleles were cloned from were as follows: pStpAT37I from Δhns lineage A , pStpAT37I/E42insert from Δhns lineages B and D , pStpAM4T from Δhns lineage C , pStpAA77D from Δhns lineage E and pStpAK38Q/F76L from Δhns lineage F . The sequences of all the plasmids constructed in this study were confirmed by Sanger Sequencing at the TCAG Sequencing Facility ( Centre for Applied Genomics , Hospital for Sick Children ) . The Salmonella enterica serovar Typhimurium 14028s strains used in this study possess a mutant rpoS allele ( called rpoS* ) that encodes a five residue in-frame deletion that significantly reduces RpoS ( σ32 ) activity [22] . The single stpA and hns chromosomal deletion strains were previously constructed using the lambda red recombinase method described by Datsenko and Wanner [96] . The stpA gene from S . Typhimurium 14028s was replaced with a kanamycin resistance cassette amplified from plasmid pKD4 , flanked by FRT recombinase sites . The kanamycin resistance cassette was subsequently flipped out of the chromosome by introducing the pCP20 plasmid expressing the FLP recombinase . This generated a S . Typhimurium ΔstpA strain without antibiotic resistance markers . To test the ability of the StpA variants to compensate for the loss of H-NS , each of the StpA complementation plasmids were transformed into the S . Typhimurium 14028s ΔstpA mutant . Next , the hns null allele was moved into the ΔstpA mutant strains harboring the StpA complementation plasmids by P22 transduction . The resulting clones were selected for on Miller's Luria Bertani ( LB ) 1% agar plates supplemented with 50 µg/ml kanamycin ( to select for the hns null mutation ) and 20 µg/ml chloramphenicol ( to select to the StpA plasmids ) . SPI-1 deletion mutants were constructed by deleting a 44 . 4 kb region spanning the SPI-1 region using the lambda red recombinase method described by Datsenko and Wanner [96] . In brief , the region between S . Typhimurium 14028s genome coordinates 3005740–3050161 ( Genbank ID CP001363 . 1 ) in each parent strain was replaced by a chloramphenicol resistance cassette flanked by FRT recombinase sites from plasmid pKD3 using primers ALO76 and ALO77 . Each knockout mutation was then transduced into a fresh strain background by P22 HT105/1 int-201 transduction . Similarly , the SPI-2 deletion mutants were generated using the lambda red recombinase method . A chloramphenicol resistance cassette was amplified from plasmid pKD3 with primers ALO83 and ALO84 , which were designed with flanking sequences complementary to the SPI-2 region . Following lambda red recombinase with the amplified PCR product , a 25 kb SPI-2 deletion spanning nucleotides 1 , 486 , 143–1 , 511 , 465 ( Genbank ID CP001363 . 1 ) was introduced into the S . Typhimurium 14028s genome . The SPI-2 mutation was then transduced into a fresh strain background by P22 HT105/1 int-201 transduction . The double ΔSPI-1/ΔSPI-2 mutants strain were generated by first flipping the ΔSPI-1 chloramphenicol resistance cassette out of the chromosome by introducing the plasmid PCP20 expressing the FLP recombinase , and then introducing the SPI-2 deletion via P22 transduction . Strains containing the hilD mutants were constructed by P22 transduction of a previously constructed mutation provided generously by the lab of Dr . Ferric Fang at the University of Washington [97] . An hns gene knockout from S . Typhimurium 14028s harboring a kanamycin resistance cassette in place of hns was moved into a fresh S . Typhimurium 14028s background containing a mutated rpoS allele ( rpoS* ) via P22 phage transduction . The transductants were selected on LB-agar plates supplemented with 50 µg/ml kanamycin . Six independently derived colonies from the original transduction were streaked twice on LB-kanamycin plates to eliminate trace P22 phage lysate , with each passage on solid media corresponding to a 16 hour incubation period at 37°C . All six transductants harbored the kanamycin resistance cassette in place of hns and were free of contaminating P22 phage as determined by PCR . These hns mutant isolates were selected to inoculate 5 ml LB in conical 25 ml polypropylene culture tubes . The cultures were grown at 37°C with 200 rpm shaking and every 24 hr , 5 µl from each culture was transferred to 5 ml of fresh media . The 1∶1000 dilution corresponds to approximately 9 . 96 doublings a day for a total of ∼300 doublings over the course of the 30 day evolution period . Daily samples from each lineage were taken and stored at −80°C in culture media supplemented with 10% DMSO for later analysis . Samples from the frozen DMSO stocks representing day 1 and day 30 of the evolution period were scraped into LB media and grown at 37°C with shaking until mid-stationary phase ( approximately 8 hours ) . The genomic DNA from approximately 4×109 cells from each culture was purified using the Qiagen DNeasy blood and tissue kit . 5 µg of the purified DNA in 130 µl water was sheared to a mean fragment size of 400 nt using a Covaris S2 focused ultrasonicator ( Woburn , Massachusetts ) . The fragmented DNA was concentrated in a centrifugal evaporator to less than 34 µl and treated with the End-IT DNA repair kit from Epicenter to blunt-end the DNA . Following a 1 hr incubation period at room temperature , 50 µl H20 and 400 µl buffer QG from the Qiaquick Gel extraction kit were added to the blunted DNA fragments . The samples were purified with the QIAquick spin columns ( Qiagen ) and eluted twice in 15 µl elution buffer ( total elution volume 30 µl ) . A-tails were added to the blunted fragments using the Klenow Exo-minus enzyme from Lucigen for 1 hr at room temperature and the reaction was terminated with the addition of 400 µl Quiagen QG buffer . After a second purification with the QIAquick spin columns , the eluted DNA ( 30 µl ) was reduced to a volume of 9 . 25 µl in the centrifugal evaporator . Preannealed dsDNA adapter oligonucleotides were ligated to each sample overnight at 16°C using the Fast-Link DNA Ligation kit ( Epicentre ) . These adapters were generated by mixing equimolar parts of a desalted common oligonucleotide ( 5′-AAT GAT ACG GCG ACC ACC GAG ATCTAC ACT CTTTCC CTA CAC GAC GCT CTT CCG ATC*T-3′ ) , where C* indicates the addition of a phosphothioate group , and a unique indexing oligonucleotide with partial complementarity ( 5′Phos-GATCGGAAGAGCGGTTCAGCAGGAATGCCGAGACCGNNNNNNNNATCTCGTATGCCGTCTTCTGCTTG-3′ ) , where N indicates a unique 8 nt barcode . The samples were separated on a 2% agarose gel and a slice containing fragments of approximately 400–450 nucleotides was extracted purified with the Qiagen gel extraction kit . The samples were then amplified in a PCR cycler for 16 cycles , purified once again with the Qiagen Gel Extraction Kit , and were quantified spectrophotometrically . Equal quantities of each library were combined and sequenced by the Donnelly Sequencing Centre ( Toronto ) in a partial lane of a 130 nt×8 nt index×100 nt paired-end run on an Illumina HiSeq2000 instrument using v3 chemistry . To achieve greater depth of coverage for the wild type E lineage at 30 days , this library was resequenced on a partial lane of a 101 nt×8 nt×101 nt HiSeq2500 run . The unique 8 nt barcode sequence present on the ligated adapters enabled the identification of each sample during downstream analysis . Paired-end Illumina reads from each strain were reference assembled to the published S . Typhimurium 14028s genome ( Genbank ID CP001363 . 1 ) using the Geneious Pro 5 . 5 . 6 software package on “medium-low sensitivity/fast” , which corresponds to the following settings: maximum gaps per read 10% , maximum gap size 15 , minimum overlap identity 80% , minimum overlap 25 nt , and maximum mismatches per read 20% . The mean genomic depth of coverage ranged from 32 . 2%–134 . 2% . Single nucleotide polymorphisms and small deletions and insertions ( SNPs/INDELS ) that arose in each lineage were identified by comparing the genomes of each lineage at Day 30 to their corresponding genome sequence at Day 1 using the Geneious Pro 5 . 5 . 6 “Find Variants/SNPs” tool with the minimum depth of sequence coverage 25-fold and the variant frequency set to 0 . 8 . In addition , the raw Illumina reads of the hns mutant lineage C genomic DNA from Day 30 were aligned to the published 14028s genome using Bowtie version 1 . 0 . 0 and de novo assembled using Velvet version 1 . 2 . 1 . 0 [67] , [68] . A list of the SNPs/INDELS from the Bowtie and Velvet assemblies of hns lineage C was generated using Samtools [69] . SNPs were then confirmed by sequencing PCR products from each strain . For each SNP , the corresponding gene was amplified from the DMSO stock of each passage by PCR using gene-specific primers: stpA ( ALO117/118 ) , rpoD ( ALO122/123 ) , idnK ( ALO139/140 ) , mutY ( ALO141/142 ) , phoP ( ALO145/146 ) , phoQ ( ALO143/144 ) , yecS ( ALO151/152 ) , yhfC ( 153/154 ) ( Table 4 ) . The resultant PCR product was then purified using EZ-10 Spin Column PCR Purification Kit ( Biobasic ) and sent for Sanger Sequencing at TCAG Sequencing Facility ( Centre for Applied Genomics , Hospital for Sick Children ) . The passage when each mutation occurred was similarly determined by sequencing individual loci from the samples stored daily during the course of the experiment . Sequencing chromatograms were visually compared for the emergence of the mutant nucleotide change . Emergence of the SPI-1 deletions were detected by PCR amplifying the region spanning the deletion sites from the DMSO stock of each passage using the following primer pairs: ALO127/ALO128 for Δhns lineage A , ALO129/ALO130 for Δhns lineage B , ALO131/ALO132 for Δhns lineage D , ALO133/ALO134 for Δhns lineage E and ALO135/ALO136 for Δhns lineage F . The PCR products were purified using EZ-10 Spin Column PCR Purification Kit ( Biobasic ) and sent for Sanger Sequencing at TCAG Sequencing Facility ( Centre for Applied Genomics , Hospital for Sick Children ) . Overnight cultures ( 5 ml LB ) were inoculated from single colonies grown for approximately 16 hours at 37°C with 200 rpm shaking . Cultures for each strain were then adjusted to an O . D . at 600 nm of 0 . 5 and then diluted an additional 1∶100 . 200 µl of each culture was then dispensed in triplicate into a clear , flat-bottom 96-well plate ( Sarstedt ) , the plate was covered with the plate lid and grown overnight with shaking at 37°C in a TECAN Infinite M200 Pro microplate reader . Optical density readings at 600 nm were recorded every 15 minutes for 18 hours . Overnight cultures ( 5 ml LB ) were initiated from single colonies and grown for 16 hour at 37°C with shaking at 200 rpm . The next day cultures were each adjusted by dilution to an O . D . at 600 nm of 0 . 1 . Equivalent colony forming units in each of the diluted cultures were verified by plating serial dilutions . 5 µl of the O . D . 600nm 0 . 1 cultures was spotted into the center of 25 ml soft agar plates ( LB 0 . 35% agar ) . The plates were incubated for 12 hours at 37°C and the radial swarming diameters were measured . The motility assays were replicated three times and in each assay the strains were plated in triplicate . Overnight cultures were diluted 1∶200 in 200 ml LB media containing 20 µg/ml chloramphenicol . Sample volumes of 50 ml , 12 ml and 1 . 5 ml were removed from the cultures at O . D . 600 nm of 0 . 1 , 0 . 6 and 1 . 5 respectively . Cells were harvested by centrifugation at 5000× g for 15 min at 4°C . The cell pellets were resuspended in cell lysis buffer containing 9 . 32 M urea , 2 . 67 M thiourea , 40 mM Tris , and 86 . 78 mM 3- ( 3- cholamidopropyl ) -dimethylammonio-1-propanesulfonate ( CHAPS; pH 8 . 5 ) . Cells were lysed by sonication and the total protein concentrations were quantified using Bradford assay ( Bio-Rad ) . 30 µg of total protein was combined with 2× SDS PAGE loading dye and separated on a 16% polyacrylamide SDS Tris-Tricine gel . Transfer to a nitrocellulose membrane was performed with the Bio-Rad semidry electrophoretic transfer cell at 15 V for 1 h . The membrane was blocked at 4°C over night in TBST 1× Tris-buffered saline , 0 . 05% Tween 20 ) with 5% skim milk powder . The membrane was probed with Rabbit anti-FLAG M2 antibody ( Sigma ) diluted 1∶1000 in TBST with 5% ( w/v ) skim milk for 1 h at room temperature , followed by goat anti-rabbit secondary antibody conjugated with horseradish peroxidase ( Sigma , diluted 1∶10 , 000 in TBST with 5% milk ) for 1 h at room temperature . DnaK was probed as a loading control using a mouse primary antibody ( Enzo Life Sciences , 1∶1 , 000 in TBST with 5% milk ) followed by a goat anti-mouse secondary antibody conjugated with horseradish peroxidase ( Enzo Life Sciences , 1∶10 , 000 in TBST with 5% milk ) . Constructs overexpressing StpAwt , StpAT37I , StpAM4T and StpAA77D were transformed in BL21Δhns ( DE3 ) strain . The resulting strains were cultured in Luria Bertani ( LB ) until OD600 nm = 0 . 6 . IPTG was added to a final concentration of 1 mM prior to growing the cultures for 16 h at 18°C . Cells were spun at 2500×g for 30 min , resuspended in 20 mL cell lysis buffer ( 20 mM Tris pH 8 , 500 mM NaCl , 5 mM imidazole , 5 mM β-mercaptoethanol ) and sonicated . The cellular debris was removed by centrifugation at 20 000 g for 15 min . Ni2+ resins were incubated with supernatant for 1 h on a rocking platform , washed twice with washing buffer ( 20 mM Tris pH 8 , 500 mM NaCl , 30 mM imidazole , 5 mM β-mercaptoethanol ) , and eluted with elution buffer ( 20 mM Tris pH 8 , 500 mM NaCl , 500 mM imidazole ) . Proteins were then purified further by size exclusion chromatography using Superdex 200 16/60 column from GE healthcare using storage buffer ( 20 mM Tris pH 8 , 1 M NaCl , 1 mM EDTA and 5% glycerol ) . Fractions containing protein were concentrated using Millipore Amicon Ultra centrifugal filter 3K and stored at −80°C . H-NS6HIS protein was purified by nickel affinity chromatography as previously described [98] . Ni2+ purified H-NS6HIS was dialyzed in buffer A ( 20 mM Tris pH 8 , 1 mM EDTA , 200 mM NaCl and 5% glycerol ) overnight prior to being loaded onto a 5 mL Hitrap Heparin HP column , and eluted using a linear gradient of low salt buffer ( 20 mM Tris pH 8 , 1 mM EDTA , 150 mM NaCl and 5% glycerol ) with high salt buffer ( 20 mM Tris pH 8 , 1 mM EDTA , 1 M NaCl and 5% glycerol ) over 120 mL . Peak fractions were analyzed by SDS-PAGE and then dialyzed in loading buffer prior to storage at −80°C . Total RNA was purified and reverse transcribed as previously described [98] . The resulting cDNA was analyzed by real-time quantitative PCR ( Q-PCR ) with primers specific to ssrA ( SSA198/199 ) , hilA ( SSA200/201 ) , proV ( SSA202/203 ) and yciG ( SSA232/233 ) . gyrB served as an internal control for normalization and was analyzed with primer set WNp233/234 . Q-PCR was performed with the SsoFast Evagreen Supermix ( Bio-Rad ) according to the manufacturer's instructions . Two DNA fragments were employed in this assay; a 289 bp fragment of hilA ( %GC = 34 ) from S . Typhimurium 14028s genomic DNA , and a 204 bp GC-rich fragment of PA3900 ( %GC = 74 ) from Pseudomonas aeruginosa strain PAO1 . The hilA fragment was amplified by PCR using primers GT068 and GT077 and the PA3900 fragment was amplified using primers GT049 and GT050 ( Table 5 ) . Various concentrations of purified H-NS , StpAWT and relevant variants of StpA were incubated with 10 nM DNA in binding buffer ( 15 mM HEPES pH 7 . 9 , 40 mM KCl , 1 mM EDTA , 0 . 5% DTT , 5% glycerol ) for 30 minutes . 4 µl of 6× Fermentas loading dye was added to each 20 µl reaction immediately prior to separation by gel electrophoresis for 2 . 5 h at 70 V on a 6% native polyacrylamide gel at 4°C ( buffered with Tris acetate EDTA ) . Gels were stained with SYBR Green for 20 minutes at room temperature , washed twice with ddH2O , and DNA complexes were visualized with ultraviolet light . | H-NS is an abundant DNA-binding protein found in enteric bacteria including the important pathogens Escherichia , Salmonella , Vibrio , and Yersinia , that plays a primary role in defending the bacterial genome by silencing AT-rich foreign genes . H-NS has been hypothesized to facilitate the evolution of bacterial species by acting as a buffer against the negative consequences that can occur when new genes are incorporated into pre-existing genetic landscapes . Here experimental evolution and whole-genome sequencing were employed to determine the factors underlying the severe growth defects displayed by Salmonella strains lacking H-NS . Through tracking the evolution of several independently derived mutant lineages , we find that compensatory mutations arise quickly and that they occur in loci related to virulence . A frequent outcome was loss of the Salmonella Pathogenicity Island-1 , the defining genetic island of the genus Salmonella . Among other things these findings demonstrate that H-NS has enabled the birth of a new and important bacterial pathogen by buffering the fitness consequences caused by overexpression of SPI-1 . These findings are likely generalizable to pathogens such as E . coli , Yersinia , Shigella , and Vibrio cholerae , all of which maintain a pool of “expensive” AT-rich virulence genes that are repressed by H-NS . | [
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"i... | 2014 | Silencing by H-NS Potentiated the Evolution of Salmonella |
Successful prediction of the likely paths of tumor progression is valuable for diagnostic , prognostic , and treatment purposes . Cancer progression models ( CPMs ) use cross-sectional samples to identify restrictions in the order of accumulation of driver mutations and thus CPMs encode the paths of tumor progression . Here we analyze the performance of four CPMs to examine whether they can be used to predict the true distribution of paths of tumor progression and to estimate evolutionary unpredictability . Employing simulations we show that if fitness landscapes are single peaked ( have a single fitness maximum ) there is good agreement between true and predicted distributions of paths of tumor progression when sample sizes are large , but performance is poor with the currently common much smaller sample sizes . Under multi-peaked fitness landscapes ( i . e . , those with multiple fitness maxima ) , performance is poor and improves only slightly with sample size . In all cases , detection regime ( when tumors are sampled ) is a key determinant of performance . Estimates of evolutionary unpredictability from the best performing CPM , among the four examined , tend to overestimate the true unpredictability and the bias is affected by detection regime; CPMs could be useful for estimating upper bounds to the true evolutionary unpredictability . Analysis of twenty-two cancer data sets shows low evolutionary unpredictability for several of the data sets . But most of the predictions of paths of tumor progression are very unreliable , and unreliability increases with the number of features analyzed . Our results indicate that CPMs could be valuable tools for predicting cancer progression but that , currently , obtaining useful predictions of paths of tumor progression from CPMs is dubious , and emphasize the need for methodological work that can account for the probably multi-peaked fitness landscapes in cancer .
Improving our ability to predict the paths of tumor progression is helpful for diagnostic , prognostic , and treatment purposes as , for example , it would allow us to identify genes that block the most common paths of disease progression [1–4] . This interest in predicting paths of progression is not exclusive to cancer ( see reviews in [5 , 6] ) . For example , in some cases antibiotic resistance shows parallel evolution with mutations being acquired in a similar order [7] , and here “Even a modest predictive power might improve therapeutic outcomes by informing the selection of drugs , the preference between monotherapy or combination therapy and the temporal dosing regimen ( … ) ” [8 , p . 243i] . But detailed information about the paths of tumor evolution and their distribution , obtained from multiple within-patient samples with timing information , is not available . Cancer progression models ( CPMs ) , such as conjunctive Bayesian networks ( CBN ) [9–11] , oncogenetic trees ( OT ) [12 , 13] , CAncer PRogression Inference ( CAPRI ) [14 , 15] , or CAncer PRogression Extraction with Single Edges ( CAPRESE ) [16] , can be used to predict paths of tumor progression . CPMs were originally developed to identify restrictions in the order of accumulation of mutations during tumor progression from cross-sectional data [17 , 18] . But CPMs also encode all the possible mutational paths or trajectories of tumor progression , from the initial genotype to the genotype with all driver genes mutated ( see Fig 1 ) ; in fact , mutational pathways and evolutionary trajectories are already mentioned in the papers that describe CBN [10] , CAPRI [14 , 15] and in general overviews of CPMs [18] . Thus , CPMs could improve our ability to predict disease progression by leveraging on the available , and growing , number of cross-sectional data sets . The first question we address in this study is whether we can predict the paths of tumor progression using CPMs . To answer this question we will examine how close to the truth are the predictions made by four CPMs ( CBN , OT , CAPRI , and CAPRESE ) about the distribution of paths of tumor progression . We have adapted the output from the CPMs ( the restrictions in the order of accumulation of mutations ) to predict mutational pathways , and assessed the quality of these predictions . When addressing this question we need to take into account possible deviations from the models assumed by CPMs . In particular , most CPMs assume that the acquisition of a mutation in a driver gene , when all its possible dependencies on other genes are satisfied , does not decrease the probability of gaining a mutation in another driver gene [19] . In other words , acquiring driver mutations ( when their dependencies on other genes are satisfied ) cannot decrease fitness . This implies that the fitness landscapes assumed by CPMs , with respect to the driver genes , only have a single fitness maximum , the genotype with all drivers mutated ( see Fig 1; note that there can be multiple fitness maxima if we include genes that are unconditionally deleterious —see section 4 of S2 Text ) . But it is likely that many cancer fitness landscapes have several local fitness maxima ( i . e . , they are rugged , multi-peaked landscapes ) : this can happen if there are many combinations of a small number of drivers , out of a larger pool of drivers [20] , that result in the escape genotypes; moreover , synthetic lethality is common in both cancer cells [21 , 22] and the human genome [27] , and it can lead to local fitness maxima when it affects mutations that individually increase fitness—see also [28] . Thus , to examine if CPMs can be used to predict paths of tumor progression we will need to assess how the quality of the predictions is affected by multi-peaked fitness landscapes . The second question addressed in this paper is whether we can use CPMs to estimate evolutionary unpredictability , regardless of the performance when predicting the actual paths of tumor progression . A model could be useful if it suggests few paths are possible , even if its actual predictions about the distribution of paths are not trustworthy . Conversely , predicting correctly the distribution of paths of tumor progression might be of little importance in scenarios where the true evolutionary unpredictability itself is very large ( where disease progression follows a very large number of possible paths ) ; for practical purposes , forecasting here would be useless . To address the above questions ( can we predict the paths of tumor progression using CPMs ? ; can we estimate evolutionary unpredictability using CPMs ? ) we use evolutionary simulations on 1260 fitness landscapes that include from none to severe deviations from the assumptions that CPMs make about the structure of fitness landscapes , and we analyze the data with four different CPMs , whose predictions about restrictions in the order of accumulation of mutations we have adapted to provide probabilities of paths of tumor progression . This paper does not attempt to understand the determinants of evolutionary ( un ) predictability ( see , e . g . , [5 , 6 , 25 , 29 , 30] ) but , instead , we focus on the effects of evolutionary unpredictability for CPMs . This is why we use variation in key determinants of evolutionary unpredictability ( e . g . , variation in population sizes and mutation rates ) but these factors are only used to generate variability in unpredictability , and not themselves the focus of the study . To better assess the quality of predictions , we use sample sizes that cover the range from what is commonly used to what are much larger sample sizes than currently available . We also include variation in the cancer detection process or detection regime ( when cancer samples are taken , or when patients are sampled ) , since previous studies have shown that it affects the quality of inferences from CPMs [31] . We have shown before [31] that the performance of two CPMs ( CBN and CAPRI ) for predicting accessible genotypes degrades considerably when the fitness landscapes contain reciprocal sign epistasis . That study focused on predicting accessible genotypes and its results cannot provide an answer to the questions about predicting paths of tumor progression and estimating evolutionary unpredictability . We are extending our previous study to answer whether CPMs can be used to predict paths of progression and to estimate evolutionary unpredictability . To address these questions we need to look directly at the prediction of paths ( not genotypes ) , and compare them with the true paths of progression , as we do in the current work . Thus , the two studies differ in objectives , methods ( here we use a larger number of CPMs , we follow evolution until fixation , and we develop procedures to compare predicted with true paths of tumor progression ) , and scenarios considered ( the types fitness landscapes used and the extent of evolutionary unpredictability ) ; see details in S1 Text . Here we find that the agreement between the predicted and true distributions of paths is generally poor , unless sample sizes are very large and fitness landscapes conform to the assumptions of CPMs . Both detection regime and evolutionary unpredictability itself have major effects on performance . But in spite of the unreliability of the predictions of paths of tumor progression , we find that CPMs can be useful for estimating upper bounds to the true evolutionary unpredictability . What are the implications of our results for the analysis and interpretation of the use of CPMs with cancer data sets ? We analyze twenty-two real cancer data sets with H-CBN , the best performing CPM in the simulations . We cannot examine how close predictions are to the truth , since the truth is unknown; thus , we use bootstrap samples to examine the reliability of the inferences . Many of the cancer data sets reflect conditions where useful predictions could be possible , based on the estimates of evolutionary unpredictability from H-CBN . But for most data sets these results are thwarted by the unreliability of the predictions themselves , which increases with the number of features analyzed . Our results question uncritical use of CPMs for predicting paths of tumor progression , and suggest the need for methodological work that can account for the probably multi-peaked fitness landscapes in cancer .
We have compared four distinct CPMs: CBN , OT , CAPRI , and CAPRESE . Two of the models used , CBN and CAPRI , have been used in two variants ( H-CBN and MCCBN for CBN , CAPRI_AIC and CAPRI_BIC for CAPRI ) , yielding a total of six different procedures for obtaining CPMs . Only a brief overview of these CPMs is provided here; detailed descriptions can be found in the original references for each model: H-CBN [9 , 10] , MCCBN [11] , OT [12 , 13] , CAPRI [14 , 15] , and CAPRESE [16] . ( Other CPMs that we are aware of include DiP [32] , bcbn [33] and RESIC [34 , 35]; we do not consider these methods here because they are too slow for routine work , have no software available , or have dependencies on non-open source external libraries —see S4 Text ) . The CPMs considered try to identify restrictions in the order of accumulation of mutations from cross-sectional data . CPMs assume that the different observations in the cross-sectional data set constitute independent realizations of evolutionary processes where the same constraints hold for all tumors [10 , 17 , 18] . Thus , a data set can be regarded as a set of replicate evolutionary experiments where all individuals are under the same genetic constraints . For the four CPMs considered in this paper , the cross-sectional data is a matrix of subjects ( or individuals ) by driver alteration events , where each entry in the matrix is binary coded as mutated or not-mutated ( or , equivalently , altered or non-altered ) . CPMs assume there are no back mutations in these events —i . e . , once gained , an alteration is not lost . CPMs further assume that the driver genes are known . For the simulations , we will refer to these driver alteration events as “genes” , but they can be individual genes , parts or states of genes , or modules or pathways made from several genes ( e . g . , [10 , 15] ) . When we analyze the twenty-two cancer data sets ( see section Cancer data sets ) we will use the generic term “features” as some of those data sets use genes whereas others use pathway or module information . CPMs assume that all tumors start cancer progression without any of the mutations considered in the study ( the above matrix of subjects by driver alterations ) , but other mutations could be present that have caused the initial tumor growth . All these other mutations are absorbed in the root node from which cancer is initiated [35]; note that the way the data are simulated to generate cross-sectional observations ( see section Overview of the simulation study ) is consistent with this assumption . The above assumptions are common to the CPMs considered . The models examined here differ , however , in the types of restrictions they can represent and on their model fitting procedures . Both OT [12 , 13] and CAPRESE [16] describe the accumulation of mutations with order constraints that can be represented as trees . Thus , among the “representable” fitness landscapes used in this paper ( section Evolutionary simulations ) , OT and CAPRESE can only faithfully model the subset that are trees , those where a gene mutation has a direct dependency on only one other gene’s mutation . A key difference between OT and CAPRESE is that CAPRESE reconstructs these models using a probability raising notion of causation in the framework of Suppes’ probabilistic causation , whereas in OT weights along edges can be directly interpreted as probabilities of transition along the edges by the time of observation [12 , p . 4] . In contrast to OT and CAPRESE , both CAPRI [14 , 15] and CBN [9–11] allow modeling the dependence of an event on more than one previous event: the output of the models are directed acyclic graphs ( DAGs ) where some nodes have multiple parents , instead of a single parent ( as in trees ) . CAPRI tries to identify events ( alterations ) that constitute “selective advantage relationships” , again using probability raising in the framework of Suppes’ probabilistic causation . We have used two versions of CAPRI , that we will call CAPRI_AIC and CAPRI_BIC , that differ in the penalization used in the maximum likelihood fit , Akaike Information Criterion ( AIC ) , or Bayesian Information Criterion ( BIC ) , respectively . For CBN we have also used two variants , H-CBN , described in [9 , 10] that uses simulated annealing with a nested expectation-maximization ( EM ) algorithm for estimation , and MCCBN , described in [11] , that uses a Monte-Carlo EM algorithm . Thus , these six procedures can be divided into three groups: models that return trees ( OT and CAPRESE ) and two families of models that return DAGs , CBN ( H-CBN and MCCBN ) and CAPRI ( CAPRI_AIC and CAPRI_BIC ) . Because ( the transitive reduction of ) a DAG of restrictions determines a fitness graph ( see Fig 1 and [31] ) , the set of paths to the maximum encoded by the output from a CPM is obtained from the fitness graph . This we did for all models . From H-CBN and MCCBN we can also obtain the estimated probability of each path of tumor progression to the fitness maximum , since both H-CBN and MCCBN return the parameters of the waiting time to occurrence of each mutation ( given its restrictions are satisfied; e . g . , [11 , p . i729] , [9 , section 2 . 2] , or [36]; details and example in section 3 of S4 Text ) . It is also possible to perform a similar operation with the output of OT , and use the edge weights from the fits of OT to obtain the probabilities of transition to each descendant genotype and , from them , the probabilities of the different paths to the single fitness maximum . It must be noted that these probabilities are not really returned by the model , since the OTs used are untimed oncogenetic trees [12 , 13] . We will refer to paths with probabilities assigned in the above way as probability-weighted paths . For CAPRESE and CAPRI , it is not possible to map the output to different probabilities of paths of progression ( see section 3 of S4 Text ) and in all computations that required probability of paths we assigned the same probability to each path . We have used simulations of tumor evolution on fitness landscapes of three different types ( see Fig 1 ) , for landscapes of 7 and 10 genes , under different initial population sizes and mutation rates . We have used a total of 1260 fitness landscapes = 2 conditions of numbers of genes x 3 types of fitness landscapes x 3 initial population sizes x 2 mutation regimes x 35 random fitness landscapes for each combination of conditions . For each one of the 1260 fitness landscapes , we simulated 20000 independent evolutionary processes ( with the specified parameters for initial population size and mutation rate ) using a logistic-like growth model; each simulated evolutionary process was run until one of the genotypes at the local fitness maxima ( or the single fitness maximum ) reached fixation . Each set of 20000 simulated evolutionary processes was then sampled under three detection regimes , so that each fitness landscape generated three sets of 20000 simulated genotypes . From each of these sets , we obtained five different splits of the genotypes for each of three sample sizes ( 50 , 200 , 4000 ) ; thus a total of 56700 ( = 1260 x 3 x 3 x 5 combinations of 1260 fitness landscapes , 3 detection regimes , 3 sample sizes , 5 splits ) data sets were produced . Each of these 56700 data sets was analyzed with every one of the CPMs compared ( H-CBN , MCCBN , OT , CAPRI_AIC , CAPRI_BIC , and CAPRESE ) , to obtain predicted paths of tumor progression . These predictions were then compared with the true , recorded , paths of tumor progression from the simulations ( see section Measures of performance and predictability ) . A schematic view of the simulation study is provided in Fig 2 . We have used three different kinds of random fitness landscapes ( see Fig 1 ) . Representable fitness landscapes are fitness landscapes for which a DAG of restrictions exists with the same accessible genotypes and accessible mutational paths . ( Accessible mutational path: a trajectory through a collection of genotypes , where each genotype is separated from the preceding genotype by one mutation , along which fitness increases monotonically [26]; accessible genotypes: genotypes along accessible mutational paths ) . An example of a representable fitness landscape with its corresponding DAG of restrictions and fitness graph is shown in Fig 1A . A defining characteristic of representable fitness landscapes is that all accessible genotypes that differ by exactly one mutation are connected in the fitness graph; thus , with respect to the driver genes , there is a single fitness maximum , the genotype with all driver genes mutated , and all accessible mutational paths in the fitness graph end in that single maximum . For representable fitness landscapes there is a one-to-one correspondence between DAGs of restrictions and fitness graphs . In local maxima fitness landscapes ( Fig 1B ) the set of accessible genotypes can be represented by a DAG of restrictions , but there are local fitness maxima and the fitness graph has missing paths; the genotype with all genes mutated might or might not be the genotype with largest fitness . In other words , in the local maxima fitness landscapes , the DAG of restrictions and the fitness landscape agree on which genotypes should be accessible and which genotypes should not be accessible; what the local maxima landscapes are missing are mutational paths to the genotype with all genes mutated , because we have introduced local fitness maxima . Once we introduce local maxima there is no longer a one-to-one correspondence between DAGs of restrictions and fitness graphs ( thus , there is no longer a one-to-one correspondence between DAGs of restrictions and sets of tumor progression paths ) . These local maxima landscapes should not be as challenging to CPMs as the DAG-derived non-representable fitness landscapes used in [31] , as those also missed some genotypes that should exist under the DAG of restrictions . This is by design: here we want to isolate the effect of multi-peaked landscapes or local maxima ( or , equivalently , missing paths ) , without the additional burden , for the CPMs , of missing genotypes . The third type of fitness landscapes used are Rough Mount Fuji ( RMF ) fitness landscapes . The RMF model [25 , 37] combines a random House of Cards model ( where fitness is assigned to genotypes by independently sampling from a fixed probability distribution ) and an additive fitness landscape ( where the reference genotype , or the genotype with largest fitness , need not be the one with all genes mutated ) . The RMF model is a very flexible one , where the ruggedness of the landscape can be modified by changing the ratio of the additive component ( the change in fitness per unit increase in Hamming distance from the reference genotype ) relative to the variance of the random fitness component ( the House of Cards component ) . The RMF model has been useful to model empirical fitness landscapes [25 , 26 , 37] . RMF fitness landscapes generally have multiple local fitness maxima and considerable reciprocal sign epistasis and thus not even the set of accessible genotypes can be represented by a DAG of restrictions ( see [31] , and Fig 1 ) . We generated the DAG-derived representable fitness landscapes by generating a random DAG of restrictions and from it the fitness graph . We then assigned birth rates to genotypes using an iterative procedure on the fitness graph where , starting from the genotype without any driver mutation with a birth rate of 1 , the birth rate of each descendant genotype was set equal to the maximum fitness of its parent genotypes times a random uniform variate between 1 . 01 and 1 . 19 ( U ( 1 . 01 , 1 . 19 ) ) yielding , therefore , an average multiplicative increase in fitness of 0 . 1 ( which is within values previously used: [4 , 38 , 39] ) . The birth rate of genotypes that were not accessible according to the DAG of restrictions was set to 0 . For example , if the DAG of restrictions was the one shown in Fig 1A , a cell with genotype “1001” would have a birth rate of 0 , since the dependencies of the DAG of restrictions are not satisfied —mutations in genes 2 and 3 must occur before a mutation in gene 4 . Therefore , this simulation scheme strictly adheres to the assumptions about accessible and non-accessible genotypes under the CPM model . ( For the growth model used here —see below— birth rates determine fitness at any population size as death rates are identical for all genotypes and depend only on population size . Genotypes with a birth rate of 0 are never added to the population and , thus , they cannot mutate before dying ) . We generated the DAG-derived local maxima fitness landscapes by first generating a random DAG and from it the fitness graph , identically to what was done for representable fitness landscapes . Though in contrast to representable fitness landscapes , before assigning fitness to genotypes a random selection of edges of the fitness graph were removed so that all accessible genotypes remained accessible but now from a possibly much smaller set of parents . Birth rate was then assigned as for the representable fitness landscapes ( using the iterative procedure on the fitness graph , where birth rate of descendant genotype = max ( birth rate parent genotypes ) * U ( 1 . 01 , 1 . 19 ) , and with all non-accessible genotypes with a birth rate of 0 ) . For each DAG we repeated this procedure 50 times , and kept the one that introduced the largest number of local maxima . Creating local maxima almost always resulted in creating reciprocal sign epistasis ( see also section Characteristics of the simulated fitness landscapes and genotypes and S2 Text ) . We generated the RMF fitness landscapes by randomly choosing the reference genotype ( i . e . , the genotype with the largest fitness ) and the decrease in birth rate of a genotype per each unit increase in Hamming distance from the reference genotype ( which affects the ruggedness of the landscape ) ; see details in S2 Text . Once a fitness landscape had been generated , we simulated 20000 evolutionary processes ( step B in Fig 2 ) . We used the continuous-time , logistic-like model of [38] , in which death rate depends on total population size , as implemented in OncoSimulR [40] , with the specified parameters of initial population size and mutation rate ( below ) . Each individual evolutionary process was run until one of the genotypes at the local fitness maxima ( or the single fitness maximum ) reached fixation ( see details in S3 Text ) . We also verified that all 7 or 10 genes had appeared in at least some genotypes , i . e . , were part of the paths of tumor progression . If this condition was not fulfilled , a new fitness landscape was generated and the processes started again . This procedure is independent of the detection process that returns the genotypes analyzed by the CPMs ( section Detection regimes and obtaining data sets from the simulations ) . We used three initial population sizes , 2000 , 50000 , and 1 × 106 cells , for the simulations; these cover a range of population sizes at tumor initiation that have previously been used in the literature ( e . g . , [10 , 38 , 41 , 42] ) . We also used two mutation regimes; in the first one , all genes had a common mutation rate of 1 × 10−5; in the second , genes had different mutation rates , uniformly distributed in the log scale between ( 1/5 ) 1 × 10−5 and 5 × 10−5 ( i . e . , the largest ratio between largest and smallest mutation rates was 25 ) , so that the arithmetic mean of mutation rates was 1 . 5 × 10−5 and the geometric mean 1 × 10−5 . These mutation rates are within ranges previously used in the literature [38 , 39 , 43] , with a bias towards larger numbers ( since we use only 7 or 10 genes relevant for population growth and we could be modeling pathways , not individual genes ) . Initial population size and mutation rates are not of intrinsic interest here ( since our focus are not the determinants of evolutionary predictability per se ) , but are used to generate variability in evolutionary predictability and to allow for deviations from the strong-selection-weak-mutation ( SSWM ) regime [25]; see section Characteristics of the simulated fitness landscapes and genotypes . Only in the representable fitness landscapes are simulations restricted to move uphill in the fitness landscapes . In all three types of fitness landscapes , mutations can lead to either increases or decreases in fitness . In the representable and local maxima fitness landscapes , as explained above , mutation events that do not fulfill the restrictions in the order of accumulation of mutations lead to a birth rate ( and , thus , fitness ) of 0 . Therefore , in the simulations in representable and local maxima fitness landscapes , no path from the “0000” genotype to a fitness maximum can ever go through a non-accessible genotype . This is by design , so that these fitness landscapes strictly adhere to the assumption of CPMs about restrictions in the accumulation of mutations . But in both RMF and local maxima fitness landscapes it is possible to move through a fitness valley ( i . e . , make moves from ancestor to descendant that are not always monotonically increasing in fitness ) , phenomena that are more frequent as we deviate from the SSWM assumption ( [25]; commented example in section 5 of S3 Text ) . ( Note that this is possible in local maxima fitness landscapes , even when non-accessible genotypes can never be part of evolutionary paths , because with no back mutations an accessible genotype can be along an uphill path when coming from one ancestor but in a valley when coming from another ancestor; no such genotypes can exist in the representable fitness landscape as in the representable landscapes all accessible genotypes that differ by exactly one mutation are connected in the fitness graph ) . In addition , in the RMF fitness landscape , we can move through fitness valleys of non-accessible genotypes as non-accessible genotypes need not have a birth rate of 0 in the RMF; see section 5 of S3 Text ) . To obtain the genotypes that were analyzed by the CPMs , we first sampled the simulated evolutionary processes , obtaining one observation per evolutionary processes , using three different detection regimes ( Fig 2C and 2D ) ; then , for each observation of each detection regime , we obtained the genotype corresponding to each observation ( Fig 2E ) , which lead to matrices of 20000 genotypes ( Fig 2F ) ; finally we split these matrices into non-overlapping subsets to be analyzed with the CPMs ( Fig 2G ) . The three detection regimes differ in the distribution of sizes of the sampled tumors ( Fig 2D ) . Under the large detection regime a large fraction of the samples correspond to large tumors . In contrast , under the small detection regime a large fraction of the samples correspond to small tumors . Finally , under the uniform detection regime the distribution of sizes of the sampled tumors is approximately uniform . Thus , the large detection regime would emulate scenarios where cancer tends to be detected at late , advanced stages , and the small detection regime would emulate scenarios where cancer tends to be detected at early stages . To implement these detection regimes , we drew random deviates from beta distributions with parameters B ( 1 , 1 ) , B ( 5 , 3 ) , and B ( 3 , 5 ) ( for uniform , large , and small , respectively ) , rescaled them to the range of the log-transformed distribution of observed tumor sizes ( log of number of cells ) , and obtained the observation with population size closest to the target ( see details in section 2 in S3 Text ) . ( We used the log-scale of tumor size because in the model of [38] tumor population size increases logarithmically with number of driver mutations; thus , distributions of sampled tumors that are biased towards large sizes in the log scale will mimic sampling of late-stage tumors —tumors with a large number of drivers— , and distributions of sampled tumors that are biased towards small sizes in the log scale will mimic sampling of early-stage tumors , as intended ) . For each observation , the genotype returned was the genotype of the most abundant clone ( Fig 2E ) . Finally , the set of 20000 genotypes ( Fig 2F ) was then split into five sets of non-overlapping data sets for each of the three sample sizes of 50 , 200 , and 4000 ( Fig 2G ) . These are the data sets that were analyzed with the CPMs . We have characterized evolutionary unpredictability using the diversity of Lines of Descent ( LODs ) . LODs were introduced by [30] and “ ( … ) represent the lineages that arrive at the most populated genotype at the final time” ( p . 572 ) . In other words , in our simulations a LOD is a sequence of parent-child genotypes , from the initial genotype to a local maximum: a LOD is the path that a tumor has taken until fixation . The final genotype in a LOD is a local fitness maximum , but there are no guarantees that any intermediate genotype in the LOD will have been the most common genotype at any time point ( especially under deviations from SSWM such as clonal interference and stochastic tunneling [25 , 30 , 44] ) . As in [30] , we can use the entropy of these paths to measure the indeterminism of the paths of evolution , or evolutionary unpredictability , and we will define Sp = −∑pi ln pi , where pi is the observed probability of each LOD ( each path ) computed from the 20000 simulations , and the sum is over all paths or LODs . Evolutionary unpredictability , as estimated by the CPMs , will analogously be defined as Sc = −∑qj ln qj , where qj is the probability of each path to the maximum according to the cancer progression model considered , and the sum is over all paths predicted by the CPMs . ( [36] normalizes predictability by dividing by the maximum entropy , similar to dividing by the prior entropy in the “information gain” statistic in [5]; but the maximum entropy is a constant for each number of genes , i . e . , 7 ! or 10 ! for our simulations ) . To measure how well CPMs predict tumor progression , we used three different statistics . To compare the overall similarity of the distribution of paths predicted by CPMs with the true observed one ( i . e . , the distribution of LODs ) we used the Jensen-Shannon divergence ( JS ) [45 , 46] , scaled between 0 and 1 ( equivalent to using the logarithm of base 2 ) . JS is a symmetrized Kullback-Leibler divergence between two distributions and is defined even if the two distributions do not have the same sample space , i . e . , even if P ( i ) ≠ 0 and Q ( i ) = 0 ( or Q ( i ) ≠ 0 and P ( i ) = 0 ) , as can often be the case for our data . A JS value of 0 means that the distributions are identical , and a value of 1 that they do not overlap . Therefore , predictions of CPMs are closer to the truth the smaller the value of JS . The sum of the probabilities of the paths in the LODs that are not among the paths allowed by the CPMs , P ( ¬DAG|LOD ) , is equivalent to 1—recall . Larger values of 1-recall mean that the CPM is not capturing a large fraction of the evolutionary paths to the maximum ( or maxima ) . The sum of the predicted probabilities of paths according to the CPMs that are not used by evolution ( i . e . , that are not LODs ) , P ( ¬LOD|DAG ) , is equivalent to 1—precision . Larger values of 1-precision mean that the CPMs predict larger numbers of paths to the maximum that are not used by evolution . In S6 Text we also use as statistic the probability of recovering the most common LOD; we will rarely refer to this statistic in the main paper since it follows a pattern very similar to recall ( section 2 in S6 Text ) . Statistics 1-recall and 1-precision can overestimate performance: they could both have a value of 0 , even when JS is very close to 1 ( see example in section 4 in S4 Text ) . Thus , the main overall performance measure will be JS . All the fitness landscapes used are shown in S1 Fig . We provide here a brief description of the main features of the three different fitness landscapes and the simulated data sets . The three types of fitness landscapes had comparable numbers of accessible genotypes but differed in the number of local fitness maxima and reciprocal sign epistasis , as shown in Figures A to C in S2 Fig ( representable fitness landscapes had a single fitness maximum with no reciprocal sign epistasis , whereas RMF landscapes had the largest of both , and local maxima landscapes were intermediate ) . Simulations resulted in varied amounts of clonal interference , as measured by the average frequency of the most common genotype ( Figures D and E in S2 Fig ) ; scenarios where clonal sweeps dominated ( i . e . , those characterized by the smallest clonal interference ) corresponded to initial population sizes of 2000 , with clonal interference being much larger at the other population sizes ( Figure D in S2 Fig ) . Simulations resulted in a wide range of numbers of paths to the maximum ( number of distinct LODs: Figure F in S2 Fig ) . LOD diversities ( Sp ) ranged from 0 . 3 to 8 . 7 ( Figure G in S2 Fig ) with RMF models showing smaller Sp; RMF landscapes had the largest number and diversity of observed local fitness maxima ( Figures H and I in S2 Fig ) and Sp was strongly associated to the number of accessible genotypes ( Figure J in S2 Fig ) . As designed , the number of mutations of the fitness maxima were 7 and 10 in the representable landscapes; the mean number of mutations were smaller in the fitness maxima of the local maxima and RMF landscapes ( Figure K in S2 Fig ) . The number of different sampled genotypes was comparable between detection regimes ( Figure L in S2 Fig ) , but diversity differed ( Figure M in S2 Fig ) , with the uniform detection regime showing generally larger sampled diversity . The mean and median number of mutations of sampled genotypes ( Figures N and O in S2 Fig ) differed between detection regimes , being largest in the large detection regime , and smallest in the small detection regime; the standard deviation and coefficient of variation in the number of mutations ( Figures P and Q in S2 Fig ) were largest in the uniform detection regime ( thus , the uniform detection regime showed both the largest variation in number of mutations of genotypes and the largest diversity of genotypes ) . Sample characteristics and the difference in sample characteristics between detection regimes were affected by type of fitness landscape ( Figures M and P in S2 Fig ) . We have used twenty-two cancer data sets ( including six different cancer types: breast , glioblastoma , lung , ovarian , colorectal , and pancreatic cancer ) . All of these data sets , except for the breast cancer data sets BRCA_ba_s and BRCA_he_s ( from [54] ) , have been used previously as input for some CPM algorithms in [10 , 14–16 , 19 , 34 , 35] , with the original sources of the data being [55–65] . Details on sources , names , and how the data were obtained and processed are provided in S5 Text . These data sets vary in sample size ( 27 to 594 samples ) , number of features ( from 7 to over 100 ) , data types ( nonsynonymous somatic mutations and copy number aberrations or both ) , levels of analysis ( genes , modules and pathways , exclusivity groups ) , patterns of number of mutations per subject and frequency of mutations analyzed , and procedures for driver selection , and restriction of patient subtypes . The data sets , therefore , are a large representative ensemble of data sets to which researchers have previously applied or might apply CPMs . We have run the CPM analyses three times per data set , limiting the number of features analyzed to the 7 , 10 , and 12 most common ones , so as to examine how our assessments depend on the number of features analyzed; the first two thresholds use the same number of features as the simulations . ( For data sets with 7 or fewer features , there are no differences in the data sets used under the 7 , 10 , and 12 thresholds; likewise for data sets with 8 to 10 features with respect to thresholds 10 and 12 ) . We do not know the true paths of tumor progression , but we can use the bootstrap to assess the robustness or reliability of the inferences . To do so , we repeated the process above with 100 bootstrap samples ( section 1 . 2 in S5 Text ) . We computed JSo , b , the average JS between the distribution of paths to the maximum from the original data set and each of the bootstrapped samples . Large differences in the distribution of paths between the analyses with the bootstrap samples and the analysis with the original sample ( i . e . , large JSo , b ) would suggest that the inferences are unreliable and cannot be trusted ( but small differences do not indicate that the inferred paths match the distribution of the true ones ) .
The CPMs used ( four , two with two variants , yielding a total of six different procedures for obtaining CPMs: H-CBN , MCCBN , OT , CAPRI_AIC , CAPRI_BIC , and CAPRESE ) can be divided into three groups: models that return trees ( OT and CAPRESE ) and two families of models that return DAGs , CBN ( H-CBN and MCCBN ) and CAPRI ( CAPRI_AIC and CAPRI_BIC ) . Comparing within groups with respect to JS one member of the pair consistently outperformed the other ( see Figure A in S6 Text ) . OT ( using probability-weighted paths , see below ) was significantly better than CAPRESE ( paired t-test over all non-missing 56595 pairs of results: t56594 = −161 . 1 , P < 0 . 0001 ) , H-CBN was significantly better than MCCBN ( t56593 = −42 . 6 , P < 0 . 0001 ) , and CAPRI_AIC was significantly better than CAPRI_BIC ( t56594 = −41 . 9 , P < 0 . 0001 ) . In what follows , therefore , and for the sake of brevity , we will focus on OT , H-CBN , and CAPRI_AIC , since the overall performance of their alternatives is worse . Fig 3 shows how the performance measures for OT , H-CBN , and CAPRI_AIC change with sample size for all combinations of type of landscape , detection regime , and number of genes ( see Figure B in S6 Text for the probability of recovering the most common LOD ) . The measures of JS and 1-precision for OT and H-CBN ( and MCCBN ) use probability-weighted paths computed as explained in Measures of performance and predictability , because there was strong evidence for all three models that the probability-weighted paths led to better results ( JS , paired t-test over all pairs: OT , t56594 = −195 . 8 , P < 0 . 0001; H-CBN: t56594 = −222 . 3 , P < 0 . 0001; MCCBN: t56593 = −149 . 0 , P < 0 . 0001; 1-precision: OT: t56594 = −187 . 6 , P < 0 . 0001; H-CBN: t56594 = −217 . 6 , P < 0 . 0001; MCCBN: t56593 = −130 . 3 , P < 0 . 0001 ) . ( See also Figures D to F in S6 Text ) . Overall , H-CBN was the model with the best performance ( P < 0 . 0001 from all pairwise comparisons between the six procedures with Tukey’s contrasts and single-step multiple testing p-value adjustment [66] on linear mixed-effects models with landscape by split replicate as random effect ) . It must be noted , however , that all CPMs can show large variability in performance ( Figure G in S6 Text ) . JS differed between type of landscape , number of genes , detection regime , and sample size , but the magnitude and even direction of effects differed between combinations of those factors , as seen in Figs 3 and 4 . Generalized linear mixed-effects models fitted to the complete data set and to the different combinations of CPM and type of landscape ( see section 11 in S6 Text ) also showed highly significant ( P < 0 . 0001 ) two- , three- , and four-way interactions between most of the factors , in particular those involving type of landscape and detection regime . Type of landscape and detection regime also had very strong effects in the variability of the estimates , with relative variabilities that could reach 20% with small sample sizes ( Figure G in S6 Text ) . Under representable fitness landscapes , performance improved with increasing sample size and with the uniform detection regime . Performance was worse in fitness landscapes of 10 genes ( Figs 3A and 4 top row ) ; the decrease in performance with increasing number of genes is related to CPMs both missing evolutionary paths ( Fig 3B ) , and allowing paths that are not used by evolution ( Fig 3C ) . With CAPRI_AIC the effect of sample size was much weaker and increases in sample size could lead to decreases in performance , specially under the uniform detection regime ( highly significant , P < 0 . 0001 , interactions of detection and sample size —section 11 in S6 Text ) . This is attributable to CAPRI_AIC excluding many paths taken during evolution ( Fig 3B ) . This behavior was caused by CAPRI_AIC sometimes allowing only a few or even just one path to the maximum ( Figure H in S6 Text ) ; see also next section . Under the RMF landscape overall performance was worse . Increasing sample size for OT and H-CBN led to minor decreases in performance ( Figs 3 and 4 bottom row ) . CPMs failed to capture about 50% of the evolutionary paths ( or fractions of paths ) to the local maxima ( Fig 3B ) and included more than 75% of paths ( or fractions of paths ) that were never taken by evolution ( Fig 3C ) . The behavior under local maxima was similar to that of representable fitness landscapes in terms of the direction of most effects , but effects were generally weaker , with the exception of evolutionary unpredictability . Evolutionary unpredictability itself had a strong effect on performance . There were highly significant interactions ( P < 0 . 0001 ) between evolutionary unpredictability ( as measured with Sp ) , detection regime , and sample size , within representable and local maxima landscapes , as well as in the overall models ( section 11 in S6 Text ) . In most scenarios , performance was worse with larger unpredictability ( larger Sp ) as seen by the positive slopes of JS on Sp ( Fig 5 ) . But under representable landscapes , in the large detection regime and for sample sizes 50 and 200 , larger evolutionary unpredictability was associated with better performance . Under RMF fitness landscapes , large evolutionary unpredictability was associated with poorer performance over all sample sizes . Under local maxima , the effect of evolutionary unpredictability depended strongly on sample size and detection regime , with reversal of effects from sample size of 50 compared to 4000 under the large detection regime , similar to the ones in representable landscapes . Fig 6A shows the ratio of inferred to true evolutionary unpredictability , Sc/Sp . Under representable fitness landscapes , for H-CBN this ratio remained close to 1 over all combinations of detection regime , number of genes , and sample size; the values were closest to one with sample size 4000 and under the uniform detection regime . This is in spite of large differences in the ratio of estimated number of paths to the maximum over true number of paths to the maximum ( Fig 6B ) . This good performance is a consequence of both using probability-weighted paths by H-CBN ( and OT ) and of changes in scale ( diversities use logarithms ) . Patterns for CAPRI_AIC seemed dominated by the tendency of CAPRI_AIC to only allow very few paths as the sample size grows large ( see also Figure H in S6 Text ) and were also the consequence of CAPRI_AIC’s inability to produce probability-weighted paths . For all CPMs type of landscape affected the quality of estimates: under local maxima and specially RMF the number and diversity of paths tended to be overestimated , sometimes by large factors . In summary , and regardless of fitness landscape , the estimates of evolutionary unpredictability from H-CBN ( Sc ) could be used to obtain an upper bound of the true evolutionary unpredictability . And how does the estimated evolutionary unpredictability change with the true evolutionary unpredictability ? Fig 6C shows that the slopes of regressions of estimated unpredictability from CPMs ( Sc ) on true unpredictability ( Sp ) changed depending on fitness landscape , detection regime , and sample size , including slopes over and under 1 , and even inversion of signs . We have used H-CBN ( the best performing model in the simulations ) on twenty-two cancer data sets to examine the estimated evolutionary unpredictability and to assess the reliability of the estimates . The results are shown in Fig 7 ( see Figure A in S5 Text for ranges of bootstrap runs ) . Unreliability ( JSo , b —section Cancer data sets ) was large for most data sets , and very large for some of them . These results would be expected , even if the true fitness landscapes were representable ones , as most of the data sets have small sample sizes ( less than 1000 ) , and we have seen that performance is poor ( large JS ) for that range of sample sizes ( Fig 3A ) . For these data sets there was no relationship between JSo , b and sample size ( Fig 7A ) , and when the same data set was analyzed using pathways/modules and genes , performance was generally better using pathways or modules ( Pan_pa vs . Pan_ge , Col_pa vs . Col_g , GBM_pa vs . GBM_ge , GBM_mo vs . GBM_CNA ) . Within data sets , and for all data sets , as the number of features analyzed increased performance either decreased or stayed the same ( i . e . , for data sets with more than 7 features , unreliability at the 10 feature threshold , J S o , b 10 , was larger or equal to unreliability at the 7 feature threshold , J S o , b 7; for data sets with more than 10 features , J S o , b 7 ≤ J S o , b 10 ≤ J S o , b 12: Figure A in S5 Text ) . There were mild trends for an association between smaller JSo , b and smaller numbers of features and smaller Sc ( Fig 7B and 7C ) , with notable exceptions: the Pancreas Pathways ( Pan_pa ) data set had very small JSo , b even for moderate number of features , and the All Pathways ( all_pa ) data set had a relatively small JSo , b even though it used 12 features and had a large Sc; the GBM CNA modules ( GBM_mo ) data set also showed moderate JSo , b in spite of having nine features and relatively large Sc . Conversely , some data sets with small Sc had extremely unreliable path predictions ( e . g . , BRCA_ba_s , Col_mss_co , Col_msi_co , GBM_ge ) . Values for Sc were well within the ranges of Sc estimated by H-CBN for the simulated data ( Figure K in S6 Text ) . As expected , Sc increased with the number of features analyzed ( see also Figure D in S5 Text ) . Given the results from section Inferring evolutionary unpredictability from CPMs , where generally Sp < Sc , this suggests that the true evolutionary unpredictability ( when analyzing up to 12 features ) for 13 of the data sets should be less than that corresponding to about 100 equiprobable paths to the maximum , but only eight are below the much more manageable , and useful , 20 equiprobable paths ( Fig 7D ) . The Pan_pa , GBM_coo , and BRCA_he_s show outstanding patterns in Fig 7 . Examination of the output from H-CBN revealed that there was one single path with estimated probability > 0 . 97 for Pan_pa , and two paths to the maximum of about equal probability that together added > 0 . 95 for GBM_coo . BRCA_he_s had only four features but mutations in SRPRA and PIK3R1 were present each in only four individuals ( different individuals for the two mutations ) ; repeated runs of H-CBN led to different sets of restrictions being inferred which , because there are few paths to the maximum , and some had large probabilities ( > 0 . 5 ) , resulted in large differences in JS statistic between runs .
The answer to the question “can we predict the likely course of tumor progression using CPMs ? ” is , unfortunately , at least for the models examined , “only with moderate success and only under representable fitness landscapes and with very large sample sizes; but even perfect predictions might be of little use if evolutionary unpredictability is large” . Estimating upper bounds to evolutionary unpredictability is a more modest , though more likely to succeed , use of CPMs . Promisingly , several cancer data sets showed low evolutionary unpredictability . There are three key difficulties for successful prediction: the sheer size of the problem even for moderate numbers of genes , the intrinsic evolutionary unpredictability in many scenarios , and the deviations from the assumptions of CPMs that are likely to hold in most cancer data . Further methodological work to allow CPMs to deal with rugged , multi-peaked , fitness landscapes could improve their usefulness to predict tumor evolution . In addition to the caveat about using these models under scenarios where performance is very poor , this paper raises the general question of what can we really predict about likely paths of tumor progression from cross-sectional data , for instance to guide therapeutic interventions . At a minimum , measures such as JSo , b and Sc with CPMs that return probability-weighted paths should probably become routine as ways of providing a sense of the reliability of predictions and for assessing whether the predictions could be of any practical use . | Knowing the likely paths of tumor progression is instrumental for cancer precision medicine as it would allow us to identify genetic targets that block disease progression and to improve therapeutic decisions . Direct information about paths of tumor progression is scarce , but cancer progression models ( CPMs ) , which use as input cross-sectional data on genetic alterations , can be used to predict these paths . CPMs , however , make assumptions about fitness landscapes ( genotype-fitness maps ) that might not be met in cancer . We examine if four CPMs can be used to predict successfully the distribution of tumor progression paths; we find that some CPMs work well when sample sizes are large and fitness landscapes have a single fitness maximum , but in fitness landscapes with multiple fitness maxima prediction is poor . However , the best performing CPM in our study could be used to estimate evolutionary unpredictability . When we apply the best performing CPM in our study to twenty-two cancer data sets we find that predictions are generally unreliable but that some cancer data sets show low unpredictability . Our results highlight that CPMs could be valuable tools for predicting disease progression , but emphasize the need for methodological work to account for multi-peaked fitness landscapes . | [
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"birth",... | 2019 | Every which way? On predicting tumor evolution using cancer progression models |
The human gut microbiota is an important metabolic organ , yet little is known about how its individual species interact , establish dominant positions , and respond to changes in environmental factors such as diet . In this study , gnotobiotic mice were colonized with an artificial microbiota comprising 12 sequenced human gut bacterial species and fed oscillating diets of disparate composition . Rapid , reproducible , and reversible changes in the structure of this assemblage were observed . Time-series microbial RNA-Seq analyses revealed staggered functional responses to diet shifts throughout the assemblage that were heavily focused on carbohydrate and amino acid metabolism . High-resolution shotgun metaproteomics confirmed many of these responses at a protein level . One member , Bacteroides cellulosilyticus WH2 , proved exceptionally fit regardless of diet . Its genome encoded more carbohydrate active enzymes than any previously sequenced member of the Bacteroidetes . Transcriptional profiling indicated that B . cellulosilyticus WH2 is an adaptive forager that tailors its versatile carbohydrate utilization strategy to available dietary polysaccharides , with a strong emphasis on plant-derived xylans abundant in dietary staples like cereal grains . Two highly expressed , diet-specific polysaccharide utilization loci ( PULs ) in B . cellulosilyticus WH2 were identified , one with characteristics of xylan utilization systems . Introduction of a B . cellulosilyticus WH2 library comprising >90 , 000 isogenic transposon mutants into gnotobiotic mice , along with the other artificial community members , confirmed that these loci represent critical diet-specific fitness determinants . Carbohydrates that trigger dramatic increases in expression of these two loci and many of the organism's 111 other predicted PULs were identified by RNA-Seq during in vitro growth on 31 distinct carbohydrate substrates , allowing us to better interpret in vivo RNA-Seq and proteomics data . These results offer insight into how gut microbes adapt to dietary perturbations at both a community level and from the perspective of a well-adapted symbiont with exceptional saccharolytic capabilities , and illustrate the value of artificial communities .
A growing body of evidence indicates that the tens of trillions of microbial cells that inhabit our gastrointestinal tracts extend our biological capabilities in important ways . Microbial enzymes process many compounds that would otherwise pass through our intestines unaltered [1] , and cases of particular nutrient substrates favoring the growth of particular taxa are being reported [2]–[5] . Changes in diet are therefore expected to lead to changes in the composition and function of the microbiota [6]–[10] . However , our understanding of diet–microbiota interactions at a mechanistic level is still in its infancy . The absence of a complete catalog of the microbial strains and associated genome sequences that comprise a given microbiota complicates efforts to describe how particular dietary substrates influence individual taxa , how taxa cooperate/compete to utilize nutrients , and how these many interactions in aggregate lead to emergent host phenotypes . Gnotobiotic mice colonized with defined consortia of sequenced human gut microbes , on the other hand , provide an in vivo model of the microbiota in which the identity of all taxa and genes comprising the system are known . Within these assemblages , expressed mRNAs and proteins can be attributed to their genome , gene , and species of origin , and findings of interest can be pursued in follow-up in vitro or in vivo experiments . These systems also afford an opportunity to tightly control experimental variables to a degree not possible in human studies and have proven useful in studying microbial invasion , microbe–microbe interactions , and the metabolic roles of key ecological guilds [11]–[15] . Studies aiming to better understand community-level assembly , resilience , and adaptation are therefore likely to benefit from a focus on such defined systems . However , the limited taxonomic and functional representation within artificial communities of modest complexity requires that caution be exercised when extrapolating results to more complex , naturally occurring gut communities ( see Prospectus ) . Culture-independent surveys of the healthy adult gut microbiota consistently conclude that it is composed primarily of members of two bacterial phyla , the Bacteroidetes and Firmicutes [16]–[21] . The dominance of these two bacterial phyla suggests that their representatives in the human gut are exquisitely adapted to its dynamic conditions , which include a constantly evolving nutrient environment . Members of the genus Bacteroides are known to be adept at utilizing both plant- and host-derived polysaccharides [22] . Comparisons of available Bacteroides genomes with those from other gut species indicate that the former are enriched in genes involved in the acquisition and metabolism of various glycans , including glycoside hydrolases ( GHs ) and polysaccharide lyases ( PLs ) , as well as linked environmental sensors that control their expression ( e . g . , hybrid two-component systems , extracytoplasmic function ( ECF ) sigma factors and anti-sigma factors ) . Many of these genes are organized into polysaccharide utilization loci ( PULs ) that are distributed throughout the genome [23] , [24] . Recent studies have focused on better understanding the evolution , specificity , and regulation of PULs in the genomes of species like Bacteroides thetaiotaomicron and Bacteroides ovatus [25] , [26] . Little is known , however , about the metabolic strategies adopted by multiple competing species in more complex communities , how dietary changes lead to reconfigurations in community structure through changes in individual species , or whether dietary context influences which genes dominant species rely on to remain competitive with other microbes , including those genes that are components of PULs . Here , we adopt a multifaceted approach to study an artificial community in gnotobiotic mice fed changing diets in order to better understand ( i ) the process by which such a community reconfigures itself structurally in response to changes in host diet; ( ii ) how aggregate community function , as judged by the metatranscriptome and metaproteome , is impacted when host diet is altered; ( iii ) how the metabolic strategies of its individual component microbes change , if at all , when the nutrient milieu is dramatically altered , with an emphasis on one prominent but understudied member of the human gut Bacteroides; and ( iv ) whether a microbe's metabolic versatility/flexibility correlates with competitive advantage in an assemblage containing related and unrelated species .
Though at least eight complete and 68 draft genomes of Bacteroides spp . are currently available [27] , there are numerous examples of distinct clades within this genus where little genomic information exists . To further explore the genome space of one such clade , we obtained a human fecal isolate whose four 16S rRNA gene sequences indicate a close relationship to Bacteroides cellulosilyticus ( Figure S1A , B ) . The genome of this isolate , which we have designated B . cellulosilyticus WH2 , was sequenced deeply , yielding a high-quality draft assembly ( 23 contigs with an N50 value of 798 , 728 bp; total length of all contigs in the assembly , 7 . 1 Mb; Table S1 ) . Annotation of its 5 , 244 predicted protein-coding genes using the carbohydrate active enzyme ( CAZy ) database [28] revealed an extraordinary complement of 503 CAZymes comprising 373 GHs , 23 PLs , 28 carbohydrate esterases ( CEs ) , and 84 glycosyltransferases ( GTs ) ( see Table S2 for all annotated genes in the B . cellulosilyticus WH2 genome predicted to have relevance to carbohydrate metabolism ) . One distinguishing feature of gut Bacteroides genomes is the substantial number of CAZymes they encode relative to those of other intestinal bacteria [29] . The B . cellulosilyticus WH2 CAZome is enriched in a number of GH families even when compared with prominent representatives of the gut Bacteroidetes ( Figure S2A ) . When we expanded this comparison to include all 86 Bacteroidetes in the CAZy database , we found that the B . cellulosilyticus WH2 genome had the greatest number of genes for 19 different GH families , as well as genes from two GH families that had not previously been observed within a Bacteroidetes genome ( Figure S2B ) . Altogether , B . cellulosilyticus WH2 has more GH genes at its disposal than any other Bacteroidetes species analyzed to date . In Bacteroides spp . , CAZymes are often located within PULs [30] . At a minimum , a typical PUL harbors a pair of genes with significant homology to the susC and susD genes of the starch utilization system ( Sus ) in B . thetaiotaomicron [30]–[32] . Other genes encoding enzymes capable of liberating oligo- and monosaccharides from a larger polysaccharide are also frequently present . The susC- and susD-like genes of these loci encode the proteins that comprise the main outer membrane binding and transport apparatus and thus represent key elements of these systems . A search of the B . cellulosilyticus WH2 genome for genes with strong homology to the susC- and susD-like genes in B . thetaiotaomicron VPI-5482 revealed an unprecedented number of susC/D pairs ( a total of 118 ) . Studies of other prominent Bacteroides spp . have found that the evolutionary expansion of these genes has played an important role in endowing the Bacteroides with the ability to degrade a wide range of host- and plant-derived polysaccharides [25] , [33] . Analysis of deeply sampled adult human gut microbiota datasets indicates that B . cellulosilyticus strains are common , colonizing approximately 77% of 124 adult Europeans characterized in one study [18] and 62% of 139 individuals living in the United States examined in another survey [20] . We hypothesized that the apparent success of B . cellulosilyticus in the gut is derived in part from its substantial arsenal of genes involved in carbohydrate utilization . To test the fitness of B . cellulosilyticus WH2 in relation to other prominent gut symbionts , and the importance of diet on its fitness , we carried out an experiment in gnotobiotic mice ( experiment 1 , “E1 , ” Figure S3 ) . Two groups of 10–12-wk-old male germ-free C57BL/6J animals were moved to individual cages within gnotobiotic isolators ( n = 7 animals/group ) . At day zero , each animal was colonized by oral gavage with an artificial community comprising 12 human gut bacterial species ( Figure 1A , Table S3 ) . Each species chosen for inclusion in this microbial assemblage met four criteria: ( i ) it was a member of one of three bacterial phyla routinely found in the human gut ( i . e . , Bacteroidetes , Firmicutes , or Actinobacteria ) , ( ii ) it was identified as a prominent member of the human gut microbiota in previous culture-independent surveys , ( iii ) it could be grown in the laboratory , and ( iv ) its genome had been sequenced to at least a high-quality draft level . Species were also selected for their functional attributes ( as judged by their annotated gene content ) in an effort to create an artificial community that was somewhat representative of a more complex human microbiota . For example , although more than half of the species in the assemblage were Bacteroidetes predicted to excel at the breakdown of polysaccharides , several were also prominent inhabitants of the human gut that are thought to have limited carbohydrate utilization capabilities ( e . g . , Firmicutes from Clostridium cluster XIVa ) . Some attributes for the 12 strains included in the artificial community are provided in Table S4 . For 2 wk , each treatment group was fed a standard low-fat/high-plant polysaccharide ( LF/HPP ) mouse chow , or a “Western”-like diet where calories are largely derived from fat , starch , and simple sugars ( high-fat/high-sugar ( HF/HS ) ) [12] . Over the course of 6 wk , diets were changed twice at 2-wk intervals , such that each group began and ended on the same diet , with an intervening 2-wk period during which the other diet was administered ( Figure S3 ) . Using fecal DNA as a proxy for microbial biomass , the plant polysaccharide-rich LF/HPP diet supported 2- to 3-fold more total bacterial growth ( primary productivity ) despite its lower caloric density ( 3 . 7 kcal/g versus 4 . 5 kcal/g for the HF/HS diet; Figure S4A ) . The HF/HS diet contains carbohydrates that are easily metabolized and absorbed in the proximal intestine ( sucrose , corn starch , and maltodextrin ) , with cellulose being the one exception ( 4% of the diet by weight versus 46 . 3% for the other carbohydrate sources ) . Thus , in mice fed the HF/HS diet , diet-derived simple sugars are likely to be rare in the distal gut where the vast majority of gut microbes reside; this may provide an advantage to those bacteria capable of utilizing other carbon sources ( e . g . , proteins/oligopeptides , host glycans ) . In mice fed the LF/HPP diet , on the other hand , plant polysaccharides that are indigestible by the host should provide a plentiful source of energy for saccharolytic members of the artificial community . To evaluate the impact of each initial diet and subsequent diet switch on the structural configuration of the artificial community , we performed shotgun sequencing ( community profiling by sequencing; COPRO-Seq ) [11] of DNA isolated from fecal samples collected throughout the course of the experiment , as well as cecal contents collected at sacrifice . The relative abundances of the species in each sample ( defined by the number of sequencing reads that could be unambiguously assigned to each microbial genome after adjusting for genome uniqueness ) were subjected to ordination by principal coordinates analysis ( PCoA ) ( Figure S5A ) . As expected , diet was found to be the predominant explanatory variable for observed variance ( see separation along principal coordinate 1 , “PC1 , ” which accounts for 52% of variance ) . The overall structure of the artificial community achieved quasi-equilibrium before the midpoint of the first diet phase , as evidenced by the lack of any significant movement along PC1 after day five . A structural reconfiguration also took place over the course of ∼5 d following transition to the second diet phase . Notably , the two treatment groups underwent a near-perfect inversion in their positions along PC1 after the first diet switch; the artificial community in animals switched from a LF/HPP to HF/HS diet took on a structure like that which arose by the end of the first diet phase in animals consuming the HF/HS diet , and vice versa . The second diet switch from phase 2 to 3 resulted in a similar movement along PC1 in the opposite direction , indicating a reversion of the artificial community's configuration to its originally assembled structure in each treatment group . These results , in addition to demonstrating the significant impact of these two diets on the structure of this 12-member artificial human gut community , also suggest that an assemblage of this size is capable of demonstrating resilience in the face of substantial diet perturbations . The assembly process and observed diet-induced reconfigurations also proved to be highly reproducible as evidenced by COPRO-Seq results from a replication of E1 ( experiment 2 , “E2” ) . In this follow-up experiment , fecal samples were collected more frequently than in E1 , providing a dataset with improved temporal resolution . Ordination of E2 COPRO-Seq data by PCoA showed that ( i ) for each treatment group in E2 , the artificial community assembles in a manner similar to its counterpart in E1; ( ii ) structural reconfigurations in response to diet occur with the same timing as in E1; and ( iii ) the quasi-equilibria achieved during each diet phase are highly similar between experiments for each treatment group ( compare Figures 1B and S5A ) . As in E1 , cecal data for each E2 treatment group overlap with their corresponding fecal samples , and DNA yields from E2 fecal samples vary substantially as a function of host diet ( Figure S4B ) . COPRO-Seq provides precise measurements of the proportional abundance of each member species present in the artificial community . Data collected in both E1 and E2 ( Table S5 ) revealed significant differences between members in terms of the maximum abundance levels they achieved , the rates at which their abundance levels were impacted by diet shifts , and the degree to which each species demonstrated a preference for one diet over another ( Figure S4C ) . Changes in each species' abundance over time replicated well across animals in each treatment group , suggesting the assembly process and diet-induced reconfigurations occur in an orderly , rules-based fashion and with minimal stochasticity in this artificial community . A species' relative abundance immediately after colonization ( i . e . , 24 h after gavage/day 1 ) was , in general , a poor predictor of its abundance at the end of the first diet phase ( i . e . , day 13 ) ( E1 R2 = 0 . 23; E2 R2 = 0 . 27 ) , suggesting that early dominance of the founder population was not strongly tied to relative success in the assembly process . In mice initially fed a HF/HS diet , four Bacteroides spp . ( Bacteroides caccae , B . cellulosilyticus WH2 , B . thetaiotaomicron , and Bacteroides vulgatus ) each achieved a relative abundance of ≥10% by the end of the first diet phase ( day 13 postgavage ) , with B . caccae attaining the highest levels ( 37 . 1±4 . 9% and 34 . 2±5 . 5%; group mean ± SD in E1 and E2 , respectively ) . In animals fed the plant polysaccharide-rich LF/HPP chow during the first diet phase , B . cellulosilyticus WH2 was dominant , achieving levels of 37 . 1±2 . 0% ( E1 ) and 41 . 6±3 . 9% ( E2 ) by day 13 . B . thetaiotaomicron and B . vulgatus also attained relative abundances of >10% . Changes in diet often resulted in rapid , dramatic changes in a species' proportional representation . Because the dynamic range of abundance values observed when comparing multiple species was substantial ( lowest , Dorea longicatena ( <0 . 003% ) ; highest , B . caccae ( 55 . 0% ) ) , comparing diet responses on a common scale using raw abundance values was challenging . To represent these changes in a way that scaled absolute increases/decreases in relative abundance to the range observed for each strain , we also normalized each species' representation within the artificial community at each time-point to the maximum proportional abundance each microbe achieved across all time-points within each mouse . Plotting the resulting measure of abundance ( percentage of maximum achieved; PoMA ) over time demonstrates which microbes are strongly responsive to diet ( experience significant swings in PoMA value following a diet switch ) and which are relatively diet-insensitive ( experience only modest or no significant change in PoMA value following a diet switch ) . Heatmap visualization of E1 PoMA values ( Figure S5B ) indicated that those microbes with a preference for a particular diet in one animal treatment group also tended to demonstrate the same diet preference in the other . Likewise , diet insensitivity was also consistent across treatment groups; diet-insensitive microbes were insensitive regardless of the order in which diets were introduced . Of the diet-sensitive taxa , those showing the most striking responses were B . caccae and B . ovatus , which strongly preferred the “Western”-like HF/HS diet and the polysaccharide-rich LF/HPP diet , respectively ( Figures 1C and S4C ) . Among the diet-insensitive taxa , B . thetaiotaomicron showed the most stability in its representation ( Figures 1C and S4C ) , consistent with its reputation as a versatile forager . Paradoxically , B . cellulosilyticus WH2 was both diet-sensitive and highly fit on its less-preferred diet; although this strain clearly achieved higher levels of representation in animals fed the LF/HPP diet , it also maintained strong levels of representation in animals fed the HF/HS diet ( Figures 1C and S4C ) . When taking into account the abundance data for all 12 artificial community members , proportional representation at the end of the first diet phase ( i . e . , day 13 ) was a good predictor of representation at the end of the third diet phase ( i . e . , day 42 ) ( E1 R2 = 0 . 77; E2 R2 = 0 . 84 ) , suggesting that the intervening dietary perturbation had little effect on the ultimate outcomes for most species within this assemblage . However , one very low-abundance strain ( D . longicatena ) achieved significantly different maximum percentage abundances across the two treatment groups in each experiment , suggesting that steady-state levels of this strain may have been impacted by diet history . In mice initially fed the LF/HPP diet , D . longicatena was found to persist throughout the experiment at low levels on both diet regimens . In mice initially fed the HF/HS diet , D . longicatena dropped below the limit of detection before the end of the first diet phase , was undetectable by the end of the second diet phase , and remained undetectable throughout the rest of the time course . This interesting example raises the possibility that for some species , irreversible hysteresis effects may play a significant role in determining the likelihood that they will persist within a gut over long periods of time . These diet-induced reconfigurations in the structure of the artificial community led us to examine the degree to which its members were modifying their metabolic strategies . To establish an initial baseline static view of expression data for each microbe on each diet , we developed a custom GeneChip whose probe sets were designed to target 46 , 851 of the 48 , 023 known or predicted protein-coding genes within our artificial human gut microbiome ( see Materials and Methods ) . Total RNA was collected from the cecal contents of each animal in E1 at the time of sacrifice and hybridized to this GeneChip . The total number of genes whose expression was detectable on each diet was remarkably similar ( 14 , 929 and 14 , 594 detected in the LF/HPP→HF/HS→LF/HPP and HF/HS→LF/HPP→HF/HS treatment groups , respectively ) . A total of 11 , 373 genes ( 24 . 3% ) were expressed on both diets ( Figure S6A ) , while 2 , 003 ( 4 . 3% ) were differentially expressed to a statistically significant degree , including 161 ( 6 . 1% ) of the 2 , 640 genes in the microbiome encoding proteins with CAZy-recognized domains . Figure S6B illustrates the fraction of the community-level CAZome and several species-level CAZomes expressed on each diet ( see Table S6 for a comprehensive list of all genes , organized by species and fold-change in expression , whose cecal expression was detectable on each diet and all genes whose expression was significantly different when comparing data from each treatment group ) . Among taxa demonstrating obvious diet preferences ( as judged by relative abundance data ) , B . caccae and B . cellulosilyticus WH2 provided examples of CAZy-level responses to diet change that were different in several respects . Our observations regarding the carbohydrate utilization capabilities and preferences of B . caccae are summarized in Text S1 . However , our ability to evaluate shifts in B . caccae's metabolic strategy in the gut was limited by its very low abundance in animals fed LF/HPP chow ( i . e . , our mRNA and subsequent protein assays were often not sensitive enough to exhaustively sample B . caccae's metatranscriptome and metaproteome ) . In contrast , the abundance of B . cellulosilyticus WH2 , which favored the LF/HPP diet , remained high enough on both diets to allow for a comprehensive analysis of its expressed genes and proteins . This advantage , along with the exceptional carbohydrate utilization machinery encoded within the genome of this organism , encouraged us to focus on further dissecting the responses of B . cellulosilyticus WH2 to diet changes . Detailed inspection of the expressed B . cellulosilyticus WH2 CAZome ( 503 CAZymes in total ) provided an initial view of this microbe's sophisticated carbohydrate utilization strategy . A comparison of the top decile of expressed CAZymes on each diet disclosed many shared elements between the two lists , spanning many different CAZy families , with just over half of the 50 most expressed enzymes on the plant polysaccharide-rich LF/HPP chow also occurring in the list of most highly expressed enzymes on the sucrose- , corn starch- , and maltodextrin-rich HF/HS diet ( Figure 2A ) . Twenty-five of the 50 most expressed CAZymes on the LF/HPP diet were significantly up-regulated compared to the HF/HS diet; of these , seven were members of the GH43 family ( Figure 2B ) . The GH43 family consists of enzymes with activities required for the breakdown of plant-derived polysaccharides such as hemicellulose and pectin . Inspection of the enzyme commission ( EC ) annotations for the most up-regulated GH43 genes shows that they encode xylan 1 , 4-β-xylosidases ( EC 3 . 2 . 1 . 37 ) , arabinan endo-1 , 5-α-L-arabinosidases ( EC 3 . 2 . 1 . 99 ) , and α-L-arabinofuranosidases ( EC 3 . 2 . 1 . 55 ) . The GH10 family , which is currently comprised exclusively of endo-xylanases ( EC 3 . 2 . 1 . 8 , EC 3 . 2 . 1 . 32 ) , was also well represented among this set of 25 genes , with four of the seven putative GH10 genes in the B . cellulosilyticus WH2 genome making the list . Strikingly , of the 45 predicted genes with putative GH43 domains in the B . cellulosilyticus WH2 genome , none were up-regulated on the “Western”-style HF/HS diet . The most highly expressed B . cellulosilyticus WH2 CAZyme on the plant polysaccharide-rich chow ( which was also highly-expressed on the HF/HS chow ) was BWH2_1228 , a putative α-galactosidase from the GH36 family . These enzymes , which are not expressed by humans in the stomach or intestine , cleave terminal galactose residues from the nonreducing ends of raffinose family oligosaccharides ( RFOs , including raffinose , stachyose , and verbascose ) , galacto ( gluco ) mannans , galactolipids , and glycoproteins . RFOs , which are well represented in cereal grains consumed by humans , are expected to be abundant in the LF/HPP diet given its ingredients ( e . g . , soybean meal ) , but potential substrates in the HF/HS diet are less obvious , possibly implicating a host glycolipid or glycoprotein target . Surface glycans in the intestinal epithelium of rodents are decorated with terminal fucose residues [34] as well as terminal sialic acid and sulfate [35] . Hydrolysis of the α-2 linkage connecting terminal fucose residues to the galactose-rich extended core is thought to be catalyzed in large part by GH95 and GH29 enzymes [36] . The B . cellulosilyticus WH2 genome is replete with putative GH95 and GH29 genes ( total of 12 and 9 , respectively ) , but only a few ( BWH2_1350/2142/3154/3818 ) were expressed in vivo on at least one diet , and their expression was low relative to many other CAZymes ( see Table S6 ) . Cleavage of terminal sialic acids present in host mucins by bacteria is usually carried out by GH33 family enzymes . B . cellulosilyticus WH2 has two GH33 genes that are expressed on either one diet ( BWH2_3822 , HF/HS ) or both diets ( BWH2_4650 ) , but neither is highly expressed relative to other B . cellulosilyticus WH2 CAZymes . Therefore , utilization of host glycans by B . cellulosilyticus WH2 , if it occurs , likely requires partnerships with other members of the artificial community that express GH29/95/33 enzymes ( see Table S6 for a list of members that express these enzymes in a diet-independent and/or diet-specific fashion ) . Among the 50 most highly expressed B . cellulosilyticus WH2 CAZymes , 12 were significantly up-regulated on the HF/HS diet compared to the LF/HPP diet , with members of family GH13 being most prevalent . While the enzymatic activities and substrate specificities of GH13 family members are varied , most relate to the hydrolysis of substrates comprising chains of glucose subunits , including amylose ( one of the two components of starch ) and maltodextrin , both prominent ingredients in the HF/HS diet . GeneChip-based profiling of the E1 cecal communities provided a snapshot of the metatranscriptome on the final day of the final diet phase in each treatment group . The analysis of B . cellulosilyticus WH2 CAZyme expression suggested that this strain achieves a “generalist” lifestyle not by relying on substrates that are present at all times ( e . g . , host mucins ) , but rather by modifying its resource utilization strategy to effectively compete with other microbes for diet-derived polysaccharides that are not metabolized by the host . To develop a more complete understanding of the dynamic changes that occur in gene expression over time and throughout the artificial community following diet perturbations , we performed microbial RNA-Seq analyses using feces obtained at select time-points from mice in the LF/HPP→HF/HS→LF/HPP treatment group of E2 ( Figure S3 ) . We began with a “top-down” analysis in which every RNA-Seq read count from every gene in the artificial microbiome was binned based on the functional annotation of the gene from which it was derived , regardless of its species of origin . In this case , the functional annotation used as the binning variable was the predicted EC number for a gene's encoded protein product . Expecting that some changes might occur rapidly , while others might require days or weeks , we searched for significant differences between the terminal time-points of the first two diet phases ( i . e . , points at which the model human gut microbiota had been allowed 13 d to acclimate to each diet ) . The 157 significant changes we identified were subjected to hierarchical clustering by EC number to determine which functional responses occurred with similar kinetics . The results revealed that in contrast to the rapid , diet-induced structural reconfigurations observed in this artificial community , community-level changes in microbial gene expression occurred with highly variable timing that differed from function to function . These changes were dominated by EC numbers associated with enzymatic reactions relevant to carbohydrate and amino acid metabolism ( see Table S7 for a summary of all significant changes observed , including aggregate expression values for each functional bin ( EC number ) at each time-point ) . Significant responses could be divided into one of three groups: “rapid” responses were those where the representation of EC numbers in the transcriptome increased/decreased dramatically within 1–2 d of a diet switch; “gradual” responses were those where the representation of EC numbers changed notably , but slowly , between the two diet transition points; and “delayed” responses were those where significant change did not occur until the end of a diet phase ( Figure 3 , Table S7 ) . EC numbers associated with reactions important in carbohydrate metabolism and transport were distributed across all three of these response types for each of the two diets . Nearly all genes encoding proteins with EC numbers related to amino acid metabolism that were significantly up-regulated on HF/HS chow binned into the “rapid” or “gradual” groups , suggesting this diet put immediate pressure on the artificial microbial community to increase its repertoire of expressed amino acid biosynthesis and degradation genes . Genes with assigned EC numbers involved in amino acid metabolism that were significantly up-regulated on the other , polysaccharide-rich , LF/HPP diet were spread more evenly across these three response types ( Figure 3 ) . Careful inspection of our top-down analysis results and a complementary “bottom-up” analysis in which normalization was performed at the level of individual species , rather than at the community level , allowed us to identify other important responses that would have gone undetected were it not for the fact that we were dealing with a defined assemblage of microbes where all of the genes in component members' genomes were known . For example , an assessment of the representation of EC 3 . 2 . 1 . 8 ( endo-1 , 4-β-xylanase ) within the metatranscriptome before and after the first diet switch ( LF/HPP→HF/HS ) initially suggested that this activity was reduced to a statistically significant degree as a result of the first diet perturbation ( day 13 versus day 27; Mann–Whitney U test , p = 0 . 03; Figure S7A ) . Aggregation by species of all sequencing read counts assignable to mRNAs encoding proteins with this EC number revealed that over 99% of the contributions to this functional bin originated from B . cellulosilyticus WH2 ( note the similarity in a comparison of Figure S7A and Figure S7B ) , implying that the community-level response and the response of this Bacteroides species were virtually one and the same . A tally of all sequencing reads assignable to B . cellulosilyticus WH2 at each time-point disclosed that although this strain maintains high proportional representation in the artificial community throughout each diet oscillation period ( range , 10 . 3–42 . 5% and 11 . 6–43 . 3% for E1 and E2 , respectively ) , its contribution to the metatranscriptome is substantially decreased during the HF/HS diet phase ( Figure S7C ) . This dramatic reduction in the extent to which B . cellulosilyticus WH2 contributes to the metatranscriptome in HF/HS-fed mice “masks” the significant up-regulation of EC 3 . 2 . 1 . 8 that occurs within the B . cellulosilyticus WH2 transcriptome following the first diet shift ( day 13 versus day 27; Mann–Whitney U test , p = 0 . 03; Figure S7D ) . A further breakdown of endo-1 , 4-β-xylanase up-regulation in B . cellulosilyticus WH2 when mice are switched to the HF/HS diet reveals that most of this response is driven by two genes , BWH2_4068 and BWH2_4072 ( Figure S7E ) . Our realization that we were unable to correctly infer the direction of one of the most significant diet-induced gene expression changes in the second most abundant strain in the artificial community when inspecting functional responses at the community level provides a strong argument for expanding the use of microbial assemblages comprised exclusively of sequenced species in studies of the gut microbiota . This should allow the contributions of individual species to community activity to be evaluated in a rigorous way that is not possible with microbial communities of unknown or poorly defined gene composition . In principle , protein measurements can provide a more direct readout of expressed community functions than an RNA-level analysis , and thus a deeper understanding of community members' interactions with one another and with their habitat [37] , [38] . For these reasons and others , much work has been dedicated to applying shotgun proteomics techniques to microbial ecosystems in various environments [39] , [40] . Though these efforts have provided illustrations of significant methodological advances , they have been limited by the complexity of the metaproteomes studied and by the difficulties this complexity creates when attempting to assign peptide identities uniquely to proteins of specific taxa . Recognizing that a metaproteomics analysis of our artificial community would not be subject to such uncertainty given its fully defined microbiome and thus fully defined theoretical proteome , we subjected cecal samples from two mice from each diet treatment group in E1 ( n = 4 total ) to high-performance liquid chromatography-tandem mass spectrometry ( LC-MS/MS; see Materials and Methods ) . We had three goals: ( i ) to evaluate how our ability to assign peptide-spectrum matches ( PSMs ) to particular proteins within a theoretical metaproteome is affected by the presence of close homologs within the same species and within other , closely related species; ( ii ) to test the limits of our ability to characterize protein expression across different species given the substantial dynamic range we documented in microbial species abundance; and ( iii ) to collect semiquantitative peptide/protein data that might validate and enrich our understanding of functional responses identified at the mRNA level , particularly with respect to the niche ( profession ) of CAZyme-rich B . cellulosilyticus WH2 . Given the evolutionary relatedness of the strains involved , we expected that some fraction of observed PSMs from each sample would be of ambiguous origin due to nonunique peptides shared between species' proteomes . To assess which species might be most affected by this phenomenon when characterizing the metaproteome on different diets , we catalogued each strain's theoretical peptidome using an in silico tryptic digest . This simulated digest took into account both the potential for missed trypic cleavages and the peptide mass range that could be detected using our methods . The results ( Figure S8A , Table S8 ) demonstrated that for an artificial community of modest complexity , the proportion of peptides within each strain's theoretical peptidome that are “unique” ( i . e . , assignable to a single protein within the theoretical metaproteome ) varies substantially from species to species , even among those that are closely related . We found the lone representative of the Actinobacteria in the artificial community , Collinsella aerofaciens , to have the highest proportion of unique peptides ( 94 . 2% ) , while B . caccae had the lowest ( 63 . 0% ) . Interestingly , there was not a strong correlation between the fraction of a species' peptides that were unique and the total number of unique peptides that species contributed to the theoretical peptidome . For example , C . aerofaciens ( 2 , 367 predicted protein-coding genes ) contributed only 81 , 894 ( 1 . 5% ) unique peptides , the lowest of any artificial community member evaluated , despite having a proteome composed of mostly unique peptides . On the other hand , B . cellulosilyticus WH2 ( 5 , 244 predicted protein-coding genes ) contributed 241 , 473 ( 4 . 5% ) unique peptides , the highest of any member despite a high fraction of nonunique peptides ( 18 . 4% ) within its theoretical peptidome . The evolutionary relatedness of the Bacteroides components of the artificial community appeared to negatively affect our ability to assign their peptides to specific proteins; their six theoretical peptidomes had the six lowest uniqueness levels . However , their greater number of proteins and peptides relative to the Firmicutes and Actinobacteria more than compensated for this deficiency; over 60% of unique peptides within the unique theoretical metaproteome were contributed by the Bacteroides . We also found that the proportion of PSMs uniquely assignable to a single protein within the metaproteome varied significantly by function , suggesting that some classes of proteins can be traced back to specific microbes more readily than others . For example , when considering all theoretical peptides that could be derived from the proteome of a particular bacterial species , those from proteins with roles in categories with high expected levels of functional conservation ( e . g . , translation and nucleotide metabolism ) were on average deemed unique more often than those from proteins with roles in functions we might expect to be less conserved ( e . g . , glycan biosynthesis and metabolism ) ( see Table S8 for a summary of how peptide uniqueness varied across different KEGG categories and pathways , and across different species in the experiment ) . However , even in KEGG categories and pathways with high expected levels of functional conservation , the vast majority of peptides were found to be unique when a particular species was not closely related to other members of the artificial community . Next , we determined the average number of proteins that could be experimentally identified in our samples for each microbial species within each treatment group in E1 . The results ( Figure S8B , Table S9 ) illustrate two important conclusions . First , although equal concentrations of total protein were evaluated for each sample , slightly less than twice as many total microbial proteins were identified in samples from the LF/HPP-fed mice as those from mice fed the HF/HS diet ( 4 , 659 versus 2 , 777 , respectively ) . While there are a number of possible explanations , both this finding and the higher number of mouse proteins detected in samples from HF/HS-fed animals are consistent with the results of our fecal DNA analysis , which indicated that the HF/HS diet supports lower levels of gut microbial biomass than the LF/HPP diet ( Figure S4A , B ) . Second , a breakdown of all detected microbial proteins by species of origin ( Figure S8B ) revealed that the degree to which we could inspect protein expression for a given species was dictated largely by its relative abundance and the diet to which it was exposed . Our ability to detect many of B . cellulosilyticus WH2's expressed transcripts and proteins in samples from both diet treatment groups allowed us to determine how well RNA and protein data for an abundant , active member of the artificial community might correlate . These data also allowed us to evaluate whether or not the types of genes considered might influence the degree of correlation between these two datasets . We first performed a spectral count-based correlation analysis on the diet-induced , log-transformed , average fold-differences in expression for all B . cellulosilyticus WH2 genes that were detectable at both the RNA and protein level for both diets . The results revealed a moderate degree of linear correlation between RNA and protein observations ( Figure S8C , black plot; r = 0 . 53 ) . However , subsequent division of these genes into functionally related subsets , which were each subjected to their own correlation analysis , revealed striking differences in the degree to which RNA-level and protein-level expression changes agreed with one another . For example , diet-induced changes in mRNA expression for genes involved in translation showed virtually no correlation with changes measured at the protein level ( Figure S8C , red plot; r = 0 . 03 ) . Correlations for other categories of B . cellulosilyticus WH2 genes , such as those involved in energy metabolism ( Figure S8C , green plot; r = 0 . 36 ) and amino acid metabolism ( Figure S8C , orange plot; r = 0 . 48 ) , were also poorer than the correlation for the complete set of detectable genes . In contrast , the correlation for the 110 genes with predicted involvement in carbohydrate metabolism was quite strong ( Figure S8C , blue plot; r = 0 . 69 ) , and was in fact the best correlation identified for any functional category of genes considered . The significant range of correlations observed in different categories of genes suggests that the degree to which RNA-based analyses provide an accurate picture of microbial adaptation to environmental perturbation may be strongly impacted by the functional classification of the genes involved . Additionally , these data further emphasize the need for enhanced dynamic range metaproteome measurements and better bioinformatic methods to assign/bin unique and nonunique peptides so that deeper and more thorough surveys of the microbial protein landscape can be performed and evaluated alongside more robust transcriptional datasets . Several of the most highly expressed and diet-sensitive B . cellulosilyticus WH2 genes in this study fell within two putative PULs . One PUL ( BWH2_4044–55 ) contains 12 ORFs that include a dual susC/D cassette , three putative xylanases assigned to CAZy families GH8 and GH10 , a putative multifunctional acetyl xylan esterase/α-L-fucosidase , and a putative hybrid two-component system regulator ( Figure 4A ) . Gene expression within this PUL was markedly higher in mice consuming the plant polysaccharide-rich LF/HPP diet at both the mRNA and protein level . Our mRNA-level analysis disclosed that BWH2_4047 was the most highly expressed B . cellulosilyticus WH2 susD homolog on this diet . Likewise , BWH2_4046/4 , the two susC-like genes within this PUL , were the 2nd and 4th most highly expressed B . cellulosilyticus WH2 susC-like genes in LF/HPP-fed animals , and exhibited expression level reductions of 99 . 5% and 93% in animals consuming the HF/HS diet . The same LF/HPP diet bias was observed for other genes within this PUL ( Figures 2A and 4B ) but not for neighboring genes . The same trends were obvious and amplified when we quantified protein expression ( Figure 4C ) . In mice fed LF/HPP chow , only three B . cellulosilyticus WH2 SusC-like proteins had higher protein levels than BWH2_4044/6 , and only two SusD-like proteins had higher levels than BWH2_4045/7 . Strikingly , we were unable to detect a single peptide from 9 of the 12 proteins in this PUL in samples obtained from mice fed the HF/HS diet , emphasizing the strong diet specificity of this locus . A second PUL in the B . cellulosilyticus WH2 genome composed of a susC/D-like pair ( BWH2_4074/5 ) , a putative hybrid two-component system regulator ( BWH2_4076 ) , and a xylanase ( GH10 ) with dual carbohydrate binding module domains ( CBM22 ) ( BWH2_4072 ) ( Figure 4A ) demonstrated a strong but opposite diet bias , in this case exhibiting significantly higher expression in animals consuming the HF/HS “Western”-like diet . Our mRNA-level analysis showed that this xylanase was the second most highly expressed B . cellulosilyticus WH2 CAZyme in animals consuming this diet ( Figure 2A ) . As with the previously described PUL , shotgun metaproteomics validated the transcriptional analysis ( Figure 4B , C ) ; with the exception of the gene encoding the PUL's presumed transcriptional regulator ( BWH2_4076 ) , diet specificity was substantial , with protein-level fold changes ranging from 10–33 across the locus ( Table S10 ) . Recent work by Cann and co-workers has done much to advance our understanding of the regulation and metabolic role of xylan utilization system gene clusters in xylanolytic members of the Bacteroidetes , particularly within the genus Prevotella [41] . The “core” gene cluster of the prototypical xylan utilization system they described consists of two tandem repeats of susC/susD homologs ( xusA/B/C/D ) , a downstream hypothetical gene ( xusE ) and a GH10 endoxylanase ( xyn10C ) . The 12-gene PUL identified in our study ( BWH2_4044–55 ) appears to contain the only instance of this core gene cluster within the B . cellulosilyticus WH2 genome , suggesting that this PUL , induced during consumption of a plant polysaccharide-rich diet , is likely to be the primary xylan utilization system within this organism . A recent study characterizing the carbohydrate utilization capabilities of B . ovatus ATCC 8483 also identified two PULs involved in xylan utilization ( BACOVA_04385–94 , BACOVA_03417–50 ) whose gene configurations differ from those described in Prevotella spp . [25] . Interestingly , the five proteins encoded by the smaller xylanase-containing PUL described above ( BWH2_4072–6 ) are homologous to the products of the last five genes in BACOVA_4385–94 ( i . e . , BACOVA_4390–4 ) . The order of these five genes in these two loci is also identical . The similarities and differences observed when comparing the putative xylan utilization systems encoded within the genomes of different Bacteroidetes illustrate how its members may have evolved differentiated strategies for utilizing hemicelluloses like xylan . Having established that expression of BWH2_4044–55 and BWH2_4072–6 is strongly dictated by diet , we next sought to determine if these PULs are required by B . cellulosilyticus WH2 for fitness in vivo . A follow-up study was performed in which mice were fed either a LF/HPP or HF/HS diet after being colonized with an artificial community similar to the one used in E1 and E2 ( see Materials and Methods ) . The wild-type B . cellulosilyticus WH2 strain used in our previous experiments was replaced with a transposon mutant library consisting of over 90 , 000 distinct transposon insertion mutants in 91 . 5% of all predicted ORFs ( average of 13 . 9 distinct insertion mutants per ORF ) . The library was constructed using methods similar to those reported by Goodman et al . ( [42]; see Materials and Methods ) so that the relative proportion of each insertion mutant in both the input ( orally gavaged ) and output ( fecal ) populations could be determined using insertion sequencing ( INSeq ) . The INSeq results revealed clear , diet-specific losses of fitness when components of these loci were disrupted ( Figure 4D ) . Additionally , as observed in E1 and E2 , expression of each PUL was strongly biased by diet , with genes BWH2_4072–5 demonstrating up-regulation on the HF/HS diet and BWH2_4044–55 on the LF/HPP diet . The extent to which a gene's disruption impacted the fitness of B . cellulosilyticus WH2 on one diet or the other correlated well with whether or not that gene was highly expressed on a given diet . For example , four of the five most highly expressed genes in the BWH2_4044–55 locus were the four genes shown to be most crucial for fitness on the LF/HPP diet . Of these four genes , three were susC or susD homologs ( the fourth was the putative endo-1 , 4-β-xylanase thought to constitute the last element of the xylan utilization system core ) . Though the fitness cost of disrupting genes within BWH2_4044–55 varied from gene to gene , disruption of any one component of the BWH2_4072–6 PUL had serious consequences for B . cellulosilyticus WH2 in animals fed the HF/HS diet . This could suggest that while disruption of some components of the BWH2_4044–55 locus can be rescued by similar or redundant functions elsewhere in the genome , the same is not true for BWH2_4072–5 . Notably , disruption of BWH2_4076 , which is predicted to encode a hybrid two-component regulatory system , had negative consequences on either diet tested , indicating that regulation of this locus is crucial even when the PUL is not actively expressed . While many genes outside of these two PULs were also found to be important for the in vivo fitness of B . cellulosilyticus WH2 , those within these PULs were among the most essential to diet-specific fitness , suggesting that these loci are central to the metabolic lifestyle of B . cellulosilyticus WH2 in the gut . The results described in the preceding section indicate that B . cellulosilyticus WH2 prioritizes xylan as a nutrient source in the gut and that it tightly regulates the expression of its xylan utilization machinery . Moreover , the extraordinary number of putative CAZymes and PULs within the B . cellulosilyticus WH2 genome suggests that it is capable of growing on carbohydrates with diverse structures and varying degrees of polymerization . To characterize its carbohydrate utilization capabilities , we defined its growth in minimal medium ( MM ) supplemented with one of 46 different carbohydrates [25] . Three independent growths , each consisting of two technical replications , yielded a total of six growth curves for each substrate . Of the 46 substrates tested , B . cellulosilyticus WH2 grew on 39 ( Table S11 ) ; they encompassed numerous pectins ( 6 of 6 ) , hemicelluloses/β-glucans ( 8 of 8 ) , starches/fructans/α-glucans ( 6 of 6 ) , and simple sugars ( 14 of 15 ) , as well as host-derived glycans ( 4 of 7 ) and one cellooligosaccharide ( cellobiose ) . The seven substrates that did not support growth included three esoteric carbohydrates ( carrageenan , porphyran , and alginic acid ) , the simple sugar N-acetylneuraminic acid , two host glyans ( keratan sulfate and mucin O-glycans ) , and fungal cell wall-derived α-mannan . B . cellulosilyticus WH2 clearly grew more robustly on some carbohydrates than others . Excluding simple sugars , fastest growth was achieved on dextran ( 0 . 099±0 . 048 OD600 units/h ) , laminarin ( 0 . 095±0 . 014 ) , pectic galactan ( 0 . 088±0 . 018 ) , pullulan ( 0 . 088±0 . 026 ) , and amylopectin ( 0 . 085±0 . 003 ) . Although one study has reported that the type strain of B . cellulosilyticus degrades cellulose [43] , the WH2 strain failed to demonstrate any growth on MM plus cellulose ( specifically , Solka-Floc 200 FCC from International Fiber Corp . ) after 5 d . Maximum cell density was achieved with amylopectin ( 1 . 17±0 . 02 OD600 units ) , dextran ( 1 . 12±0 . 20 ) , cellobiose ( 1 . 09±0 . 08 ) , laminarin ( 1 . 08±0 . 08 ) , and xyloglucan ( 0 . 99±0 . 04 ) . Total B . cellulosilyticus WH2 growth ( i . e . , maximum cell density achieved ) on host-derived glycans was typically very poor , with only two substrates achieving total growth above 0 . 2 OD600 units ( chondroitin sulfate , 0 . 50±0 . 04; glycogen , 0 . 99±0 . 02 ) . The disparity between total growth on plant polysaccharides versus host-derived glycans , including O-glycans that are prevalent in host mucin , indicates a preference for diet-derived saccharides , consistent with our in vivo mRNA and protein expression data . We also determined how the growth rate of B . cellulosilyticus WH2 on these substrates compared to the growth rates for other prominent gut Bacteroides spp . After subjecting B . caccae to the same phenotypic characterization as B . cellulosilyticus WH2 , we combined our measurements for these two strains with previously published measurements for B . thetaiotaomicron and B . ovatus [25] . The results underscored the competitive growth advantage B . cellulosilyticus WH2 likely enjoys when foraging for polysaccharides in the intestinal lumen . For example , of the eight hemicelluloses and β-glucans tested in our carbohydrate panel , B . cellulosilyticus WH2 grew fastest on six while B . ovatus grew fastest on two ( Table S11 ) . B . caccae and B . thetaiotaomicron , on the other hand , failed to grow on any of these substrates . Across all the carbohydrates for which data are available for all four species , B . cellulosilyticus WH2 grew fastest on the greatest number of polysaccharides ( 11 of 26 ) and tied with B . caccae for the greatest number of monosaccharides ( 6 of 15 ) . B . thetaiotaomicron and B . caccae did , however , outperform the other two Bacteroides tested with respect to utilization of host glycans in vitro , demonstrating superior growth rates on four of five substrates tested ( Table S11 ) . B . cellulosilyticus WH2's rapid growth to high densities on xylan , arabinoxylan , and xyloglucan , as well as xylose , arabinose , and galactose , is noteworthy given our prediction that two of its most tightly regulated , highly expressed PULs appear to be involved in the utilization of xylan , arabinoxylan , or some closely related polysaccharide . To identify specific mono- and/or polysaccharides capable of triggering the activation of these two PULs , as well as the 111 other putative PULs within the B . cellulosilyticus WH2 genome , we used RNA-Seq to characterize its transcriptional profile at mid-log phase in MM ( Table S12 ) plus one of 16 simple sugars or one of 15 complex sugars ( Table S13 ) ( see Materials and Methods; n = 2–3 cultures/substrate; 5 . 2–14 . 0 million raw Illumina HiSeq reads generated for each of the 90 transcriptomes ) . After mapping each read to the B . cellulosilyticus WH2 reference gene set , counts were normalized using DESeq to allow for direct comparisons across samples and conditions . Hierarchical clustering of the normalized dataset resulted in a well-ordered dendrogram in which samples clustered almost perfectly by the carbohydrate on which B . cellulosilyticus WH2 was grown ( Figure 5A ) . The consistency of this clustering illustrates that ( i ) technical replicates within each condition exhibit strong correlations with one another , suggesting any differences between cultures in a treatment group ( e . g . , small differences in density or growth phase ) had at best minor effects on aggregate gene expression , and ( ii ) growth on different carbohydrates results in distinct , substrate-specific gene expression signals capable of driving highly discriminatory differences between treatment groups . The application of rigorous bootstrapping to our hierarchical clustering results also revealed several cases of higher level clusters in which strong confidence was achieved . These dendrogram nodes ( illustrated as white circles ) indicate sets of growth conditions that yield gene expression patterns more like each other than like the patterns observed for other substrates . Two notable examples were xylan/arabinoxylan ( which are structurally related and share the same xylan backbone ) and L-fucose/L-rhamnose ( which are known to be metabolized via parallel pathways in E . coli [44] ) . Importantly , these findings suggested that by considering in vitro profiling data alongside in vivo expression data from the artificial community , it might be possible to identify the particular carbohydrates to which B . cellulosilyticus WH2 is exposed and responding within its gut environment . To explore this concept further , we compared expression of each gene in each condition to its expression on our control treatment , MM plus glucose ( MM-Glc ) . The results revealed a dynamic PUL activation network in which some PULs were activated by a single substrate , some were activated by multiple substrates , and some were transcriptionally silent across all conditions tested . Of the 118 putative susC/D pairs in B . cellulosilyticus WH2 that we have used as markers of PULs , 30 were dramatically activated on one or more of the substrates tested; in these cases , both the susC- and susD-like genes in the cassette were up-regulated an average of >100-fold relative to MM-Glc across all technical replicates ( Figure 5B ) . At least one susC/D activation signature was identified for every one of the 17 oligosaccharides and polysaccharides and for six of the 13 monosaccharides tested ( Table S14 ) . The lack of carbohydrate-specific PUL activation events for some monosaccharides ( fructose , galactose , glucuronic acid , sucrose , and xylose ) was expected , given that these loci are primarily dedicated to polysaccharide acquisition . Further inspection of gene expression outside of PULs disclosed that B . cellulosilyticus WH2 prioritizes use of its non-PUL-associated carbohydrate machinery , such as putative phosphotransferase system ( PTS ) components and monosaccharide permeases , when grown on these monosaccharides ( Table S14 ) . Several carbohydrates activated the expression of multiple PULs . Growth on water-soluble xylan and wheat arabinoxylan produced significant up-regulation of five susC/D-like pairs ( BWH2_0865/6 , 0867/8 , 4044/5 , 4046/7 , and 4074/5 ) . No other substrate tested activated as many loci within the genome , again hinting at the importance of xylan and arabinoxylan to this strain's metabolic strategy in vivo . Cecal expression data from E1 showed that 15 of these activated PULs were expressed in vivo on one or both of the diets tested ( see circles to the right of the heatmap in Figure 5B ) . In mice fed the polysaccharide-rich LF/HPP chow , B . cellulosilyticus WH2 up-regulates three susC/D pairs ( BWH2_2717/8 , 4044/5 , 4046/7 ) whose expression is activated in vitro by arabinan and xylan/arabinoxylan . The three most significantly up-regulated susC/D pairs ( BWH2_1736/7 , 2514/5 , 4074/5 ) in mice fed the HF/HS diet rich in sugar , corn starch , and maltodextrin are activated in vitro by amylopectin , ribose , and xylan/arabinoxylan , respectively . All three PULs identified as being up-regulated at the RNA level in LF/HPP-fed mice were also found to be up-regulated at the protein level ( Figure 5B ) . Two of the three PULs up-regulated at the mRNA level in HF/HS-fed mice were up-regulated at the protein level as well . The presence of an amylopectin-activated PUL among these two loci is noteworthy , given the significant amount of starch present in the HF/HS diet . The up-regulation of four other PULs in HF/HS-fed animals was only evident in our LC-MS/MS data , reinforcing the notion that protein data both complement and supplement mRNA data when profiling microbes of interest . Two of the five susC/D pairs activated by xylan/arabinoxylan form the four-gene cassette in the previously discussed PUL comprising BWH2_4044–55 that is activated in mice fed the plant polysaccharide-rich chow ( see Figure 4A ) . Another one of the five is the susC/D pair found in the PUL comprising BWH2_4072–6 that is activated in mice fed the HF/HS “Western”-like chow ( see Figure 4A ) . Thus , we have identified a pair of putative PULs in close proximity to one another on the B . cellulosilyticus WH2 genome that encode CAZymes with similar predicted functions , are subject to near-identical levels of specific activation by the same two polysaccharides ( i . e . , xylan , arabinoxylan ) in vitro , but are discordantly regulated in vivo in a diet-specific manner . The highly expressed nature of these PULs in the diet environment where they are active , their shared emphasis on xylan/arabinoxylan utilization , and their tight regulation indicate that they are likely to be important for the in vivo success of this organism in the two nutrient environments tested . However , the reasons for their discordant regulation are unclear . One possibility is that in addition to being activated by xylan/arabinoxylan and related polysaccharides , these loci are also subject to repression by other substrates present in the lumen of the gut , and this repression is sufficient to block activation . Alternatively , the specific activators of each PUL may be molecular moieties shared by both xylan and arabinoxylan that do not co-occur in the lumenal environment when mice are fed the diets tested in this study . Elucidating generalizable “rules” for how microbiota operate under different environmental conditions is a substantial challenge . As our appreciation for the importance of the gut microbiota in human health and well-being grows , so too does our need to develop such rules using tractable experimental models of the gut ecosystem that allow us to move back and forth between in vivo and ex vivo analyses , using one to inform the other . Here , we have demonstrated the extent to which high-resolution DNA- , mRNA- , and protein-level analyses can be applied ( and integrated ) to study an artificial community of sequenced human gut microbes colonizing gnotobiotic mice . Our efforts have focused on characterizing community-level and species-level adaptation to dietary change over time and “leveraging” results obtained from in vitro assessments of individual species' responses to a panel of purified carbohydrates to deduce glycan exposures and consumption strategies in vivo . This experimental paradigm could be applied to any number of questions related to microbe–microbe , environment–microbe , and host–microbe interactions , including , for example , the metabolic fate of particular nutrients of interest ( metabolic flux experiments ) , microbial succession , and biotransformations of xenobiotics . Studying artificial human gut microbial communities in gnotobiotic mice also allows us to evaluate the technical limitations of current molecular approaches for characterizing native communities . For example , the structure of an artificial community can be evaluated over time at low cost using short read shotgun DNA sequencing data mapped to all microbial genomes within the community ( COPRO-Seq ) . This allows for a much greater depth of sequencing coverage ( i . e . , more sensitivity ) and much less ambiguity in the assignment of reads to particular taxa than traditional 16S rRNA gene-based sequencing . Short read cDNA sequences transcribed from total microbial community RNA can also often be assigned to the exact species and gene from which they were derived , and the same is also often true for peptides derived from particular bacterial proteins . However , substantial dynamic range in species/transcript/protein abundance within any microbiota , defined or otherwise , imposes limits on our ability to characterize the least abundant elements of these systems . The effort to obtain a more complete understanding of the operations and behaviors of minor components of the microbiota is an area deserving of significant attention , given known examples of low-abundance taxa that play key roles within their larger communities and in host physiology [2] , [45] . Developing such an understanding requires methods and assays that are collectively capable of assessing the structure and function of a microbiota at multiple levels of resolution . The need for high sensitivity and specificity in these approaches will become increasingly relevant as we transition towards experiments involving defined communities of even greater complexity , including bacterial culture collections prepared from the fecal microbiota of humans [46] . We anticipate that the study of sequenced culture collections transplanted to gnotobiotic mice will be instrumental in determining the degree to which physiologic or pathologic host phenotypes can be ascribed to the microbiota as well as specific constituent taxa . The recent development of a low-error 16S ribosomal RNA amplicon sequencing method ( LEA-Seq ) and the application of this method to the fecal microbiota of 37 healthy adults followed for up to 5 years indicated that individuals in this cohort contained 195±48 bacterial strains representing 101±27 species [47] . Furthermore , stability follows a power-law function , suggesting that once acquired , most gut strains in a person are present for decades . New advances in the culturing of fastidious gut microbes may one day allow us to capture most ( or all ) of the taxonomic and functional diversity present within an individual's fecal microbiota as a clonally arrayed , sequenced culture collection , providing a perfectly representative and defined experimental model of their gut community . In the meantime , first-generation artificial communities of modest complexity such as the one described here offer a way of studying many questions related to the microbiota . However , the limited complexity and composition of our 12-species artificial community , and the way in which it was assembled in germ-free mice , make it an imperfect model of more complex human microbiota . Native microbial communities , for example , are subject to the influence of variables that are notably absent from this system , such as intraspecies genetic variability and exogenous microbial inputs . There are also taxa ( e . g . , Proteobacteria , Bifidobacteria ) and microbial guilds ( e . g . , butyrate producers ) typical of human gut communities that are absent from our defined assemblage that could be used to augment this system in order to improve our understanding of how their presence/absence influences a microbiota's response to diet and a spectrum of other variables of interest . These future attempts to systematically increase complexity should reveal what trends , patterns , and trajectories observed in artificial assemblages such as the one reported here map or do not map onto natural communities . Finally , one of the greatest advantages of studying defined assemblages in mice is that they afford us the ability to interrogate the biology of key bacterial species in a focused manner . The artificial community we used in our experiments included B . cellulosilyticus WH2 , a species that warrants further study as a model gut symbiont given its exceptional carbohydrate utilization capabilities , its apparent fitness advantage over many other previously characterized gut symbionts , and its genetic tractability . This genetic tractability should facilitate future experiments in which transposon mutant libraries are screened in vivo as one component of a larger artificial community in order to identify this strain's most important fitness determinants under a wide variety of dietary conditions . Identifying the genetic elements that allow B . cellulosilyticus to persist at the relatively high levels observed , regardless of diet , should provide microbiologists and synthetic biologists with new “standard biological parts” that will aid them in developing the next generation of prebiotics , probiotics , and synbiotics .
All experiments involving mice used protocols approved by the Washington University Animal Studies Committee in accordance with guidelines set forth by the American Veterinary Medical Association . Trained veterinarians from the Washington University Division of Comparative Medicine supervised all experiments . The laboratory animal program at Washington University is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International ( AAALAC ) . A strain of B . cellulosilyticus designated “WH2” ( see Figure S1A , B ) was isolated from a human fecal sample during an iteration of the Microbial Diversity Summer Course overseen by A . Salyers ( University of Illinois , Urbana-Champaign ) at the Marine Biological Laboratory ( Woods Hole , MA ) . The genome of this isolate was sequenced using a combination of long-read and short-read technologies , yielding 51 , 819 plasmid and fosmid end reads ( library insert sizes: 3 . 9 , 4 . 9 , 6 . 0 , 8 . 0 , and 40 kb; ABI 3730 platform ) , 333 , 883 unpaired 454 reads ( FLX+ and XL+ chemistry ) , and 10 million unpaired Illumina reads ( HiSeq; 42 nt read length ) . A hybrid assembly was constructed using MIRA v3 . 4 . 0 ( method , de novo; type , genome; quality grade , accurate ) with default settings [48] , [49] . Gene calling was performed using the YACOP metatool [50] . Additionally , the four ribosomal RNA ( rRNA ) operons within the B . cellulosilyticus WH2 genome were sequenced individually to ensure high sequence accuracy in these difficult-to-assemble regions . Further details for the B . cellulosilyticus WH2 assembly are provided in Table S1 . Details regarding the 12 bacterial strains used in this study are provided in Table S4 . Cells were grown in supplemented TYG ( TYGS; [42] ) at 37°C under anaerobic conditions in a Coy anaerobic chamber ( atmosphere: 75% N2 , 20% H2 , 5% CO2 ) . After reaching stationary phase , cells were pelleted by centrifugation and resuspended in TYGS medium supplemented with 20% glycerol . Individual aliquots containing 400–800 µL of each cell suspension were stored at −80°C in 1 . 8 mL borosilicate glass vials with aluminum crimp tops . The identity of each species was verified prior to its use in experiments by extracting DNA from a frozen aliquot of cells , amplifying the 16S rRNA gene by PCR using primers 8F/27F ( AGAGTTTGATCCTGGCTCAG; [51] ) and 1391R ( GACGGGCGGTGWGTRCA; [52] ) , sequencing the entire amplicon with an ABI 3730 capillary sequencer ( Retrogen , Inc . ) , and comparing the assembled 16S rRNA gene sequence to the known reference sequence . Details regarding the construction of each inoculum are provided in Table S3 . The inocula used to gavage germ-free mice in each experiment were prepared either directly from frozen stocks ( experiment 1 , E1 ) or from a combination of frozen stocks and overnight cultures ( experiment 2 , E2 ) . The recoverable cell density for each batch of frozen stocks used in inoculum preparation was determined prior to pooling , while the same values for overnight cultures were calculated after pooling . To do so , an aliquot of cells from each overnight culture or set of frozen stocks was used to prepare a 10-fold dilution series in phosphate-buffered saline ( PBS ) , and each dilution series was plated on brain-heart-infusion ( BHI; BD Difco ) agar supplemented with 10% ( v/v ) defibrinated horse blood ( Colorado Serum Co . ) . Plates were grown for up to 3 d at 37°C under anaerobic conditions in a Coy chamber , colonies were counted , and the number of colony-forming units per milliliter ( CFUs/mL ) was calculated . The volume of each cell suspension included in the final inoculum was normalized by its known or estimated viable cell concentration in an effort to ensure that no species received an early advantage during establishment of the artificial community in the germ-free animals . Total CFUs per gavage were estimated at 8 . 0×107 and 4 . 2×108 for experiments E1 and E2 , respectively . Experiments were performed using protocols approved by the animal studies committee of the Washington University School of Medicine . For each experiment , two groups of 10–12-wk-old male germ-free C57BL/6J mice were maintained in flexible film gnotobiotic isolators under a strict 12 h light cycle , during which time they received sterilized food and water ad libitum . Animals were fasted for 4 h prior to gavage with 500 µL of a cell suspension inoculum containing the 12 sequenced , human gut-derived bacterial symbionts . After gavage , animals were maintained in separate cages throughout the course of the experiment . Fresh fecal pellets were periodically collected directly into screw-cap sample tubes that were immediately frozen in liquid nitrogen . At the time of sacrifice , the contents of each animal's cecum were divided into thirds and snap-frozen in liquid nitrogen for later use in DNA , RNA , and total protein isolations . Animals were subjected to dietary oscillations comprising three consecutive phases of 2 wk each ( see Figure S3 ) . Prior to inoculation , germ-free mice were maintained on a standard autoclaved chow diet low in fat and rich in plant polysaccharides ( LF/HPP , B&K rat and mouse autoclavable chow #73780000 , Zeigler Bros , Inc ) . Three days prior to inoculation , one group of germ-free animals was switched to a sterile “Western”-like chow high in fat and simple sugars ( HF/HS , Harlan Teklad TD96132 ) , while the other continued to receive LF/HPP chow . After gavage , each group of animals was maintained on its respective diet for 2 wk , after which each treatment group was switched to the alternative diet . Two weeks later , the mice were switched back to their original starting diet and were retained on this diet until the time of sacrifice . DNA and RNA were extracted from fecal pellets and cecal contents as previously described [11] . COPRO-Seq measurements of the proportional representation of all species present in each fecal/cecal sample analyzed were performed as previously described [11] using short-read ( 36 nt ) data collected from an Illumina sequencer ( data were generated using a combination of the Genome Analyzer I , Genome Analyzer II , and Genome Analyzer IIx platforms ) . After demultiplexing each barcoded pool , reads were trimmed to 25 bp and aligned to the reference genomes . An abundance threshold cutoff of 0 . 003% was set for determining an artificial community members' presence/absence , based on the proportion of reads from each experiment that were found to spuriously align to distractor reference genomes of bacterial species not included in this study . Normalized counts for each bacterial species in each sample were used to calculate a simple intrasample percentage . In order to make changes in abundance over time more easily comparable between species with significantly different relative abundances , these percentages were also in some cases normalized by the maximum abundance ( % ) observed for a given species across all time-points from a given animal . This transformation resulted in a value referred to as the percentage of maximum achieved ( “PoMA” ) that was used to evaluate which species were most/least responsive to dietary interventions . COPRO-Seq proportional abundance data were subjected to ordination using scripts found in QIIME v1 . 5 . 0-dev [53] . Data from both E1 and E2 were combined to generate a single tab-delimited table conforming to QIIME's early ( pre-v1 . 4 . 0-dev ) OTU table format . This pseudo-OTU table was subsequently converted into a BIOM-formatted table object that was used as the input for beta_diversity . py to calculate the pairwise distances between all samples using a Hellinger metric . PCoA calculations were performed using principal_coordinates . py . These coordinates and sample metadata were passed to make_3d_plots . py to generate PCoA plots . Plots shown are visualized using v2 . 21 of the KiNG software package [54] . The ability of B . cellulosilyticus WH2 and B . caccae ATCC 43185 to grow on a panel of 47 simple and complex carbohydrates was evaluated using a phenotypic array whose composition has been previously described [25] . Growth measurements were collected in duplicate ( two wells per substrate ) over the course of 3 d at 37°C under anaerobic conditions . A total of three independent experiments were performed for each species tested ( n = 6 growth profiles/substrate/species ) . Total growth ( Atot ) was calculated from each growth curve as the difference between the maximum and minimum optical densities ( OD600 ) observed ( i . e . , Amax−Amin ) . Growth rates were calculated as total growth divided by time ( Atot/ ( tmax−tmin ) ) , where tmax and tmin correspond to the time-points at which Amax and Amin , respectively , were collected . Consolidated statistics from all six replicates for each of the 47 conditions tested for each species are provided in Table S11 . Whole genome transposon mutagenesis of B . cellulosilyticus WH2 was performed using protocols originally developed for B . thetaiotaomicron [42] , [46] , with some modifications . Initial attempts to transform B . cellulosilyticus WH2 with the pSAM_Bt construct reported by Goodman et al . yielded very low numbers of antibiotic-resistant clones , which we attributed to poor recognition of one or more promoters in the mutagenesis plasmid . Replacement of the promoter driving expression of the transposon's erythromycin resistance gene ( ermG ) with the promoter for the gene encoding EF-Tu in B . cellulosilyticus WH2 ( BWH2_3183 ) dramatically improved the number of resistant clones recovered after transformation . The resulting library consisted of 93 , 458 distinct isogenic mutants , with each mutant strain containing a single randomly inserted modified mariner transposon . Of all predicted ORFs , 91 . 5% had insertions covering the first 80% of each gene ( mean , 13 . 9 distinct insertion mutants per ORF ) . At 11 wk of age , male germ-free C57BL/6J mice ( individually caged ) were fed either a diet low in fat and rich in plant polysaccharides ( LF/HPP ) or high in fat and simple sugars ( HF/HS ) . After a week on their experimental diet , animals received a single gavage containing the B . cellulosilyticus WH2 transposon library and 14 other species of bacteria ( i . e . , this artificial community consisted of the 12 species listed in Figure 1A , plus B . thetaiotaomicron 7330 , E . rectale ATCC 33656 , and Clostridium symbiosum ATCC 14940 ) . After 16 d , fecal pellets were collected , and total fecal DNA was extracted . 500 ng of each fecal DNA extraction was diluted in 15 µL of TE buffer and digested with MmeI ( 4 U , New England Biolabs ) in a 20 µL reaction supplemented with 10 pmoles of 12 bp DNA containing an MmeI restriction site ( to improve the efficiency of restriction enzyme digestion ) [42] . The reaction was incubated for 1 h at 37°C and then terminated ( 80°C for 20 min ) . MmeI-digested DNA was subsequently purified using 125 µL of AMPure beads ( after washing the beads once with 100 µL of sizing solution ( 1 . 2 M NaCl and 8 . 4% PEG 8000 ) ) . The digested DNA was added to the beads and the solution incubated at room temperature for 5 min . Beads were pelleted with a magnetic particle collector ( MPC ) , washed twice ( each time using a mixture composed of 20 µL TE buffer ( pH 7 . 0 ) and 100 µL sizing solution , with bead recovery via MPC after each wash ) , followed by two ethanol washes ( 180 µL 70% ethanol/wash ) and air-drying for 10 min . Samples were resuspended in 18 µL TE buffer ( pH 7 . 0 ) , and DNA was removed after pelleting beads with the MPC . Ligation of adapters was performed in a 20 µL reaction that contained 16 µL of purified DNA , 1 µL of T4 Ligase ( 2000 U/µL; NEB ) , 2 µL 10× ligase buffer , and 10 pmol of barcoded adapter ( incubation for 1 h at 16°C ) . Ligations were subsequently diluted with TE buffer ( pH 7 . 0 ) to a final volume of 50 µL , mixed with 60 µL of AMPure beads , and incubated at room temperature for 5 min . Beads with bound DNA were pelleted using the MPC and washed twice with 70% ethanol as above . After allowing the ethanol to evaporate for 10 min , 35 µL of nuclease-free water was added , and the mixture was incubated at room temperature for 2 min before collecting the beads with the MPC . Enrichment PCR was performed in a final volume of 50 µL using 32 µL of the cleaned up sample DNA , 10 µL 10× Pfx amplification buffer ( Invitrogen ) , 2 µL 10 mM dNTPs , 0 . 5 µL 50 mM MgSO4 , 2 µL of 5 µM amplification primers ( forward primer: 5′CAAGCAGAAGACGGCATACG3′ , reverse primer: 5′AATGATACGGCGACCACCGAACACTCTTTCCCTACACGA3′ ) , and 1 . 5 µL Pfx polymerase ( 2 . 5 U/µL; Invitrogen ) ( cycling conditions: denaturation at 94°C for 15 s; annealing at 65°C for 1 min; extension at 68°C for 30 s; total of 22 cycles ) . The 134 bp PCR product from each reaction was purified ( 4% MetaPhor gel; MinElute Gel Extraction Kit ( Qiagen ) ) in a final volume of 20 µL and was quantified ( Qubit , dsDNA HS Assay Kit; Invitrogen ) . Reaction products were then combined in equimolar amounts into a pool that was subsequently adjusted to 10 nM and sequenced ( Illumina HiSeq 2000 instrument ) . All short read Illumina data used for COPRO-Seq and RNA-Seq analyses , GeneChip data , and genome sequencing/assembly data are available through GEO SuperSeries GSE48537 and NCBI BioProject ID PRJNA183545 . The draft genome assembly for B . cellulosilyticus WH2 has been deposited at DDBJ/EMBL/GenBank under accession number ATFI00000000 . Raw MS data are available from the Dryad Digital Repository: http://dx . doi . org/10 . 5061/dryad . 7fj1k . | Our intestines are populated by an almost unimaginably large number of microbial cells , most of which are bacteria . This species assemblage operates as a microbial metabolic organ , performing myriad tasks that contribute to our well-being , including processing components of our diet . The way this incredible machine assembles itself and operates remains mysterious . One approach to understanding its properties is to create artificial communities composed of a limited number of sequenced human gut bacterial species and to install them in the guts of germ-free mice that are then fed different diets . In this report , we adopt this approach . We describe the genome sequence of a new gut bacterial isolate , Bacteroides cellulosilyticus WH2 , which is equipped with an unprecedented number of carbohydrate active enzymes . Deploying four different “omics” technologies , we characterize the response to diet , the relative stability , and the temporal dynamics of a 12-species artificial bacterial assemblage ( including B . cellulosilyticus WH2 ) implanted in germ-free mouse guts . We also combine high-throughput substrate utilization screens and RNA-Seq to generate reference data analogous to a “Rosetta stone” in order to decipher what types of carbohydrates B . cellulosilyticus encounters and uses within the gut , and how it interacts with other organisms that have similar and/or distinct “professions . ” This work sets the stage for future ecological and metabolic studies of more complex assemblages that more fully emulate the properties of our native gut communities . | [
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"com... | 2013 | Effects of Diet on Resource Utilization by a Model Human Gut Microbiota Containing Bacteroides cellulosilyticus WH2, a Symbiont with an Extensive Glycobiome |
Emergomyces africanus is a thermally dimorphic fungus that causes a systemic mycosis in immunocompromised persons in South Africa . Infection is presumed to follow inhalation of airborne propagules . We developed a quantitative PCR protocol able to detect as few as 5 Es . africanus propagules per day . Samples were collected in Cape Town , South Africa over 50 weeks by a Burkard spore trap with an alternate orifice . We detected Es . africanus in air samples from 34 days ( 10% ) distributed over 11 weeks . These results suggest environmental exposure to airborne Es . africanus propagules occurs more commonly in endemic areas than previously appreciated .
Emergomyces africanus is an emerging opportunistic dimorphic fungal pathogen that causes emergomycosis , a systemic and often-fatal HIV-associated mycosis in South Africa [1 , 2] . It is a member of a newly-described genus within the family Ajellomycetaceae called Emergomyces , so-named because of the striking appearance or recognition of new dimorphic fungal pathogens reported globally [2] . In addition to Emergomyces africanus , which has been reported from South Africa and Lesotho [1–3] , the genus includes Emergomyces pasteurianus ( formerly Emmonsia pasteuriana , reported from Europe , Asia and Africa [4–10] ) and Emergomyces orientalis ( reported from China [11] ) . Similar but distinct fungi have also been reported from North America [12] . Although , the true incidence is unknown , cases of disease caused by Emergomyces species other than Es . africanus are uncommon , with only 13 cases reported to date . On the other hand , disease caused by Es . africanus is relatively common: although only described in 2013 ( as Emmonsia species [1] ) , Es . africanus is now recognized to cause the most frequently diagnosed dimorphic fungal infection in South Africa [10] . In Cape Town , a clinical and laboratory surveillance study at public hospitals over a 15-month period identified 14 cases of culture-proven emergomycosis [13] . Emergomycosis is an opportunistic infection of immunocompromised hosts . In South Africa , one patient was a kidney transplant recipient , and the remainder have occurred in patients with advanced HIV infection ( among whom the median CD4 lymphocyte count was 16 cells/μL ) [1 , 3 , 14 , 15] . Patients most commonly present with widespread skin lesions and pulmonary disease [3] , and the disease often only becomes clinically apparent after the initiation of antiretroviral therapy [3 , 13] . The reported case-fatality ratio is 50% [3] . Most patients with emergomycosis caused by Es . africanus have been diagnosed in the Western Cape province [3] , although cases have been reported from 6 of 9 South African provinces and Lesotho [3 , 10] . The ecological niche of Es . africanus is still being elucidated , although an environmental reservoir of the mycelial phase in the soil is supported by molecular detection of Es . africanus in 30% of soils sampled from the Western Cape [16] . Nonetheless , most patients diagnosed with emergomycosis do not report occupational or other frequent exposure to soil [13] . Based on what is known of the pathogenesis of most other dimorphic fungal infections [17] , Es . africanus infection likely follows inhalation of conidia or other infectious propagules . However , no attempts have been made to detect Es . africanus in the air . We developed a protocol for the molecular detection and quantification of Es . africanus propagules from air samples , and used this to evaluate air samples collected from Cape Town , Western Cape .
We sampled airborne propagules using a Hirst-type volumetric 7-day spore trap ( Burkard Manufacturing Co . Ltd . , Rickmansworth , Hertfordshire , England ) [18] fitted with an alternate orifice ( Burkard ) [19] . This spore trap samples air continuously at a rate of 10 l/min ( 14 . 4 m3/day ) [18] . The spore trap was set up on the roof of a building 4 m high in Bellville , an urban area of Cape Town , Western Cape , South Africa near where cases of Es . africanus infection have been diagnosed [3] . Permission was provided for sampling by the Air Pollution Department of the City of Cape Town , the owner of the property . Sampling took place over a period of 50 weeks from 15 September 2015 to 29 August 2016 . Melinex tape was fixed to the sampler drum , greased with petroleum jelly and replaced weekly . Tapes were stored in individually labeled storage boxes at room temperature and shipped to the Aerobiology Lab at the University of Tulsa for molecular analysis . For processing , each Burkard tape was cut into 7 48-mm segments , with each segment representing a 24-hour period . The 48-mm segments were temporarily fixed on microscope slides labeled with the sampling date . Each 48-mm tape segment containing the daily air sample was removed from the slide , cut into small strips and loaded into a sterile 2 ml bead-beating tube containing 0 . 3–0 . 4 g of 0 . 5 mm glass beads . The DNA extraction protocol was a variation of the cetrimonium bromide ( CTAB ) method [20] . A 2% CTAB extraction solution ( pH 8 . 0 ) was added to the tube at a volume of 500 μl , along with 10 μl of β-mercaptoethanol . The tubes were loaded into the bead beater and shaken for 3 min at max speed . Tubes were incubated at 70°C for 1 hr before being centrifuged at 15 , 000 g for 1 min . The supernatant was transferred to a 2-ml centrifuge tube and centrifuged at 15 , 000 g for 10 min . The supernatant was transferred to another tube and 500 μl of chloroform:isoamyl-alcohol ( 24:1 ) was added , and vortexed for 10 sec . To separate the phases , tubes were centrifuged at 15 , 000 g for 10 min . The upper aqueous phase was collected and transferred to a clean tube with 100 μl of 5 M sodium acetate and 500 μl of ice-cold isopropyl , and the tubes vortexed for 5 sec . Tubes were stored at -20°C overnight . The next day , tubes were centrifuged at 15 , 000 g for 15 min at 4°C to pellet DNA . The supernatant was removed and discarded before the DNA pellet was washed with 800 μl ice-cold 70% ethanol at 4°C and centrifuged at 15 , 000 g for 10 min at 4°C . The supernatant was removed and the DNA pellet was air dried for ~30 min . The DNA pellet was suspended in 50 μl of enzyme free water and vortexed for 5 s and centrifuged at 15 , 000 g for 10 min before the DNA extract ( from the daily air sample ) was stored at -20°C . For each week , fractions of the DNA from 7 daily air samples were pooled to produce a weekly air sample , which allowed testing of daily and weekly samples . A 15 μl aliquot of each 50 μl daily air sample ( 30% ) was transferred to a 2-ml centrifuge tube , which produced a weekly sample with a final volume of 105 μl . Both the daily and weekly air sample DNA extracts were stored at 4°C until analyzed . qPCR assays specific for Es . africanus isolates ( CBS 136730 and CBS 136260 ) were developed for regions of the nuclear ribosomal internal transcribed spacer ( ITS ) and the β-tubulin gene . All accessions of Es . africanus and gene constructs of closely related species used in this study are listed in S1 and S2 Tables . Gene constructs were manufactured by Eurofins MWG Operon USA , Louisville , KY . DNA sequence alignments were performed using the Multiple Sequence Comparison by Log-Expectation ( MUSCLE ) algorithm implemented in European Bioinformatics ( EMBL-EBI; http://www . ebi . ac . uk ) web browser . An alignment of Es . africanus JX398299 ( formerly Emmonsia sp . ) , Es . pasteurianus KR150770 ( formerly Ea . pasteuriana ) , Emmonsia crescens AF038336 , Histoplasma capsulatum NR_149341 , H . capsulatum KX646004 , Ajellomyces capsulatus KM361509 , A . capsulatus AF322386 , Ajellomyces dermatitidis HQ026734 , Paracoccidioides brasiliensis AY374339 , P . brasiliensis JQ675762 , Ajellomyces grisea AY527404 , Blastomyces percursus KY195964 , B . percursus KY 195963 , Onygenales sp . KX148665 , and Onygenales sp . KX148661 was performed in silico to test the specificity of the primers and probe for the Es . africanus assay . TaqMan ( Applied Biosystems , Foster City , CA ) primer and probe design was performed manually using the alignment files with the following criteria: the maximum melting temperature differences between primers used in an assay were ±1 . 6°C and probes >7 . 5°C when compared to primers . The resulting primers and probe are shown in Table 1; these were manufactured by Eurofins MWG Operon USA . The probe was labeled with a fluorescein dye ( 6-FAM ) at the 5’ end and a Black Hole Quencher 1 ( BHQ-1 ) nonfluorescent quencher . The specificity of the Es . africanus qPCR assay was validated in silico using the BLAST alignment tool in NCBI . The in silico specificity was also validated against the NCBI nucleotide database and there were no closely related isolates homologous for the assay target , including two isolates of the dimorphic fungus Blastomyces percursus ( CBS 139878 and NCPF 4091 ) that is endemic to South Africa [2] . The specificity of the primers EmeITS ( F ) and EmeITS ( R ) was further validated in vitro by end-point PCR using Es . africanus gDNA and gene constructs from other closely related species ( S1 Table ) , as well as a mock community of fungal species potentially present in air samples . The mock community consisted of gDNA from Alternaria sp . , Aspergillus niger , Cladosporium sp . , Epicoccum nigrum , and Trichoderma sp . Each member of the mock community was isolated from air samples and identified by morphology . A single fragment of 129 bp was amplified from the Es . africanus gDNA . No product was observed with the gene constructs of Es . pasteurianus or Ea . crescens ( accessions KR150770 and AF038336 , respectively ) or the air sample mock community gDNA . Development of the qPCR assay included the addition of the TaqMan probe . Quantitative PCR using the Es . africanus ITS primers described above and Taqman probe was performed with a StepOnePlus System ( Applied Biosystems , Foster City , CA ) . All reactions were performed in a final volume of 25 μl and contained 12 . 5 μl of 2× TaqMan Gene Expression Master Mix ( Applied Biosystems ) , 2 . 5 μl of each 5 μM primer solution , 2 . 5 μl of 2 . 5 μM TaqMan probe solution , and 5 μl of template DNA . PCR thermocycling conditions were set at 95°C for 15 min , 40 cycles at 95°C for 15 s and 61 . 9°C for 30 s . All air samples were tested in triplicate . To produce amplicon standards for the standard curve , end-point PCR was performed with the ITS primers using 20 ng of Es . africanus ( CBS 136260 ) gDNA . Gel electrophoresis was used to remove the unincorporated nucleotides from the PCR amplicons; this was followed by column purification with Illustra GFX PCR DNA and Gel Band Purification Kit , isolating the amplicons . Purified amplicons were quantified with a Qubit 2 . 0 and dsDNA HS assay Kit . The quantified amplicon solution was diluted based on the mass of amplicon and concentration of amplicon in the initial solution ( Applied Biosystems , 2003 ) . This resulted in solutions containing 60 , 000 , 6 , 000 , 600 , 60 , and 6 target gene copies in 5 μl of solution , which were used to create the standard curve . The positive control was 20 ng/μl of gDNA and the negative control was water in replacement of the gDNA . For the qPCR assay , each 96-well plate included reactions for a standard curve , positive control , and negative control with three replicates for all reactions . In addition , for days positive for Es . africanus , quantification was repeated on a different day to increase the number of replicates and to confirm there were no false positives . Deducing the number of Es . africanus cells or propagules from the number of target sequences detected by absolute qPCR required the knowledge of target copy number per cell . The β-tubulin gene is a single-copy gene [21–24]; therefore , the comparisons between the β-tubulin target and ITS targets for the same gDNA concentration enabled calculation of the number of ITS targets per genome . The ITS targets for our assay were normalized to the single β-tubulin target , allowing use of the amplicon standard curve to create an assay with a sensitivity below one propagule per 5 μl sample volume analyzed . Standards were developed for an absolute qPCR β-tubulin SYBR Green assay . To produce known standards , end-point PCR was carried out with the β-tubulin and ITS primers using 20 ng of Es . africanus gDNA; each PCR product was column purified with Illustra GFX PCR DNA and Gel Band Purification Kit ( GE Healthcare , Chicago , IL ) . Cleaned PCR product was quantified with a Qubit 2 . 0 and dsDNA HS assay Kit ( Thermofisher Scientific , Waltham , MA ) , and each quantified PCR product was diluted based on mass of amplicon and concentration of amplicon in the initial solution [25] . This resulted in solutions containing 60 , 000 , 6 , 000 , 600 , 60 , and 6 target gene copies in 5 μl of solution for each gene . These solutions were used to generate qPCR reactions with final volumes of 20 μl with 10 μl of 2× PowerUp SYBR Green PCR Master Mix ( Applied Biosystems , Foster City , CA ) , 2 μl of each 5 μM primer solution , 1 μl of enzyme free water , and 5 μl of standard template solution . Quantitative PCR was performed with a StepOnePlus System ( Applied Biosystems ) . PCR thermocycling conditions were set at 95°C for 2 min , 40 cycles at 95°C for 15 s and 60°C for 1 min . Fluorescence was read at the end of each extension step and there were four replicates for all standards . An absolute qPCR β-tubulin SYBR Green assay was used to test the mean copy number of targets from two isolates ( CBS 136260 and CBS 136730 ) of Es . africanus gDNA . Genomic DNA reactions containing 60 , 6 , and 0 . 6 ng were performed in a final volume of 20 μl , containing 10 μl of 2× PowerUp SYBR Green PCR Master Mix , 2 μl of each 5 μM primer solution ( Table 1 ) , 3 μl of enzyme free water , and 3 μl of template DNA . There were four replicates for each gDNA concentration . For the 0 . 6 ng reaction only isolate CBS 136260 was used . The quantity of ITS and β-tubulin target copy numbers were determined by comparing the Ct values of different gDNA concentrations to those of the known target number of the standard curve ( Fig 1 ) . The absolute quantification resulted in ITS target copy numbers of 5 . 97x108 and 5 . 04x108 ( for the 60 ng reactions ) 1 . 33x106 and 1 . 03x105 ( 6 ng reactions ) and 2 . 98x103 ( 0 . 6 ng reactions ) and β-tubulin target copy number of 5 . 92x107 and 5 . 09x107 ( 60 ng ) , 9 . 08x104 and 5 . 85x103 ( 6 ng ) , and 1 . 08x102 ( 0 . 6 ng ) respectively . When ITS target numbers were divided by the β-tubulin target numbers for the same DNA concentration , the calculated ITS targets per genome from highest to lowest DNA concentration were 10 , 10 , 15 , 18 , and 28 , respectively . The mean ( n = 5 ) ± SE ITS target number using all gDNA concentrations was 16 ± 3 with a SD of 7 . 3 , and after removal of two outliers was 12 ± 2 with a SD of 2 . 7 . Accordingly , 12 ITS targets were used to determine the number of Es . africanus propagules . The SYBR Green quantification was followed with a melting curve analysis , which produced single peaks for both the ITS and β-tubulin amplicons ( S1 Fig ) . DNA extracts were tested for PCR inhibitors to confirm the ability to amplify DNA . The qPCR master mix was spiked with a known concentration of a gene construct ( S1 Table ) of the reflectin gene of cuttlefish ( Sepia officinalis ) , an ocean living mollusk ( the DNA of which should not be found in air samples ) , and the Ct value of the DNA extract from air samples tested [26] . All weekly air samples were amplified with cuttlefish primers ( Table 1 ) and had a mean ( ± SD ) Ct value of 11 ± 0 . 47 ( Fig 2 ) . When the daily air samples from the Emergomyces positive weeks were tested , 6 samples showed the presence of inhibitors . Samples were cleaned by repeating the chloroform: isoamyl alcohol and subsequent steps of the DNA extraction . After cleaning , all 77 daily samples amplified with cuttlefish primers and had a mean ( ± SD ) Ct value of 10 ± 0 . 78 . StepOne Software v2 . 0 ( Applied Biosystems ) was used to determine the quantity of ITS target copy numbers for weekly ( pooled daily samples ) and daily air samples . The targets in the total DNA extract sample volume ( 105 μl for the weekly samples and 50 μl for the daily samples ) and the resulting total propagules were determined by comparing the Ct values of gDNA concentrations from air samples to the Ct values of the standard curve ( Fig 3A and 3B ) . The mean ITS target copy numbers were computed using all replicates of daily air samples ( Fig 3B ) . Daily meteorological data was obtained for the sampling period from South African Weather Service for Cape Town International Airport 6 . 8 km away . This included wind speed ( collected at the times 0800 , 1400 and 2000 ) , daily maximum and minimum temperatures , and daily rainfall . Mean values for each week were plotted using GraphPad Prism version 6 . 00 against the number of days in that week that Es . africanus propagules were detected .
A standard curve for the qPCR assay was obtained from 5 different amplicon concentrations from 6 to 60 , 000 ITS target copies ( Fig 1 ) . There was amplification of all amplicon dilutions and the positive control . The limit of quantification was 6 ITS target copies . Therefore , target quantities with fewer than 6 were only used for qualitative assessments of the presence of Es . africanus DNA . No amplification was detected for the no-template control . Es . africanus DNA was detected among weekly samples during 11 of 50 weeks . Ten of the 11 weeks produced ITS target numbers that were within the range of the standard curve ( Fig 3A ) and could be quantified ( Table 2 ) . During the 11th week in which Es . africanus DNA was detected , fewer than 6 targets were amplified . Aliquots of DNA from the daily air samples ( n = 77 ) from the 11 positive weekly samples were analyzed for Es . africanus . Initially , there were 31 daily samples positive for Es . africanus DNA . Although the weekly samples did not show PCR inhibition , all daily air samples from the Es . africanus positive weeks were also tested for the presence of PCR inhibition . DNA extracts from 6 daily air samples were determined to contain PCR inhibitors; after cleaning , the 6 samples were retested for Emergomyces . Three of the 6 previously inhibited daily samples were positive for Es . africanus , bringing the total number of Es . africanus positive daily samples to 34; however , only 11 daily samples were within the range of the standard curve and quantified ( Table 3 ) . This left a total of 23 daily samples that were positive for Es . africanus DNA but outside the standard curve ( Fig 3B ) and thus below the limit of quantification ( S2 Fig ) . The respective relationships between the detection of Es . africanus ( 34 days during the 11 positive weeks ) at our sampling site and the prevailing mean wind speed , maximum and minimum daily temperatures , and mean rainfall at Cape Town International Airport are demonstrated in Fig 4 . Although not enough Es . africanus positive weeks were detected to attempt statistical correlations with meteorological variables , it is possible that cooler temperatures ( during winter and spring ) and rainfall coincided with Es . africanus propagules in the air samples .
Emergomyces africanus is an important cause of an AIDS-related mycosis in South Africa [13] . We developed a qPCR assay that is highly specific and sensitive for the detection and quantification of Es . africanus in spore trap air samples , and demonstrated the frequent airborne circulation of Es . africanus in an industrial area of Cape Town . Other studies have used different protocols for molecular detection of Es . africanus in South Africa . Using a conventional PCR , Cronjé et al failed to detect Es . africanus in tissues of 1402 small terrestrial mammals from across South Africa [27] . Schwartz et al used a nested ( conventional ) PCR strategy and found Es . africanus in 30% of soil samples assessed [16] . The main advantage of the protocol presented here is the ability of qPCR to quantify the number of propagules present . Additional advantages of our assay include the high specificity for Es . africanus , which could be clearly distinguished from other closely related fungi in addition to a distantly related mock community . Moreover , our assay was highly sensitive , detecting as few as five propagules per day; the minimum inhaled dose of Es . africanus propagules required to cause infection is unknown . Limitations of our study include the fact that only a single spore trap was used from a single location . Additionally , the climatic data in our study is from a meteorological station 6 . 8 km away from the sampling site , and conditions at the spore trap may have been different from those measured . A limitation of the Burkard spore trap is the inability to use culture-based analyses [28] . Consequently , we cannot definitively conclude the infectivity of the detected propagules . Alternatively , the Burkard spore trap can be a robust sampling technique that allows molecular analyses of samples [29 , 30] . Our study is useful in demonstrating that airborne propagules of Es . africanus can be detected . Future investigations should include multiple concurrent spore traps in different locations to further characterize the range of detection as well as clarify the factors associated with the presence of airborne Es . africanus propagules . That Es . africanus propagules were frequently detected in an urban setting suggests that exposure to Es . africanus is common in Cape Town ( and perhaps other areas where infection has been diagnosed ) . While emergomycosis has only been reported in patients who are immunocompromised , many similarly immunocompromised patients do not develop the disease [13] . Further research should consider the question of which environmental , host and/or pathogen factors influence whether infection leads to disease . | Emergomyces africanus is a recently described dimorphic fungus that causes a serious and often fatal disease in immunocompromised patients in South Africa . Infection is presumed to occur via inhalation of infectious propagules; however , no attempts have yet been made to detect this fungus in the air . We developed a highly specific and sensitive protocol for the molecular detection and quantification of Es . africanus , and tested air samples collected over a 50-week period from an urban area of Cape Town , Western Cape , South Africa , where the disease is endemic . We detected Es . africanus in the air on 10% of all days sampled , suggesting that exposure to this fungus is common in this area . This study lays the groundwork for further investigations that might explore environmental , host , and pathogen factors that influence the development of this fungal disease . | [
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"extract... | 2018 | Molecular detection of airborne Emergomyces africanus, a thermally dimorphic fungal pathogen, in Cape Town, South Africa |
Persistent infections are subject to constant surveillance by CD8+ cytotoxic T cells ( CTL ) . Their control should therefore depend on MHC class I-restricted epitope presentation . Many epitopes are described for γ-herpesviruses and form a basis for prospective immunotherapies and vaccines . However the quantitative requirements of in vivo immune control for epitope presentation and recognition remain poorly defined . We used Murid Herpesvirus-4 ( MuHV-4 ) to determine for a latently expressed viral epitope how MHC class-I binding and CTL functional avidity impact on host colonization . Tracking MuHV-4 recombinants that differed only in epitope presentation , we found little latitude for sub-optimal MHC class I binding before immune control failed . By contrast , control remained effective across a wide range of T cell functional avidities . Thus , we could define critical engagement thresholds for the in vivo immune control of virus-driven B cell proliferation .
The gamma-herpesviruses ( γHVs ) infect >90% of humans and cause diseases including nasopharyngeal carcinoma , African Burkitt's lymphoma and Kaposi's Sarcoma . Their colonization of circulating memory B cells is crucial to persistence and hence to disease ontogeny . Viral latency gene expression in B cells provides an immune target [1] that has been exploited to prevent lymphoproliferative disease in acutely immunodeficient patients by T cell transfer [2] . However , extending this approach to established cancers and developing related vaccines have proved difficult . A significant problem is that the narrow species tropisms of human γHVs severely restrict in vivo analysis , and hence an understanding of how empirical therapies such as adoptive T cell transfer work . Immune recognition can be assayed in vitro; but while Epstein-Barr virus ( EBV ) latency gene products drive autonomous B cell proliferation in vitro , most in vivo infected cells are resting memory B cells that have passed though lymphoid germinal centers ( GCs ) [3] . This makes difficult in vitro analysis of in vivo immune control . One way to make progress is to study related viruses that are experimentally more accessible . Probably the best characterized is Murid Herpesvirus-4 ( MuHV-4 , archetypal strain MHV-68 ) [4]–[6] . MuHV-4 is more closely related to the Kaposi's Sarcoma-associated Herpesvirus ( KSHV ) than to EBV [7] . However it shares many features of host colonization with EBV , for example it exploits lymphoid GCs to establish persistence in circulating memory B cells [8]–[10] . Therefore it can be used to reveal fundamental mechanisms of γHV/host interaction . MuHV-4 studies have shown that γHV-driven lymphoproliferation occurs in complex lesions incorporating T cell evasion and infected cells with distinct patterns of viral gene expression [10] . In addition to cis-acting T cell evasion during episome maintenance [11] , [12] , EBV inhibits the transporter associated with antigen processing ( TAP ) via BNLF2a [13]–[15] and MHC class I export to the cell surface via BILF1 [16] , [17]; KSHV degrades MHC class I and other immune receptors via K3 and K5 [18]; and MuHV-4 degrades MHC class I and TAP via MK3 [19]–[21] . Disrupting MK3 impairs virus-driven lymphoproliferation [22] . The γHVs also evade immune recognition during latency by expressing few CTL targets . However a gene that modulates signaling through the B cell receptor - M2 in MuHV-4 [23]–[26] , LMP-2A in EBV [27] and K1 in KSHV [28] - is expressed more widely than growth program genes [3] , and shows protein sequence diversity [29]–[33] consistent with immune selection . More directly , the presence of an H2Kd binding epitope in M2 [34] , [35] significantly reduces long-term MuHV-4 latent loads in BALB/c mice [29] . Therefore despite viral evasion , CTL help to regulate long-term infection [36] , [37] , and CTL recognition of M2/K1/LMP-2A , which in EBV may extend also to EBNA3A/B/C [38] , [39] , provides a potential point of attack . LMP-2A is also a candidate vaccine target for nasopharyngeal carcinoma [40] . Thus , how M2/K1/LMP-2A recognition works in vivo is important to understand . CTL effector capacity broadly correlates with functional avidity , as determined by the capacity of T cell receptor ( TcR ) engagement to trigger CTL proliferation , cytokine production and target cell lysis at limiting antigen dose [41] . Therefore with limited γHV protein expression during latency , peptide affinity for MHC class I and TcR functional avidity are likely to be crucial for immune control . The diversity of LMP-2A , K1 and M2 prompted us to analyze in vivo the consequences of varying MHC class I binding and TcR functional avidity for a single epitope derived from M2 . These parameters affected dramatically the control of virus-driven lymphoproliferation , even in the context of immune evasion . The capacity of MuHV-4 to correlate biochemical interactions with in vivo immune function allowed us to establish quantitative guidelines for infection control .
To understand the CTL recognition requirements for γHV infection control , we expressed from MuHV-4 a well-characterized , H2Kb-restricted epitope comprising amino acid residues 257–264 of ovalbumin ( OVA ) , or APL derivatives ( Figure 1A ) . OVA binds to H2Kb with high affinity ( KD = 4 . 1 nM ) [42] . We compared OVA and APL binding by H2Kb stabilization on TAP-deficient RMA/S cells ( Figure 1B ) [43] . The OVA concentration giving 50% maximal stabilization ( EC50 ) was 40 nM , in close agreement with published data [44] . APLs Q4 , V4 , G4 and R4 were similar to OVA ( EC50 within 2-fold ) , consistent with residue 4 being solvent-exposed in the H2Kb-peptide complex [45] . E1 required 6-fold more peptide for equivalent H2Kb stabilization , consistent with this residue being only partly exposed; A8 , which has a mutated anchor residue , required 10-fold more peptide again; and the control peptide A5A8 , with 2 mutated anchor residues , gave no significant stabilization . The H2Kb/OVA/β2M complex has an estimated half-life of 8 h [44] . Its stability is determined primarily by the peptide off-rate , so the E1 complex is likely to have a half-life of approximately 1 . 3 h . We assessed the functional avidity of the H2Kb-OVA-specific TcR of OT-I [46] for each APL by ex vivo stimulation of CD8+ T cells from OT-I mice with graded peptide doses ( Figure 1C ) . There was a clear hierarchy in dose-response , with OVA>Q4 ( 14-fold ) >V4 ( a further 279-fold ) >G4 ( 53-fold further still ) , consistent with published data [47] . The R4 antagonist peptide [48] , [49] gave no stimulation . As predicted E1 and A8 , which have lower MHC class I binding , generated the lowest dose-responses . We next introduced each epitope at the MuHV-4 M2 C-terminus to ensure expression in latency without compromising M2 function [29] . CTL recognition of an endogenous M2 epitope reduces long-term MuHV-4 latent loads in H2d mice [29] . The lack of an endogenous H2b-restricted M2 epitope therefore allowed us to introduce new targets in a context where this is known to be important . Each recombinant virus was also made with a yellow fluorescent protein ( YFP ) reporter construct [50] to aid infection tracking ( Figure S1 ) . Correct epitope insertion and assembly of the surrounding genome were demonstrated by PCR of plaque-purified viral DNA ( Figure 1D ) . Each recombinant virus showed equivalent in vitro growth ( Figure 1E ) , equivalent lytic replication in the lungs of intranasally ( i . n . ) infected C57BL/6 mice ( Figure 1F ) - with peak titers at 4–7 days post-inoculation and clearance by day 11 - and normal latency establishment in H2d BALB/c mice - with equivalent splenic infectious center assay titers 14 days after i . n . inoculation ( Figure 1G ) . Therefore none showed a replication defect independent of H2b-restricted latent epitope expression . We then tested latency establishment in H2b mice . Infectious center assays ( Figure 2A ) showed attenuation of any virus with an H2Kb binding epitope attached to M2 ( vOVA , vQ4 , vV4 , vG4 , vR4 ) : splenic infection was established at day 11 , but then cleared rather than amplified by days 14–21 . In contrast , the virus expressing a poorly binding epitope ( vA8 ) was indistinguishable from the epitope-negative wild-type ( vWT ) . Interestingly vE1 , which expresses an epitope with 6-fold lower EC50 for H2Kb stabilization ( Figure 1B ) , showed an intermediate phenotype with normal titers at day 11 followed by a gradual reduction . Not every latently infected cell necessarily reactivates its virus ex vivo . We therefore used PCR of viral DNA at limiting dilution ( Figure 2B; Table 1 ) as a second measure of infected cell frequency . We looked at the peak of latent infection ( 14 days post-inoculation ) and at the steady state ( 50 days ) . These results supported the infectious centre assays: vOVA , vQ4 , vV4 , vG4 and vR4 were all markedly attenuated ( >100-fold reduction ) ; vA8 was equivalent to vWT; and vE1 showed an intermediate phenotype , with strongly decreased acute titers but long-term titers close to vA8 and vWT . MuHV-4-specific CTL responses peak at 14–21 days post-infection [51] . Thus a weakly binding latent epitope ( E1 ) allowed some control when CTL responses were at their peak , but not in the long-term when CTL responses decrease in size . MuHV-4 colonizes multiple cell types in acutely infected spleens . Many are B cells , which change in phenotype as they pass through germinal centers; others are myeloid cells . The main proliferating population is GC B cells , and these also connect most directly to the long-term latency reservoir of resting memory B cells [9] , [10] . Therefore to understand better the relationship between acute and long-term viral loads , we measured viral genome prevalence in flow cytometrically sorted GC B cells ( Figure 2C; Table 2 ) . They showed marked reductions for vOVA , vQ4 , vV4 , vG4 and vR4 , equivalent frequencies for vA8 and vWT , and intermediate frequencies for vE1 . These data were further supported by in situ hybridization for latently expressed viral tRNA/miRNA homologs [29] ( Figure 2D ) , which showed abundant GC infection by vWT and vA8 , severely impaired infection by vOVA , vQ4 , vV4 , vG4 and vR4 , and intermediate infection by vE1 . Therefore susceptibility to CTL attack during acute lymphoproliferation varied with cell type , and the relative sparing of vE1+ GC B cells appeared to allow high long-term viral loads . We measured epitope-specific CTL responses with H2Kb-peptide tetramers ( Figure 2E ) and by staining for intracellular IFN-γ after ex vivo stimulation ( Figure 2F ) . Responses to vA8 were uniformly low despite high viral loads , presumably because this epitope was not produced in sufficient amounts to compensate for its poor H2Kb binding . Responses to vOVA , vQ4 , vV4 , vG4 and vR4 were detectable , although small compared to those reported for lytic antigens [51] . Surprisingly , the largest CTL response was elicited by the intermediate phenotype virus , vE1 . This could not be explained by lytic infection , since this was high in lungs for all viruses ( Figure 1F ) . We confirmed the functionality of vE1-specific CTL by in vivo killing of CFSE-labelled , peptide-exposed targets ( Figure 2G , H ) : vE1-induced CTL showed target cell elimination comparable to vOVA , whereas mice infected with vWT or vA8 showed none . Therefore the relatively weak H2Kb binding of E1 was sufficient to stimulate large , functional CTL responses , but not for those CTL to curtail efficiently virus-driven lymphoproliferation . This result suggested that at least for vE1 , most CTL stimulation comes from a population distinct from that engaged in lymphoproliferation . The capacity of C57BL/6 mice to control MuHV-4-driven lymphoproliferation through the recognition of latently expressed OVA , Q4 , V4 , G4 or R4 indicated that the key requirement in a polyclonal TcR setting is the availability of an epitope capable of strong MHC class I binding: T cells from the naive repertoire could recognize either OVA or an APL . However responses to EBV can involve oligoclonal or even monoclonal CTL expansions [52]–[54] . Therefore to understand better the quantitative requirements of TcR functional avidity for in vivo γHV control , we focussed on the well-characterized OT-I TcR ( Figure 3 ) . We first infected OT-I mice with MuHV-4 expressing OVA or APLs with comparable H2Kb binding ( Q4 , V4 , G4 , R4 ) , and measured host colonization by infectious center assay of spleens 9 and 11 days later ( Figure 3A ) . vE1 and vA8 were not utilized since they bind MHC class I less efficiently precluding analysis of T cell functional avidity because target concentrations are different . There was a clear correlation between CTL functional avidity ( Figure 1C ) and in vivo virus control . The antagonist epitope ( R4 ) allowed no control - titers were equivalent to those of the epitope-negative vWT; the others showed a hierarchy of control ( OVA>Q4>V4>G4 ) that matched exactly their hierarchy of functional avidity ( and not their minor differences in H2Kb binding ) . Low titers of pre-formed infectious virus were found in some mice , but generally in proportion to their latent titers , consistent with reactivation of a fixed fraction of the latent viral load; we saw no evidence that M2-associated epitope presentation created a significant new lytic CTL target . To confirm that the immune control was by CTL , we treated mice with a depleting , CD8-specific mAb from the time of infection ( Figure 3B–D ) . Each virus then reached equivalent titers to the wild-type . While the depletions were highly effective ( Figure 3C ) , they had little effect on the day 11 spleen titers of vWT ( Figure 3D ) . This result was consistent with previous publications [36] , [55] and with the lack of known H2b-restricted MuHV-4 latency epitopes . Thus , introducing latent epitope recognition caused new , CD8-dependent virus attenuation in proportion to the functional avidity of that epitope for the dominant TcR . OT-I mice provided a useful starting point for in vivo analysis of single TcR function . However their limited CD4+ T cell repertoire impairs GC formation and so the ability of MuHV-4 to drive B cell proliferation . Hence , to define the impact of TcR functional avidity in an environment more conducive to lymphoproliferation , we adoptively transferred lymphocytes from Rag-1−/−OT-I mice and purified CD4+ T cells from C57BL/6 mice into TcRα−/− recipients ( Figure 4A ) . Thus the reconstituted mice had polyclonal CD4+ T cells and a TcRαβ+CD8+ T cell compartment of modest size that was restricted to OT-I cells . ( Most CD8+ T cells of TcRα−/− mice are TcRγδ+TcRαβ− . ) Infecting these with vWT led to a robust proliferation of infected GC B cells ( Figure S2 and S3 ) . Infecting them with vOVA elicited a strong OT-I response ( Figure 4B ) and suppression of splenic colonization ( Figure 4C ) ; by contrast vR4 , which expressed an antagonist epitope , elicited no OT-I response and reached high titers ( Figure 4C ) . Therefore these mice provided a new and informative window onto how TcR engagement by a latency epitope affects virus-driven lymphoproliferation . We then infected reconstituted mice with MuHV-4 expressing OVA or APLs ( Figure 5 ) . At day 16 post-infection OT-I T cell expansion was greatest for vOVA , reduced for vQ4 , reduced further for vV4 , and close to background for vG4 and vR4 ( Figure 5A ) . Thus it correlated well with the epitope functional avidity measured in Figure 1C ( OVA>Q4>V4>G4>R4 ) . Specifically , the 14-fold avidity reduction of Q4 only modestly reduced CTL cell expansion , and the 4000-fold reduction of V4 caused further reduction but still did not ablate it entirely . The CTL response declined to background only when the avidity was reduced 200 , 000-fold ( G4 ) . Therefore the immune response showed a surprisingly large tolerance for sub-optimal TcR engagement . Similar results were obtained for OT-I T cell activation ( loss of CD62L , Figure 5B ) . We analyzed CTL function further by intracellular staining for IFN-γ ( Figure 5C ) and Granzyme B ( Figure 5D ) after ex vivo stimulation with the corresponding peptide epitope . The responses to vG4 and vR4 were hard to assess due to low CTL numbers; but those to vQ4 and vV4 showed comparable functionality to vOVA . ( Note that the peptide concentration used was only just sufficient for maximal stimulation by V4 in Figure 1C ) Therefore there was no sign of vQ4 and vV4 eliciting CTL responses that were functionally impaired ( or functionally enhanced ) ; they simply elicited responses that were smaller . Virus titers ( Figure 5E ) were reduced markedly by OVA expression , only marginally less by Q4 , and not significantly by G4 or R4 . V4 expression gave an intermediate phenotype , with titers significantly below those of the vWT control and significantly above those of vOVA . The frequencies of viral DNA+ cells in spleens ( Figure 5F and Table S1 ) showed a similar hierarchy ( vWT = vG4 = vR4>vV4>vQ4>vOVA ) . The viral DNA+ frequencies of flow cytometrically sorted GC B cells ( Figure 5G and Table 3 ) showed less discrimination . Nonetheless the trends were similar , and these results were further corroborated by analysis of YFP expression in GC B cells ( Figure S4 ) . Therefore high functional avidity ( vOVA ) gave marked CTL expansion and low virus titers; a 14-fold avidity reduction ( vQ4 ) have remarkably similar results; a 200 , 000-fold avidity reduction abolished virus control ( vG4 ) ; and a 4000-fold reduction gave an intermediate phenotype ( vV4 ) . OT-I TcR engagement by M2-derived OVA was therefore considerably above the threshold required for in vivo viral control , and low functional avidity compromised viral control via reduced CTL expansion , rather than by differentially affecting CTL effector function .
Gamma-herpesvirus epitope recognition by CTL has been studied extensively [1] , [54] , but ours is the first quantitative assessment of how epitope/MHC class I/TcR complex formation affects host colonization . Where no latency epitope expression existed , introducing one led to a profound , CTL-dependent suppression of virus-driven lymphoproliferation . This was consistent with the impact of endogenous epitope presentation in H2d mice [29] . The latter affected only long-term viral loads; OVA expression in H2b mice also conferred susceptibility to CTL during acute lymphoproliferation , when trans-acting immune evasion operates [1] . This greater effect of epitope presentation possibly reflected differences in host susceptibility to immune evasion: the MuHV-4 K3 degrades H2Kb relatively poorly [19] and degrades TAP better in H2d than H2b cells [20] . The precise cellular targets for CD8+ T cell recognition of M2-linked epitopes remain unknown . One possibility is proliferating germinal centre B cells , as B cells are a major site of M2 expression [10] , [34] . Infected B cells could also be recognized before the onset of proliferation; and as myeloid cells transfer infection to B cells [56] , CD8+ T cells could also suppress lymphoproliferation indirectly , by targeting infected myeloid cells [1] . A key point for physiologically relevant epitope presentation is that it conforms to normal latent gene expression . Exogenous promoters such as HCMV IE1 show activity independent of endogenous viral gene expression [57] and this can lead to attenuation [58] . Previous analysis of endogenous M2 epitope [29] established its importance for determining the different long-term latent loads of H2d and H2b mice . Here , to identify presentation thresholds , we made use of the well-characterized SIINFEKL epitope , attaching it to a neutral region of M2 ( its C-terminus ) . This allowed the generation of a very well-defined model epitope with the kinetics and copy number of a known endogenous epitope . Epitope presentation varies with MHC class I genotype . C57BL/6 mice have only 2 MHC class I molecules and appear not to recognize an endogenous M2 epitope . In this context , M2-SIINFEKL illustrated the impact of strong epitope presentation , and wild-type M2 ( or M2-vA8 ) that of poor epitope presentation . The SIINFEKL variants covered the range between , and so allowed us to identify functional recognition thresholds . Small differences ( <1 . 6-fold ) in H2Kb epitope binding had no obvious impact on in vivo CTL efficacy , but a 60-fold reduction abolished protection and a 6-fold reduction showed a partial phenotype . Thus , M2-linked epitope presentation left little room for sub-optimal MHC class I binding . By contrast when H2Kb binding was maintained , reducing TcR functional avidity 14-fold had little effect , reducing it 200 , 000-fold abolished control , and reducing it 4 , 000-fold gave an intermediate phenotype . Therefore this aspect of recognition was more flexible even for monoclonal , Rag-1−/− CTL , and a polyclonal population could attack any epitope so long as its MHC class I binding was strong . In complex viral infections , larger CTL responses are not necessarily more effective responses . These parameters can correlate: MuHV-4 lacking its K3 evasion gene elicits more CTL and achieves lower titers [22]; and our reconstituted mice showed a correlation between more CTL and less virus . But as with latent epitope presentation downstream of ORF73 [11] , OVA-specific CTL responses that completely suppressed lymphoproliferation were small compared to lytic epitope responses [51]; and mice infected with vE1 made large epitope-specific responses yet showed poor virus control . We hypothesize that CTL can be stimulated by the key , self-renewing population of infected B cells , when infection is suppressed , but also by infected cells less important to host colonization , when large responses may achieve little . Crucially , viral evasion may make the self-renewing population harder to target . Thus , vE1 showed a strong acute reduction in total viral DNA+ cell frequencies , but relative sparing of GC B cells and consequently high long-term virus loads . A position 1 mutation also impairs the control by Rag-1−/−OT-I mice of MuHV-4 expressing OVA from an HCMV IE1 promoter [59] . However such mice lack B cells or CD4+ T cells , and without CD4+ T cells MuHV-4 causes a lethal , chronic lytic infection even with a strong , polyclonal CTL response [60] , [61] . Our reconstituted mice maintained both virus-driven lymphoproliferation and infection control without outgrowth of CTL escape mutants . Thus we could relate directly quantitative changes in epitope recognition to the control of lymphoproliferation . An important task with EBV is to predict in vivo CTL efficacy . Extrapolating from CTL numbers and in vitro assays alone is clearly problematic . For example , large responses to lytic epitopes in infectious mononucleosis [54] could be interpreted as important , or simply as poor latency epitope recognition when better recognition might preclude large lytic responses and avoid symptoms . The precise relatedness of EBV memory B cell colonization via GCs to MuHV-4 memory B cell colonization via GCs is unknown . But all γHVs have evolved to colonize lymphocytes with maximal efficiency , within limits set ultimately by the immune system , so similar quantitative thresholds would not be surprising . Our data therefore have important general implications for γHV-specific CTL function , and for predicting in vivo CTL efficacy from biochemical measures .
The study accorded with the Portuguese official Veterinary Directorate ( Portaria 1005/92 ) , European Guideline 86/609/EEC , and Federation of European Laboratory Animal Science Associations guidelines on laboratory animal welfare . It was approved by the Portuguese official veterinary department for welfare licensing ( protocol AEC_2010_017_PS_Rdt_General ) and by the IMM Animal Ethics Committee . CD45 . 1 C57BL/6 , OT-I , Rag-1−/− and TcRα−/− mice were obtained from Jackson Laboratories . CD45 . 1 Rag-1−/− OT-I mice were obtained by breeding OT-I onto a CD45 . 1 Rag-1−/− background . C57BL/6 and BALB/c mice were purchased from Charles River Laboratories . All mice were housed under specific pathogen-free conditions at the Instituto de Medicina Molecular and used when 6–12 weeks old . For adoptive transfers to TcRα−/− mice , CD4+ T cells were purified by negative selection from pooled lymph nodes of naïve C57BL/6 mice using the CD4+ T cell isolation kit ( Miltenyi Biotech ) . OT-I T cells were obtained from pooled lymph nodes of naïve CD45 . 1 Rag-1−/− OT-I mice . 2×106 CD4+ T cells and 106 CD45 . 1 Rag-1−/− OT-I T cells were adoptively transferred to TcRα−/− recipients via tail vein injection one day prior to infection . MuHV-4 recombinants were generated from BAC-cloned viral genomes [29] . OVA and APL epitopes were introduced by PCR at the M2 C-terminus . Briefly , the M2 downstream region ( genomic co-ordinates 3846-4029 ) containing a HindIII restriction site followed by the epitope coding region and a stop codon were PCR amplified ( Table S2 ) to attach each epitope to the M2 C-terminus . The PCR products were inserted downstream of a HinDIII/XhoI MuHV-4 genomic fragment ( nt 4029–5362 ) in pSP72 ( Promega ) , using a genomic BglII site ( nt 3846 ) and the engineered HinDIII ( nt 4029 ) restriction site . The constructs were then subcloned into a HinDIII-E MuHV-4 genomic fragment in the pST76K-SR shuttle plasmid , using genomic BlnI ( nt 3908 ) and XhoI ( nt 5362 ) restriction sites . All PCR-derived regions were sequenced to confirm the integrity of the introduced epitopes and the M2 flanking region . Each recombinant HinDIII-E shuttle plasmid was transformed into E . coli carrying the wild type MuHV-4 BAC ( pHA3 ) or a YFP+ BAC [50] obtained from Dr Samuel Speck ( Emory Vaccine Center , Atlanta ) . Following multi-step selection , recombinant BAC clones were identified by restriction digestion with HinDIII . The integrity of each BAC was confirmed by digestion with BamHI and EcoRI . All viruses were reconstituted by transfecting BAC DNA into BHK-21 cells using FuGENE 6 or X-tremeGENE HP ( Roche Applied Science ) . The loxP-flanked BAC cassette was then removed by viral passage through NIH-3T3-CRE cells and limiting dilution cloning . The integrity of each reconstituted virus was checked by PCR of viral DNA across the HinDIII-E region and DNA sequencing across M2 . Murine RMA/S cells were cultured in RPMI 1640 with 10% fetal calf serum , 2 mM glutamine and 100 U/ml penicillin and 100 µg/ml streptomycin . NIH-3T3 ( ATCC ) -CRE cells [22] were grown in Dulbecco's modified Eagle's medium ( DMEM ) with 10% fetal calf serum , 2 mM glutamine , 100 U/ml penicillin and 100 µg/ml streptomycin . Baby hamster kidney fibroblast cells ( BHK-21 , ATCC ) were cultured in Glasgow's modified Eagle's medium ( GMEM ) supplemented as above plus 10% tryptose phosphate broth . To prepare viral stocks , low multiplicity infections ( 0 . 001 PFU per cell ) of NIH-3T3-CRE or BHK-21 cells were harvested after 4 days and titrated by plaque assay [29] . H2Kb stabilization was determined with TAP-deficient RMA/S cells . These were incubated overnight at 26°C to promote the export of empty H2Kb complexes , then loaded with graded concentrations of OVA or APL peptides ( Thermo Scientific ) for 2 h at 26°C and subsequently transferred to 37°C for 2 h to destabilize empty MHC molecules [43] . The cells were then washed twice , stained with anti-H2Kb ( AF6-88 . 5 . 5 . 3 , eBioscience ) , and analysed on a LSR Fortessa ( BD Biosciences ) . Mean fluorescence intensities were determined with FlowJo ( Tree Star ) . To measure the ex vivo stimulation of naïve OT-I T cells by OVA and APLs , CD8+ T cells from the spleens of naïve OT-I mice were purified by negative selection ( CD8+ T cell isolation kit , Miltenyi Biotech ) ; for equivalent peptide/MHC class I numbers , irradiated ( 7500 rads ) RMA/S cells were loaded with different peptides at 26°C , then incubated at 37°C; and 5×104 OT-I T cells were cultured with 2 . 5×104 RMA/S cells for 72 h at 37°C . IFNγ levels in culture supernatants were measured by ELISA ( DuoSet ELISA development kit , R&D Systems ) . The data were fitted to sigmoidal dose-response curves and EC50 values calculated using GraphPad Prism . Groups of 6- to 8-week old BALB/c and C57BL/6 mice were inoculated i . n . with 104 PFU of MuHV-4 . 8- to 12-week old OT-I and TCRα−/− mice were inoculated i . n . with 103 PFU of MuHV-4 . All virus inoculations were in 20 µl of PBS under isofluorane anaesthesia . At different days post-infection lungs or spleens were removed and processed for subsequent analysis . Titres of infectious virus were determined by plaque assay of freeze-thawed lung or spleen homogenates using BHK-21 cells . Latent virus loads were quantified by explant co-culture of splenocytes with BHK-21 cells . Plates were incubated for 4 ( plaque assay ) or 5 ( explant co-culture assay ) days , then fixed with 4% formaldehyde and stained with 0 . 1% toluidine blue . Viral plaques were counted with a plate microscope . The frequency of MuHV-4 genome-positive cells was determined by limiting dilution combined with real time PCR [10] . Splenocytes were pooled from 4–5 mice . GC B cells ( CD19+CD95hiGL7hi ) were purified from pools of 4 or 5 spleens using a BD FACSAria Flow Cytometer ( BD Biosciences ) . Cells were serially two-fold diluted and eight replicates of each dilution were analysed by real time PCR ( Rotor Gene 6000 , Corbett Life Science ) . The primer/probe sets were specific for the MuHV-4 M9 gene ( 5′ primer: GCCACGGTGGCCCTCTA; 3′ primer: CAGGCCTCCCTCCCTTTG; probe: 6-FAM-CTTCTGTTGATCTTCC-MGB ) . Samples were subjected to a melting step of 95°C for 10 min followed by 40 cycles of 15 s at 95°C and 1 min at 60°C . Real-time PCR data was analysed on the Rotor Gene 6000 software . The purity of sorted populations was always >96% . In situ hybridization with a digoxigenin-labelled riboprobe encompassing MuHV-4 vtRNAs 1–4 and microRNAs 1–6 was performed on formalin-fixed , paraffin-embedded spleen sections [29] , using probes generated by T7 transcription of pEH1 . 4 . Splenocytes from naïve CD45 . 1 C57BL/6 mice were used as targets and controls . Targets were pulsed with 1 µM OVA , E1 or A8 peptides for 1 h at 37°C , then labeled with 1 µM carboxyfluorescein succinimidyl ester ( CFSE ) ( Molecular Probes ) . Controls were left unpulsed and labeled with 0 . 1 µM CFSE . Cells were washed three times then injected intravenously as a 50∶50 mix of CFSEhi and CFSElo cells ( 4×106 ) into mice infected with vWT , vOVA , vE1 or vA8 . The same mixes were injected intravenously into vWT infected C57BL/6 controls to ensure equal transfer . On the next day splenocytes were harvested and the proportion of CFSEhi and CFSElo cells among CD45 . 1 splenocytes was analysed by FACS . Target cell killing was calculated as ( % CFSElo/% CFSEhi ) , with % = 100− ( ratio in vWT infected/ratio in vOVA , vE1 or vA8 infected ) ×100 . MuHV-4 infected OT-I mice were depleted of CD8+ T cells by 5 intraperitoneal injections of 200 µg monoclonal antibody YTS 169 . 4 . Splenocytes from control or depleted mice were stained with anti-CD8α ( 53-6 . 7 ) ( BD Pharmingen ) and analysed on a LSR Fortessa ( BD Biosciences ) . Splenocytes ( 2×106 ) from infected mice were stimulated for 5 h at 37°C with 10 µg/ml peptide ( OVA , APLs or VSV NP52-59 ) in RPMI 1640/10% fetal calf serum/2 mM glutamine/100 U/ml penicillin/100 µg/ml streptomycin/50 µM 2-mercaptoethanol/10 U/ml recombinant murine IL-2 ( PeproTech ) /10 µg/ml Brefeldin A . Cells were then washed , blocked with anti-CD16/32 ( 2 . 4G2 ) ( BD Pharmingen ) , surface stained with anti-CD8α ± anti-CD45 . 1 ( for OT-I T cells ) , fixed and permeabilized with Foxp3 staining buffer ( eBioscience ) and stained with anti-IFNγ ( XMG1 . 2 ) ( BD Pharmingen ) , anti-Granzyme B ( NGZB ) or anti-IgG2ak Isotype control ( eBioscience ) . Samples were analysed on a LSR Fortessa ( BD Biosciences ) . Splenocytes were treated with red blood cell lysis buffer , blocked with anti-CD16/32 ( 2 . 4G2 , BD Pharmingen , 10 min ) , and stained at 4°C in PBS/2% FCS 30 minutes: anti-CD95 ( Jo2 ) , anti-CD19 ( 1D3 ) , anti-CD8α ( 53-6 . 7 ) , anti-IFNγ ( XMG1 . 2 ) ( BD Pharmingen ) ; anti-CD45 . 1 ( A20 ) , anti-CD45 . 2 ( 104 ) , anti-CD44 ( IM7 ) , anti-CD62L ( MEL-14 ) ( Biolegend ) ; anti-GL7 ( GL7 ) , anti-H2Kb ( AF6-88 . 5 . 5 . 3 ) , anti-TCRβ ( H57-597 ) , anti-GranzymeB ( NGZB ) , anti-IgG2ak Iso control ( eBR2a ) ( eBioscience ) . For biotinylated antibodies , an additional 20 minutes incubation with streptavidin was performed . MuHV-4 infected cells were identified by YFP expression . H2Kb tetramers conjugated to PE were a kind gift from Dr Hidde L . Ploegh ( Whitehead Institute for Biomedical Research , Massachusetts Institute of Technology , Cambridge ) . Conditional ligand was exchanged for SIINFEKL ( OVA ) , SIIQFEKL ( Q4 ) , SIIVFEKL ( V4 ) , SIIGFEKL ( G4 ) , SIIRFEKL ( R4 ) , EIINFEKL ( E1 ) or RGYVYQGL ( VSV NP52-59 ) peptides ( Thermo Scientific ) . Streptavidin-APC or -PerCP ( BD Pharmingen ) was used to reveal biotinylated antibodies . Samples were acquired on a LSR Fortessa using DIVA ( BD Biosciences ) and analysed with FlowJo ( Tree Star , Inc . ) . Data comparisons between groups were performed by an unpaired two-tailed t-test or ordinary one-way ANOVA as appropriate . Mean +/− SEM and statistics were calculated with GraphPad Prism Software . For limiting dilution analysis 95% confidence intervals were determined as described [10] . Primers used for attaching each epitope to MuHV-4 M2 C-terminus are detailed in supplemental Table S2 . | Chronic viral infections cause huge morbidity and mortality worldwide . γ-herpesviruses provide an example relevant to all human demographics , causing , inter alia , Hodgkin's disease , Burkitt's lymphoma , Kaposi's Sarcoma , and nasopharyngeal carcinoma . The proliferation of latently infected B cells and their control by CD8+ T cells are central to pathogenesis . Although many viral T cell targets have been identified in vitro , the functional impact of their engagement in vivo remains ill-defined . With the well-established Murid Herpesvirus-4 infection model , we used a range of recombinant viruses to define functional thresholds for the engagement of a latently expressed viral epitope . These data advance significantly our understanding of how the immune system must function to control γ-herpesvirus infection , with implications for vaccination and anti-cancer immunotherapy . | [
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"infect... | 2014 | Defining Immune Engagement Thresholds for In Vivo Control of Virus-Driven Lymphoproliferation |
Aedes mediovittatus mosquitoes are found throughout the Greater Antilles in the Caribbean and often share the same larval habitats with Ae . Aegypti , the primary vector for dengue virus ( DENV ) . Implementation of vector control measures to control dengue that specifically target Ae . Aegypti may not control DENV transmission in Puerto Rico ( PR ) . Even if Ae . Aegypti is eliminated or DENV refractory mosquitoes are released , DENV transmission may not cease when other competent mosquito species like Ae . Mediovittatus are present . To compare vector competence of Ae . Mediovittatus and Ae . Aegypti mosquitoes , we studied relative infection and transmission rates for all four DENV serotypes . To compare the vector competence of Ae . Mediovittatus and Ae . Aegypti , mosquitoes were exposed to DENV 1–4 per os at viral titers of 5–6 logs plaque-forming unit ( pfu ) equivalents . At 14 days post infectious bloodmeal , viral RNA was extracted and tested by qRT-PCR to determine infection and transmission rates . Infection and transmission rates were analyzed with a generalized linear model assuming a binomial distribution . Ae . Aegypti had significantly higher DENV-4 infection and transmission rates than Ae . mediovittatus . This study determined that Ae . Mediovittatus is a competent DENV vector . Therefore dengue prevention programs in PR and the Caribbean should consider both Ae . Mediovittatus and Ae . Aegypti mosquitoes in their vector control programs .
Dengue virus ( DENV , Family Flaviridae , Genus Flavivirus ) is most commonly transmitted to humans by the bite of an infected Aedes aegypti mosquito . Worldwide , Ae . aegypti and Aedes albopictus are the main vectors for DENV transmission , however Ae . albopictus has not been found in Puerto Rico ( PR ) . The most common container Aedes mosquito species in PR are Ae . aegypti and the Caribbean treehole mosquito , Aedes mediovittatus . Ae . mediovittatus mosquitoes inhabit both natural water-holding containers in cooler , shady forested areas and artificial containers in low density housing and rural areas while Ae . aegypti mosquitoes are more abundant in areas of high density urban housing [1–3] . Despite the apparent habitat differences between the two mosquito species , vector control personnel in Cuba reported Ae . mediovittatus larvae exploiting the same artificial aquatic habitats normally occupied by Ae . aegypti mosquitoes after an intensive Ae . aegypti elimination campaign [4] . Since Ae . mediovittatus mosquitoes inhabit peridomestic containers and feed on potentially DENV-infected humans , they may be potential secondary DENV vectors in PR and the Caribbean [5] . Dengue epidemics in PR have been documented since 1915 and multi-serotype epidemics have frequently occurred on the island [6–8] . To better understand why DENV was being maintained in rural Puerto Rican communities between epidemics , Gubler et al . examined the vector competence of Ae . mediovittatus a mosquito frequently found in these communities . Vector competence is measured by the number of mosquitoes which become infected and transmit virus following an infectious bloodmeal [9] . Gubler and colleagues compared DENV-1 and DENV-2 infection and transmission rates and reported that Ae . mediovittatus were infected with DENV at a higher rate than Ae . aegypti [7] . They concluded that Ae . mediovittatus mosquitoes were efficient vectors for DENV and may maintain DENV transmission during inter-epidemic periods . Collectively , mosquito surveillance reports from Cuba and the vector competence work by Gubler et al . suggested that vector control efforts that only target Ae . aegypti mosquitoes may not be successful in controlling dengue in PR [4 , 7] . The paucity of effective vector control methods available to stop dengue epidemics has prompted development of non-insecticidal mosquito suppression techniques to reduce DENV transmission . New non-insecticidal control techniques , lethal traps , refractory mosquitoes , and lethal genetic modifications , target mosquitoes in an attempt to reduce DENV transmission [10–12] . All of these methods control dengue by reducing or modifying the Ae . aegypti populations . Our goal was to expand our understanding of Ae . mediovittatus vector competence for DENV by comparing Ae . mediovittatus and Ae . aegypti DENV infection and transmission rates for laboratory strains of all four DENV serotypes . To compare DENV competence between mosquito species , we exposed Ae . mediovittatus and Ae . aegypti mosquitoes with DENV- ( 1–4 ) and determined viral titers and infection and transmission rates . To compare the vector competence by DENV serotype , we analyzed the vector competence results within species by DENV serotype .
Mosquito colonies were established in 2012 using Ae . aegypti and Ae . mediovittatus mosquito eggs collected in ovitraps from the Patillas municipality in PR . Colonies were supplemented with field-collected eggs every six months to maintain characteristics of wild populations . F5–6 eggs were hatched and reared to adults . A taxonomic key of PR mosquitoes ( CDC Dengue Branch Entomology ) was used to verify species identity . Adult mosquitoes were placed into 1m3 cages and allowed to mate freely . Both colonies were maintained at 25–27°C , with 75% relative humidity ( RH ) , and a 12:12 light: dark cycle in separate rooms to prevent cross-contamination . Colonies were offered pig’s blood from a local butcher three times per week . Ae . mediovittatus was conditioned two days prior to Ae . aegypti to account for its slower development . Eggs were conditioned for 24 hours by placing egg papers on edge in 200ml of water , allowing water to wick onto the eggs . The egg papers ( Anchor Paper , Saint Paul , MN ) were then submerged for an additional 24 hour period with 0 . 05g of ground rabbit food ( Amigo Supermarket brand , San Juan , PR ) to stimulate hatching . To avoid the effects of larval competition on vector competence , 150 larvae were reared in pans containing 1 liter ( L ) water [13] . Food was provided daily and proportional to nutritional requirements . Mosquitoes were transferred to cages at the pupal stage . Caged adults were maintained with 10% sucrose solution . Laboratory strains of DENV-1 ( Hawaii ) , DENV-2 ( New Guinea C ) , DENV-3 ( H87 ) , and DENV-4 ( H241 ) were grown in 33°C adapted C6/36 ( Ae . albopictus ) cells [14] . To prepare stock virus , a 75-cm3 flask of C6/36 cells at 80% confluency was infected at a multiplicity of infection ( MOI ) of 0 . 01 . Cells were incubated for three days at 33°C in Dulbecco’s Modified Eagle Medium ( DMEM ) supplemented with 10% heat-inactivated fetal bovine serum ( FBS ) , 1% of 7 . 5% sodium bicarbonate solution , 1%-non-essential amino acids , 1% MEM vitamins , 1% sodium pyruvate . After three days of growth , supernatant was transferred to 75-cm3 flask of C6/36 cells at 80% confluency . Five days after transfer of supernatant , stock virus supernatant was harvested and stored with 5% FBS at -80°C . Virus for all infectious bloodmeals was cultured using an aliquot of stock virus and the three day infection , five day passage method just described . Plaque forming unit ( pfu ) equivalents were preferred to genome equivalents in saliva specimens , because pfus were better measure of infectious virions and thus risk of DENV transmission . Therefore we used DENV laboratory strains that reliably produced plaques than recent PR DENV isolates in this experiment [15] . DENV was extracted from cultures using QIAamp viral RNA mini kit ( Qiagen , Carlsbad , CA ) , according to the manufacturer’s instructions and tested via quantitative reverse transcriptase polymerase chain reaction ( RT-PCR ) , as described below , to ensure titers of all four serotypes ( DENV1–4 ) were within 1 . 0 log10 pfu equivalents . Virus titers were 5–6 log10 pfu equivalents for all infectious bloodmeals . To prepare infectious bloodmeals , virus supernatant from infected cells was harvested , and mixed: 5 parts virus , 4 parts pig blood , 1 part 10 . 0 mM adenosine triphosphate disodium salt . Prior to mixing with virus bloodmeal suspension , the pig blood was treated with sodium citrate anticoagulant ( 108 mM sodium citrate , 16 mM citric acid , 16 mM sodium phosphate , 1 . 5 mM adenine solution ) . We standardized singleplex real-time RT-PCR assay genome copies to pfu equivalents to generate a standard curve which allowed us to report mosquito specimen viral titers in pfu equivalents [16] . Briefly , virus stocks were serially diluted ( 101–106 ) in phosphate buffered saline ( PBS ) with 30% FBS . Half of each dilution was used for plaque assay , and half was extracted and tested by singleplex real-time RT-PCR [15] . Plaque assay serial dilutions were incubated in twelve-well plates ( Corning Costar , Corning , NY ) with Vero cell monolayers for one hour . An agarose overlay consisting of 1% agarose in 2X Ye-Lah medium was added to the plate . Ye-Lah medium consists of 2x Earle’s balanced salt solution without phenol red in 1L sterile distilled water supplemented with 0 . 15g yeast extract , 0 . 75g lactalbumin hydrolysate , 2% FBS , 2x fungizone and gentamycin . Plates were incubated for four days at 37°C 10% C02 then stained with 3 . 2% neutral red diluted in PBS . Plaques were counted at five days and every day ( up to seven days ) until no additional plaques were observed . Final plaque counts were correlated with singleplex real-time RT-PCR results and used to generate a standard curve . For quality control , iQ5 multicolor Real-Time PCR detection system ( BioRad ) was calibrated using iCycler iQ calibration kit according to manufacturer’s instructions . Real-time RT-PCR results were considered valid when R ≥0 . 90 . Approximately 200 mosquitoes per treatment were sucrose-starved for 36 hours and water-deprived for 12 hours prior to bloodfeed . The bloodmeal mixture described in the virus methods was warmed to 37°C using a Hemotek feeding system ( Discovery Workshops , UK ) and mosquitoes were allowed to feed for one hour . Aliquots of the bloodmeal were taken before and after feeding and tested by qRT-PCR to ensure there was minimal loss of virus titer . After bloodfeeding , fully engorged mosquitoes were retained and maintained 100/carton for 14 days post-bloodmeal at temperature 25°C and 53–83% RH . Insectary temperature and relative humidity were dependent on ambient conditions . Experimental feed rates were 59% and 64% for Ae . aegypti and Ae . mediovittatus , respectively . Note: Ae . mediovittatus mosquitoes fed poorly on DENV-4 bloodmeal so another batch of Ae . mediovittatus mosquitoes were bloodfed DENV-4 a day later than the Ae . aegypti . Viral titers for both bloodfeeds were between 1–2 × 106 log10 pfu . Those data are reported only for the second batch of Ae . mediovittatus mosquitoes . There were no infected mosquitoes from the first batch of Ae . mediovittatus . At 14 days , the experimental survival rates were 67% for Ae . aegypti and 74% for Ae . mediovittatus . A representative sample of 60 mosquitoes from each treatment was used for further analysis . Fourteen days after infectious bloodmeal , mosquitoes were anesthetized with FlyNap ( Carolina Biological Supply Company ) , transferred to a petri dish , and the proboscis of each mosquito inserted into an individual capillary tube . Capillary tubes were 1 . 1–1 . 2 mm diameter , 70 µl capacity Thermo Fisher Scientific , Waltham , MA ) , scored and broken into 2 . 5 cm lengths and contained approximately 23 . 3 µl salivation fluid . Salivation fluid was 10% aqueous sucrose ( wt/vol ) solution supplemented with 10% FBS . Mosquitoes were allowed to salivate into capillary tubes for 15 minutes , and capillary tubes were transferred to individual 1 . 5 ml microcentrifuge tubes and centrifuged at 3 , 000x g for 3 minutes to expel fluid then stored at -80°C until tested . Mosquitoes were tested for the presence of viral RNA 14 days after challenge with an infectious bloodmeal . Mosquito saliva and bodies were stored at -80°C until tested . Individual mosquito bodies were homogenized in 0 . 5ml BA-1 diluent ( 1x M199-Hank’s salts , 2mM L-glutamine , 0 . 05M Tris buffer ( pH 7 . 5 ) , 1% bovine serum albumin ( pH 7 . 0 ) , 100 units penicillin , 0 . 35 mg sodium biocarbonate , 100µg streptomycin , and 1µg Amphotericin B per ml ) with 2 Copperhead Premium BBs at four min , frequency 25/s in Qiagen Tissue Lyser ( Qiagen ) . Saliva samples were centrifuged 3 , 000 rpm , three min with 0 . 5ml BA-1 . RNA was extracted from 200 µl mosquito body samples using Qiagen M48 robot and MagAttract Mini M48 kit ( Qiagen ) according to the manufacturer’s instructions . Two hundred microliters of each saliva sample was extracted by hand , to minimize loss of low volume saliva samples , using QIAamp viral RNA mini kit ( Qiagen ) , according to the manufacturer’s instructions . All samples were analyzed by singleplex real time RT-PCR standardized to a curve consisting of serial RNA dilutions . All statistical analyses of infection , transmission rates ( number of mosquitoes that transmitted DENV/ total number of mosquitoes exposed to DENV ) , and transmission efficiency ( number of positive transmissions/number of mosquitoes infected with DENV ) were performed with R software v2 . 15 . 1 . Comparisons of infection , transmission rates , and transmission efficiency were analyzed using generalized linear models ( glm ) assuming binomial distribution and logit link . Serotype , species , and their interaction were included as explanatory variables . Multiple comparisons on all pair-wise means using Sidak’s method with simultaneous 95% confidence intervals were conducted on statistically significant effects . In order to determine if higher infection titers were associated with increased likelihood of transmission , transmission status was regressed on body titer and its interaction with species and serotype using a glm assuming a binomial distribution with logit link . Finally , body titers by species and serotype were compared using a glm assuming a Poisson distribution with log link . Graphs representing viral titers were produced with GraphPad Prism 5 and mean , 25th and 75th percentile titers are indicated .
Twenty-five percent of Ae . mediovittatus mosquitoes exposed to DENV-2 became infected with DENV-2 with an average viral titer of 3 . 0±0 . 8 log 10pfu/mosquito at 14 days post infection ( dpi ) compared to 18% infected with DENV-3 ( mean = 1 . 0±1 . 3 log10 pfu/mosquito ) , 13% with DENV-1 ( mean = 2 . 7±0 . 4 log10 pfu/mosquito ) , and 2% with DENV-4 ( mean = 3 . 1±0 . 0 log10 pfu/mosquito ) ( Table 1 , S1–S5 Tables in S1 Text ) . There were no significant differences in Ae . mediovittatus infection rates or viral titers between DENV serotypes . Sixty-two percent of Ae . aegypti mosquitoes exposed to DENV-4 became infected by 14 dpi with an average viral titer of 3 . 2±0 . 8 log10 pfu/mosquito compared to 17% infected with DENV-2 ( mean = 3 . 3±1 . 0 log10 pfu/mosquito ) , 15% with DENV-1 ( mean = 2 . 3±1 . 2 log10 pfu/mosquito ) , and 10% with DENV-3 ( mean = 2 . 0±1 . 4 log10 pfu/mosquito ) ( Table 1 , S1–S5 Tables in S1 Text ) . There was a significant difference in the Ae . aegypti infection rate with DENV-4 compared to the other serotypes ( p < 0 . 05 ) ( Table 1 ) . The infection rate for DENV-4 at 14 dpi was significantly higher for Ae . aegypti than Ae . mediovittatus mosquito species ( glm , p < 0 . 001 . Additionally , individual viral infection titers were variable for both species ( Fig . 1 ) thus there were no significant differences between viral infection titers between species for DENV1–3 ( Table 1 , Fig . 1 ) . Twelve percent of Ae . mediovittatus mosquitoes artificially transmitted DENV-1 at 14 dpi with an average titer of 0 . 8±0 . 5 log 10pfu/mosquito compared to 7% transmitting DENV-2 ( mean = 1 . 5±0 . 3 log10 pfu/mosquito ) , 2% transmitted DENV-3 ( mean = 0 . 1±0 . 0 log10 pfu/mosquito ) , and 2% transmitted DENV-4 ( mean = 2 . 9±0 . 0 log10 pfu/mosquito ) ( Table 2 , S1–S5 Tables in S1 Text ) . There were no significant differences in Ae . mediovittatus transmission rates between serotypes . Forty-two percent of Ae . aegypti mosquitoes artificially transmitted DENV-4 on 14 dpi with an average titer of 1 . 9±0 . 4 log10 pfu/mosquito compared to 5% transmitted DENV-2 ( mean = 2 . 1±0 . 9 log10 pfu/mosquito ) , 3% transmitted DENV-1 ( mean = 0 . 3±0 . 4 log10 pfu/mosquito ) , and 2% transmitted DENV-3 ( mean = 0 . 4±0 . 0 log10 pfu/mosquito ) ( Table 2 , S1–S5 Tables in S1 Text ) . Significantly more Ae . aegypti transmitted DENV-4 ( 42% ) than DENV-1 , -2 , and-3 ( 3% , 5% , and 2% respectively ) ( p < 0 . 05 ) ; there were no significant differences in Ae . aegypti saliva titers by DENV serotype . When challenged with DENV-4 , significantly more Ae . aegypti ( 42% ) were transmitting DENV-4 than Ae . mediovittatus ( 2% ) ( p < 0 . 05 ) ( Table 2 ) . There were no significant differences in transmission rates between species for DENV-1 , 2 , 3 ( Table 2 , Fig . 1 ) . Eighty-eight percent ( 7/8 ) of DENV-1 infected Ae . mediovittatus mosquitoes artificially transmitted DENV compared to 27% ( 4/15 ) for DENV-2 , 9% ( 1/11 ) for DENV-3 , and only 1 Ae . mediovittatus mosquito transmitted DENV-4 for 100% ( 1/1 ) of infected mosquitoes transmitting ( Table 3 ) . Sixty-eight percent ( 25/37 ) of DENV-4 infected Ae . aegypti mosquitoes artificially transmitted DENV compared to 30% ( 3/10 ) for DENV-2 , 22% ( 2/9 ) for DENV-1 , and 17% ( 1/6 ) for DENV-3 ( Table 3 ) . Transmission efficiency between mosquito species was not statistically significant ( S3 Table in S1 Text ) . Transmission efficiency between DENV serotypes was analyzed , independent of mosquito species by pooling data across mosquito species . DENV-1 and DENV-4 are the most efficiently transmitted DENV serotypes . Fifty-three percent of Aedes mosquitoes infected with DENV-1 transmitted compared to 12% of mosquitoes infected with DENV-3 ( p < 0 . 05 ) . Sixty-eight percent of Aedes mosquitoes infected with DENV-4 transmitted compared to 28% infected with DENV-2 or 12% infected with DENV-3 ( p < 0 . 05 ) ( Table 3 ) . Our analysis indicated that one log10 increase in the infection titer of a mosquito did not increase the likelihood that a mosquito could transmit DENV .
Dengue is a vector bone disease of major public health importance , so many researchers are developing non-insecticidal mosquito control techniques to reduce dengue transmission . Most of these non-insecticidal control techniques , lethal traps , refractory mosquitoes , and lethal genetic modifications , target primarily Ae . aegypti mosquitoes . Eliminating only the primary DENV vector , Ae . aegypti , may have unexpected consequences in the presence of other secondary vectors ( e . g . , Ae . albopictus and Ae . mediovittatus ) that are capable of transmitting DENV . We determined that Ae . aegypti and Ae . mediovittatus mosquitoes are comparatively competent to transmit DENV1–3 but differ in competence for DENV-4 . Our results have several implications for DENV transmission in PR , most interesting are the implications for non-insecticidal control techniques . Since both Ae . aegypti and Ae . mediovittatus are competent DENV vectors , non-insecticidal mosquito control techniques that target Ae . aegypti may not be effective in PR because they do not account for local secondary vectors . If such methods are used to eradicate Ae . aegypti , secondary DENV vectors such as Ae . mediovittatus , could expand their populations and drive DENV transmission negating the utility of non-insecticidal mosquito control techniques in the elimination of dengue [4] . Consequently , the use of non-insecticidal control techniques to control dengue requires careful assessment of local DENV vectors . | Dengue is a potentially life-threatening tropical disease caused by four serotypes of virus , dengue virus 1 , -2 , -3 , and -4 . Worldwide , as many as 390 million people become infected with dengue virus each year after being bitten by infectious Aedes mosquitoes . Unfortunately , there is no commercially available vaccine to prevent dengue; so , dengue prevention is attempted by controlling Aedes mosquitoes . Since the Aedes aegypti mosquito is responsible for most dengue virus infections worldwide , most dengue control efforts target this mosquito . However , Aedes mediovittatus , a common mosquito in the Caribbean , may also transmit dengue virus in Puerto Rico . Our goal was to compare dengue virus transmission by Aedes mediovittatus and Aedes aegypti mosquitoes for four serotypes of dengue virus . In the laboratory , we exposed Aedes mediovittatus and Aedes aegypti mosquitoes with dengue virus-1–4 . We found that similar numbers of Aedes mediovittatus and Aedes aegypti mosquitoes became infected with dengue virus-1–3 , but differed in dengue virus 4 infection rates . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Comparison of Vector Competence of Aedes mediovittatus and Aedes aegypti for Dengue Virus: Implications for Dengue Control in the Caribbean |
Seeds of flowering plants can be formed sexually or asexually through apomixis . Apomixis occurs in about 400 species and is of great interest for agriculture as it produces clonal offspring . It differs from sexual reproduction in three major aspects: ( 1 ) While the sexual megaspore mother cell ( MMC ) undergoes meiosis , the apomictic initial cell ( AIC ) omits or aborts meiosis ( apomeiosis ) ; ( 2 ) the unreduced egg cell of apomicts forms an embryo without fertilization ( parthenogenesis ) ; and ( 3 ) the formation of functional endosperm requires specific developmental adaptations . Currently , our knowledge about the gene regulatory programs underlying apomixis is scarce . We used the apomict Boechera gunnisoniana , a close relative of Arabidopsis thaliana , to investigate the transcriptional basis underlying apomeiosis and parthenogenesis . Here , we present the first comprehensive reference transcriptome for reproductive development in an apomict . To compare sexual and apomictic development at the cellular level , we used laser-assisted microdissection combined with microarray and RNA-Seq analyses . Conservation of enriched gene ontologies between the AIC and the MMC likely reflects functions of importance to germline initiation , illustrating the close developmental relationship of sexuality and apomixis . However , several regulatory pathways differ between sexual and apomictic germlines , including cell cycle control , hormonal pathways , epigenetic and transcriptional regulation . Enrichment of specific signal transduction pathways are a feature of the apomictic germline , as is spermidine metabolism , which is associated with somatic embryogenesis in various plants . Our study provides a comprehensive reference dataset for apomictic development and yields important new insights into the transcriptional basis underlying apomixis in relation to sexual reproduction .
In flowering plants , both sexual and asexual reproduction through seeds ( apomixis ) is common . Apomixis occurs in more than 400 plant species belonging to over 40 families , but it is poorly represented in crop species . Apomixis leads to clonal offspring by conservation of the maternal genotype through the absence of meiosis and fertilization [1]–[4] . Engineering of apomixis in crop species is perceived as one of the greatest challenges faced by modern agriculture [5] . However , achieving this goal proved to be difficult , particularly as the knowledge about the genetic basis and regulatory programs underlying apomictic reproduction is very limited . Sexual reproduction and apomixis only differ in a number of key developmental steps [6] , [7] . During sexual reproduction , the female and male reproductive lineages are initiated by spore formation from a spore mother cell during megasporogenesis and microsporogenesis , respectively . The megaspore mother cell ( MMC ) is the first cell of the female germline . It is specified by selection of one subepidermal , somatic ( sporophytic ) cell within an ovule , the precursor of the seed . The MMC undergoes meiosis and gives rise to a tetrad of reduced megaspores . Typically , only one of those - the functional megaspore ( FMS ) - survives to form the female gametophyte ( embryo sac ) . The FMS divides mitotically and subsequently cellularizes to form the mature female gametophyte harbouring the gametes ( egg cell and central cell ) and several accessory cells , including the synergids that play an important role in fertilization [8] . Double fertilization of the egg and central cell with one sperm each initiates the development of embryo and endosperm , respectively . In contrast , in gametophytic apomixis an unreduced sporophytic cell of the ovule in proximity to the MMC ( apospory ) , or the MMC itself becoming an apomictic initial cell ( AIC ) that omits or aborts meiosis ( diplospory ) , gives rise to an unreduced embryo sac ( apomeiosis ) [9] . The egg cell subsequently develops into an embryo without fertilization ( parthenogenesis ) . Endosperm development can either be autonomous or require fertilization ( pseudogamy ) . It is likely that signals from sporophytic ovule tissues are important for the development of the sexual and apomictic germline [6] , [9] . During meiosis the MMC is shielded by incorporation of callose into its cell wall [10] , which may temporarily reduce or block such signaling . However , to our knowledge such signaling events have so far not been investigated in detail . While recent studies uncovered the transcriptional basis of key steps of female germline development in the sexual model species Arabidopsis thaliana [11]–[13] , relatively little is known about the genetic and transcriptional basis governing apomictic reproduction . Gametophytic apomixis is genetically controlled by usually two or more loci - or potentially clusters of linked loci - in different aposporous and diplosporous species [14]–[24] . In the Boechera genus , there is evidence for a complex genetic control of apomixis [25] . At the transcriptional level it has been hypothesized that apomixis is derived from a deregulation of the sexual pathway [6] , [7] , [26] . Indeed , evidence for differential regulation of many genes between apomictic and sexual accessions comes from comparative gene expression analyses . These studies mostly use ovule or flower tissues from a variety of species , including Boechera spp . [27] , [28] , Brachiaria spp . [29] , [30] , Hieracium perforatum [31] , Pennisetum spp . [32] , [33] , Paspalum spp . [34]–[36] , apomeiotic mutants of Medicago falcata [37] , Panicum maximum [38] , and Poa pratensis [39] , [40] . In addition , recent findings indicate spatial and temporal shifts in the expression of genes important for reproductive development between sexual and apomictic plants [27]–[29] , [41] . To coordinate such complex transcriptional deregulation , the involvement of epigenetic regulatory pathways has been proposed [3] , [6] , [7] . Epigenetic pathways play important roles in regulating developmental and cell-fate decisions through the modification of gene activity by histone modifications , DNA methylation or gene silencing by small RNAs . Interestingly , features of apospory or diplospory have recently been observed in Arabidopsis and maize carrying mutant alleles of genes involved in DNA methylation and small RNA pathways [42]–[44] . In Arabidopsis plants carrying mutations in ARGONAUTE9 ( AGO9 ) , or genes encoding additional members of a small RNA pathway ( RNA-DEPENDENT RNA POLYMERASE 6 ( RDR6 ) , SUPPRESSOR OF GENE SILENCING 3 ( SGS3 ) ) , additional MMC-like cells in the ovule gave rise to developing female gametophytes in a process resembling apospory [42] . Maize plants with mutations in homologues of the Arabidopsis DNA methyltransferases DOMAINS REARRANGED METHYLASE1 ( DRM1 ) /DRM2 and CHROMOMETHYLTRANSFERASE3 ( CMT3 ) show also features of apospory [43] . However , in maize plants carrying mutations in AGO104 , a homologue of Arabidopsis AGO9 , formation of unreduced viable gametes occurs by a diplospory-like mechanism [44] . In addition , features of apospory have been observed in Arabidopsis plants carrying mutations in the RNA helicase gene MNEME ( MEM ) , which restricts germline fate to one cell per ovule [12] . As in ago9 mutants , the additional MMC-like cells initiate development of unreduced female gametophytes [12] . Apomeiosis has also been achieved by mutating important meiotic genes in Arabidopsis , such as DYAD/SWITCH1 ( SWI1 ) , a regulator of meiotic chromosome organisation , or a combination of three mutations in the MiMe triple mutant ( sporulation11-1 ( spo11-1 ) ; omission of second division1 ( osd1 ) ; recombination8 ( rec8 ) ) [45] , [46] . However , to date the potential role of these genes in naturally occurring apomixis has not been elucidated . To study the transcriptional basis of key steps of apomictic reproduction we used the triploid , diplosporous species Boechera gunnisoniana as an apomictic model . The genus Boechera is closely related to the sexual model species Arabidopsis thaliana , facilitating comparative studies . We demonstrate the obligate apomictic behaviour of B . gunnisoniana by analysing the ploidy of embryo and endosperm in single seeds by means of a flow cytometric seed screen [47] . As no annotated , genome-wide sequence information is available for this species , we used RNA-Seq ( Illumina HiSeq2000 ) to generate a reference transcriptome based on ovule tissues isolated by microdissection at the developmental stages of interest . We annotated the reference transcriptome , including the identification of homologous genes in Arabidopsis . Using a combination of laser-assisted microdissection ( LAM ) , Affymetrix GeneChip profiling ( ATH1 ) , and RNA-Seq ( SOLiD ) , we studied the transcriptome of isolated AICs , as well as egg , central and synergid cells from B . gunnisoniana . Statistical data analysis revealed the significant enrichment of polyamine and spermidine metabolism in the AIC as compared to the cells of the mature female gametophyte in Boechera . In addition , we compared the gene expression profiles of the AIC and the MMC , egg cells and central cells between apomictic Boechera and sexual Arabidopsis . This uncovered differential expression of genes in important regulatory pathways , including protein degradation , hormonal pathways , cell cycle control , signal transduction , transcriptional regulation , and epigenetic pathways .
B . gunnisoniana has previously been described as diplosporous apomict [48] , [49] . While the embryo develops parthenogenetically , the endosperm requires fertilization ( pseudogamy ) [48] , [49] . Based on flow cytometric studies of single seeds , a high variability of the reproductive mode - ranging from obligate sexual to obligate apomictic - has been reported among 71 Boechera accessions analysed [50] . We applied this technique to test the frequency of apomictic reproduction in B . gunnisoniana . From 84 individual seeds tested , ∼98% showed a 3C∶9C ( embryo∶endosperm ) ploidy ratio in the seed , as expected for a triploid , pseudogamous apomict ( Figure 1A ) . In two seeds ( ∼2% ) a 6C embryo resulted from fertilization of an unreduced egg cell ( Figure 1B ) . In conclusion , B . gunnisoniana reproduces obligatory by pseudogamous apomixis . In all seeds analysed an unreduced egg cell gave rise to the embryo , and embryos developed parthenogenetically at very high frequency . Nevertheless , the possibility of developmental variations during germline formation cannot be excluded based on a flow cytometric analysis alone . We used ovule and seed clearings for cytological analyses to address the question whether there is potential variation of reproductive development . In young ovules typically a single enlarged subepidermal cell specified to an AIC ( Figure S1A , B ) , while in 3 . 6% of all ovules ( N = 551 ) an additional enlarged , subepidermal cell was observed ( Figure S1A ) . As previously reported , the AICs give rise to the formation of dyads [48] , [49] , [51] . Dyad formation was seen at a frequency of 85% ( N = 224; Figure S1E , Q ) . In an additional 10% of all ovules , either dyads accompanied by large parietal cells and or triads were formed ( Figure S1F , Q ) . These two possibilities could not clearly be discriminated based on morphology . Unexpected numbers of nuclei during AIC division or the formation tetrads were observed in ∼2% of all cases ( Figure S1G , Q ) . In the remaining 3% of ovules the AICs apparently failed to divide ( Figure S1C , Q ) , likely leading to developmental arrest ( Figure S1D ) . Formation of a mature gametophyte was observed in 92% of all ovules ( N = 353 ) in agreement with previously published results [49] , the majority showing a delay or defect in the fusion of the polar nuclei ( Figure S1I , J , R ) . In 7 . 4% of the ovules development was arrested early ( at AIC or FMS stage ) , was delayed , or resulted in an unexpected number of nuclei ( Figure S1R ) . At a very low frequency ( 0 . 6% ) more than one gametophyte developed in a single ovule ( Figure S1K , R ) . In agreement with previous reports , in the absence of pseudogamous fertilization no evidence for the initiation of embryo development was observed [48] , [51] . After fertilization , 62% of the seeds developed normally ( N = 477; Figure S1L , M ) . In the remainder , ovules harbouring mature gametophytes or enlarging seeds due to seed coat growth without embryo or endosperm development were observed , or only embryo or endosperm development initiated ( Figure S1N–P ) , suggesting a problem in fertilization . In summary , in B . gunnisoniana the large majority of mature gametophytes are formed by diplospory and 98% of the seeds are derived parthenogenetically under our growing conditions . Thus B . gunnisoniana is well suited as a model species for molecular studies of apomixis . The close relation of the apomict B . gunnisoniana with the sexual model species A . thaliana provides an excellent basis for comparative analyses . However , while genome sequencing projects for Boechera species are currently ongoing ( http://www . jgi . doe . gov/ ) , this initiative does not include B . gunnisoniana , which is fast cycling and obligatory diplosporous . Thus , as a tool for transcriptomic studies , we generated a reference transcriptome for this species . We isolated ovules at the two developmental stages of interest , megasporogenesis ( i . e . ovule stages from the initiation of integument development until the integuments start to overgrow the nucellus; Figure S1A , B ) and mature gametophyte stage ( Figure S1I–K ) . The highly enriched ovule samples included some pistil tissue , particularly for the early developmental stage . We prepared two libraries that were sequenced with the Illumina HiSeq2000 . Both libraries were assembled together using trinity [52] . Following removal of reads with low average quality scores ( Q<30 ) or adaptor sequences , and trimming of low quality ( Q<20 ) ends , around 697 million reads were assembled into 112'232 sequences corresponding to 30'298 distinct genes with 50% having a sequence length of ≥2'153 bp . The reference transcriptome was annotated using Blast2GO [53] and BLAT [54] ( Table S1 ) . Using Blast2GO , 51% of all hits matched best to A . thaliana and an additional 25% to A . lyrata sequences . Gene ontology ( GO ) terms could be successfully assigned to 62% of all hits . In addition , we aligned the sequences to cDNA ( TAIR10 ) using BLAT and identified 19'617 close A . thaliana homologues of B . gunnisoniana genes ( hereafter denoted as Arabidopsis homologues , Table S2 ) . In summary , the length of assembled sequences and annotation results indicate a good quality of our apomictic reference transcriptome . For the sexual model plant Arabidopsis , transcriptomes of the cell types of the mature gametophyte ( egg , central , and synergid cells ) and the MMC have been described [11]–[13] . From these studies , important new insights into the transcriptional basis of sexual germline development could be gained . We applied LAM to isolate the AIC and the surrounding sporophytic nucellus tissue , as well as the egg , central , and synergid cells from B . gunnisoniana ( Figure 2A , B; Figure S2A ) . For the AIC , small contamination with surrounding nucellus tissue cannot be completely avoided ( Figure 2A , B ) . Due to the dense structure of the mature embryo sac , samples for egg , central , and synergid cells are highly enriched in these cell types , but contain some contamination from neighbouring gametophytic cells ( Figure S2A ) . For transcriptional profiling , 300–650 cell- or tissue-specific sections were pooled per sample . Transcriptional profiling was done using two alternative strategies: heterologous hybridization of amplified and labelled Boechera RNA to the Affymetrix ATH1 GeneChip designed for Arabidopsis and SOLiD V4 sequencing ( Table 1 , Figure 2C , D ) . For GeneChip analysis , the extracted RNA was subjected to linear amplification , labelled and hybridized to the microarray as described [12] . Cross-species hybridization of microarrays with RNA from a species other than the original target species is largely influenced by the degree of sequence similarities between the probes on the array and the mRNA sequence of the species under investigation [55] . To account for this effect we used an adapted BgPANP algorithm for the generation of presence/absence p values , similar to the AtPANP previously shown to outperform the default algorithm [11] . These algorithms use probes that do not match to the reference genome or transcriptome of the target species as “negative probes” to estimate the true background of each array . For our BgPANP algorithm probes not aligning to the reference transcriptome ( allowing for three mismatches ) were defined as negative . In this way , several thousand genes were detected significantly above background ( hereafter referred to as present/“P” ) in each cell type-specific sample ( Table 1 , Figure 2C , Figure S2B , C , Table S3 ) . For RNA-Seq , the isolated RNA was subjected to linear amplification following an established protocol [13] , [56] . Each library was sequenced on one eights of a slide , resulting in 53'701'313 ( AIC , apo_initial3 ) , 50'453'327 ( egg cell , egg_cell2 ) , 49'331'759 ( central cell , central_cell2 ) , and 46'240'916 ( synergid cells , synergid_cell2 ) reads . Reads were processed and aligned to the assembled reference transcriptome as described [13] . Under the applied criteria , between 30% and 37% of the reads had at least one valid alignment , corresponding to 16'371'464 ( apo_initial3 ) , 18'783'550 ( egg_cell2 ) , 17'348'718 ( central_cell2 ) , and 15'353'384 ( synergid_cell2 ) weighted alignments . Gene expression values were calculated as the sum of expression of individual variants ( Table S4 ) . We identified 16'385 , 17'828 , 19'091 , and 10'409 B . gunnisoniana genes to be expressed ( i . e . to have at least 5 mapped reads ) in the AIC , egg , central , and synergid cells , respectively ( Table 1 ) . This corresponds to 13'047 , 13'811 , 14'893 , and 9'390 expressed ( ≥5 read counts ) Arabidopsis homologues in the AIC , egg , central , and synergid cells , respectively ( Table 1 , Table S2 , Table S4 ) . Between ∼2'000 and 6'000 genes were consistently identified in at least two independent cell type-specific samples ( Table 1 ) , in agreement with previous observations on the comparability of microarray and RNA-Seq data and the higher sensitivity and genome-wide coverage reached by RNA-Seq [13] . Apomixis and sexual reproduction are interrelated developmental processes . Therefore , it is likely that the cell type-specific transcriptome profiles are largely overlapping between the sexual and apomictic mode of reproduction . Nevertheless , differences in expression of a subset of genes are expected due to the differences in reproductive mode and species . To compare the cell type-specific transcriptome profiles between Boechera and Arabidopsis , we used genes designated as P in two ( for AIC ) or one ( for egg and central cell ) microarray sample ( s ) , or were identified as an expressed Arabidopsis homologue using RNA-Seq ( Table 1 ) . For Arabidopsis we used the 9'115 genes with evidence of expression in the MMC [12] , 12'769 genes expressed in the egg cell ( as described in [11] , [12] and SOLiD reads aligned to the reference genome of Arabidopsis thaliana ( TAIR10 ) ) , and 14'661 genes expressed in the central cell ( [11] , [12] and both samples from [13] ) . Comparing the genes with evidence of expression from Arabidopsis and Boechera for MMC/AIC , egg and central cells , we found overlapping expression of 7'606 , 9'883 , and 10'772 genes , respectively ( Figure 2C , D; Figure S2B , C ) . In addition , we selected several genes for independent data confirmation by in situ hybridization . Based on our analyses , these genes were expressed in the Boechera AIC but not in the Arabidopsis MMC ( Table S5 ) . Probes for in situ hybridization on B . gunnisoniana ovule sections were designed based on the Arabidopsis Col-0 cDNA for three transcription factors ( Figure 3 , ( A–D ) AT1G06170 , basic helix-loop-helix ( bHLH ) DNA-binding superfamily protein; ( E–G ) AT1G28050 , B-BOX DOMAIN PROTEIN 13; ( H–J ) AT1G76580 , Squamosa promoter-binding protein-like ( SBP domain ) transcription factor family protein ) , an oligopeptide transporter ( AT1G59740 , Figure 3 K , L ) , and a HIGH MOBILITY GROUP A protein ( HMGA , AT1G14900 , Figure 3 M–O ) . The probes were designed to have significant sequence homologies only to the respective Boechera homologue ( Figure S3 , Supporting Information S1 ) . For all selected genes we could confirm enriched expression in the AIC . Taken together , our analyses confirm the accuracy of the B . gunnisoniana transcriptome dataset . Between sexual and apomictic reproduction , there are important differences in cell specification and cell fate decisions . Heterochronic shifts in expression patterns have been reported previously using isolated Boechera ovules from sexual and apomictic accessions [27] , [28] . However , gene expression has not yet been profiled in a germline-specific manner without the confounding effects of the surrounding sporophytic tissue in Boechera . Based on genes significantly enriched in the MMC as compared to the cell types of the mature gametophyte , we previously identified translational control pathways and the activity of RNA-helicases as crucial for the acquisition of germline fate and MMC specification in Arabidopsis [12] . To see if similar or different functions are prominent in the Boechera AIC as compared to the mature gametophyte , we used read counts obtained by mapping to the Boechera reference transcriptome . To identify genes significantly enriched we used NOIseq-sim , a non-parametric approach for differential expression analysis based on simulated replicate samples [57] . We identified 1'487 genes to be significantly enriched in the AIC as compared to the cell types of the mature gametophyte ( Figure 4A ) . In addition , 3'509 , 1'466 , and 1'806 genes were significantly enriched in the egg , central , and synergid cells , respectively , as compared to the other three cell types of the germline under investigation . In a gene ontology ( GO ) analysis , we identified functions important for pollen germination and sperm cell and pollen maturation as significantly enriched in the AIC ( p<0 . 01 , Table 2 ) . In addition , different metabolic and transport processes were upregulated , in addition to spermidine metabolism and polyamine biosynthesis ( p<0 . 01 , Table 2 ) . Functions related to plant cell wall modification and epigenetic regulatory pathways ( histone H3K4 demethylation and maintenance of DNA methylation ) were also amongst the enriched functions ( p<0 . 01 , Table 2 ) . Furthermore , cytokinin catabolism was among the near-significantly enriched processes ( p = 0 . 012 , Table 2 ) . In the egg cell of Boechera , cytokinin metabolism is a dominant molecular function as discovered by analysis of GO enrichment based on the 3'509 significantly upregulated genes , in addition to transcription factor activity ( Table S6A ) . The central cell transcriptome is dominated by different epigenetic regulatory pathways , cell cycle regulation , and regulation of cell fate decisions ( p<0 . 01 , Table S6B ) . At higher stringency , using EdgeR with an estimated biological variation coefficient of 0 . 8 , we identified 142 genes to be significantly enriched in all pairwise comparisons of the AIC with the transcriptomes of cells of the mature gametophyte ( adjusted p value ( FDR ) <0 . 05 , Benjamini-Hochberg adjustment; Figure 4B ) [58] . Based in these genes , GO enrichment analysis confirmed spermidine metabolism , cytokinin catabolism , and functions related to pollen development and germination as significantly enriched in the AIC ( p<0 . 01; Table S7 ) . Notably , also the term “sexual reproduction” was an enriched function based on upregulated genes . In addition , 3'792 genes were differentially expressed in any pairwise comparison between the cell types of the mature gametophyte ( FDR≤0 . 05 for comparisons between synergid cells and egg- or central cell , or an unadjusted p value≤0 . 001 for comparisons between egg cell and central cell ( Figure 4B ) ) . In summary this indicates interesting differences in the functions underlying the specification of the germline lineage and the female gametes in the apomict B . gunnisoniana as compared to the sexual pathway in Arabidopsis . Consistently , spermidine metabolism was identified as enriched in the AIC . Our analysis also indicated a distinct regulation of cytokinin metabolism and degradation in the apomictic germline lineage . To analyse differences in gene activity between the sexual and apomictic germline in more detail , we identified Arabidopsis genes and their homologues only expressed in a certain cell type in Arabidopsis or Boechera . Boechera genes were designated as expressed when having at least 5 read counts by mapping against the reference transcriptome , or a P call on one or both microarrays . For a conservative estimate of genes only expressed in Arabidopsis , we also aligned the SOLiD reads to the reference genome of A . thaliana ( TAIR10 ) and only considered genes with at least 5 read counts . We included the latter method as annotation of the closest Arabidopsis homologue is not always unambiguous . Sometimes sequence variants for one Boechera gene have their highest sequence similarity to different Arabidopsis genes ( see below ) , complicating a direct comparison . Of the 9'115 MMC-expressed genes , no evidence of expression has been found for 852 genes in the AIC . GO analysis on this set of genes identified a significant enrichment of different molecular functions , including metabolism , regulation of physiological responses , auxin turnover , translation initiation , and functions related to cell wall structure and cell cycle control ( p<0 . 01 , Table 3A ) . Also the “core cell cycle genes” were found to be significantly enriched ( Fisher's exact test , p = 0 . 006 ) , in agreement with the meiotic fate of the MMC . In addition , 14 protein family ( PFAM ) domains were identified as enriched ( Fisher's exact test , p value<0 . 01 , Table S8 ) including F-box domain and F-box related domains , as well as the cyclin C- and N-terminal domains . This suggests that protein ubiquitinylation and degradation , as well as cell cycle control , may be differentially regulated between MMCs and AICs . Using a similar approach , out of 12'679 genes expressed in the Arabidopsis egg cell ( Figure S2B ) we identified 1'731 for which no homologues were expressed in the Boechera egg cell . GO analysis in this set of genes identified biological processes related to RNA modification and splicing , transport and metabolism , and methylation-dependent chromatin silencing as significantly enriched , and also functions related to double fertilization and endosperm formation ( p<0 . 01 , Table 4A ) . In addition , two transcription factor families , the “AtRKD Transcription Factor Family” and the “MYB Transcription Factor Family” were identified as significantly enriched gene families ( Fisher's exact test , p = 0 . 0087 and p = 0 . 0038 , respectively ) . For the Arabidopsis central cell , out of 14'661 expressed genes ( Figure S2C ) no evidence for expression of homologues in the Boechera central cell was found for 2'146 genes . As in the Arabidopsis egg cell , biological processes related to RNA modification and splicing ( GO:0000154 , rRNA modification , p = 5 . 1e-17; GO:0045292 , nuclear mRNA cis-splicing , via splicosome , p = 5 . 4e-5 ) and “endosperm development” ( p = 0 . 0078 ) were significantly enriched . In addition , out of the 12 PFAM domains identified as enriched , three were related to F-box domains ( Fisher's exact test , p<0 . 01 , Table S9 ) . For the identification of genes only expressed in the apomictic Boechera germline and not in Arabidopsis , we used the Arabidopsis homologues identified and mapping to the Boechera reference transcriptome , combined with the microarray data . We identified 5'273 and 4'902 genes expressed in the apomictic egg and central cell , respectively , that were absent in the corresponding Arabidopsis cell type . We used more restrictive criteria to identify the 901 genes expressed in the AIC but not in the MMC: we considered only Arabidopsis homologues with ≥5 reads in the SOLiD dataset and detected as P in at least one microarray dataset of the AIC . Interestingly , for all three cell types of the apomictic Boechera germline , GO and/or PFAM analyses revealed a significant enrichment of signal transduction processes and protein kinases ( Table 3B , Table 4B , Table 5 , Table S10 ) . For instance , we identified the significant enrichment of “MAP kinase kinase activity” in the AIC ( p<0 . 01 , Table 3B ) . In addition , transport and metabolic processes were enriched , and spermidine metabolism was confirmed as an important feature ( p<0 . 01 , Table 3B ) . Analysis of gene families revealed the Squamosa promoter Binding Proteins as enriched ( Fisher's exact test , p value<0 . 01 , SBP transcription factor family ) . Analysis of gene families and PFAM domains also identified a significant enrichment of the AMINO ACID/AUXIN PERMEASE ( AAAP ) family , the ARF transcription factor family , and the protein domain of the AUX/IAA family during apomictic germline specification ( Fisher's exact test , p<0 . 01 , Table 6 ) . Also the family of B3 transcription factors ( B3_TFs ) , including the ARF transcription factor family , was identified as significantly enriched ( Fisher's exact test , p<0 . 01 , Table 6 ) . In the parthenogenetic Boechera egg cell , GO analysis suggests the importance of signal transduction pathways , cell cycle regulation , and transcription factor activity ( p<0 . 01 ) . In general , in the female gametes analysis of gene families identified the enriched expression of several transcription factor families , particularly the basic Helix-Loop-Helix transcription factors both in the egg and central cell ( Table S11 ) . In summary , our analysis reveals interesting differences in the regulatory programs underlying the acquisition of germline fate and between the female gametes . While the subset of genes only expressed in the sexual germline is significantly enriched in protein degradation pathways , the apomictic Boechera germline is marked by the activity of signal transduction processes . In addition , indications for a role of auxin signalling and metabolism were observed in both germlines . Among the genes identified as active in the apomictic germline lineage only , we found the enrichment of different transcription factors families , particularly basic helix-loop-helix transcription factors in the female Boechera gametes . The comparison between the sexual and apomictic germlines further revealed differential regulation of genes involved in cell cycle control and posttranscriptional regulatory processes , including mRNA splicing , and epigenetic regulatory pathways related to methylation-dependent chromatin modifications . For a number of genes , enriched expression in the Arabidopsis MMC or the aposporic initial cell of Hieracium praealtum have previously been described [12] , [31] . In addition , for sexual or apomictic germline development , evidence for the importance of different genes including core cell cycle genes , meiotic genes , and genes involved in epigenetic regulatory pathways has previously been reported based on mutant analyses or expression patterns [42] , [43] , [59] . Thus , we compared the expression of selected genes of interest upon sexual and diplosporic germline initiation . From a list of 89 core cell cycle genes as defined before [60] , [61] , 75 are represented on the ATH1 array and for 66 Arabidopsis genes , homologues were identified in the Boechera reference transcriptome . From these , 41 genes are expressed in the Arabidopsis MMC and 49 homologues are present in the Boechera AIC . 16 and 24 cell cycle regulators have only been detected in the MMC or the AIC , respectively ( Table S12 ) . In particular , the genes only detected in the apomict upon germline specification include genes involved in different cell cycle transitions , e . g . G1/S phase , including a number of genes involved in the cyclin D/retinoblastoma/E2F pathway ( Table S12 ) . The observed differences in cell cycle regulation are in agreement with the different mechanisms of cell division in the meiotic MMC versus the diplosporous AIC . Interestingly , for 14 selected meiotic genes and genes expressed in the sexual MMC no evidence for expression was found in the H . praealtum aposporous initial cell [31] . However , although the aposporous and the diplosporous initial cell both give rise to unreduced embryo sacs , cell lineage and developmental fate are markedly different . So far it is unknown whether common regulators underlie apomeiosis in these distinct types of apomixis . Interestingly , for all 14 genes except for SWITCH/DYAD and SPO11-2 evidence for expression was found in the Boechera AIC; although at very low levels for most genes ( Figure 5 ) . The Arabidopsis male meiocytes cluster separately from the expression data of different Arabidopsis cell- and tissue-types publicly available [13] , [62]–[66] . Furthermore , the RNA helicase MEM previously identified as predominantly expressed in the Arabidopsis MMC is only expressed at low levels in the AIC but higher in the apomictic egg cell ( Figure 5 ) . Interestingly , this indicates differences in the expression of genes previously identified to have important functions for MMC specification and meiosis in Arabidopsis . In agreement with the differences in developmental fate , the data also suggest differences in cell specification of aposporous and diplosporous initial cells . Nevertheless , the majority of 35 genes described as enriched in the H . praealtum aposporous initial cell or the early apomictic embryo sac as compared to sporophytic ovule tissues [31] was also expressed in the Boechera AIC , except for HISTONE ACETYLTRANSFERASE OF THE CBP FAMILY1 , LIKE HETEROCHROMATIN PROTEIN1 , BEL1-LIKE HOMEODOMAIN1 , CONSTITUTIVE DISEASE RESISTANCE1 , genes involved in lipid localization ( AT1G03103 , AT5G38170 , AT3G18280 , AT1G43666 ) , and a pathogenesis-related lipid-transfer protein gene ( AT2G18370 ) . Increasing evidence suggests the involvement of epigenetic regulatory pathways in the discrimination between sexual reproduction and apomixis . Therefore , we were interested in a closer investigation of the expression of 69 genes involved in DNA methylation and small RNA pathways ( as used in [12] ) . 58 of these genes have annotated homologues in Boechera ( Table S1 ) . 40 genes are consistently present both in the AIC and in the MMC , supporting the important role of epigenetic regulatory pathways for the initiation of germline development [12] . Heatmap clustering suggests the closest relation between the AIC dataset and the datasets of the Boechera female gametes ( Figure S4 ) . Together , these datasets cluster with the Arabidopsis egg and synergid cells , but distantly from male meiocytes or the central cell of the sexual germline lineage ( Figure S4 ) . Nevertheless , a number of genes were only detected in the MMC or the AIC , respectively ( Supporting Information S2 ) . Genes only detected in the AIC included ENHANCED SILENCING PHENOTYPE3 ( ESP3 ) . Also AGO9 and RDR6 , mutations in which cause an apospory-like behaviour in Arabidopsis [42] , were both detected at low levels in the Boechera AIC ( Figure S4 , Figure S5 ) . In summary , for a subset of genes involved in DNA methylation and small RNA pathways , we observed distinct expression patterns during germline specification in sexual Arabidopsis MMCs versus apomictic Boechera AICs , which may be of importance to distinguish fate decisions between these alternative reproductive modes . Particularly within a gene family , the assignment of the closest Boechera homologues to Arabidopsis genes is not always unambiguous . For selected gene families of interest we aimed to test the influence of sequence divergence and annotation criteria on the expression estimates for B . gunnisoniana homologues of A . thaliana genes . Identification of the closest homologues in the Boechera reference transcriptome was based on the highest bit score sum with BLAT , using only the best mappings per Arabidopsis gene . For this analysis we selected the AtRKD gene family ( Figure 6 , 7 ) . In addition , similar analysis of the ARIADNE ( ARI ) gene family , and the AGO gene family are shown in “Supporting Information S2” ( Figure S5 , Figure S6 , Figure S7 , Supporting Information S2 ) . The RKD gene family has been identified in our analysis to be enriched among the genes expressed only in the Arabidopsis but not the Boechera egg cell . Instead of the five members of the Arabidopsis RKD family , two gene models of homologues with one variant each have been identified in the Boechera reference transcriptome ( Figure 6 ) . This suggests either that the gene family is smaller in Boechera as compared to Arabidopsis , or that additional members of this family are not expressed in Boechera ovules at the developmental stages used to generate the reference transcriptome . Analysis of sequence similarities indicates the closest similarity between comp76373_c0_seq1 and AtRKD2 . In agreement , counts for reads mapped to comp76373_c0_seq1 are assigned to AtRKD2 . However , while clustering of comp83606_c0_seq1 indicates higher sequence divergence from all AtRKDs , the reads are assigned to AtRKD5 . The expression and role of members of the RKD family in Arabidopsis , where they play a role in egg cell specification , has been described previously [67] . As the two Boechera gene models homologous to the AtRKD genes are expressed in the egg apparatus ( egg and synergid cells; Figure 6 , 7 ) , the Arabidopsis family as a whole is predominantly expressed in the Arabidopsis egg apparatus ( Figure 7 ) , in agreement with our gene set enrichment analysis .
To investigate apomictic reproduction , the female germline is in particular of interest , as in apomicts clonal offspring genetically identical to the mother plant is generated . In B . gunnisoniana , based on a flow cytometric seed screen using single seeds , we observed exclusively apomeiotic behaviour and only a very low percentage of fertilized , unreduced egg cells . In agreement , the formation of dyads and mature Polygonum type embryo sacs were observed at high frequencies . At low frequencies , developmental variations during germline development were observed , including the formation of more than one female gametophyte per ovule . This could either be due to a failure of degradation of the second megaspore resulting from diplospory , or indicate the rare occurrence of apospory . Interestingly , parthenogenesis remains repressed in the absence of pseudogamous fertilization . In maturing siliques , likely due to a lack of successful fertilization , not all female gametes give rise to an embryo or endosperm . As a consequence of deviations from apomictic germline development and fertilization , reproductive development seems to arrests , so that the vast majority of mature seeds are derived apomictically . This obligate apomictic behaviour , together with its fast cycling ( about 4 months from seed to seed ) and the close relation to the sexual model species A . thaliana , make B . gunnisoniana an ideal system to study apomixis . We generated the first comprehensive , annotated reference transcriptome for reproductive development in B . gunnisoniana , including the identification of Arabidopsis homologues , as an essential tool for further studies . Previously , similarities of germline development were reported even across kingdoms , between the plant and animal germline . These are likely of general importance for the acquisition of germline fate [12] . Nevertheless , cell type specification and developmental fate is markedly different during germline specification in sexual , aposporous , and diplosporous species . Consistently , a number of differences in gene expression profiles have been observed between the apomictic and the sexual germline . In the B . gunnisoniana AIC , a number of functions related to pollen development and germination were enriched , consistent with gene activities observed during germline development in apomeiotic , non-parthenogenetic hybrids of Pennisetum glaucum [33] . Polyamine biosynthesis and spermidine metabolism were also identified as features of the Boechera AIC . Interestingly , spermidine synthesis is essential for embryo development in Arabidopsis [68] . In addition , a possible role of polyamines in promoting somatic plant embryogenesis was reported [69]–[71] . This indicates the importance of spermidine for plant reproduction and provides an interesting link between polyamines and somatic embryogenesis , a form of asexual reproduction different from gametophytic apomixis . Interestingly , spermidine is involved in the protection of DNA from oxidative stress by quenching free radicals mostly arising from reactive oxygen species ( ROS ) [72] . In line with the high activity of spermidine metabolism in the apomeiotic AIC , it has been hypothesized that repair of DNA damage after oxidative stress has been a major driving force for the evolution of meiosis [73] . Apart from being cytotoxic , the role of ROS in signalling and for plant reproductive development has recently been demonstrated [74] . Notably , a spermine/spermidine synthase has previously been identified to be present in the apospory-specific region of P . squamulatum and hypothesized to be expressed [75] , supporting a potential role of these substances for the specification of the apomictic germline . However , further studies will be required to conclude which , if any , role polyamine and spermidine metabolism plays during germline development or the determination of the asexual reproductive fate . In addition to polyamine and spermidine metabolism , the activities of important hormonal pathways were also observed in the AIC . Upregulation of cytokinin degradation was detected upon apomictic germline specification as compared to the mature gametophyte , while the egg cell is marked by gene activities leading to cytokinin modifications . In addition , genes involved in auxin signalling were enriched in the set of genes expressed in the AIC but not in the sexual MMC , in line with the identification of genes involved in auxin signal transduction in the H . praealtum apospory initial cell [31] . In the Boechera AIC , we detected an enriched activity of the AUX/IAA and the ARF transcription factor gene families . These play crucial roles in auxin-regulated gene expression , for example to control cell type-specific auxin responses during Arabidopsis embryo development [76] , [77] . Evidence for differential expression of ARF genes has previously been reported during early stages of reproductive development in a comparative cDNA-AFLP analysis targeting sexual and apomictic Paspalum simplex flowers [35] . In contrast , genes active only during sexual reproduction and MMC specification are marked by an enrichment of F-box proteins . F-box proteins play important roles in ubiquitin-dependent protein degradation involved in signal transduction pathways , cell cycle control , and a variety of other processes [78] , [79] . The expression of miRNAs targeting genes encoding F-box proteins and ARF transcription factors in Boechera floral tissues supports the importance of these pathways in plant reproductive development [80] . This is in line with the identification of a truncated ARI allele with homology to Arabidopsis ARI7 as a candidate apospory locus in Hypericum perforatum [81] . ARI7 encodes a ring finger protein predicted to be involved in ubiquitin-dependent protein degradation [82] . Interestingly , we found evidence for higher activity of ARI family members in the sexual MMC compared to the AIC . In addition to miRNAs targeting F-box proteins and ARF transcription factors , miRNAs involved in regulation of SPL and MYB transcription factors have been identified in Boechera spp . [80] . Together with the enrichment observed for SPL transcription factors in the B . gunnisoniana AIC and of MYB transcription factors in the sexual Arabidopsis MMC and egg cell , respectively , this suggests that these transcription factors play important roles in plant reproduction . Differences in activity were also observed for additional transcription factor families in agreement with previously identified differences in transcriptional regulation at later developmental stages in sexual and apomictic P . simplex flowers [35] . In the sexual Arabidopsis egg cell as compared to the apomictic Boechera egg cell , we observed the enriched expression of the RKD transcription factor family , which are important regulators of egg cell gene expression programs in Arabidopsis and wheat [67] . This suggests that RKD transcription factors might be specifically involved in the determination of the developmental fate of the sexual egg cell . Taken together , our findings indicate differences in the activity of important regulatory pathways during sexual and apomictic germline specification and development . Development of an unreduced embryo sac from an AIC is common to both diplospory and apospory . However , the founder cell of the female germline differs in position and cell fate between these two types of gametophytic apomixis . It is unknown whether a common regulator or a set of regulatory genes determines apomeiosis , or whether apomeiosis is mediated by unrelated developmental programs during apospory and diplospory . Interestingly , a number of important differences in gene expression have been observed in the aposporous initial cell in H . praealtum and the AIC of diplosporous B . gunnisoniana . This is consistent with the differences in cellular fate and identity between these apomicts . While the aposporous initial cell acquires a FMS-like fate without intervening cell division , the transcriptome of the AIC in a diplosporous apomict is expected to be more similar to the sexual MMC . This is in agreement with the lack of expression of several meiotic genes and other genes expressed in the sexual MMC in the aposporous initial cell in H . praealtum [31] , differing from the transcriptome of the AIC in Boechera . Interestingly , in the Boechera AIC we did not observe evidence of expression of DYAD/SWITCH . In Arabidopsis , mutations in this gene have previously been shown to cause a diplospory-like phenotype with rare seed formation by the fertilization of unreduced egg cells [45] . The manipulation of cell cycle progression or meiotic genes has also been shown to lead to the formation of unreduced gametophytes [46] , [83] , [ reviewed in 84] . The comparison between the Arabidopsis MMC and the Boechera AIC identified a number of core cell cycle genes to be differentially regulated . While a small number of genes important for meiotic cell cycle progression in Arabidopsis has already been described [46] , [ reviewed in 84] , detailed functional studies of candidate genes showing differential expression in the MMC and AIC will be required to elucidate their putative role in the discrimination between meiosis and apomeiosis . Interestingly , the Arabidopsis gene encoding WEE1 is only detected in the Arabidopsis MMC . The WEE1 protein is specifically removed to allow progression of mitosis [85] . In addition , homologues of three members of the Arabidopsis E2F transcription factor family have only been detected in the Boechera AIC but not in the Arabidopsis MMC . Members of this family are involved in the regulation of the centromer-specific histone 3 variant CENH3 in Arabidopsis [86] . Manipulation of CENH3 can induce genome elimination , a capacity that has already been successfully applied for the generation of synthetic clonal seeds from Arabidopsis in combination with dyad or MiMe mutants [83] . Based on our transcriptome analysis , different levels of CENH3 expression have been observed in the Boechera germline as compared to Arabidopsis . In contrast to very low expression or absence in Arabidopsis gametes , higher expression levels of the CENH3 homologue have been observed in Boechera gametes . It is thus possible that the absence of DYAD/SWITCH expression in the AIC combined with elevated expression levels of CENH3 in apomictic Boechera as compared to sexual gametes might play a role in naturally occurring diplospory . In addition to unknown parthenogenesis factors , the regulation of CENH3 activity might provide an additional control mechanism to secure the absence of a paternal contribution in the offspring . While mutations in the gene encoding for DYAD lead to features of diplospory , mutations in MEM , AGO9 and additional genes involved in a small RNA pathway have recently been reported to cause phenotypes reminiscent of apospory [12] , [42] , [43] . We identified additional genes involved in gene silencing and small RNA pathways to be differentially expressed in the MMC and the AIC . The expression of ESP3 in the AIC is reminiscent of the previous identification of ESP4 among the transcripts form the apospory-specific region in P . squamulatum [87] . This supports the importance of epigenetic regulatory pathways for sexual and apomictic reproduction . Taken together , upon specification of the apomictic and sexual germline a number of differences involving regulatory processes such as hormone signalling , cell cycle control , and protein turnover have been observed . In addition , increased activity of signal transduction processes was identified as a typical feature of the apomictic germline . The potential role of positioning of the MMC or AIC and the signalling from the surrounding sporophytic tissues has previously been discussed [88] , [89] , and our study has shown that signalling pathways are indeed modulated in the two modes of reproduction . In conclusion , our study provides the first comprehensive transcriptional analysis of germline cells at key steps of apomictic reproduction in B . gunnisoniana . The generation and annotation of an apomictic reference transcriptome forms an essential basis for further analyses and allows the comparison of gene expression to Arabidopsis as sexual model species . Important differences in the development of the apomictic as compared to the sexual germline have been observed . While translational regulation is a feature conserved in both types of germline , polyamine and spermine/spermidine metabolism is only enriched upon initiation of the apomictic germline . In addition , key regulatory mechanisms are differentially regulated , involving hormone pathways , cell cycle control , signal transduction , and epigenetic regulatory processes . Thus , our analysis provides important new insights into gene regulation during apomictic germline development .
A . thaliana Col-0 plants were used to isolate RNA for cloning of in situ probes . Plants were grown as described previously [12] . Seeds of B . gunnisoniana were kindly provided by Bitty Roy ( University of Oregon , previously ETH Zürich ) [48] . Seeds were surface sterilized and grown on MS plates for 10–14 days before transfer to a mixture of soil ( ED73 , Universalerde , Germany ) and sand ( 5∶1 ) , fertilized with Plantomaag ( Syngenta , Basel , Switzerland ) and Osmocote ( Scotts , Marysville , USA ) . Plants were grown in a greenhouse chamber with 60% humidity and 16 h light/ 8 h darkness at 20°C and 16°C , respectively . Matured green seeds were harvested from B . gunnisoniana plants and individually analysed in a Quanta SC MPL flow cytometer ( Beckman-Coulter , Nyon , Switzerland ) . Seeds were individually transferred to 1 . 2 ml cluster tubes ( Thermo Scientific , Wohlen , Switzerland ) containing 80 µl 0 . 1 M citric acid and 0 . 1% Triton X-100 . A 3 mm stainless steel bead ( Schieritz & Hauenstein AG , Zwingen , Switzerland ) was added to each tube prior to shaking for 4 minutes at 30 Hz on a mixer mill ( MM300 , Retsch GmbH , Germany ) . Afterwards , 80 µl of 0 . 1 M citric acid containing 1% Triton X-100 was added and each tube was inverted 40 times . The solution containing the nuclei was filtered though fritted deep well plates ( Nunc , Thermo Scientific , Wohlen , Switzerland ) into 96-well V-bottom plates ( Sarstedt , Numbrecht , Germany ) . Nuclei were collected by filtering in a centrifuge for 5 minutes at 150 g ( Centrifuge 5810R , Eppendorf , Schönebuch , Switzerland ) . The nuclei were resuspended in 30 µl 0 . 1 M citric acid containing 1% Triton X-100 . The samples were either analysed directly by flow cytometer robotics ( Quanta SC MPL , Beckman-Coulter , Nyon , Switzerland ) or stored at 4°C overnight prior to analysis . 120 µl of staining solution ( 0 . 4 M Na2HPO4 , 2 . 6 ml H2O , 27 . 4 µl DAPI ( 5 . 5 µg/µl ) , 0 . 2 µl β-mercaptoethanol ) were added 2 min prior to analysis . The protocol was set to count nuclei for six minutes or until a maximum of 10'000 counts was reached . The Photo Multiplier Tube and the gain were set to have the embryo peak at around 200 on the linear fluorescent scale . B . stricta nuclei were used as external standard . To quantitatively characterize developmental stages during germline development in B . gunnisoniana , material of 5 plants was used and averaged . Tissues were fixed in an ice-cold solution of ethanol∶acetic acid ( 3∶1; v/v ) , vacuum infiltrated on ice two times for 15 min , and left in fixative on ice over night before replacing the fixative with 70% ethanol . Tissues were cleared in chloral hydrate/glycerol/water ( 8∶1∶2; w/v/v ) , and microdissected with dissecting needles . Pictures were taken as previously described [12] . Genes for data confirmation by in situ hybridization were selected based on the following criteria: ( 1 ) expression in the B . gunnisoniana AIC and no evidence of expression in the A . thaliana MMC , ( 2 ) representing different expression levels ( Table S5 ) , ( 3 ) high homology only to the respective homologue in B . gunnisoniana ( 82–96% identity between A . thaliana and B . gunnisoniana nucleotide sequences; Figure S3; Supporting Information S1 ) , and ( 4 ) gene specificity in A . thaliana . Total RNA was isolated from Arabidopsis Col-0 inflorescences and from B . gunnisoniana buds and opened flowers using the RNeasy Plant Mini Kit ( QIAGEN , Hilden , Germany ) . During the isolation procedure , RNA was treated with DNAseI on column . Reverse transcription was done as previously described ( [12]; see Table S13 for a summary of primers and cDNA templates used ) . Fragment cloning and in situ hybridizations were done as previously described with modifications [12] , [51] , [90]: in situ hybridizations were performed on 8 µm thick sections of fixed and embedded Boechera buds or flowers . Pictures were taken and processed as previously described [12] . To prepare samples for LAM , buds with ovules harbouring the AIC were chosen as previously described for selection of buds with ovules harbouring the MMC in Arabidopsis [12] with modifications: for Boechera individual buds were harvested instead of inflorescences . To obtain ovules harbouring mature gametophytes , flowers were emasculated ∼7 hours prior to fixation . The buds and flowers were fixed on ice in farmer's fixative ( ethanol∶acetic acid 3∶1; v/v ) , vacuum infiltrated on ice two times for 15 min , and stored on ice over night before replacing the fixative with 70% ethanol . Embedding , microdissection , and LAM were done as previously described [12] . On average ∼60 sections of AICs were collected per day , or ∼25 sections for each cell-type of the mature female gametophyte . Egg and synergid cells from Arabidopsis were isolated as described previously for the central cell of Arabidopsis [13] . LAM samples were stored dry at −80°C before RNA isolation . RNA isolation and quality control was done as previously described [12] , [13] . RNA amplification and labelling was done with the MessageAmpII Kit ( Ambion , Foster City , USA ) as described previously . ∼15 mg labeled aaRNA was fragmented and hybridized onto the Arabidopsis ATH1 GeneChip ( Affymetrix ) for 16 h at 45°C as described in the technical manual . The hybridization , staining , washing , and subsequent array scanning were performed as described previously [12] . Original data files are deposited under the Gene Expression Omnibus at NCBI ( Accession Number GSE51996 ) . RNA isolation , amplification , library preparation , and SOLiD Sequencing were performed as described previously [12] , except that SOLiD V4 was used for paired-end sequencing . Original data files are deposited in the NCBI database ( Accession Number: SRP032961 ) . As a tool for our data analysis we generated a reference transcriptome from female reproductive tissues of B . gunnisoniana at the two developmental stages of interest: ( I ) at megasporogenesis and ( II ) at the mature gametophyte stage . After isolation of mRNA and library preparation , sequencing was performed on an Illumina HiSeq 2000 instrument ( see Supporting Methods S1 for details ) . Original data files are deposited in the NCBI SRA database ( Accession Number SRP032960 ) . The Transcriptome Shotgun Assembly project has been deposited at DDBJ/EMBL/GenBank under the accession GBAD00000000 . The version described in this paper is the first version , GBAD01000000 . After quality filtering , pre-processed reads were assembled using Trinity ( version r2012-06-08 ) with default parameter settings , except that min_kmer_cov was set to 2 . For annotation with Blast2GO , trinity assembled transcripts were compared to the NCBI non-redundant protein database ( nr ) using blastx ( in blastall version 2 . 2 . 21 ) . E-value cutoff was set to 0 . 00001 . Top five hits were recorded . BLASTX results in XML format were analysed using b2g4pipe ( version 2 . 5 , [53] ) to assign GO terms to the query transcript sequences . The BLAT ( version 34 ) comparison of the Boechera reference transcriptome and the TAIR10 cDNA sequences ( updated 12/14/2010 ) was done with default parameters for cross species DNA mapping ( -q = dnax -t = dnax ) . The top hits were selected using the blat utility script pslCDnafilter ( globalNearBest , globalNearBest plus minCov of 80% ) . TAIR10 cDNA annotation of the top hits was then transferred to the query transcripts . To obtain expression values based on the assembled Boechera reference transcriptome , short read data was processed as described in [13] . Gene-wise expression values were then defined as the sum of the expression values of individual transcript variants . Expression values based on the A . thaliana reference genome ( TAIR10 ) were likewise calculated as described in [13] . To identify potential homologues of known genes from A . thaliana in the assembled reference transcriptome of B . gunnisoniana we used BLAT ( version 34 , [54] ) . Sequences from Boechera were aligned to Arabidopsis cDNAs ( TAIR10 ) , allowing for a maximal intron size ( -maxIntron ) of 2 kb . Individual alignment scores ( bitScore ) and lengths between a given pair of Boechera and Arabidopsis sequences were then summed up . For each gene of interest from Arabidopsis , the Boechera homologue was then defined as the gene with the highest bitScore sum ( or none if no alignments were reported or the total alignment length was below 100 bp ) . To estimate the extent of sequence divergence between a certain set of genes from A . thaliana and B . gunnisoniana we used ClustalX ( version 2 . 1 , [91] ) with default settings ( complete alignment , draw tree ) . Tree files were then used to cluster the genes in the heatmap plots ( R packages ape , version 3 . 0-8 [92] and gplots , version 2 . 11 . 0 , cran . r-project . org/web/packages/gplots/index . html ) . Microarray data were processed as described in [12] , except using an updated annotation of the ATH1 microarray ( brainarray . mbni . med . umich . edu , TAIRG , version 14 ) , and an alternative list of probesets for the background estimation ( “negative probes” ) . Probe sequences were aligned to the assembled Boechera reference transcriptome using bowtie ( version 0 . 12 . 7 , [93] ) , allowing three mismatches . Probes without any alignments were considered as “negative probe” for the PANP algorithm [11] . We used the NOISeq-sim algorithm ( downloaded in April 2012 , http://bioinfo . cipf . es/noiseq/doku . php , [57] ) to analyse differential expression of genes between RNA-Seq samples of the Boechera germline ( apo_initial3 , egg_cell2 , central_cell2 , synergid_cell2 ) . Reads were aligned to the Boechera reference transcriptome . The normalization method was set to tmm ( Trimmed mean of M , [94] ) , no correction for feature length was applied , and default settings were used for all other parameters , including q = 0 . 9 as threshold to determine differentially expressed genes . Genes identified as significantly upregulated in all three pairwise comparisons of one cell type with the other three Boechera germline samples were described as enriched in the cell type . For higher stringency analysis EdgeR was used with the biological coefficient of variation ( bcv ) set to 0 . 8 and Benjamini-Hochberg multiple testing corrections . Genes with an adjusted p value ( FDR ) below 0 . 05 were considered to be significantly differentially expressed . To identify genes differentially expressed between egg cell and central cell we applied an unadjusted p value<0 . 001 . See Supporting Methods S1 . Gene Ontology ( GO ) terms associated with A . thaliana genes were extracted from the functional descriptions and GOSLIM mappings available on TAIR ( ftp . arabidopsis . org/home/tair/Proteins/TAIR10_functional_descriptions , and ( ftp . arabidopsis . org/home/tair/Ontologies/Gene_Ontology/ATH_GO_GOSLIM . txt ) . GO terms associated with the genes of Boechera were obtained with b2g4pipe ( version 2 . 5 , [54] ) . Protein family ( PFAM ) and gene family ( FAM ) annotation was used as described [95] . We used the Bioconductor package topGO [96] for gene ontology analysis . To test for overrepresentation of GO terms we used a Fisher's exact test in combination with the function “weight” . As gene universe in the test for Arabidopsis MMC the whole ATH1 array genome was used , otherwise all genes annotated in the respective GO annotation were used . We used a two-sided Fisher's exact test and comparison against the gene universe as defined above to test for misrepresentation of protein family domains ( PFAM ) and gene families ( FAM ) . Heatmaps were generated using the Bioconductor package gplots [97] . Hierachical agglomerative clustering ( complete linkage ) and euclidean distance was used . Normalization of RNA-Seq reads was done with the Bioconductor package DESeq [98] . Heatmaps were based on normalized log2-transformed total read counts for RNA-Seq data or log2-scale expression values generated by RMA for microarray data as previously described [12] . Venn diagrams were made with the online tool BioVenn ( http://www . cmbi . ru . nl/cdd/biovenn/ ) . | In flowering plants , asexual reproduction through seeds ( apomixis ) likely evolved from sexual ancestors several times independently . Only three key developmental steps differ between sexual reproduction and apomixis . In contrast to sexual reproduction , in apomicts the first cell of the female reproductive lineage omits or aborts meiosis ( apomeiosis ) to initiate gamete formation . Subsequently , the egg cell develops into an embryo without fertilization ( parthenogenesis ) , and endosperm formation can either be autonomous or depend on fertilization . Consequently , the offspring of apomicts is genetically identical to the mother plant . The production of clonal seeds bears great promise for agricultural applications . However , the targeted manipulation of reproductive pathways for seed production has proven difficult as knowledge about the underlying gene regulatory processes is limited . We performed cell type-specific transcriptome analyses to study apomictic germline development in Boechera gunnisoniana , an apomictic species closely related to Arabidopsis thaliana . To facilitate these analyses , we first characterized a floral reference transcriptome . In comparison , we identified several regulatory pathways , including core cell cycle regulation , protein degradation , transcription factor activity , and hormonal pathways to be differentially regulated between sexual and apomictic plants . Apart from new insights into the underlying transcriptional networks , our dataset provides a valuable starting point for functional investigations . | [
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] | 2014 | Apomictic and Sexual Germline Development Differ with Respect to Cell Cycle, Transcriptional, Hormonal and Epigenetic Regulation |
Exome sequencing studies in complex diseases are challenged by the allelic heterogeneity , large number and modest effect sizes of associated variants on disease risk and the presence of large numbers of neutral variants , even in phenotypically relevant genes . Isolated populations with recent bottlenecks offer advantages for studying rare variants in complex diseases as they have deleterious variants that are present at higher frequencies as well as a substantial reduction in rare neutral variation . To explore the potential of the Finnish founder population for studying low-frequency ( 0 . 5–5% ) variants in complex diseases , we compared exome sequence data on 3 , 000 Finns to the same number of non-Finnish Europeans and discovered that , despite having fewer variable sites overall , the average Finn has more low-frequency loss-of-function variants and complete gene knockouts . We then used several well-characterized Finnish population cohorts to study the phenotypic effects of 83 enriched loss-of-function variants across 60 phenotypes in 36 , 262 Finns . Using a deep set of quantitative traits collected on these cohorts , we show 5 associations ( p<5×10−8 ) including splice variants in LPA that lowered plasma lipoprotein ( a ) levels ( P = 1 . 5×10−117 ) . Through accessing the national medical records of these participants , we evaluate the LPA finding via Mendelian randomization and confirm that these splice variants confer protection from cardiovascular disease ( OR = 0 . 84 , P = 3×10−4 ) , demonstrating for the first time the correlation between very low levels of LPA in humans with potential therapeutic implications for cardiovascular diseases . More generally , this study articulates substantial advantages for studying the role of rare variation in complex phenotypes in founder populations like the Finns and by combining a unique population genetic history with data from large population cohorts and centralized research access to National Health Registers .
After widespread success with genome-wide association studies ( GWAS ) of common variants , several studies have recently begun to identify rare ( with <0 . 5% allele frequency ) and low-frequency ( 0 . 5–5% ) variants in complex diseases and traits such as triglycerides [1] , insulin processing [2] , bone mineral density [3] , Alzheimer's disease [4] , impulsivity [5] , and prostate cancer [6] , some of which confer protection from disease [4] . Protective loss of function variants that can be tolerated in a homozygote state in humans are of particular interest as potential safe targets for therapeutic inhibition . Interestingly , many of these studies that have discovered rare and low-frequency variants use isolated populations that have undergone bottlenecks resulting in frequency enrichment of the associated variants . In contrast to the large number of extremely rare variants present in out-bred populations , such bottlenecked populations have a smaller spectrum of rare variation . This observation has been borne out in examples of Mendelian disease where , for example , Finns and Ashkenazi Jews have characteristic high incidence of recessive diseases because of the enrichment of specific mutations [7] , [8] , [9] – in the wider European population these same diseases are rarer and have mutational spectra involving a more diverse array of extremely rare mutations . It has not yet been assessed to which extent these population structures , so advantageous to Mendelian studies but of little importance to common variant GWAS , might generally improve the power to identify low-frequency loss-of-function ( LoF ) variants in studies of complex disease . To explore this question , we used exome sequencing to characterize the allelic architecture of the Finnish population compared with a set of non-Finnish Europeans ( NFEs ) from the United States , Great Britain , Germany and Sweden . We demonstrate that Finns carry a significant enrichment of low-frequency ( 0 . 5–5% ) LoF variation , defined here as nonsense and essential splice sites that are rare in NFEs . In addition to the isolate population structure , Finland has nationwide health records that provide decades of follow-up data that can be linked to epidemiological studies . The availability of nationwide health records in a population isolate structure triggered us to study the impact of low-frequency variants on risk factors and disease outcomes and their risk factors . The Sequencing Initiative Suomi ( The SISu project ) aims to combine these resources and build knowledge and tools for genome health initiatives . We genotyped 83 LoF variants discovered through our exome sequencing , in several large well-phenotyped population-based cohorts comprised of 36 , 262 Finns and tested for association to 60 quantitative traits and used data from the 13 disease outcomes assessed using the National Health Registers . We demonstrate that 5 of these variants have significant associations with clinically relevant phenotypes , illustrating the general value of the Finnish population for the study of low-frequency variants studies in complex as well as Mendelian diseases . We further confirm two LoF variants that significantly reduce lipoprotein ( a ) levels are associated with protection from cardiovascular disease .
As part of the SISu Project , we assembled 3 , 000 whole-exome sequences from Finns in projects including GoT2D , ENGAGE , migraine , METSIM and the 1000 Genomes Project along with 3 , 000 whole exome-sequences of NFEs from GoT2D , ESP , NIMH and 1000 Genomes project using the same data generation and processing pipelines ( Table S1 ) . The raw BAM files from these projects were compressed and re-processed at the Broad Institute and variant calling was performed in a unified manner to minimize potential batch effects . We compared the number and frequency of variable sites in 3 , 000 Finns and 3000 NFEs ( Fig . 1A ) and observed several expected hallmarks of the isolated bottlenecked Finnish population history . There was a depletion of ‘singletons’ , or variants that were observed only once in 3 , 000 individuals , in Finns compared to NFEs . An average Finn had 3 . 7 times fewer singleton variants in these data ( binomial P<1×10−6 ) . On the other hand , there was an excess of low-frequency variants in Finns versus NFEs ( binomial P<1×10−6 ) , collectively suggesting that while most rare variants did not survive the bottleneck , the variants that did have become substantially elevated in frequency [10] , while the rates of common variation were not different between Finns and NFEs . All these findings are consistent with an expected impact of the Finnish population bottleneck . We then stratified the variants according to their functional annotations – LoF variants , missense variants and synonymous variants . We found a higher proportion of LoF variants in Finns compared to NFEs across the rare and low-frequency allelic spectrum ( Fig . 1A , Table S2 ) and for missense variants predicted to be deleterious by PolyPhen2 ( Fig . S1 ) . We found a similar observation when comparing the Finns to an equivalent number of Swedes ( Fig . S2 ) . This is also a direct consequence of the bottleneck: alleles that are elevated in frequency through the bottleneck are drawn at random from extremely rare variants in the parental population , where there is a higher proportion of LoF variants that arose recently or were kept at low frequencies because of negative selection . This is clearly demonstrated with the decreasing proportions of LoF variants with increasing allele frequencies ( Fig . 1B ) . The observation that LoF variants in the 0 . 5–5% range are enriched in Finns and our hypothesis that some of these variants might have health related phenotypic consequences , motivated the targeted association study described below ( Fig . 2 ) . Despite the reduced overall variation in the isolated population , the existence of a greater number of low frequency LoF variants results in an average Finn harboring 0 . 16 homozygous LoF variants compared to only 0 . 095 in an average NFE , driven primarily by homozygosity in the 0 . 5 to 5% allele frequency range ( Fig . S3B ) . These features of the Finnish population have already been well described as they pertain to Mendelian diseases: many characteristic “Finnish founder mutations” exist at unusually high frequencies , even up to 1% , for highly penetrant and reproductively lethal disorders while such variants are extremely rare or absent in NFEs [11] . We confirmed with simulations that while such variants are inevitably pushed to extremely low frequency after 1 , 000 or more generations , they can easily persist at frequencies between 0 . 1 and 1% up to 100 generations after a bottleneck ( Fig . S4 ) . Table S3 shows a table of a set of Finnish Disease Heritage ( www . findis . org ) variants and their population frequencies . The extent to which such variants contribute to more common diseases , either through highly-penetrant recessive subtypes or modest risk to carriers , will correspond to advantages in rare and low-frequency association studies in isolated populations . Given our empirical observations of proportionally more LoF variants in the 0 . 5–5% allele frequency range in Finns , we next conducted a test of this hypothesis that some of the Finnish-enriched low-frequency LoF variants might have strong phenotypic effects . We successfully genotyped 83 low-frequency LoF variants ( protein-truncating nonsense , essential splice site variants and frameshift variants ) enriched in Finns based on their ability to multiplex in four Sequenom MALDI-TOF genotyping pools ( Table S4 ) . Of these 83 variants , 76 variants were more than 2-fold enriched and 26 were more than 10-fold enriched . in Finns vs . NFEs . Three genes ( SERPINA10 , LPA and FANCM ) contained two LoF variants each; we combined these pairs and tested them as single composite LoF variants , resulting in a total of 80 independent LoF variants tested in this study . These 83 variants were genotyped in a total of 36 , 262 individuals from three population cohorts: FINRISK [12] ( 26 , 245 individuals ) , Health2000 ( 7 , 363 individuals ) and Young Finns [13] ( 2 , 654 individuals ) . As these three studies are population-based cohorts , we were able to assess whether any of the homozygous LoF variants result in such a severe phenotype that these individuals would not be able to participate in a population survey for instance , due to lethality in fetal life of early infancy . Study-wide , there was a modest excess of homozygotes of the variants ( 1 . 23-fold versus Hardy-Weinberg expectation ) arising from within population substructure . A nonsense variant ( Q246X ) in the Translation Elongation Factor , Mitochondrial gene ( TSFM ) that is present at 1 . 2% allele frequency in Finns and absent in NFEs , was not found in a homozygous state in >36 , 000 Finns ( Hardy Weinberg Equilibrium ( HWE ) P = 0 . 0077 ) . This suggests that complete loss of TSFM might result in embryonic lethality , severe childhood diseases in humans , or that the individuals might not have been ascertained by the studies employed , i . e . if the individuals are too sick to be included in the studies . A lookup of this variant in another 25 , 237 Finnish samples in exome chip genotyping data from the GoT2D studies confirmed that the variant is present at 1 . 2% in Finns , but again with no homozygotes observed ( combined HWE P = 1 . 6×10−4 ) . Recessive missense variants in TSFM have been reported to result in mitochondrial translation deficiency [14] , [15] and Finnish mitochondrial disease patients from two families have been identified with compound heterozygosity of this nonsense variant ( each with a different second hit in TSFM ) ( personal communication ) - lending strong evidence to the hypothesis that complete loss of this gene is not tolerated in humans . Neither did we observe strong associations for the TSFM Q246X heterozygotes across major diseases ( Table S5 ) . Several other LoF variants occur in genes where recessive mutations have been noted to cause severe Mendelian diseases from the Online Mendelian Inheritance in Man database ( OMIM ) [16] . For instance , the Fanconi anemia complementation group M gene ( FANCM ) was initially discovered in one family with Fanconi anemia [17] , but we did not observe any deficit of homozygous LoFs in FANCM from our dataset ( expected = 5 , observed = 7 ) , which we would typically observe for a disease causing recessive variant . Furthermore , examination of the hospital discharge records did not provide any evidence for blood diseases , increased cancer events or any other chronic diseases in these individuals with homozygous LoFs in FANCM . We also had blood counts for two homozygote individuals . Both of them had normal hemoglobin , erythrocyte size and counts as well as leukocyte and thrombocyte counts . Singh et al . reported that the initial case that led to the association of FANCM with Fanconi anemia also harbor biallelic , functional mutations in FANCA , a well-established Fanconi anemia gene [18] . Our findings in this study , combined with the findings by Singh et al . do not support the hypothesis that FANCM is a Fanconi anemia gene but rather suggest that the initial FANCM association was not causative . In addition to FANCM , we further evaluated evidence for two other genes COL9A2 and DPYD that were previously implicated in other Mendelian diseases ( Supplementary Methods ) . The FINRISK cohort had collected 60 biochemical and physiological quantitative measurements of cardiovascular or immunologic relevance ( Table S6 ) , some of which are highly correlated . We tested the 80 variants across the 60 traits and report from this initial screen all associations with p<2×10−4 – that is , a value where we would expect only one chance observation in the entire study . In total , we observed 41 associations that exceeded this significance threshold ( Table 1 ) , far beyond the expected . If the phenotype was available in the Young Finns and Health 2000 cohorts , replication was attempted for these initial scan hits and significant associations are highlighted below when the combined p-value was smaller than a conservative study-wide Bonferroni-corrected threshold of 0 . 05/ ( 80*60 ) = 1×10−5 . Three of these association have been previously reported and represent positive controls for our approach: a strong association for the 2 splice variants ( c . 4974-2A>G and c . 4289+1G>A ) in the Lipoprotein ( a ) gene ( LPA ) with lipoprotein ( a ) measurements in plasma ( Pdiscovery = 2 . 17×10−81 , Pdiscovery+replication = 1 . 53×10−117 , combined = −0 . 64 or −8 . 77 mg/dL per allele , Table S7 ) , the W154X variant in Fucosyltransferase 2 ( FUT2 ) with increased Vitamin B12 levels [19] ( = 0 . 2 , P = 3 . 7×10−26 or 43 pg/mL per allele , Table S8 ) and the R225X variant in the Citrate Lyase Beta Like gene ( CLYBL ) with decreased Vitamin B12 levels [20] ( = −0 . 2 , P = 1 . 8×10−5 or −43 pg/mL per allele , Table S9 ) [21] . The boxplots for these associations are shown in Fig . S5 . In addition to a strong correlation between circulating lipoprotein ( a ) levels and cardiovascular disease , it has been previously reported that genetic variants that elevate circulating lipoprotein ( a ) levels are cardiovascular risk factors [22] , [23] . The converse , critical for evaluation of the therapeutic hypothesis of inhibition , that lowering lipoprotein ( a ) levels can confer cardiovascular protection has not yet been evaluated . With access to National Health Records , we utilized the strong lipoprotein ( a ) lowering variants discovered here to evaluate the impact of lipoprotein ( a ) lowering via Mendelian randomization . Using a Cox proportional hazards model for incident cardiovascular disease in these cohorts ( adjusted for age , gender and therapies ) , the composite LPA variant was found to protect against coronary heart disease ( Hazard Ratio HR = 0 . 79 , P = 6 . 7×10−3 ) , demonstrating that lowering lipoprotein ( a ) levels are likely to confer protection for cardiovascular diseases . We adjusted the association for the composite LPA variant with a previously published risk variant ( rs3798220 ) [22] , but observed a similarly protective effect ( N = 18 , 270 , HR = 0 . 79 , P = 0 . 014 ) , suggesting that the splice variants are independent from the previously reported risk variants in LPA . We confirmed this finding using three independent non-Finnish datasets: an early onset myocardial infarction dataset of 18 , 000 individuals and two studies from the Estonian Biobank ( 4 , 600 and 7 , 953 individuals respectively ) , which collectively replicated the observation that the LPA variants confer cardioprotective effect ( OR = 0 . 87 , P = 0 . 016 ) . After meta-analyzing all the datasets , the final odds ratio was found to be 0 . 84 ( P = 3×10−4 , Fig . 3 ) . We found 227 individuals who are homozygous or compound heterozygous for the two LPA splice variants with no evidence for increased morbidity or mortality based on National Health Records . This suggests that reduction of lipoprotein ( a ) is well-tolerated and might constitute a potential drug target for cardiovascular diseases . A survey across other diseases showed potential association between the LPA variants with acute coronary disease and myocardial infarction but not Type 2 Diabetes ( Table S10 ) . In addition , we surveyed the LPA variants across other cardiovascular risk factors and observed that the LPA variants were associated with mildly increased glucose levels but not high-density lipoproteins ( HDL ) , low-density lipoproteins ( LDL ) or triglycerides ( Table S11 ) . In addition , we observed novel associations for the FGL1 , MS4A2 and ATP2C2 variants . The 1-bp c . 545_546insA frameshift in the Fibrinogen-like 1 gene ( FGL1 ) was associated with increased D-dimer levels ( = 0 . 21 , P = 6 . 1×10−6 or 52 . 23 ng/mL per allele , Table S12 ) . D-dimers are products of fibrin degradation and their concentration in the blood flow is clinically used to monitor thrombotic activity . The role of FGL1 in clot formation remains unclear: although FGL1 is homologous with fibrinogen , it lacks the essential structures for fibrin formation , with one study suggesting its presence in fibrin clots [24] . In addition , given prior links between variants associated with D-dimer levels and stroke , we utilized the same Mendelian randomization approach as for LPA above and found a nominally significant association between FGL1 c . 545_546insA and increased risk of ischemic stroke ( OR = 1 . 32 , P = 0 . 024 ) . If replicated , this would be consistent with modest risk increase for stroke that other variants associated to circulating D-dimer levels , such as reported for variants in coagulation Factor V , Factor III and FGA [25] . We found suggestive associations for the c . 637-1G>A splice variant in the membrane-spanning 4-domains , subfamily A , member 2 gene ( MS4A2 ) with triglycerides ( Pdiscovery = 7 . 80×10−5 , Pdiscovery+replication = 1 . 31×10−6 , = 0 . 14 or 0 . 14 mmol/L per allele , Table S13 ) . This observation is consistent with our previously published study of 631 individuals in the DILGOM subset of FINRISK showing that whole blood expression of MS4A2 was strongly negatively associated with total triglycerides ( = −1 . 62 , P = 2 . 1×10−27 , Fig . S6 ) [26] and a wide range of systemic metabolic traits [27] . A similar but insignificant trend was observed in 15 , 696 individuals from the D2D2007 , DPS , FUSION , METSIM and DRSEXTRA cohorts ( = 0 . 04 , P = 0 . 32 ) . The MS4A2 gene encodes the β-subunit of the high affinity IgE receptor , a key mediator of the acute phase inflammatory response . The c . 2482-2A>C splice variant in the ATPase Ca++ Transporting Type 2C Member 2 gene ( ATP2C2 ) was associated with increased systolic blood pressure ( Pdiscovery = 1 . 25×10−5 , Pdiscovery+replication = 1 . 3×10−6 , = 0 . 12 or 2 . 13 mmHg per allele ( an association that is undisturbed by correction for lipid lowering medication ( = 0 . 12 , P = 1 . 75×10−5 ) or blood pressure lowering medication ( = 0 . 13 , P = 1 . 3×10−5 ) , Table S14 ) . Based on its structure , ATP2C2 is predicted to catalyze the hydrolysis of ATP coupled with calcium transport . Interestingly , the ATP2C2 c . 2482-2A>C variant is also significantly associated to several highly correlated immune markers , such as granulocyte colony-stimulating factor ( = 0 . 26 , P = 6 . 98×10−7 ) , interleukin-4 ( = 0 . 27 , P = 2 . 48×10−6 ) , interferon-γ ( = 0 . 26 , P = 3 . 24×10−6 ) and interleukin-6 ( = 0 . 25 , P = 4 . 58×10−6 ) .
The empirical data of this study sheds light on an active debate in population genetics theory whether or not bottlenecked populations have an excess burden of deleterious alleles . Lohmueller et al . first observed that there were proportionally more deleterious variants in European American individuals compared to African American individuals [28] . They performed a series of forward simulations to demonstrate that such an observation is consistent with an Out-of-Africa bottleneck experienced by the European populations from which the European-American individuals descend , and illustrated that bottlenecked populations are likely to accumulate a higher proportion of deleterious alleles . A recent study by Simons et al . showed conflicting results suggesting that there are similar burdens of deleterious alleles in Europeans and West Africans and that demography is unlikely to contribute to the proportions of deleterious alleles in human populations [29] . The comparison of Finns , with a well-documented bottleneck , with non-Finnish Europeans here provides strong empirical data on these questions . While the distribution of common alleles , both synonymous and non-synonymous , is as expected unchanged by the bottleneck , when exploring the rare and low-frequency allelic spectrum where the Finns and NFEs demonstrate distinct distributions , we indeed observe a significant excess of deleterious variants in the Finns – despite the considerable deficit in variable sites in the population overall . This suggests that negative selection has had insufficient time to suppress the frequency of deleterious alleles dramatically elevated in frequency through the founding bottleneck , an observation that generalizes the intuitive understanding of the existence of characteristic and unusually common Mendelian recessive disorders in Finland . However , we note that while we observe a strong influence of the founding bottleneck , the observed results , particularly the proportional enrichment of rare deleterious variants , are also influenced by other elements in the unique history of the Finnish population and will not necessarily apply to all populations influenced by a bottleneck . This excess of presumably deleterious variants motivated the subsequent association study and indeed , the absence of homozygotes at TSFM ( contemporaneously identified as an early-onset mitochondrial disease gene ) suggests that low-frequency variants in Finns , beyond those already identified in Mendelian disease , do include more unusually strong acting alleles than in non-founder populations . In this study , both replicated results and novel associations demonstrate the association of low-frequency LoF variants with various complex traits and diseases . In addition , we discovered a novel cardiovascular protective effect from splice variants in the LPA gene , suggesting that knocking down levels of circulating lipoprotein ( a ) , or Lp ( a ) , can confer a protection from cardiovascular diseases . Given that we detected numerous individuals in these adult population cohorts , healthy and in the expected Hardy-Weinberg proportions , carrying a complete knockout of LPA ( homozygous or compound heterozygous for the 2 splice variants ) , this suggests that knocking out the gene in humans does not result in severe medical consequences . As such , this study provides data suggesting that LPA may be an effective target for therapeutic purposes . As more Finnish samples are being sequenced , these enriched variants can also be imputed with high precision to the large number of existing samples with array-based GWAS genotypes . This advantage is likely to be more pronounced for the much larger pool of missense variation – while one can presume all LoF variants in a gene might have a comparable effect on phenotype ( and thereby burden tests of LoF variants in an out-bred sample is not at a great disadvantage compared to isolated populations ) , it is evident that many rare missense variants within the same gene will not all have the same impact on gene function . Thus the ability to assess single low-frequency variants conclusively , especially since they will include an excess of damaging variants enriched through a bottleneck , rather than perform burden tests on heterogeneous sets of extremely rare variants , will offer substantial ongoing advantage to isolated population studies as indicated by these and other recent findings .
Raw Binary Sequence Alignment/Map ( BAM ) files from the various projects were jointly processed at the Broad Institute and joint variant calling was performed on all exomes to minimize batch differences . Functional annotation was performed using the Variant Effect Predictor ( VEP v2 . 5 ) tool from Ensembl ( http://useast . ensembl . org/info/docs/tools/vep/ ) . We modified it to produce custom annotation tags and additional loss-of-function annotations . The additional annotations were applied to variants that were annotated as STOP_GAINED , SPLICE_DONOR_VARIANT , SPLICE_ACCEPTOR_VARIANT , and FRAME_SHIFT and the variants were flagged if any filters failed . A loss-of-function variant was predicted as high confidence if there is one transcript that passes all filters , otherwise it is predicted as low confidence . In our genotyping study , we had used loss-of-function variants that were predicted to be high confidence . For quality control , we required all variants to pass the basic GATK filters and required all genotypes to have a quality score of ≥30 , read depth of ≥10 and allele balance of between 0 . 3 and 0 . 7 for heterozygous calls and <0 . 1 for homozygous calls . Allele counts and frequencies were calculated within the 3 , 000 individuals for Finns and NFEs respectively . To estimate the amount of substructure or homozygosity by descent , we fitted a regression model on all coding variants with the intercept set to 0 , where q is the allele frequency of the alternate allele and FST is the proportion of allelic variance explained by population structure . Here we fit FST to capture the empirical departure from Hardy-Weinberg equilibrium arising from population substructure to insure this is not creating the observed difference between Finnish and NFE samples:Using the whole-exome sequencing data for the 3 , 000 NFEs , we estimated the parameters:Using the whole-exome sequencing data for the 3 , 000 Finns , we estimated the parameters:As shown , there is little substructure in the 3 , 000 Finns compared to the 3 , 000 NFEs , given that the estimates for FST are similar in both populations . All frameshifts and loss-of-function single nucleotide variants with allele frequencies of 0 . 5–5% in Finns and at least 2-fold enriched in Finns compared to NFEs were selected for genotyping . To minimize the false positives in our variant selection , we performed Fisher's Exact Test for each variant between two independent NFE datasets and kept variants whose allele frequencies were highly concordant between the two NFE datasets ( P>1×10−5 ) . The high concordance between the allele frequencies in two independent NFE datasets ensures that the variants are unlikely to arise from alignment or sequencing artifacts and that these variants are unlikely to reside in a region of the exome that is difficult to sequence or genotype , which can result in highly variable allele frequencies from different experiments . Genotyping was performed using the iPLEX Gold Assay ( Sequenom Inc . ) . Assays for all SNPs were designed using the eXTEND suite and MassARRAY Assay Design software version 3 . 1 ( Sequenom Inc . ) . Amplification was performed in a total volume of 5 µL containing ∼10 ng genomic DNA , 100 nM of each PCR primer , 500 µM of each dNTP , 1 . 25× PCR buffer ( Qiagen ) , 1 . 625 mM MgCl2 and 1 U HotStar Taq ( Qiagen ) . Reactions were heated to 94°C for 15 min followed by 45 cycles at 94°C for 20 s , 56°C for 30 s and 72°C for 1 min , then a final extension at 72°C for 3 min . Unincorporated dNTPs were SAP digested prior to iPLEX Gold allele specific extension with mass-modified ddNTPs using an iPLEX Gold reagent kit ( Sequenom Inc . ) . SAP digestion and extension were performed according to the manufacturer's instructions with reaction extension primer concentrations adjusted to between 0 . 7–1 . 8 µM , dependent upon primer mass . Extension products were desalted and dispensed onto a SpectroCHIP using a MassARRAY Nanodispenser prior to MALDI-TOF analysis with a MassARRAY Analyzer Compact mass spectrometer . Genotypes were automatically assigned and manually confirmed using MassARRAY TyperAnalyzer software version 4 . 0 ( Sequenom Inc . ) . The genotyped variants were then checked for concordance in allele frequencies with the exome sequencing data . Data on disease status from National Health registers ( Hospital Discharged Registers maintained by THL ( Institute for Health and Welfare , Finland ) , Cause of Death Register , Statistics Finland and Prescription Medication Register , THL ) for FINRISK , Health2000 and the Young Finns Study participants of this study were collected and curated . A description of each cohort is provided in the Supplement . To analyze the effects of the LoF variants on gene expression , we used RNA sequencing data from two major studies: the GEUVADIS project [30] with RNA sequencing data from lymphoblastoid cell lines of 462 individuals participants from the 1000 Genomes Project [31] ) , and the GTEx project with RNA-sequencing data from a total of 175 individuals with 1–30 tissues each ( http://www . broadinstitute . org/gtex/ ) [32] . The processing of the GEUVADIS data and the methods for allele-specific expression analysis are described in Lappalainen et al . [30] and the GTEx data were analyzed using similar methods . Allele-specific expression analysis was used primarily to capture nonsense-mediated decay . Additionally , to assess whether LoF variants lead to decreased exon expression levels overall or for individual exons , we calculated an empirical p-value for each exon of all the LoF genes with respect to all other exons genome-wide , denoting the proportion of all exons where carriers of the LoF variants are more extreme than in the each studied exon in LoF variant genes . The analyses were performed separately in each studied tissue: lymphoblastoid cell lines from the GEUVADIS data and nine tissues from the GTEx data . The significance threshold after correcting for the total number of tested exons across all tissues is 0 . 05/1070 = 4 . 67×10−5 . Inverse rank-based normalization was performed on the quantitative measurements in males and females separately , with linear regression residuals using age and age2 as covariates . Linear regression was then performed on the normalized Z-scores using R to obtain the statistics for the associations . We tested the correlations between the quantitative measurements and disease outcomes using two one-tailed t-tests to assess the significance of observing higher levels of the quantitative measurements in cases ( individuals with the disease outcomes ) versus controls ( individuals without the disease outcomes ) , as well as lower levels of the quantitative measurements in cases versus controls . To test the association of the variants with the prevalent disease outcomes , we performed a logistic regression in R to obtain the reported statistics . In addition , a Fisher's Exact Test on the homozygous counts in cases and controls were performed to test for association with the homozygotes . The results for the LPA with cardiovascular disease association from MIGen ExA and the Estonian Biobank were meta-analyzed using METAL [33] and the combined results with FINRISK were obtained using the Fisher's Combined P method with 4 degrees of freedom . We fit a linear model in which the log2-normalised gene probe expression of individual i was regressed on the LoF genotype , which was encoded as Xi = 0 , 1 or 2 for the LoF genotypes −/− , +/− or +/+ respectively and association analysis of MS4A2 gene expression and triglycerides was performed as previously reported [26] . Briefly , we used a multivariate linear regression adjusted for age , gender , and use of cholesterol or blood pressure lowering medication . We further tested for association between MS4A2 c . 637-1G>A and triglycerides using a 2-sided t-test . | We explored the coding regions of 3 , 000 Finnish individuals with 3 , 000 non-Finnish Europeans ( NFEs ) using whole-exome sequence data , in order to understand how an individual from a bottlenecked population might differ from an individual from an out-bred population . We provide empirical evidence that there are more rare and low-frequency deleterious alleles in Finns compared to NFEs , such that an average Finn has almost twice as many low-frequency complete knockouts of a gene . As such , we hypothesized that some of these low-frequency loss-of-function variants might have important medical consequences in humans and genotyped 83 of these variants in 36 , 000 Finns . In doing so , we discovered that completely knocking out the TSFM gene might result in inviability or a very severe phenotype in humans and that knocking out the LPA gene might confer protection against coronary heart diseases , suggesting that LPA is likely to be a good potential therapeutic target . | [
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"computa... | 2014 | Distribution and Medical Impact of Loss-of-Function Variants in the Finnish Founder Population |
The study of HIV-infected “controllers” who are able to maintain low levels of plasma HIV RNA in the absence of antiretroviral therapy ( ART ) may provide insights for HIV cure and vaccine strategies . Despite maintaining very low levels of plasma viremia , controllers have elevated immune activation and accelerated atherosclerosis . However , the degree to which low-level replication contributes to these phenomena is not known . Sixteen asymptomatic controllers were prospectively treated with ART for 24 weeks . Controllers had a statistically significant decrease in ultrasensitive plasma and rectal HIV RNA levels with ART . Markers of T cell activation/dysfunction in blood and gut mucosa also decreased substantially with ART . Similar reductions were observed in the subset of “elite” controllers with pre-ART plasma HIV RNA levels below conventional assays ( <40 copies/mL ) . These data confirm that HIV replication persists in controllers and contributes to a chronic inflammatory state . ART should be considered for these individuals ( ClinicalTrials . gov NCT01025427 ) .
HIV-infected “controllers” are individuals who are HIV-seropositive but are able to maintain low levels of plasma HIV RNA in the absence of antiretroviral therapy ( ART ) [1] . These individuals are rare , comprising less than 1–7% of the HIV-infected population , depending upon the plasma HIV RNA criteria that are used to define the group [2] , [3] , [4] . Most controllers have evidence of strong host immune responses , which have been widely assumed to be responsible for durable viral control . Because knowledge regarding these protective immune responses might lead to novel interventions aimed at preventing or curing HIV infection , there has been intense interest in further characterizing these unique individuals . Multiple groups have examined how HIV is controlled by these individuals [5] , [6] , 7 , 8 , 9 . More recently , our group has focused on defining the potential clinical consequences of long-term , host-mediated , virologic control . We and others have shown that: ( 1 ) the vast majority of controllers have stable low-level viremia [10] , [11]; ( 2 ) controllers have elevated levels of microbial translocation and T cell activation compared to HIV-negative and ART-suppressed individuals [12] , [13]; ( 3 ) a minority ( 7–10% ) of controllers with high levels of T cell activation progress immunologically to AIDS despite preservation of virologic control [12]; and ( 4 ) controllers have accelerated measures of atherosclerosis compared to HIV-negative individuals , even after adjustment for traditional cardiovascular risk factors [14] , [15] . Collectively , these data suggest that very low levels of viral replication may lead to disproportionately high levels of immune activation in HIV-infected controllers , which may lead to an increased risk of AIDS- and non-AIDS defining events . However , the degree to which viral replication contributes to these outcomes is not known . No prospective ART studies have been performed in controllers , because it has generally been assumed that most controllers do not need ART due to their ability to control plasma viremia to very low levels . We therefore conducted the first , prospective study of antiretroviral therapy in a cohort of asymptomatic HIV-infected controllers , in order to determine the virologic and immunologic effects of treating controllers with ART . We also measured changes in biomarkers of inflammation and coagulation . Multiple biomarkers ( e . g . , high sensitivity C-reactive protein and D-dimer ) remain elevated in both untreated and treated non-controllers [16] , and have been shown to be strongly predictive of morbidity and all-cause mortality in ART-treated non-controllers [17] , [18] , [19] . We therefore examined whether ART initiation led to a reduction in biomarkers of inflammation and coagulation in controllers , in order to assess whether low-level viral replication has any potential immunologic and clinical consequences in these individuals .
Sixteen asymptomatic controllers were prospectively treated with open-label raltegravir+tenofovir/emtricitabine for 24 weeks . Controllers were defined by the following inclusion criteria: ( 1 ) HIV-seropositive; ( 2 ) ART untreated; and ( 3 ) plasma HIV RNA <1 , 000 copies/mL for ≥12 months . Exclusion criteria included: ( 1 ) known rheumatologic conditions ( e . g . , systemic lupus erythematosus ) , because of the potential for biologic false-positive testing on HIV antibody tests; ( 2 ) known kidney disease; ( 3 ) known bone disease , including pathologic fractures; ( 4 ) chronic hepatitis B infection , because of the potential risk of liver abnormalities after starting and stopping tenofovir/emtricitabine in patients with chronic hepatitis B infection; ( 5 ) serious illness requiring hospitalization or parental antibiotics within the preceding 3 months; and ( 6 ) pregnant or breastfeeding women . Subjects were seen every four weeks . Plasma and peripheral blood mononuclear cells ( PBMCs ) were collected and detailed interviews were conducted at the majority of visits . Thirteen out of 16 subjects consented to undergo 3 serial colorectal biopsies at weeks −2 , 6 , and 22 . Five out of 16 subjects also underwent leukapheresis at weeks −4 and week 21 in order to obtain large PBMC samples for measurement of integrated HIV DNA . Adherence to study drug was measured at every study visit by self-report and pill-count . An independent Data Monitoring Committee comprised of three individuals from the scientific community met at 12 , 24 , 48 , and 60 weeks after the enrollment of the first subject , and at 60 weeks after the enrollment of the last subject . All subjects had a baseline plasma HIV RNA level <1 , 000 copies/mL in the absence of ART . The median baseline plasma HIV RNA level using a standard assay ( Abbott Real Time assay , lower limit of detection <40 copy/mL ) was 77 copies/mL; 4/16 subjects had an “undetectable” ( <40 copies/mL ) baseline plasma HIV RNA level with this assay . The median baseline plasma HIV RNA level using an ultrasensitive “single copy assay” ( lower limit of detection <0 . 3 copy/mL ) was 23 copies/mL . The median baseline age was 49 years; most subjects ( 88% ) were men . The median baseline CD4+ and CD8+ T cell counts were 616 and 897 cells/mm3 , respectively; the median baseline CD4+ to CD8+ T cell count ratio was 0 . 71 . The median nadir CD4+ T cell count was 590 cells/mm3 . The median self-reported duration of known HIV diagnosis was 10 years . Antiretroviral therapy was well tolerated and all subjects completed 24 weeks of ART . No significant adverse events occurred during the study . The majority of controllers ( 11/16 ) elected to continue ART after the 24-week study period . Five out of 16 subjects elected to discontinue ART after the 24-week study period at various times ( median 10 . 0 weeks , interquartile range [IQR] 1 . 0 to 19 . 0 weeks ) after the end of the treatment study . They have subsequently been followed for a median 63 . 0 ( IQR 47 . 4 to 66 . 3 ) weeks after discontinuing ART , and at the time of last follow-up the plasma HIV RNA level using a standard assay ( Abbott Real Time assay , lower limit of detection <40 copy/mL ) was a median <40 ( IQR<40 to 73 ) copies/mL . Of the 5 subjects who elected to discontinue ART after the end of the treatment study , 4/5 of the subjects had an “undetectable” pre-ART plasma HIV RNA level at baseline using a standard assay ( Abbott Real Time assay , lower limit of detection <40 copy/mL ) . Controllers did not have a statistically significant increase in peripheral CD4+ T cell counts ( mean 1 . 00-fold increase in CD4+ T cells at week 24 , 95% confidence interval [CI] 1 . 05-fold decrease to 1 . 06-fold increase , p = 0 . 93 ) ( Fig . 1A ) . Similarly , controllers did not have a statistically significant increase in %CD3+CD4+ T cells in the rectum ( mean +0 . 4% , 95% CI −0 . 8% to 1 . 6% , p = 0 . 50 ) ( Fig . 1B ) . Despite having low pre-ART plasma HIV RNA levels by conventional assays , controllers had an early and persistent decrease in ultrasensitive plasma HIV RNA levels after initiation of ART ( mean 66-fold decrease in S/Co at week 24 , 95% CI 155 to 28-fold decrease , p<0 . 001 ) ( Fig . 2A ) . In addition , we examined change in HIV antibody levels as a surrogate measure of antigenic stimulation and viral persistence [10] , [20] , [21] , [22] , [23] . Controllers also had an early and persistent decrease in HIV antibody levels ( mean −7 . 2 S/Co at week 24 , 95% CI −9 . 6 to −4 . 8 , p<0 . 001 ) ( Fig . 2B ) . At baseline , the median ( IQR ) levels of cell-associated HIV RNA and total HIV DNA in PBMCs were 6 . 9 ( 3 . 5 , 45 . 7 ) S/Co per million CD4+ T cells and 57 ( 34 , 138 ) copies/million CD4+ T cells , respectively . In PBMCs , controllers did not have a substantial decrease in cell-associated HIV RNA ( mean 1 . 20-fold decrease in S/Co per million CD4+ T cells , 95% CI 2 . 4-fold decrease to 1 . 62-fold increase , p = 0 . 58 ) or total HIV DNA ( mean 1 . 22-fold decrease in copies/million CD4+ T cells , 95% CI 1 . 95-fold decrease to 1 . 32-fold increase , p = 0 . 41 ) at week 24 . However , controllers did have an early and persistent decrease in rectal cell-associated HIV RNA after initiation of ART , with a mean decrease of 0 . 61 log10 copies/million CD4+ cells , which corresponded to a 4 . 1-fold decrease ( 95% CI 12 . 0 to 1 . 40-fold decrease , p = 0 . 010 ) at week 22 ( Fig . 3A ) . There was a similar trend towards a decrease in rectal total HIV DNA , with a mean decrease of 0 . 28 log10 copies/million CD4+ cells , which corresponded to a 1 . 91-fold decrease ( 95% CI 5 . 1-fold decrease to 1 . 38-fold increase , p = 0 . 19 ) at week 22 ( Fig . 3B ) . We also measured integrated HIV DNA levels in PBMCs obtained through leukapheresis in 5 controllers . In these subjects , there was a statistically significant decrease in integrated HIV DNA after initiation of ART , with a mean decrease of 0 . 32 log10 copies/million PBMCs , which corresponded to a 2 . 1-fold decrease ( 95% CI 2 . 7 to 1 . 13-fold decrease , p = 0 . 027 ) at week 21 ( Fig . 4 ) . Markers of T cell activation/dysfunction in blood and gut also decreased substantially with ART . In PBMCs , controllers had a mean decrease of 1 . 9% in %CD38+HLA-DR+ CD4+ T cells ( 95% CI −2 . 8% to −0 . 9% , p<0 . 001 ) and a mean decrease of 9 . 0% in %CD38+HLA-DR+ CD8+ T cells ( 95% CI −12 . 4% to −5 . 6% , p<0 . 001 ) at week 24 ( Fig . 5 ) . Controllers also had a mean decrease of 1 . 6% in %PD-1+ CD4+ T cells ( 95% CI −3 . 1% to −0 . 1% , p = 0 . 04 ) and a mean decrease of 4 . 5% in %PD-1+ CD8+ T cells ( 95% CI −6 . 4% to −2 . 6% , p<0 . 001 ) in PBMCs at week 24 . In the rectum , controllers had a trend towards a decrease in %CD38+HLA-DR+ CD4+ T cells ( mean −0 . 9% , 95% CI −2 . 3% to +0 . 5% , p = 0 . 20 ) and a statistically significant mean decrease of 12 . 2% in %CD38+HLA-DR+ CD8+ T cells ( 95% CI −21 . 9% to −2 . 5% , p = 0 . 014 ) at week 22 ( Fig . 6 ) . At baseline , the median ( IQR ) levels of high sensitivity C-reactive protein ( hsCRP ) , interleukin-6 ( IL-6 ) , soluble CD14 ( sCD14 ) , and D-dimer were 1 . 20 ( 0 . 55 , 3 . 03 ) ug/mL , 1 . 71 ( 1 . 28 , 4 . 42 ) pg/mL , 1696 ( 1446 , 1971 ) ng/mL , and 0 . 37 ( 0 . 28 , 0 . 56 ) ug/mL , respectively . After ART initiation , there was a trend towards a decrease in hsCRP , with a mean 1 . 74-fold decrease ( 95% CI 3 . 2-fold decrease to 1 . 04-fold increase , p = 0 . 069 ) at week 4 , and a mean 1 . 67-fold decrease ( 95% CI 3 . 0-fold decrease to 1 . 09-fold increase , p = 0 . 093 ) at week 24 ( Fig . 7 ) . We also observed similar trends in IL-6 ( mean 1 . 34-fold decrease , 95% CI 2 . 1-fold decrease to 1 . 19-fold increase , p = 0 . 22 ) , sCD14 ( mean −44 . 2 , 95% CI −138 . 6 to +50 . 3 , p = 0 . 36 ) , and D-dimer ( mean 1 . 30-fold decrease , 95% CI 2 . 1-fold decrease to 1 . 25-fold increase , p = 0 . 29 ) levels after 24 weeks of ART , although these trends did not reach statistical significance . At baseline , the median ( IQR ) levels of percentage of Gag-specific IFNγ+IL2+ CD4+ and CD8+ T cell responses in PBMCs were 0 . 10% ( 0 . 03% , 0 . 17% ) and 1 . 53% ( 0 . 37% , 2 . 55% ) , respectively . Controllers did not have a substantial change in the percentage of Gag-specific IFNγ+IL2+ CD4+ T cell responses in PBMCs ( mean 1 . 07-fold decrease , 95% CI 1 . 40-fold decrease to 1 . 22-fold increase , p = 0 . 62 ) , although there was a trend towards a decrease in the percentage of Gag-specific IFNγ+IL2+ CD8+ T cell responses in PBMCs ( mean 1 . 28-fold decrease , 95% CI 1 . 68-fold decrease to 1 . 03-fold increase , p = 0 . 075 ) at week 24 . At baseline , the median ( IQR ) levels of percentage of total ( IFNγ , IL-2 , TNFα , and/or CD107a ) Gag-specific CD4+ or CD8+ in the rectum were 0 . 56% ( 0% , 1 . 3% ) and 0 . 22% ( 0 . 08% , 0 . 32% ) , respectively . Controllers did not have a substantial change in the percentage of total Gag-specific CD4+ ( mean 1 . 26-fold increase , 95% CI 1 . 82-fold decrease to 2 . 9-fold increase , p = 0 . 58 ) or CD8+ ( mean 1 . 61-fold decrease , 95% CI 3 . 6-fold decrease to 1 . 37-fold increase , p = 0 . 24 ) T cell responses in the rectum at week 22 . At baseline , 4/16 controllers had an “undetectable” pre-ART plasma HIV RNA level using a standard assay ( Abbott Real Time assay , lower limit of detection <40 copy/mL ) . Despite having this extremely low pre-ART plasma HIV RNA level , this subset of so-called “elite” controllers had a statistically significant decrease in ultrasensitive plasma HIV RNA levels after initiation of ART ( mean 14-fold decrease in S/Co at week 24 , 95% CI 115 to 1 . 74-fold decrease , p = 0 . 013 ) ( Fig . 8A ) . Similarly , this subset of controllers had a statistically significant decrease in HIV antibody levels ( mean −4 . 2 S/Co at week 24 , 95% CI −7 . 9 to −0 . 5 , p = 0 . 027 ) ( Fig . 8B ) . Finally , we observed similar trends in immune activation in these 4 controllers after initiation of ART . There was a mean decrease of 6 . 0% in %CD38+HLA-DR+ CD8+ T cells in PBMCs at week 24 ( 95% CI −13 . 0% to +0 . 10% , p = 0 . 091 , Fig . 8C ) and a mean decrease of 24 . 3% in %CD38+HLA-DR+ CD8+ T cells in the rectum at week 22 ( 95% CI −54 . 1% to +5 . 6% , p = 0 . 11 , Fig . 8D ) .
In this first prospective study of antiretroviral therapy initiation in asymptomatic HIV-infected controllers , 24 weeks of ART was safe and well-tolerated . Despite being able to maintain very low plasma HIV RNA levels in the absence of ART , controllers had readily measurable levels of HIV RNA and DNA in the gut . Antiretroviral therapy led to statistically significant decreases in ultrasensitive plasma HIV RNA levels , HIV antibody levels , rectal cell-associated HIV RNA , and immune activation in the blood and gut . Collectively , these data suggest that HIV in most controllers is replication-competent [24] , [25] , [26] , and that host rather than virologic factors account for the remarkable degree of viral control in these unique individuals . We also observed a statistically significant decrease in levels of integrated HIV DNA with ART , while total HIV DNA levels remained stable . These findings may be due to an excess of unintegrated HIV DNA in controllers , as previously reported by our group [27] . We observed that measures of immune activation/dysfunction decreased as measures of virologic burden and HIV antigenic stimulation decreased with ART . We also observed a trend towards a decrease in hsCRP ( a measure of systemic inflammation ) with ART; similar trends were observed with IL-6 , sCD14 , and D-dimer . These biomarkers have been shown to be strong , consistent , and independent predictors of increased morbidity and mortality in HIV infection [18] , [19] , [28] . Because the confidence intervals were wide , however , we cannot assess with certainty whether the observed decrease in hsCRP levels has any clinical relevance; it would be important to pursue these findings in future , larger studies . Taken together , however , these data suggest that there may be immunologic consequences to even very low levels of viral replication . This latter finding may have important implications for HIV-infected non-controllers as well [29] , [30] , [31] , [32] , [33] . Importantly , our study also shifts the field's working definition of a “functional cure . ” On one hand , our data suggest that a complete block of viral replication is not necessary to achieve long-term virologic control . However , natural long-term virologic control appears to be coming at an immunologic and/or clinical “cost , ” at least as defined by increased levels and manifestations of immune activation . Thus , although further study of controllers is warranted , untreated HIV-infected controllers may not represent the best model of a functional cure , if we believe that a cure should require a disease-free ( and not just treatment-free ) state . Several limitations of our study deserve comment . First , this was a small pilot study and our findings should be replicated in a larger study of HIV-infected controllers with a longer duration of follow-up . In our study of controllers who had relatively high baseline CD4+ T cell counts , 24 weeks of ART did not appear to confer a CD4+ T cell count benefit . In studies of HIV-infected non-controllers , a greater absolute decrease in plasma HIV RNA levels during the early period after ART initiation has been shown to be a consistent predictor of an early increase in CD4+ T cell counts ( with much of the increase assumed to be due to redistribution ) [34] . In our study , although we did observe a significant decrease in ultrasensitive plasma HIV RNA levels with initiation of ART , the absolute change was small compared to that observed in HIV-infected non-controllers; this may have partially accounted for the limited changes in peripheral CD4+ T cell counts . It is possible that with a much longer duration of follow-up , an increase in CD4+ T cell count may have been observed . Second , there was a trend towards a decrease in the percentage of Gag-specific IFNγ+IL2+ CD8+ T cell responses in PBMCs , although a similar trend was not observed in the rectum . This observation raises the possibility that initiation of ART in controllers may reduce host mechanisms of virologic control , leading to rebound in viremia if ART is discontinued . However , in the 5 subjects who elected to discontinue ART after the 24-week study period , there was no evidence of rebound in plasma viremia after discontinuation of ART . Nevertheless , the long-term safety of ART in controllers should be confirmed . Third , we enrolled a relatively heterogeneous group of controllers . As we and others have shown , however , controllers are a heterogeneous group with varying levels of steady-state viremia; there appears to be a continuum of viremia across controllers [1] , [10] , [12] , . In order to determine whether there is a differential effect of ART on a spectrum of controllers , we enrolled individuals whose baseline plasma HIV RNA levels spanned from nearly 0 to 1000 copies RNA/mL for at least 12 months ( median duration of HIV diagnosis 10 years , IQR 4 . 5 to 24 years ) . Thus , our study included controllers who had both low-level but detectable and undetectable pre-ART plasma HIV RNA levels using conventional assays . Remarkably , however , even amongst the latter group of “elite” controllers who had undetectable pre-ART plasma HIV RNA levels at baseline , we observed a statistically significant decrease in ultrasensitive plasma HIV RNA levels and HIV antibody levels , and a trend towards a decrease in immune activation with ART . Fourth , although 24 weeks of ART significantly decreased levels of CD4+ and CD8+ T cell activation , it did not normalize them to levels observed in HIV-uninfected individuals [41] . Thus , at least in HIV-infected controllers , low-level viral replication is unlikely to be the only factor contributing to immunologic disease . The potential role of other factors that might contribute to immune activation—including co-infections and substance abuse—could not be addressed in this pilot study , but might be addressed in future studies with larger cohorts . It would also be important to systematically assess the individual and potentially synergistic contributions of ART and lifestyle modifications towards decreasing inflammation , immune activation , and clinical disease in HIV-infected controllers [14] . Finally , it is worth noting that there may be multiple pathways to virologic control , some of which may represent an appropriate model of a “functional cure” and may not receive an additional benefit from ART . In summary , 24 weeks of ART was safe and well-tolerated in chronically HIV-infected controllers . Antiretroviral therapy in controllers led to significant decreases in ultrasensitive plasma and rectal HIV RNA , HIV antibody levels , and markers of immune activation/dysfunction in blood and gut , confirming that HIV replication persists in controllers and contributes to a chronic inflammatory state . We acknowledge that this was a small pilot study and that our findings would be ideally replicated in a larger , randomized , clinical-endpoint study . However , the relative rarity of HIV-infected controllers may make such a study impractical , if not impossible . In the absence of such a study , clinicians will need to weigh the potential benefits of ART ( suggested by the changes in immune activation and biomarkers observed in our study ) with the potential risks and costs associated with long-term antiretroviral therapy .
All subjects provided written informed consent . This study was approved by the University of California San Francisco ( UCSF ) Committee on Human Research . The isothermal Transcription Mediated Amplification ( TMA ) assay ( Aptima , Gen-Probe/Hologic ) was used to measure ultrasensitive plasma HIV RNA levels at weeks 0 , 4 , 12 , and 24 . This is a nucleic acid-amplification test that has been FDA-approved for the early detection of HIV infection in blood donors [42] , [43] , [44] . It is a highly specific and sensitive assay , with a singlicate 50% detection limit of 3 . 6–14 copies/mL [45] , [46] . The assay was performed in triplicate on 0 . 5 mL plasma ( 1 . 5 mL total plasma ) , improving the overall 50% detection limit to <5 copies/mL . The output is a signal/cutoff ( S/Co ) ratio ( range 0–30 ) , with S/Co<1 . 0 = “negative” and S/Co≥1 . 0 = “positive . ” Ultrasensitive plasma HIV RNA levels were also measured at weeks 0 and 12 with a “single copy assay” ( lower limit of detection <0 . 3 copy/mL ) , using a median 7 . 3 mL of plasma [47] . A “de-tuned” or less-sensitive enzyme immunoassay ( LS-VITROS ) was used to measure HIV antibody levels at weeks 0 , 4 , 12 , and 24 . The VITROS ( Ortho-Clinical Diagnostics ) is an FDA-approved diagnostic assay for the detection of IgM/IgG antibodies to HIV-1/-2 . The less-sensitive modification tests 1∶400 dilutions of plasma and calculates a S/Co ratio ( range 0–80 ) , and has been validated as a method to identify early HIV infection [48] . Cell-associated HIV RNA and total HIV DNA were measured from PBMCs at weeks 0 , 4 , and 24 . Cell-associated HIV RNA was measured using modifications of published methods ( Aptima , Gen-Probe/Hologic ) [10] , [49] . The output is a S/Co ratio ( range 0–30 ) , with S/Co<1 . 0 = “negative” and S/Co≥1 . 0 = “positive . ” All S/Co ratios were normalized to per million CD4+ T cells . Total HIV DNA was measured using modifications of published methods with an overall sensitivity of 1 copy/3 µg of DNA ( 450 , 000 PBMCs ) [10] , [50] , [51] , [52] . All total HIV DNA levels were normalized to per million CD4+ T cells . Integrated HIV DNA was measured from PBMCs at weeks −4 and 21 . DNA was prepared ( Qiagen Mid ) and integrated HIV DNA was measured using a published repetitive sampling method because integration levels are known to be low in controllers [27] , [53] . At least 42 Alu-gag PCR reactions were performed with 150 , 000 diploid genomes per PCR , for a total of 6 . 3 million diploid genomes assayed per subject . PBMCs were isolated from whole blood , cryopreserved , and stored at the UCSF AIDS Specimen Bank . Markers of T cell activation/dysfunction and antigen-specific T cell responses were measured at weeks 0 , 4 , and 24 at the UCSF Core Immunology Laboratory , using published methods that have been optimized and validated for cryopreserved PBMCs [54] . Briefly , cryopreserved PBMCs were rapidly thawed in warm media , counted on an Accuri C6 ( BD Biosciences ) with the Viacount assay ( Millipore ) , and washed and stained the same day ( T cell immunophenotyping ) or rested overnight ( cytokine flow cytometry [CFC] ) . The average viability of thawed cells was 93% ( range 61–98%; 80% of samples had viability >90% ) . For T cell immunophenotyping , the percent of activated ( CD38+/HLA-DR+/PD1+ ) CD4+ and CD8+ T cells were measured; these markers of immune activation/dysfunction have been shown to be strong and independent predictors of HIV disease progression [12] , [41] , [55] , [56] , [57] . Cells were stained with Aqua Amine Reactive Dye ( AARD , Invitrogen ) to discriminate dead cells , washed , and stained with fluorescently-conjugated monoclonal antibodies: CD3-Pacific Blue ( BD Pharmingen ) , CD38-PE , HLA-DR-FITC , PD1- Alexa647 ( BD Biosciences ) , CD4-PE Texas Red , and CD8-QDot 605 ( Invitrogen ) . In each experiment a fluorescent-minus one control was included for CD38 , HLA-DR , and PD-1 . Stained cells were washed , fixed in 0 . 5% formaldehyde ( Polyscience ) , and held at 4C until analysis . For CFC , rested PBMCs were stimulated for 18–22 h at 37C with overlapping peptide pools corresponding to HIV-1 Con B Gag peptides ( NIH 8117 ) in the presence of 0 . 5 ug/mL Brefeldin A and 0 . 5 ug/mL Monensin ( Sigma-Aldrich ) . A control well with no stimulation was run in parallel for each sample . Cells were washed and stained with AARD , fixed , and permeabilized for intracellular staining with antibodies against CD3-Pacfic Blue , IFNγ-FITC , IL-2-PE ( BD BioScience ) , CD4-PE Texas Red , and CD8-QDot 605 ( Invitrogen ) . Cells were washed and stored at 4C until analysis . We focused on Gag-specific IFNγ+IL2+ T cell responses given that we have shown that these responses are associated with control of HIV replication in controllers [5] , [35] , [58] . Stained cells were run on a customized BD LSR II ( BD Bioscience ) . 100 , 000 and 500 , 000 lymphocytes were collected for immunophenotyping and CFC samples , respectively . Data were compensated and analyzed using FlowJo ( Tree Star ) to determine the proportion of CD4+ and CD8+ T cells expressing each of the T cell or cytokine markers . Combinations of markers were calculated in FlowJo using the Boolean gate function . For CFC data , results from control wells with no stimulation were subtracted from stimulated results . High sensitivity C-reactive protein ( hsCRP ) , interleukin-6 ( IL-6 ) , soluble CD14 ( sCD14 ) , and D-dimer levels were measured on stored fasting plasma samples at weeks 0 , 4 , and 24 at the Laboratory for Clinical Biochemistry Research at the University of Vermont . hsCRP was measured with a BN II nephelometer ( Siemens Diagnostics , Deerfield , IL ) , IL-6 was measured with Chemiluminescent Sandwich enzyme-linked immunosorbent assay , sCD14 with a standard ELISA ( both R&D Systems , Minneapolis , MN ) , and D-dimer was measured with an immunoturbidometric method on the Sta-R analyzer , Liatest D-DI ( Diagnostica Stago , Parsippany , NJ ) . Interassay coefficients of variation for a number of different control materials of different values averaged ∼10% or less for all assays . Thirty colorectal biopsy specimens were obtained 10–20 cm from the anal verge using 3 mm jumbo forceps at weeks −2 , 6 , and 22 . Eighteen to 24 biopsy pieces were placed into 10 mL RPMI-1640 media containing fetal calf serum ( 15% ) , penicillin ( 100 U/mL ) , streptomycin ( 100 ug/mL ) , and L-glutamine ( 2 mM ) . Fresh colorectal cells were isolated on the same day using a modification of a published protocol designed to optimize yield and viability of mucosal lymphocytes without compromising the detection of most surface antigens [59] . Briefly , biopsy pieces underwent two rounds of digestion in 0 . 5 mg/mL collagenase type II ( Sigma-Aldrich ) . Each digestion was followed by disruption of the tissue with a syringe bearing a 16-gauge blunt end needle and subsequent passage through a 70 µm cell strainer . Yields were 9 . 5–31×106 ( mean 18×106 ) total rectal cells . One aliquot of cells was set aside for flow cytometry and stained with CD45-FITC , CD3-APC and CD4-PE ( BD biosciences ) for 15 min at 25C . Propidium iodide was added to stain non-viable cells and samples were run on an Accuri C6 to determine the total number of viable mononuclear cells and proportion and absolute number of viable CD45+ leukocytes and CD4+ T cells . Another aliquot of cells was frozen at −80C for subsequent nucleic acid extraction . Total HIV RNA was measured from rectal cells using a published method [40] . Three replicates of up to 500 ng RNA were assayed for total processive HIV RNA transcripts using primers ( HXB2 positions 522–543 , 626–643 ) and probe ( 559–584 ) from the LTR region [60] . Genomic HIV RNA standards ( 2 . 5×100 to 2 . 5×105 ) were prepared from lab stocks of NL4-3 virions by extracting and quantifying HIV RNA using the Abbot Real Time assay . HIV RNA copy numbers were normalized to cellular input into the PCR , as determined by RNA mass ( assuming 1 ng RNA = 1000 cells [61] ) , which has been shown to correlate with levels of GAPDH RNA [62] . Results were further normalized by the percent of cells that were CD3+CD4+ by flow cytometry and expressed as copies/106 CD4+T cells . Total HIV DNA was measured from rectal cells using a published method [40] . Three replicates of up to 500 ng DNA were assayed for HIV DNA using a modification of a published TaqMan PCR assay that uses primers/probe from the LTR region ( as above ) . External standards ( 105 to 1 ) were prepared from DNA extracted from known numbers of 8E5 cells ( NIH AIDS Reagent Program ) , each of which contains one integrated HIV genome per cell . HIV DNA copy numbers were normalized to cellular input into the PCR , as determined by DNA mass ( assuming 1 ug DNA = 160 , 000 cells ) . Results were further normalized by the percent of cells that were CD3+CD4+ by flow cytometry and expressed as copies/106 CD4+T cells . Markers of T cell activation ( CD38+/HLA-DR+ ) and total Gag-specific responses ( Gag-specific CD4+ and CD8+ T cells expressing one or more of IFNγ , IL-2 , TNFα , and/or CD107a [63] , [64] , [65] ) were measured from rectal cells at weeks −2 , 6 , and 22 . We focused on these responses given that we have shown that these mucosal T cell responses are associated with control of HIV replication in controllers [36] . For T cell immunophenotyping of freshly isolated rectal cells , similar methods were used as for PBMCs [59] . For CFC , freshly isolated rectal cells were rested overnight at 37C , 5%CO2 , in R15 containing 0 . 5 mg/mL piperacillin-tazobactam , then similar methods were used as for PBMCs [59] . To account for the lower numbers of events and elevated baseline cytokine staining in mucosal samples , response data from peptide-stimulated wells were first compared against unstimulated controls using a published algorithm to determine statistical significance , prior to background subtraction [36] , [66] . Mixed effect linear models with random slopes and intercepts were used to examine change in virologic and immunologic measurements over time . Changes in integrated HIV DNA levels were assessed by estimating the mean change and its bias-corrected and accelerated non-parametric confidence intervals , and using a paired t-test to obtain a corresponding p-value [67] . All statistical analyses were conducted with Stata version 11 . 1 ( Stata Corp ) . | HIV-infected “controllers” are rare individuals who are HIV-seropositive but are able to maintain low levels of plasma HIV RNA in the absence of antiretroviral therapy ( ART ) . There has been intense interest in characterizing these unique individuals because they have been considered as a potential model for a “functional cure” of HIV . Previously , our group has shown that controllers have elevated levels of T cell activation and accelerated atherosclerosis , suggesting that very low levels of viral replication may lead to disproportionately high levels of immune activation . However , the degree to which viral replication contributes to these outcomes is not known . We therefore conducted the first , prospective study of ART initiation in a cohort of asymptomatic HIV-infected controllers , in order to determine the virologic and immunologic effects of treating controllers with ART . Controllers had a significant decreases in ultrasensitive plasma HIV RNA , rectal HIV RNA , and markers of T cell activation/dysfunction in blood and gut mucosa with ART . Similar reductions were observed in the subset of “elite” controllers with extremely low pre-ART plasma HIV RNA levels ( <40 copies/mL ) . These data suggest that HIV replication persists in controllers and contributes to a chronic inflammatory state . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | Prospective Antiretroviral Treatment of Asymptomatic, HIV-1 Infected Controllers |
Intracellular Salmonella enterica serovar Typhimurium ( STM ) deploy the Salmonella Pathogenicity Island 2-encoded type III secretion system ( SPI2-T3SS ) for the massive remodeling of the endosomal system for host cells . This activity results in formation of an extensive interconnected tubular network of Salmonella-induced filaments ( SIFs ) connected to the Salmonella-containing vacuole ( SCV ) . Such network is absent in cells infected with SPI2-T3SS-deficient mutant strains such as ΔssaV . A tubular network with reduced dimensions is formed if SPI2-T3SS effector protein SseF is absent . Previous single cell live microscopy-based analyses revealed that intracellular proliferation of STM is directly correlated to the ability to transform the host cell endosomal system into a complex tubular network . This network may also abrogate host defense mechanisms such as delivery of antimicrobial effectors to the SCV . To test the role of SIFs in STM patho-metabolism , we performed quantitative comparative proteomics of STM recovered from infected murine macrophages . We infected RAW264 . 7 cells with STM wild type ( WT ) , ΔsseF or ΔssaV strains , recovered bacteria 12 h after infection and determined proteome compositions . Increased numbers of proteins characteristic for nutritional starvation were detected in STM ΔsseF and ΔssaV compared to WT . In addition , STM ΔssaV , but not ΔsseF showed signatures of increased exposure to stress by antimicrobial defenses , in particular reactive oxygen species , of the host cells . The proteomics analyses presented here support and extend the role of SIFs for the intracellular lifestyle of STM . We conclude that efficient manipulation of the host cell endosomal system by effector proteins of the SPI2-T3SS contributes to nutrition , as well as to resistance against antimicrobial host defense mechanisms .
Salmonella enterica is an invasive , facultative intracellular pathogen causing frequent infection of humans and animal hosts that range from gastroenteritis to typhoid fever , a systemic infection caused by human-restricted S . enterica serovars such as Typhi . S . enterica serovar Typhimurium ( STM ) is causative agent of gastroenteritis in humans and causes systemic infection in susceptible mouse lines . The intracellular lifestyle of STM has been intensively studied , and prior work revealed that this pathogen inhabits a unique membrane-bound compartment , that is referred to as Salmonella-containing vacuole or SCV [1] . The SCV has certain features of a late endosomal compartment . However , virulence factors of STM converted the SCV into an intracellular habitat that is permissive for intracellular survival and replication . A remarkable phenotype induced by intracellular Salmonella is the massive remodeling of the endosomal system of the host cell . In addition to the recruitment of various vesicles to the SCV , the massive formation of tubular membrane compartments was observed [2] . Collectively , these compartments are referred to as Salmonella-induced tubules ( SIT ) [3 , 4] . One subset of SIT , termed Salmonella-induced filaments or SIFs , is characterized by the presence of late endosomal/lysosomal markers such as lysosomal glycoproteins ( lgp , such as LAMP1 ) . Inside the SCV , STM metabolism is altered and global changes in gene expression were observed [5] . One key factor is the SsrAB virulon [6 , 7] , a large group of genes located in Salmonella Pathogenicity Island 2 ( SPI2 ) encoding a type III secretion system ( T3SS ) , a subset of effector proteins and their chaperones , the SsrAB two-component system , and various genes outside of SPI2 encoding effector proteins of the SPI2-T3SS [8 , 9] . Mutant strains defective in the SPI2-T3SS are highly attenuated in systemic disease in the murine model , as well as in intracellular survival and proliferation . Induction of SIFs strictly depends on the active translocation of SPI2-T3SS effector proteins . Mutant strains defective in the SPI2-T3SS lack SIF formation [4] . Ultrastructural analyses revealed that SIFs induced by WT STM are composed of two membranes with ’tubule within tubule’ architecture ( double membrane SIF , dm SIF ) , while SIFs induced by STM ΔsseF have thinner diameter and are composed of one membrane with single tubule architecture ( single membrane SIF , sm SIF ) [10] . An effector protein with central role in SIF formation is SifA . SifA-deficient STM not only lack SIF formation [11] , but also show defects in maintaining the integrity of the SCV [12] , resulting in increased release of bacteria into the host cell cytosol . Mutant strains lacking effector proteins SseF and SseG maintain intact SCV and show identical phenotypes including reduced intracellular proliferation and formation of aberrant SIFs [13] . We recently demonstrated that SIFs contribute to nutrition of intracellular STM within the SCV [14] . Connection of the SCV to the extensive network of tubular membrane compartments provides access to endosomal cargo and allows rapid proliferation of STM WT within the SCV . STM without SIF formation are excluded from this form of nutritional supply . We also demonstrated that lack of SseF results in reduced access to endosomal cargo [14] . Thus , we hypothesize that the nutritional conditions of intracellular STM WT are distinct from STM unable to induce SIF formation ( e . g . STM ΔssaV ) , or STM with reduced induction of SIFs ( e . g . STM ΔsseF ) . Various antimicrobial factors act on STM in the SCV , for example reactive oxygen species ( ROS ) and/ or reactive nitrogen species ( RNS ) , lysosomal hydrolases or antimicrobial peptides [reviewed in 15 , 16] . The connection of the SCV to a SIF network may increase exchange of compounds delivered to the SCV with luminal content of SIFs . Such exchange could reduce the concentration of antimicrobial host factors . Thus , we hypothesize that STM strains unable to induce SIF formation or showing altered architecture of SIFs are exposed to a higher degree to stress induced by antimicrobial host defenses . To test these hypotheses , we performed proteome analyses of intracellular STM recovered from macrophages . We performed infection with STM WT , ΔssaV , or ΔsseF for comparison of the strains with full capability to induce SIFs , absence of SIF formation , or formation of aberrant SIFs , respectively . Proteome analyses indicate individual signatures of these strains and support distinct nutritional conditions and exposure to host cell defense mechanisms as a function of SIF formation .
Previous studies demonstrated the contribution of SIFs to the nutritional supply of intracellular STM and additional function in dilution of antimicrobial compounds was assumed [14] . In order to test these hypotheses , we performed a proteomic analysis of STM with different SIF phenotypes , i . e . STM WT , ΔsseF , and ΔssaV , isolated from RAW264 . 7 macrophages . For each biological replicate , we isolated 4 x 108 to 1 . 9 x 109 bacteria and detected 975 , 884 and 766 proteins for STM WT , ΔssaV , and ΔsseF strains , respectively . In total 1 , 307 distinct proteins were identified , of which 556 proteins were in common between the three strains tested ( see Fig 1 , S1 Table ) . We detected the highest overlap between WT and STM ΔssaV , with 678 proteins in common , whereas proteomes of WT and STM ΔsseF shared 606 proteins . Uniquely identified were 249 , 137 and 91 proteins for STM WT , ΔssaV , and ΔsseF strains , respectively . To test if the proteome profiles are the response to an altered intracellular environment rather than caused by the mutation , we analyzed the proteome of STM WT and ΔsseF after growth under conditions inducing the SsrAB regulon [17] . Cultures were grown in PCN minimal medium to the stage of transition into stationary phase and protein content was profiled using data-independent mass spectrometry . Altogether , 934 proteins were identified and quantified in both strains ( S2 Table ) . No significant differences in the abundance of these proteins were detected between the in vitro grown STM WT and ΔsseF . Thus , the mutation had no discernible effects during in vitro growth in defined synthetic medium . With these data sets for reference , we proceeded to analyze the proteome profiles of WT and STM ΔsseF , isolated from RAW264 . 7 macrophages . We first analyzed the transition from culture in defined synthetic medium to conditions within macrophages . A total of 242 proteins were found to be differentially abundant in STM WT from macrophages compared to growth in PCN , indicating a significant response to the intracellular environment ( S3 Table ) . More importantly , we observed a clear induction of pathways and regulons previously associated with the intracellular lifestyle of STM . Examples include elements of the PhoPQ , OmpR , Fur , Hfq , YdcR and SlyA regulons . Thus , our data are in excellent agreement with previous studies [18–24] . Interestingly , for 115 proteins the ΔsseF strain showed a similar tendency of increase during macrophage infection as for WT . These proteins likely form the core of proteins involved in the adaptation to intracellular growth . Among these proteins are again well known infection-associated proteins such as SlyA/B , PhoP/N , Fur , Hfq , YdcR , SodC1 and PntA . However , almost an equal amount of proteins ( 127 and 123 in STM WT and ΔsseF , respectively ) were either only differentially increased in STM WT or ΔsseF , or were regulated in different directions ( i . e . increased in STM WT and decreased in STM ΔsseF , or vice versa ) . These large differences suggest that lack of SseF creates a somewhat altered intracellular environment , necessitating specific adaptations . This is evidenced by the reduced intracellular replication of STM ΔsseF and even lower of STM ΔssaV in epithelial cells [25] and macrophages [13] . Since this reduced intracellular proliferation coincides with an aberrant SIF formation for STM ΔsseF , or in case of STM ΔssaV , the loss of SIFs , the question arises whether the proteomic changes can help to pinpoint the cause of these growth retardations . Potentially , the alteration of the STM-containing compartment due to the lack of SseF and SsaV , respectively , could limit nutrient availability and/or have a higher presence of stressors compared to fully formed SIFs in the WT . To this end , we classified the surveyed proteomes of WT as well as both mutants according the Clusters of Orthologous Groups ( COG ) schemes and determined the relative protein abundance for each category . To account for different protein yields due to growth conditions as well as variability in injection , we normalized abundance values to the total protein abundance measured for each sample injection . We focused on metabolic functions as these provide the most insight into the availability of nutrients for the respective strains during intracellular growth and extracted proteins involved in carbon , lipid , amino acid and inorganic ion transport and metabolism . We considered transport and metabolic enzymes in each category separately in order to get a better view on which nutrients may be available for each respective strain . The COG categorization of transporters showed an increase of overall transporter abundance in STM ΔsseF ( 7 . 89 relative abundance , RA ) and STM ΔssaV ( 7 . 7 RA ) compared to WT ( 6 . 4 RA ) . Looking closer at the different COG categories ( Fig 2 ) , it becomes apparent that the largest amount of transporters is involved in inorganic ion transport in all three strains , with STM ΔsseF exhibiting a larger abundance than the other strains . For amino acid and lipid transport both mutants exhibited a higher abundance of transporters than the WT , but lower abundance for carbohydrate transport . An analysis of metabolism-related proteins revealed that overall STM ΔssaV had the highest abundance of metabolism-related proteins ( 22 RA ) compared to STM ΔsseF ( 16 RA ) and WT ( 18 RA ) . The increase is mostly driven by increased proteins in the category of inorganic ion and lipid metabolism . It should be noted that proteins characterized as inorganic ion metabolism according to COG are in fact stress-related proteins , such as the superoxide dismutase SodA ( increased 4 . 0-fold in STM ΔssaV , 2 . 2-fold in STM ΔsseF ) or the unspecific DNA-binding protein Dps , which is involved in protecting the DNA during starvation and oxidative stress [26] . Enzymes involved in lipid metabolism were increased in both mutants ( Fig 2 ) . For example , the PhoPQ-regulated non-specific acid phosphatase was elevated in both mutants , an indication of reduced phosphate availability in the mutants compared to the WT . Even though amino acid transport was elevated in both mutants , amino acid metabolism was mostly reduced . Relative protein abundances of proteins involved in carbon metabolism were unexpectedly similar for WT and ΔssaV ( 8 . 77 and 8 . 8 RA , respectively ) and reduced for ΔsseF ( 6 . 87 RA ) . This overview demonstrated significant changes in the abundance of metabolic functions indicative of different growth situations in the examined strains . While in both mutants there are signatures of increased phosphate limitation compared to WT , there are also differences especially related to stress responses and carbohydrate- and amino acid metabolism , as well as membrane-lipid metabolism . Taken together , the data indicate the overall reduced activity of STM ΔsseF , whereas in STM ΔssaV a number of metabolic shifts are connected to adaptation to stress . We hypothesized that SIF biogenesis decreases host defense-mediated stress on STM in the SCV and anticipated stronger stress response for STM ΔssaV and an intermediate level for STM ΔsseF . Proteins detected for all strains were classified according to Gene ontology ( biological process ) and filtered for the group ‘response to stress’ . Considering the relative protein amount of stress-related proteins we obtained similar values for STM WT and ΔsseF ( 5 . 1 and 5 . 3 RA ) , but elevated levels for STM ΔssaV ( 7 . 96 RA ) , indicating an increased stress response for STM ΔssaV ( Fig 3A ) . To determine if infection of macrophages by STM WT , ΔsseF and ΔssaV results in similar or distinct cellular response , we analyzed the generation of ROS . Dihydrorhodamine 123 ( DHR 123 ) is a cell-permeable dye that is oxidized by hydrogen peroxide or peroxynitrite to rhodamine 123 [27] . The resulting increase in green fluorescence can be quantified by flow cytometry on a single cell level . Infection or RAW264 . 7 cells by STM WT lead to increased rhodamine 123 fluorescence with about twofold increase of x-median relative fluorescence units ( S1A and S1B Fig ) . We observed that the relative amount of ROS in infected cells was independent of the strain used . Infection of cells and addition of diphenylene-iodonium chloride ( DPI ) , an inhibitor of ROS generation by NADPH oxidase and other enzymes [28] , resulted in signal intensities as low as in non-infected cells . Infection with STM WT , ΔsseF , or ΔssaV resulted in the same increase of rhodamine 123 fluorescence intensities ( S1C and S1D Fig ) . We conclude that STM infection of macrophages stimulates ROS production regardless of the function of the SPI2-T3SS and effector SseF . Next , we focused on the individual proteins and their abundance ( see Table 1 ) . Significantly more abundant in STM ΔsseF were DegP ( = Ptd or HtrA , periplasmic serine endoprotease; 2 . 91-fold ) , Dps ( DNA protection during starvation protein; 2 . 58-fold ) , HtpG ( chaperone protein; 1 . 63-fold ) and Ssb ( single-stranded DNA binding protein 1; 1 . 59-fold ) , indicating an enhanced response towards oxidative stress and misfolded proteins . Proteins Lon , TrxB , ClpB , AhpC , RecA are involved in similar mechanisms and were less abundant in STM ΔsseF compared to WT ( 1 . 77- to 4 . 97-fold lower ) . We observed a different pattern for the comparison of STM WT and ΔssaV . Seven proteins were significantly more abundant in the mutant strain , whereby SodA ( superoxide dismutase A ) was the strongest induced stress-related protein ( 4 . 0-fold ) . Beside Dps ( 2 . 55-fold ) and DegP ( = Ptd or HtrA; 2 . 79-fold ) , which were induced in STM ΔsseF as well , STM ΔssaV showed a higher level of universal stress proteins E ( 2 . 66-fold ) and G ( 2 . 28-fold ) , trehalase A ( 2 . 32-fold ) and YqjD ( 1 . 81-fold ) , a protein localizing ribosomes to the membrane during stationary phase in E . coli [29] ( see Fig 3B ) . Four proteins were significantly down-regulated , of which three were also more abundant in WT compared to STM ΔsseF ( Lon , ClpB , RecA; 1 . 7- to 3 . 15-fold ) . Additionally , the chaperone DnaJ was detected in a higher abundance for WT compared to STM ΔssaV ( 2 . 12-fold ) . We conclude that dm SIFs induced by STM WT and sm SIFs induced by STM ΔsseF both result in reduced stress exposure of intracellular STM , while lack of SPI2-T3SS effector protein translocation and SIF biogenesis result in increased exposure to hostile intracellular environments . To further corroborate the role of the manipulation of the host cell endosomal system and the degree of stress exposure and response by intracellular STM , we deployed dual color fluorescence protein reporter analyses . The encoded proteins were identified directly , or as part of a response regulon , as increased in intracellular STM ΔsseF and ΔssaV compared to STM WT . trxA encodes cytoplasmic thioredoxin , an enzyme reducing cysteine after radical-induced oxidization , msrA encodes a methionine reductase required for repair of damaged methionine , and treA encodes a trehalase that supports adaptation to osmotic stress . The promoters of these genes were used to generate sfGFP reporter fusions on plasmids that also encoded constitutively expressed DsRed . We analyzed the expression of the various reporter fusions in STM WT , ΔsseF and ΔssaV background on the level of single intracellular bacteria recovered 12 h after infection from RAW264 . 7 macrophages ( S2 Fig ) . For comparison , the expression levels were determined for bacterial strains grown o/n in LB for preparation of the inoculum . In WT , ΔsseF , and ΔssaV , expression of msrA and trxA reporters was highly induced in intracellular bacteria compared to the inoculum ( Fig 4 ) . Expression levels of the treA reporter did not increase in intracellular bacteria , indicating the similar stress levels are present under extracellular and intracellular conditions . With focus on expression by intracellular STM , we observed that signals of all three reporters was highest in STM ΔssaV , while expression in ΔsseF background was increased compared to WT , but lower than in STM ΔssaV . These results obtained for single intracellular bacteria indicate the increased expression of stress response mechanisms in mutant strains with aberrant or absent ability in manipulation of the host endosomal system . This induction may reflect the increased exposure to host cell defense mechanisms such as ROS generation . We assessed the effect of ROS generation on intracellular proliferation of STM WT , ΔssaV and ΔsseF in RAW264 . 7 macrophages ( S3 Fig ) . After infection , cells were treated with phenylarsine oxide ( PAO ) or DPI , or left untreated . While intracellular proliferation of STM ΔsseF and ΔssaV was reduced compared to STM WT in non-treated cells , the inhibition of ROS generation fully abrogated the defects of the mutant strains ( S3 Fig ) . Our data indicate that STM infection of macrophages induces ROS generation ( S1 Fig ) , and ROS affect intracellular survival and replication of STM ΔsseF and ΔssaV more severely than STM WT . Recent work demonstrated that SIFs have a critical role in the intracellular nutrition of STM [14] . As the analysis of RA regarding proteins involved in metabolism led to the unexpected results , we focused on differentially regulated proteins involved in the central carbon and amino acid metabolism of STM WT , ΔsseF and ΔssaV and classified the identified proteins according to the Kyoto Encyclopedia of Genes and Genome ( KEGG ) [30] ( Fig 5 ) . 19 proteins of the carbon and amino acid ( AA ) metabolism had lower levels in ΔsseF compared to WT STM . Similar tendencies were obtained for STM ΔssaV . We analyzed if central carbon metabolism ( CCM ) including glycolysis , pentose phosphate pathway ( PPP ) and TCA cycle was modulated in STM ΔsseF and ΔssaV . Both mutant strains showed a high number of proteins with reduced abundance of all CCM pathways compared to STM WT ( Fig 6 , S4 Fig ) , as well as reduced AA metabolism ( Fig 7 , S5 Fig ) . Many enzymes were only identified for STM WT , indicating very low levels in STM ΔsseF . Especially proteins involved in synthesis of tryptophan , tyrosine and phenylalanine were only found in STM WT . To validate the down-regulation of AA metabolism , we performed qPCR analyses . RNA of pooled bacterial pellets from large-scale infection experiments with STM WT and the ΔsseF strain was isolated . RNA isolated 12 h p . i . did not reveal differential expression , using RNA from STM isolated 10 h p . i . accompanied our proteomic data . Results of qPCR of glyA ( serine and glycine metabolism ) confirmed proteomics data ( S6 Fig ) . Whereas a high number of proteins involved in CCM and AA metabolism were reduced in their abundance in STM ΔsseF compared to WT , specific proteins were up-regulated in a significant manner: GpmA ( phosphoglyceromutase ) was slightly increased ( 1 . 38-fold ) , and CysK ( cysteine synthase A ) was 1 . 8-fold more abundant . Interestingly , these proteins were up-regulated even higher in STM ΔssaV compared to WT , i . e . 1 . 7- and 2 . 56-fold for GpmA and CysK , respectively . Thus , we anticipate regulation mechanisms and roles of these two enzymes during nutrient limitation to be distinct from all other metabolic proteins detected in our approach . Successful adaptation of STM to intracellular life demands expression of transporters for access to host-derived nutrients [5] . As analyses of RA demonstrated increased and decreased amounts of proteins involved in amino acid and carbohydrate transport , respectively , in the mutant strains , we interrogated our proteomic data for presence of subunits of ABC transporters and phosphotransferase systems ( PTS ) with significantly changed amounts in STM WT , ΔsseF and ΔssaV ( Fig 8 , S7 Fig ) . We detected significantly enhanced levels of eight ABC transporter subunits in ΔsseF compared to STM WT ( 1 . 41- to 3 . 19-fold ) . These transporters are responsible for the uptake of amino acids ( arginine , cysteine , histidine and methionine ) , ions ( molybdate , putrescine , spermidine ) , and monosaccharides ( sn-glycerol-3-P ) . If including proteins only detected in STM ΔsseF , or with non-significantly increased levels compared to STM WT , 24 ABC transporter subunits were identified . ABC transporter subunits with significantly less abundant levels were not detected for STM ΔsseF . 16 proteins for ABC transporter subunits were only detected in STM WT or had non-significantly lower levels in STM ΔsseF . We confirmed the induction of two subunits of ABC transporters for methionine and arginine , i . e . metQ and artJ , by qPCR ( S6 Fig ) . For PTS , we observed in STM ΔsseF significantly reduced abundance of PtsI and MtlA that are important for uptake of phosphoenolpyruvate ( PEP ) and mannitol , respectively . Furthermore , transporters involved in uptake of glucose ( PtsH ) or galactitol ( GatB ) were only detected in the mutant strain . PtsG and PtsN were increased in the proteomic data set in a non-significant manner . Crr , important for the uptake of several members of the glucose family ( glucose , maltose , trehalose , glucosides and N-acetyl-muramic acid ) was increased by a factor of 1 . 38 . Regarding levels of ABC transporter and PTS systems , STM ΔssaV showed a pattern similar to STM ΔsseF ( see S7 Fig ) . The abundance of 23 ABC transporter subunits was increased in STM ΔssaV compared to WT , even though only significantly different for three proteins . Furthermore , we determined higher levels of 6 PTS subunits , including significantly increased amounts of Crr ( 2 . 01-fold ) ( S7 Fig ) . Taken together STM ΔsseF and ΔssaV showed increased levels of proteins involved in the transport and incorporation of nutrients important for intracellular growth .
STM is a ‘metabolic all-rounder’ [31 , 32] with the ability to use a wide range of C-sources during life inside host cells [5 , 32] . Previous results obtained from proteomic analysis of intracellular STM isolated from RAW264 . 7 macrophages , epithelial cell lines and mouse spleen indicated that a broad range of metabolic pathways is active [5 , 33–35] . Our analyses also detected in STM WT proteins of all central pathways ( S8 Fig ) and indicated that our infection models and proteomic analyses are comparable to prior results . The reduced abundance of proteins involved in metabolic processes in STM ΔsseF and ΔssaV indicates a global down-regulation of AA metabolism and CCM . Former work suggested that remodeling of the host cell endosomal system requires function of SsaV and SseF , and that this remodeling is important for the intracellular nutrition of STM [14 , 36] . Thus , the global down-regulation of metabolic pathways indicates nutritional limitations for STM ΔsseF and ΔssaV . The first response to limited amounts of C-sources such as glucose and AA are increasing concentrations of cAMP and the alarmones pppGpp and ppGpp ( further referred to as ( p ) ppGpp ) . The cAMP-CRP complex activates the expression of many enzymes involved in the CCM and represses expression of CRP [37] . The reduced abundance of CRP in STM ΔsseF ( 2 . 22-fold , see S1 Table ) thus is an indicator for cAMP-CRP regulation at the onset of starvation . The reduction of CRP in STM ΔssaV was less pronounced and this may indicate the different time course of nutritional limitation , and for the ΔssaV strain onset of starvation is expected much earlier than 12 h p . i , the time point for sampling for proteomics . In the course of stringent response , RelA and SpoT synthesize ( p ) ppGpp . High ( p ) ppGpp levels lead to up-regulation of AA , carbon , and lipid metabolism , and down-regulation of translation machinery [38] . However , in E . coli suffering C-starvation , the cAMP level is rising only in the first hour , and later it decreases [39] . The same observation has been made for ( p ) ppGpp levels . If enhanced biosynthesis and proteolytic degradation do not restore AA levels , ( p ) ppGpp levels decrease [40 , 41] . The nutrient deprivation of STM ΔssaV and ΔsseF would lead to down-regulation of metabolic proteins , with early or late onset in ΔssaV and ΔsseF , respectively . A third metabolic adaptation observed for STM ΔssaV and STM ΔsseF is the increased abundance of high-affinity ABC transporters that are expressed if specific AA are limiting [42 , 43] . These transporters are not known to be regulated by ( p ) ppGpp , but by distinct regulators such as ArgR and MetR . The metabolic adaptations of STM ΔssaV and ΔsseF , i . e . down-regulation of AA metabolism and CCM , and up-regulation of high-affinity transporters , are characteristics of response to limiting nutrient availability . The reduction of metabolic activity and translation occurs in order to save remaining energy and peptide resources for adaptation to long periods of starvation , while upregulation of ABC transporters allows access to external nutrient pools , if environmental conditions become more favorable [44 , 45] . Thus , we expect STM ΔssaV and ΔsseF to undergo starvation and induction of the starvation stress response ( SSR ) during presence in the SCV in macrophages . In contrast , the induction of SIFs with large interconnected volume and double membrane architecture of tubules by STM WT leads to sufficient nutritional supply of bacteria in the SCV . The intracellular nutrition strategy of STM is the induction of vesicle fusions to the SCV and the formation of the extensive interconnected network of SIFs [14] . Other pathogens such as C . trachomatis also highjack host cells endosomes to obtain nutrients , but the resulting PCV exhibits distinct features , such as inclusion , a single PCV with large number of C . trachomatis cells . Complementary to the role of SIF formation for the growth of intracellular STM previously reported on a single cell level [14] , this work shows , by a population-based approach , the bacterial response to different degrees of nutritional limitation . Both STM ΔssaV , unable to induce SIF formation , and STM ΔsseF , inducing SIF network with reduced volumes show proteomic signatures of nutrient starvation and the compensatory increase of uptake systems for limiting nutrients . The combination of SIF biogenesis and the metabolic flexibility of STM allow proliferation in host cells , such as macrophages , for long periods of time . If host cell nutrients are exploited and a maximal number of intracellular bacteria are reached , escape from host cells with the lowest level of damage by host defense mechanisms occurs , although the molecular mechanisms of such ‘exit strategies’ are not known in detail . Another part of the virulence strategy of STM is the formation of persisters upon nutritional limitation and exposure to antimicrobial defense mechanisms [46] . This ability allows STM intracellular survival , possibly spread within hosts , and reentering rounds of proliferation once growth restriction is released . Although the metabolic status of STM ΔsseF and ΔssaV appears to be similar under the conditions tested , intracellular proliferation of STM ΔssaV is highly reduced compared to STM ΔsseF . Action of the SPI2-T3SS reduces exposure of STM to antimicrobial host functions by redirection of NADPH oxidase [47–49] , iNOS [50] , and reduced delivery of active cathepsins [51] . In addition , the formation of a tubular SIF network was suggested to reduce exposure of STM in the SCV to antimicrobial effector mechanisms of the host cell [14] . This function could result from dilution of antimicrobial effectors in the SCV if connections to the SIF network are established . Our proteomic analyses indicate strongly enhanced abundance of proteins related to stress response in STM ΔssaV , while STM ΔsseF and WT exhibited lower levels with similar patterns . The ability to cope with attacks of antimicrobial defense systems of the host is indispensable for successful intracellular survival and replication , especially for pathogens residing in macrophages , being exposed to high levels of reactive oxygen and nitrogen species ( ROS , RNS ) . STM and other intracellular pathogens have evolved various strategies to counteract antimicrobial compounds such as ROS . Upregulation of universal stress proteins , catalases , SODs and alkyl hydroperoxide reductase under oxidative stress was demonstrated for L . monocytogenes [52] , mycobacteria [53] , F . tularensis [54] , L . pneumophila [55] , STM [56] and others . The inability to neutralize various harmful compounds often leads to attenuation . Deletion of genes of the kai operon involved in stress response of L . pneumophila , led to reduced growth in Acanthamoeba castellanii [57] . hfq deletion represses stress response regulators in Shigella spp . and led to attenuation [58] . Deletion of OxyR , a key regulator for the response to oxidative stress also in STM , and catalase G leads to attenuation of F . tularensis in the murine infection model [59] . STM defective in the ABC efflux pump MacAB has a strongly decreased replication rate in J774 . 16 macrophages [60] and a mutant strain defective in methionine sulfoxide reductase shows reduced proliferation in interferon γ-activated RAW264 . 7 macrophages [61] . Thus , the response to harmful conditions is an important virulence function of bacteria like STM . Several studies focused on the importance of SPI2-T3SS activity for survival of the oxidative burst in immune cells . The SPI2-T3SS-dependent evasion of NADPH oxidase was reported [47] , and explained by interference with assembly of functional NADPH-oxidase on the SCV by function of the SPI2-T3SS [48 , 49] . Aussel et al . [56] used a ROS-dependent reporter in STM and did not observe an effect of SPI2-T3SS on ROS exposure by STM in macrophages . In contrast , van der Heijden et al . [62] demonstrated increased redox stress of a mutant strain deficient in SPI2-T3SS subunit SsaR in THP-1 cells . ROS damage DNA in intracellular STM and defects in base-excision repair system nth/nei resulted in decreased intracellular survival comparable to the attenuation of the SPI2-T3SS-deficient STM [63] . Recognition of outer membrane proteins by sensor SCAM was shown critical for activation of NADPH oxidase in macrophages , the STM WT and a SPI2-T3SS translocon-deficient strain stimulated similar activation [64] . Our data are in line with a model that similar stimulation of ROS is induced by STM WT and mutant stains in ssaV or sseF . If SPI2-T3SS-dependent redirection of vesicular transport and remodeling of the endosomal system is initiated , the exposure of STM to ROS is reduced and ROS-induced damages are ablated . Thus , interpretation of effects of ROS should consider the dynamics of the host-pathogen interplay . While stimulation and initial effects of defense mechanisms may not be controlled by STM , the later exposure to ROS is affected by the manipulation of the host cell . Liss et al . calculated that induction of sm SIFs led to a luminal volume of the SIF network of about 50% of that of dm SIFs [14] . The data reported here indicate that proteomes of STM ΔsseF have the same abundance of stress related proteins as WT [14] . However , the luminal content of WT in SCV without connection to SIFs was calculated to be 34-fold decreased compared to STM ΔsseF inducing sm SIFs [14] , thus sm SIFs could already lead to sufficient dilution of antimicrobial activities . We hypothesize that the increased stress level STM ΔssaV is exposed to results from both , the disability to avoid recruitment of iNOS and NADPH oxidase to the SCV membrane , and the strongly reduced volume of the continuum STM ΔssaV resides in , leading to reduced luminal interchange and dilution . At the current state of art , proteomics of intracellular pathogens is not possible on single cell level . Rather , amounts of 109 recovered STM cells were required to reach sufficient coverage and reproducibility in quantitative proteomics . It is important to consider that population-based analyses are generally affected by the heterogeneity of phenotypes of intracellular STM . For example , STM WT may form actively replicating subpopulations , while other individual cells enter a persister state or fail to activate the proper set of virulence factors and are killed by the host cell [46 , 65] . We anticipate that proteomic profiles contain mixtures of different subpopulations resulting in leveling of protein amounts . In turn , the differences in amounts of proteins involved in nutrition or stress response may actually be more pronounced in specific subpopulations . Thus , future analyses should interrogate the levels of candidate proteins in correlation with the physiological state on a single cell level . Single cell analyses will also be important to understand the orchestration of metabolic adaptation and stress management of intracellular STM by means of SIF formation . Such analyses could involve the time-resolved analyses of reporter activities for individual genes , or global analyses such as RNA-seq on single cell level [65] . In conclusion , based on our proteomics datasets , we propose that i ) induction of SIFs , but also the formation of dm SIFs is required for sufficient nutritional supply , ii ) that sm SIFs and dm SIFs are sufficient to cope with the antimicrobial activities of the host cell , and iii ) that nutritional limitations reduce the replication of STM ΔsseF slightly , whereas the additional inability to neutralize host defense mechanisms leads to the strongly attenuated phenotype of STM ΔssaV ( summarized in Fig 9 ) . Efficient manipulation of the host cell endosomal system by SPI2-T3SS effector proteins contributes to nutrition as well as to resistance against antimicrobial host defense mechanisms . Proteomics analyses presented here support and extend the previously proposed role of SIF formation for the intracellular lifestyle of STM in mammalian host cells .
Salmonella enterica serovar Typhimurium NCTC12023 was used as wild-type strain ( WT ) . Isogenic mutant strains MvP1890 ( ΔssaV::FRT , defective in SPI2-T3SS apparatus ) and MvP1980 ( ΔsseF::FRT , defective in SPI2-T3SS effector protein SseF ) were constructed by λ Red-mediated mutagenesis [66] . Primers required for mutagenesis , removal of resistance cassettes and control of proper insertion are listed in S4 Table . Transfer of mutant alleles into fresh strain background was mediated by P22 transduction , and aph resistance cassettes were removed by FLP-mediated recombination as described [36] . Bacterial strains were routinely cultured in LB Luria broth at 37°C overnight ( o/n ) using a roller drum at 60 rpm . STM WT and ΔsseF were grown o/n in minimal medium ( PCN minimal medium , pH 7 . 4 , 1 mM PO4- ) [36] . Cells of 1 ml culture were pelleted by centrifugation and washed thrice with SPI2-inducing medium ( PCN minimal medium , pH 5 . 8 , 0 . 4 mM PO4- ) . The pellet was resuspended in SPI2-inducing medium and the optical density was determined . 25 ml SPI2-inducing medium was inoculated to an OD600 of 0 . 01 . Bacteria were cultured in a shaking water bath at 37°C for 5 h . OD600 was determined and 6 x 109 bacteria were pelleted by centrifugation . Supernatant was removed , and the bacterial pellets frozen in liquid nitrogen . To gain sufficient amounts of bacteria from infected host cells , infection parameters described by Popp et al . [36] were adjusted . Increased MOI , infection times , and centrifugal fields were compared in gentamicin protection assays , before being applied to proteome profiling of intracellular STM ( see S9 Fig ) . Modifications of the protocol did not affect SPI2-dependent replication . Finally , for proteomics analyses 8 x 106 RAW264 . 7 macrophages ( obtained from CLS Heidelberg , Germany ) per flask were seeded into 16 cell culture flasks ( 75 cm2 , TPP ) one day before infection , or at 4 x 106 cells per flask two days before infection . RAW264 . 7 macrophages were infected with a MOI of 25 with STM strains . Infection assays were centrifuged at 1 , 250 x g for 5 min . , infection proceeded for 45 min . at 37°C in an atmosphere of 5% CO2 . Infected cells were washed thrice with PBS and remaining extracellular bacteria were killed by incubation in cell culture medium containing 100 μg x ml-1 gentamicin for 1 h . Afterwards , the medium was exchanged to cell culture medium containing 10 μg x ml-1 gentamicin for further 11 h . During isolation of intracellular bacteria , samples were stored continuously on ice and centrifugation occurred at 4°C . The infected cells were washed thrice with pre-warmed PBS . Cells were lysed by addition of 5 ml 1% Triton X-100 in PBS for 10 min . at 4°C . Cell lysates were pooled and centrifuged for 5 min . at 300 x g . The supernatant was centrifuged again for 10 min . at 20 , 000 x g . Pelleted bacteria were resuspended in PBS containing 0 . 05% SDS and 0 . 5% desoxycholate and centrifuged again for 10 min . at 20 , 000 x g . The washing step was repeated , and the pellet resuspended in 1 ml PBS . Bacteria were pelleted again for 10 min . at 20 , 000 x g , the supernatant discarded , and samples were stored at -80°C . Proteins from bacteria were isolated using Trizol ( Thermo Fisher Scientific ) , following the manufacturers protocol . Bacterial pellets were therefore resuspended in 750 μl Trizol and incubated at RT for 10 min . After addition of 150 μl chloroform and 3 min . incubation at RT , samples were centrifuged at 12 , 000 x g for 15 min . to isolate the organic phase . To remove DNA contamination , 225 μl of 100% ethanol were added to the organic phase . After 3 min . incubation at RT , DNA was pelleted by centrifugation for 5 min . at 2 , 000 x g . Protein-containing supernatant was precipitated with 1 , 125 μl ice-cold isopropyl alcohol and stored at -20°C o/n . Protein precipitates was pelleted by centrifugation at 12 , 000 x g for 10 min . Pellets were subsequently washed twice with 70% ethanol , subsequently rinsed with 100% ethanol before air dried at RT . Afterwards protein pellets were re-suspended in 50 mM ammonium-bicarbonate buffer ( pH 8 . 0 ) containing 1% SDS , before reduced and alkylated with 5 mM DTT and 20 mM iodoacetamide , respectively . Subsequently the samples were rebuffered into 50 mM ammonium-bicarbonate buffer on filter columns ( MWCO 10 kDa , Amicon , Millipore ) and protein amount was determined by Pierce BCA protein assay kit . Protein digest and LC-MS measurement was performed as described in Hansmeier et al . [67] . Briefly , proteins were digested using trypsin gold ( Promega , Madison ) according manufacturers instruction before vacuum dried . Dried digested samples were resuspended in 0 . 1% formic acid and 3% acetonitrile and spiked with rabbit phosphorylase B ( Waters Corporation , Milford , MA ) for quantification before LC-MS analyses . Each sample was analyzed via a Waters NanoAcquity system coupled to a Waters Synapt G2 HDMS . A Waters NanoAcquity UPLC Symmetry C18 trap column ( 180 μm × 20 mm , dp: 5 μm ) was used for desalting and focusing of peptides prior to their elution onto the Waters Acquity UPLC M-class HSS T3 analytical column ( 75 μm × 200 mm , dp: 1 . 8 μm ) using a 120 min . gradient from 3% acetonitrile/0 . 1% formic acid to 45% acetonitrile/0 . 1% formic acid at a flow rate of 0 . 35 μl/min . Eluting peptides were analyzed in positive MSE resolution mode with 1 s scan time . Collision energy was set to constant 4 eV for low energy scans and ramped between 18 and 42 eV for high energy scans . To ensure mass accuracy , leucine enkephaline was measured as lock mass every 30 s . The resulting spectra were processed with the ProteinLynx Global Server ( PLGS ) v . 3 . 02 with Identity ( Waters ) and searched against a protein sequence database build with the Uniprot Salmonella reference proteome ( downloaded March 2015 ) , supplemented with the sequences of the Waters PhosB standard for quantitation . Search parameter were set to mass tolerance 8 ppm , trypsin specificity , one missed cleavage , stable modification carbamidomethyl , variable modification methionine oxidation , false discovery rate 1% . To adjust for variances between injections , the concentration values for each chromatographic run were normalized against the total femtomole of protein quantified per analysis . To determine significant differential protein amounts in STM mutants compared to WT , we employed Student’s t-test and used the Benjamini−Hochberg method to adjust for multiple hypothesis testing . Cultivation and infection of RAW264 . 7 macrophages was performed as above . For inhibition of NADPH oxidase , DPI ( 10 μM final concentration ) was added to the cells with gentamicin from 1 h p . i . to the end of the experiment . At 8 h p . i . , macrophages were recovered and pelleted by centrifugation ( 5 min . with 500 x g at RT ) . Supernatant was discarded and the cells resuspended in 200 μl fresh pre-warmed medium . Relative quantification of ROS in the host cells was performed as described [27] . Briefly , dihydrorhodamine ( DHR ) 123 ( Chemodex; St . Gallen , Switzerland; final concentration 0 . 42 mM ) was added to the tube lid . By short centrifugation , mixing of host cell suspension and DHR 123 solution was synchronized and cells were incubated for 20 min at 37°C . Cells were incubated on ice for 10 min , pelleted by centrifugation ( 5 min . at 500 x g , 4°C ) , supernatant was discarded , and cells fixed with 3% PFA in PBS for 15 min . at 4°C . Cells were recovered by centrifugation and resuspended in Attune Focusing fluid ( Thermo Fisher Scientific ) supplemented with BSA . Samples were directly subjected to flow cytometry on an Attune NxT cytometer and green fluorescence intensities per cell were measured . Macrophages were seeded into 24 well plates at 200 , 000 cells per well ( TPP , Switzerland ) one day before infection . Host cells were infected with STM o/n cultures with a MOI of 1 , and infection was synchronized by centrifugation for 5 min . at 500 x g at RT . After 25 min . of infection , extracellular bacteria were killed by 100 μg x ml-1 gentamicin for 1 h , followed by 10 μg x ml-1 for the remaining period of time . For inhibition of NADPH oxidase , PAO or DPI ( Sigma-Aldrich ) at 0 . 5 and 5 μM , respectively , were added 1 h p . i . 1 and 8 h p . i . Host cells were washed thrice with PBS and lysed by incubation with 0 . 1% Triton-X-100 in PBS for 10 min . on a rocking platform . Lysates were diluted if required and plated on MH agar plates . Colony forming units ( CFU ) of inoculum , and of lysates at 1 and 8 h p . i . were counted and the intracellular replication rate ( x-fold ) was calculated as quotient between CFU ( 1 h ) and CFU ( 8 h ) . For RNA extraction and qPCR , cells were infected as described before and harvested at 10 h p . i . Bacteria were isolated with minor modification as previously described . After washing with PBS containing 0 . 05% SDS and 0 . 5% desoxycholate , bacteria were pelleted by centrifugation . Resulting pellets were resuspended in 1 ml PBS supplemented with 200 μl ice-cold stop solution ( 95% ethanol , 5% phenol , saturated with 0 . 1 M citrate-buffer , pH 4 . 3 ) , before shock-frozen in liquid nitrogen . Afterwards , samples were thawed on ice and bacteria were pelleted by centrifugation at 6 , 344 x g and 4°C for 20 min . RNA was prepared according to the ‘hot phenol’ method [68 , 69] . Pellets of three experiments were pooled for each strain . In brief , cells were lysed using a lysis buffer ( 0 . 5 mg x ml-1 lysozyme ( Sigma-Aldrich ) in TE buffer pH 8 . 0 ( Promega ) with 2% SDS ( Sigma-Aldrich ) ) . Afterwards , samples were buffered with 3 M sodium acetate buffer ( pH 5 . 2 ) ( Life technologies ) , before RNA was extracted with Roti-Aqua phenol ( Roth ) at 64°C . After centrifugation ( 15 . 000 x g , 4°C , 20 min . ) , the watery-phase was loaded on heavy phase lock gel tubes ( 5PRIME GmBH , Hilden ) supplemented with chloroform for further purification . Nucleic acids were precipitated twice with a 30:1 mixture of absolute ethanol and 3 M NaOAc ( pH 5 . 2 ) with o/n incubation steps at -20°C . Pellets were washed with 75% ethanol , air-dried and solved in RNase-free water . Samples were treated with RNase-free DNase I ( NEB ) , before RNA concentrations were determined using a nano-photometer ( Implen ) . cDNA synthesis was performed with the RevertAid First strand cDNA synthesis kit ( Thermo Fisher Scientific ) , using 1 μg RNA and random hexamer primers . qPCR was performed with the Maxima SYBR Green/Fluorescein qPCR Master Mix ( Thermo Fisher Scientific ) using the iCycler with MyiQ module ( BioRad ) . Data were normalized to expression levels of a house-keeping gene ( gapA ) and calculated in consideration of primer efficiencies determined using serial dilutions of cDNA . The qPCR of sseF was used as negative control and internal confirmation of gene deletion . Student’s t-test was used for statistical analyses . Oligonucleotides used in this study are listed in S4 Table . Plasmid p4889 ( PEM7::DsRed PuhpT::sfGFP ) with constitutive expression of DsRed and regulated expression of sfGFP was used as basis vector for the generation of dual color reporter plasmids for flow cytometry analyses . The uhpT promoter was replaced by promoter fragments of treA , msrA , or trxA by Gibson assembly of PCR fragments generated by PCR using primers listed in S4 Table and the resulting dual color reporter plasmids are described in Table 2 . For generation of a plasmid as negative control , a frame shift mutation was introduced in the 5’ region of sfGFP . RAW264 . 7 macrophages were infected with STM WT , ΔsseF or ΔssaV strains harboring various reporter plasmids . Infected cells were lysed 12 h p . i . in order to release intracellular bacteria . After removal of host cell debris by centrifugation for 5 min . at 500 x g , bacteria were recovered from supernatant by centrifugation for 10 min . at 20 , 000 x g , fixed with 3% PFA in PBS , washed and recovered in PBS . Bacteria from the inoculum were directly harvested by centrifugation and fixed as described above ( S2 Fig ) . Flow cytometry was performed on an Attune NxT instrument ( Thermo Fischer Scientific ) at a flow rate of 25 μl x min . -1 . At least 10 , 000 bacteria were gated by virtue of the constitutive DsRed fluorescence . The intensity of the sfGFP fluorescence per gated STM cell was recorded and x-medians for sfGFP intensities were calculated . Proteins were classified according to Gene Ontology ( GO ) and COG classification schemes and mapped onto pathways using KEGG . Transport proteins were extracted from the data file by using queries in protein descriptions using keywords such as “transport” , “transferase” and “import” , followed by manual curation . Protein groups were visualized using STRING . Proteome data sets are available at PeptideAtlas ( http://www . peptideatlas . org/PASS/PASS01301 ) . | The facultative intracellular bacterium Salmonella enterica has evolved sophisticated mechanisms to adapt to life inside a pathogen-containing vacuole in mammalian host cells . Intracellular Salmonella manipulate the host cell endosomal system resulting in formation of a complex network of tubular vesicles , termed Salmonella-induced filaments ( SIFs ) . We applied quantitative proteomics to intracellular Salmonella in murine macrophages and compared the wild-type strain to mutant strains with aberrant SIF architecture , or no capacity for induction of SIF . We determined that those mutant strains contain higher amounts of transporters for nutrient uptake , and lower amounts of proteins for central carbon metabolism . These observations indicate response to nutrient restriction in absence of fully established SIF . In addition , the mutant strain unable to induce SIF formation showed increased amounts of proteins required for response to antimicrobial factors of the host cells . These data show that the massive remodeling of the endosomal system of host cells by intracellular Salmonella serves to essential needs , i . e . to enable access to nutrients for efficient proliferation of the pathogen , and to withstand hostile conditions within the pathogen-containing vacuole . | [
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"mu... | 2019 | Proteomics of intracellular Salmonella enterica reveals roles of Salmonella pathogenicity island 2 in metabolism and antioxidant defense |
Manipulation of sex determination pathways in insects provides the basis for a wide spectrum of strategies to benefit agriculture and public health . Furthermore , insects display a remarkable diversity in the genetic pathways that lead to sex differentiation . The silkworm , Bombyx mori , has been cultivated by humans as a beneficial insect for over two millennia , and more recently as a model system for studying lepidopteran genetics and development . Previous studies have identified the B . mori Fem piRNA as the primary female determining factor and BmMasc as its downstream target , while the genetic scenario for male sex determination was still unclear . In the current study , we exploite the transgenic CRISPR/Cas9 system to generate a comprehensive set of knockout mutations in genes BmSxl , Bmtra2 , BmImp , BmImpM , BmPSI and BmMasc , to investigate their roles in silkworm sex determination . Absence of Bmtra2 results in the complete depletion of Bmdsx transcripts , which is the conserved downstream factor in the sex determination pathway , and induces embryonic lethality . Loss of BmImp or BmImpM function does not affect the sexual differentiation . Mutations in BmPSI and BmMasc genes affect the splicing of Bmdsx and the female reproductive apparatus appeared in the male external genital . Intriguingly , we identify that BmPSI regulates expression of BmMasc , BmImpM and Bmdsx , supporting the conclusion that it acts as a key auxiliary factor in silkworm male sex determination .
Genetic systems for sex determination in insects show high diversity in different species . Sex determination in the fruit fly , Drosophila melanogaster , is controlled hierarchically by X:A > Sxl > tra/tra2 > dsx and fru [1 , 2] . The X:A ratio of 1 promotes transcription of Sex-lethal ( Sxl ) and results in feminization , while 0 . 5 to Sxl suppression and male differentiation [3–10] . Sxl proteins control the splicing of female transformer ( tra ) mRNAs that give rise to functional proteins , while no functional Sxl proteins exist in the male [11 , 12] . The search of homolog genes in the sex determination of D . melanogaster has been found a conserved relationship among dsx/tra across dipterans [13 , 14] In another Diptera , Musca domestica , female employs a FD allele which is encoded by the tra gene [15] . The medfly , Ceratitis capitata , has an as yet unidentified dominant male-determining factor on the Y chromosome [16] . The sex determination factors ( F or M ) in these two insects control the downstream gene doublesex ( dsx ) to generate sex-specific splicing isoforms . In contrast to Drosophila , Cctra and Mdtra seem to initiate an autoregulatory mechanism in XX embryos that provides continuous tra female-specific function and acts as a cellular memory maintaining the female pathway [17–19] . Many other insect species also exploit tra as the sex determination factor . For example , the honeybee , Apis mellifera , uses the complementary sex determiner gene ( csd ) to regulate feminization , which activates the feminizer gene ( fem ) by directing splicing to form the female functional Fem protein [20] . This fem gene is considered an orthologue of Cctra gene [21] . The tra gene in the red flour beetle , Tribolium castaneum , controls female sex determination by regulating dsx sex-specific splicing [22] . Also , Lucilia cuprina and Nasonia vitripennis , are reported to use tra as a female-determining signal [23 , 24] . Recently , Hall et al . identified Nix a distant homolog of transformer2 ( tra2 ) from Aedes aegypti as the male-determining factor [25] . These data shows that the dsx/tra axis is conserved in many insect species and tra is the key gene around which variation in sex determining mechanisms has evolved in all insect species with the exception of Aedes and Lepidopteran insects . Species of lepidoptera exhibit markedly different sex determination pathways from those seen in the flies , bees and beetles [26] . In the silkworm Bombyx mori , females possess a female-determining W chromosome have heteromorphic sex chromosomes ( ZW ) and males are homomorphic ( ZZ ) [27] . No tra ortholog has been identified in this order , possibly as a result of highly divergent sequence [28] . Bioinformatic analyses fail to identify a tra ortholog in B . mori and no dsxRE ( dsx cis-regulatory element ) binding sites are found in the target gene ortholog , Bmdsx , resulting in the default mode of female-specific splicing of the latter [29 , 30] . BmPSI ( P-element somatic inhibitor ) and BmHrp28 ( hnRNPA/B-like 28 ) were reported to regulate Bmdsx splicing through binding CE1 sequences of the female-specific exon 4 of Bmdsx pre-mRNA [30 , 31] . Another potential regulator , BmImp ( IGF-II mRNA binding protein ) , enhances the male-specific splicing of Bmdsx pre-mRNA by increasing RNA binding activity of BmPSI [32] . The Z-linked BmImp binds to the A-rich sequences in its own pre-mRNA to induce the male-specific splicing of its pre-mRNA , and this splicing pattern is maintained by an autoregulatory mechanism , being BmImpM ( the male-specific splicing form of BmImp ) able to bind its corresponding pre-mRNA [33] . Since BmPSI or BmImp products do not exhibit any sequence similarities to known Ser/Arg ( SR ) proteins , such as Tra and Tra2 , the regulatory mechanisms of sex-specific alternative splicing of Bmdsx that of exon skipping is distinct from that of Dmdsx that of 3’ alternative splice[34] . Recently , the product of the W chromosome-derived B . mori sex determination factor fem ( female-enriched PIWI-interacting RNA ) was identified to target the downstream gene BmMasc for controlling Bmdsx sex-specific splicing [35] . This remarkable finding reveals that fem is the primary female sex determinator in the silkworm . However , the genetic relationship among these genes in B . mori sex determination is still in mystery . We use here a binary transgenic CRISPR/Cas9 system to generate somatic mutations in sex determination pathway genes in B . mori . Three genes , BmMasc , BmPSI and BmImp , are involved in sex regulation only in lepidopteran insects , and two , Bmtra2 and BmSxl , are structural orthologs of the key sex regulation factors in Drosophila . We focus on the sexually dimorphic traits of reproductive structures and sex-specific alternative splicing forms of Bmdsx , the bottom gene of the sex determination . The results show that BmPSI and BmMasc affect Bmdsx splicing and the male reproductive tissues , supporting the conclusion that they have roles in sex determination . Disruption of BmImp or Bmtra2 causes severe developmental defects in both sexes , supporting their critical roles other than sex determination . Furthermore , loss-of-function mutations of BmPSI altered transcriptional or post-transcriptional ( splicing ) of BmMasc , BmImp and Bmdsx in males . These data strongly support the conclusion that BmPSI plays a key auxiliary in male sex determination in B . mori .
We established a binary transgenic CRISPR/Cas9 system to make somatic mutagenesis targeting selected genes . This system contains two separated lines , one is to express Cas9 protein under the control of a B . mori nanos promoter ( nos-Cas9 ) and another is to express sequence-specific sgRNAs under the control of a B . mori U6 promoter ( U6-sgRNA ) . Two independent lines of nos-Cas9 and from three to 29 independent lines of each U6-sgRNA construct were obtained following piggyBac-mediated transgenesis ( S1 Table ) . Genomic mutagenesis in F0 animals was confirmed by genomic PCR , and subsequent physiological phenotypes were investigated ( Fig 1 ) . The results confirm that the transgenic CRISPR/Cas9 system works effectively ( S1–S5 Figs ) . Bmdsx is the conserved downstream component of the silkworm sex determination pathway and mutations in BmPSI and BmMasc had an effect on its splicing . Mutations in BmSxl , BmImp and BmImpM resulted in the appearance of a larger BmdsxM ( the male-specific splicing form of Bmdsx ) mRNA isoform while the profile of BmdsxF ( the female-specific splicing form of Bmdsx ) appeared unaltered ( Fig 2 , lanes 4 , 10 and 12 ) . Sequence analysis of the larger BmdsxM isoform revealed an 81base-pairs ( bp ) length fragment insertion , creating a novel splice variant ( S6 Fig ) . All Bmdsx isoforms were absent in both sexes in individuals carrying mutations in Bmtra2 ( Fig 2 , lanes 5 and 6 ) . Mutations in BmMasc and BmPSI in males resulted in a decreased accumulation of BmdsxM and the appearance of BmdsxF ( Fig 2 , lanes 8 and 14 ) , which was consistent with previous report when BmMasc expression was disrupted by using RNAi [35] . RT-PCR-based analysis revealed that mutations in BmSxl , BmMasc , BmImp and BmImpM had no or minor effect on other genes at transcriptional or post-transcriptional ( splicing ) levels except Bmdsx . The male-specific BmImp ( BmImpM ) isoform appeared in female Bmtra2 mutants ( Fig 2 , lanes 5 and 6 ) . This supports the conclusion that Bmtra2 is not only involved in regulating Bmdsx , but also has a role in splicing regulation of BmImpM . Interestingly , the abundance of BmMasc and BmImpM transcripts decreased in BmPSI mutant males ( Fig 2 , lane 14 ) . Q-RT-PCR analysis showed that BmImpM and BmMasc mRNA levels decreased by 92% and 60% , respectively , in BmPSI mutant males ( Fig 3 ) . These results support the conclusion that BmPSI regulates BmMasc and BmImpM at the splicing level . In contrast to the results in males , with the exception of Bmtra2 , no effects on transcript profiles were seen in any of the mutant females . The morphology of the external and internal genitalia provides a direct index of sexual differentiation in B . mori . Mutations in BmMasc result in males with degenerative testes similar to that observed with Bmdsx mutants ( Fig 4A , lanes 2 and 4 ) . The external genitalia of males exhibit characteristics of the copulatory organs of both males and females including the female-specific ventral chitin plate and genital papillae ( Fig 4B , lanes 2 and 4 ) . The testes and external genitalia of mutant BmPSI males are similar phenotypes of mutations in BmdsxM or BmMasc and result in male sterility ( Fig 4A , lane 7; Fig 4B , lane 7 ) . The external and internal genitalia appear normal in both males and females with mutations in BmSxl , BmImp , and BmImpM , ( Fig 4A , lanes 3 , 5 , 6; Fig 4B , lanes 3 , 5 , 6 ) . The putative dsx target male-specific expression genes in the male olfactory system , pheromone binding protein 1 ( BmPBP1 ) , olfactory receptors ( BmORs ) BmOR1 , BmOR3 , were significantly down-regulated in the BmMasc and BmPSI male mutants ( Fig 5A–5C ) . In contrast , the female-specific expression genes in the female oogenesis and olfactory system , vitellogenin ( BmVg ) , BmOR19 , BmOR30 , were significantly up-regulated in the BmMasc and BmPSI male mutants ( Fig 5D–5F ) . These morphological results and corresponding gene expression profiles provide additional support for the conclusion that BmPSI and BmMasc control male sexual differentiation . Mutations in Bmtra2 result in lethality at later embryonic stages ( Fig 6A and 6B ) . This phenotype is similar to that reported in the honeybee in which down-regulation of Amtra2 causes embryonic viability and affects the female-specific splicing of fem and Amdsx transcripts [36] . Also , in T . castaneum , only a few eggs could be produced by animals after the parental RNAi of Tctra-2 and these eggs ultimately failed to hatch [37] . This is different from Dipteran species , in which tra2 has no vital function in embryogenesis [38] . The similarity of these phenotypes supports the hypothesis that Bmtra2 and its orthologs have an essential , ancestrally- and evolutionarily-conserved function in embryogenesis that is not related to sex determination that predates the divergence of the Lepidoptera , Hymenoptera and Coleoptera . Two distinct types of mutations were induced in BmImp , the first one targets all splice variants ( BmImp ) , and the second one is only in the male-specific splice variant ( BmImpM ) ( S4 Fig ) . However , neither had an effect on the morphology of the genitalia despite the observed effect on BmdsxF . While the growth indices of BmImpM mutant silkworms were normal , the body size and weight of the BmImp mutants was smaller than wild-type animals ( Fig 6C and 6D ) . They failed to molt at each larval instar and the majority died at the later larval and prepupal stages ( Fig 6E and 6F ) .
We provide here genetic evidence for the proposed sex determination pathway in B . mori that emphasizes the key roles of the products of the BmPS1 and BmMasc genes in male determination and differentiation ( Fig 7 ) . B . mori has the ZZ/ZW sex chromosome system in which the female is ZW ( heterogametic ) and the male is ZZ ( homogametic ) . The Fem piRNA gene is located on the W chromosome and maintains feminization through downregulating BmMasc expression . Without the BmMasc protein in ZW embryos , the default ( full-length ) splicing isoform of Bmdsx ( BmdsxF ) activates downstream gene expression and produces female-specific development . BmPSI is not involved in this pathway , since mutations in it have no observable effects on female differentiation . BmPSI protein in males interacts with the Bmdsx pre-mRNA and generates the male-specific Bmdsx splice variant ( BmdsxM ) [30] . The BmMasc product might play the role of a recruitment or splicing factor to participate in this event . BmMasc is expressed normally in males due to lack of Fem piRNA , and thus may be controlled by BmPSI . Significant differences are evident in the B . mori sex-determination system when compared to D . melanogaster . Sxl has a major and early role in sex determination in the fruit fly . Its ‘on/off’ status serves as the primary signal to trigger somatic sex differentiation by controlling its own splicing ( autocatalytically ) and of tra [38] . However , Sxl does not show sex-specific splicing and function in other dipterans [39] . Furthermore , the silkworm ortholog , BmSxl , has two splicing isoforms , neither of which is regulated in a sex-specific manner [40] . Depletion of Bmsxl induced a longer BmdsxM sex-specific splicing form appeared , just as seen in BmImp or BmImpM disruption . Although the potential role of this splicing form was unclear , it did not bring any phenotypic consequence in male sexual dimorphism . The presence of a longer BmdsxM splicing form might because the spliceosome is complex machinery containing up to 300 proteins and loss of additional auxiliary factors having little roles [41 , 42] . Dmtra2 is a co-binding protein of sex determination key gene Dmtra , and acts on the D . melanogaster sex-determination cascade to regulate both somatic sexual differentiation and male fertility [43] . However , the ortholog encoding the tra appears to not exist in the silkworm , and therefore Bmtra2 might have distinct roles . Bmtra2 could regulate female-specific splicing of Dmdsx and RNAi knockdown of Bmtra2 in the early embryo could cause abnormal testis formation in B . mori [44] . Our data support the conclusion that Bmtra2 regulates Bmdsx in both males and females , and has sex-independently essential roles during embryogenesis although the mechanism of embryonic lethality induced by Bmtra2 mutagenesis is not known . Another phenomena is BmImpM appeared in the Bmtra2 mutants , indicating that Bmtra2 is involved in regulating the upstream transcriptional factors of BmImpM . It is similar with Dmtra2 which regulates many downstream genes [45] . The BmImp gene was firstly identified in the silkworm as a co-binding protein with BmPSI [32] . BmImp produces two splice variants , one of which ( BmImpM ) is expressed in various tissues only in males and is proposed to be an essential regulator in the B . mori sex determination cascade [33] . BmImpM could bind the Bmdsx pre-mRNA and knock-down of its product induced the female-specific Bmdsx splicing form in male cell lines and embryos [33 , 46] . We cannot account for the differences in our results , but speculate that they could be a consequence of different methods between RNAi and Cas9-mediated gene silencing , or different materials between cell lines and the intact animal . However , our phenotypic results are consistent with those seen in D . melanogaster and the mouse . In the fruit fly , loss-of-function Imp mutations were zygotic lethal which through imprecise P excision , or mutants die later as pharate adults by two loss-of-function alleles , H44 and H149 [47 , 48] . Imp1 mutations in mice produced animals that were ~40% smaller than wild-type controls , and these exhibited high perinatal mortality [49] . Our experiments in which we mutate all isoforms of BmImp or only BmImpM did not result in any effects on sexual organs or sex determination genes . We conclude that BmImp and BmImpM are not essential for sex regulation but are needed for development . BmMasc had been identified as a downstream target of Fem-piRNA which could induce BmdsxF in male embryos after siRNA-mediated knocking down [35] . BmMasc also controls dosage compensation in male embryos , and male embryos injected with BmMasc siRNA did not hatch normally [35] . Kiuchi et al . [35] used two siRNAs to knockdown BmMasc and reported detecting BmdsxF in male embryo and the down-regulation of BmImpM . They proposed that BmImpM and Bmdsx are located downstream of BmMasc in the sex determination cascade . In contrast , our genetic analyses show no evidence of an effect of BmMasc on BmImpM . We propose that BmMasc controls male sexual differentiation by regulating Bmdsx but has no regulatory effect on BmImpM . It is still unclear how BmMasc regulates Bmdsx . F or M factors have been identified as the tra or tra2 gene in several insect species , and these factors directly regulated the dsx gene [25 , 50] . Previous reports concluded that BmPSI could directly bind Bmdsx pre-mRNA and that the BmImpM product increased BmPSI RNA binding activity in vitro [32] . Mutations of DmPSI in D . melanogaster strongly affect 43 splicing events and DmImp is one of the downstream genes targeted by it [51 , 52] . We found that BmImp and BmImpM had minor effects on BmdsxF splicing in our molecular genetic analyses . Furthermore , BmPSI did regulate expression of Bmdsx , BmImpM and BmMasc in vivo . It is unknown how BmPSI regulates the splicing of Bmdsx and other potential splicing factors involved in this process . Nonetheless , these data support the conclusion that BmPSI is at least a key auxiliary factor in the silkworm male sexual differentiation gene cascade , and provide the basis for the hypothesis that the BmPSI gene has a major initial role in the sex determination cascade .
Silkworms of the same genetic background ( Nistari , a multivoltine , nondiapausing strain ) were used in all experiments . Wild-type ( WT ) and mutant larvae were reared on fresh mulberry leaves under standard conditions [53] . A binary transgenic CRISPR/Cas9 system was established to target selected genes . The piggyBac-based plasmid , pBac[IE1-EGFP-nos-Cas9] ( nos-Cas9 ) , was constructed to express the Cas9 nuclease in germ-line cells under the control of the B . mori nanos ( nos ) promoter with the EGFP fluorescence marker gene under the control of the IE1 promoter . The plasmid pBac[IE1-DsRed2-U6-sgRNA] ( U6-sgRNA ) was constructed to express single guide RNAs ( sgRNA ) under the control of the silkworm U6 promoter and the DsRed fluorescence marker gene also under control of the IE1 promoter . The sgRNAs targeting sequences were designed by manually searching genomic regions that match the 5′-GG-N18-NGG-3′ rule [54] . sgRNA sequences were checked bioinformatically for potential off-target binding using CRISPRdirect ( http://crispr . dbcls . jp/ ) by performing exhaustive searches against silkworm genomic sequences [55] . All sgRNA and oligonucleotide primer sequences for plasmid construction are listed in S2 Table . U6-dsx-sgRNA line from our previous report [56] . Each nos-cas9 or U6-sgRNA plasmid mixed with a piggyBac helper plasmid [53] was microinjected separately into fertilized eggs at the preblastoderm stage . G0 adults were mated to WT moths , and resulting G1 progeny scored for the presence of the marker gene product using fluorescence microscopy ( Nikon AZ100 ) ( S1 Table ) . Each U6-sgRNA transgenic line was mated individually with the nos-Cas9 line to derive mutated F0 animals . Genomic DNA of mutated animals was extracted at the embryonic or larval stage using standard SDS lysis-phenol treatment after incubation with proteinase K , followed by RNase treatment and purification . The resulting individual DNA samples from mutant animals were separated by sex using gene amplification with primers specific to the W chromosome ( S2 Table ) . For two sgRNA sites , mutation events were detected by amplification using gene-specific primers which set on the upstream or downstream of the each target ( S2 Table ) . Amplified products were visualized by 2% agarose gel electrophoresis running 30 min at 100V . Amplicons were sub-cloned into the pJET-1 . 2 vector ( Fermentas ) and the positive clones of each we pick six were sequenced . For the one sgRNA , a restriction enzyme ( HpyAV , New England Biolabs , Ipswich , MA , USA ) cutting site adjacent to the AGG ( PAM sequence ) was selected to analyze the putative mutations by restriction enzyme digestion ( RED ) assay . The RED assay was carried out as previous report [57] . Phenotypic analysis focused light-microscope examination of the morphology of secondary sexual characteristics including internal and external genitalia . Photographs were taken with NRK-D90 ( B ) or DS-Ri1 ( Nikon , Tokyo , Japan ) digital cameras . Testes or ovaries were dissected from fourth-day , fifth-instar larvae and fixed overnight in Qurnah’s solution ( anhydrous ethanol: acetic acid: chloroform = 6:1:3v/v/v ) . Tissues were dehydrated , cleared three times using anhydrous ethanol and xylene , respectively , and embedded in the paraffin overnight . Tissue sections ( 8 μm ) were cut ( Leica; RM2235 ) and stained using a mixture of hematoxylin and eosin solution . All pictures were taken under a microscope ( Olympus BX51 ) using differential interference contrast with the appropriate filter . Total RNA was extracted from silkworm at different stages using Trizol reagent ( Invitrogen ) and treated with RNase-free DNAse I ( Ambion ) . cDNAs were synthesized using the Omniscript Reverse transcriptase kit ( Qiagen ) in a 20 μl reaction mixture containing 1 μg total RNA . RT-PCR reactions were carried out using KOD plus polymerase ( Toyobo ) with gene-specific primers ( S2 Table ) . Amplification of the B . mori ribosomal protein 49 ( Bmrp49 ) was used as a positive control . Quantitative real-time RT-PCR ( Q-RT-PCR ) assays were performed using SYBR Green Realtime PCR Master Mix ( Thermo Fisher Scientific ) on an Eppendorf Real-time PCR System Mastercycler realplex ( Eppendorf ) . Q-RT-PCR reactions were carried out with gene-specific primers ( S2 Table ) . A 10-fold serial dilution of pooled cDNA was used as the template to make standard curves . Quantitative mRNA measurements were performed in three independent biological replicates and normalized to Bmrp49 mRNA . Experimental data were analyzed with the Student’s t-test . t-test: * , p < 0 . 05 , ** , p < 0 . 01 , *** , p < 0 . 001 . At least three independent replicates were used for each treatment and the error bars show means ± S . E . M . | The sex determination system extremely diverse among organisms including insects in which even each order occupy a different manner of sex determination . The silkworm , Bombyx mori , is a lepidopteran model insect with economic importance . The mechanism of the silkworm sex determination has been in mystery for a long time until a Fem piRNA was identified as the primary female sex determinator recently . However , genetic and phenotypic proofs are urgently needed to fully exploit the mechanism , especially of the male sex determination . In the current study , we provided comprehensively genetic evidences by generating CRISPR/Cas9-mediated knockout mutations for those genes BmSxl , Bmtra2 , BmImp , BmImpM , BmPSI and BmMasc , which were considered to be involved in insect sex determination . The results showed that mutations of BmSxl , BmImp and BmImpM had no physiological and morphological effects on the sexual development while Bmtra2 depletion caused Bmdsx splicing disappeared and induced embryonic lethality . Importantly , the BmPSI regulates expression of BmMasc , BmImpM and Bmdsx , supporting the conclusion that it acts as a key auxiliary factor to regulate the male sex determination in the silkworm . | [
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"research",... | 2017 | Bombyx mori P-element Somatic Inhibitor (BmPSI) Is a Key Auxiliary Factor for Silkworm Male Sex Determination |
The traditional view of cancer as a genetic disease that can successfully be treated with drugs targeting mutant onco-proteins has motivated whole-genome sequencing efforts in many human cancer types . However , only a subset of mutations found within the genomic landscape of cancer is likely to provide a fitness advantage to the cell . Distinguishing such “driver” mutations from innocuous “passenger” events is critical for prioritizing the validation of candidate mutations in disease-relevant models . We design a novel statistical index , called the Hitchhiking Index , which reflects the probability that any observed candidate gene is a passenger alteration , given the frequency of alterations in a cross-sectional cancer sample set , and apply it to a mutational data set in colorectal cancer . Our methodology is based upon a population dynamics model of mutation accumulation and selection in colorectal tissue prior to cancer initiation as well as during tumorigenesis . This methodology can be used to aid in the prioritization of candidate mutations for functional validation and contributes to the process of drug discovery .
Our computational approach is based upon the evolutionary dynamics of the accumulation of driver and passenger mutations in a population of cells ( Fig 1 . ( a ) ) . There are two phases ( Fig 1 . ( b ) ) : a pre-initiation phase and a clonal expansion phase . During the pre-initiation phase , the first driver mutation has not yet emerged and the population is maintained at a homeostatic cell number . Cells proliferate according to a stochastic process: at each time step , a cell is chosen at random proportional to its fitness to divide , and its offspring replaces another randomly chosen cell . During each cell division , a mutation may emerge with probability u . Each mutation confers an additive change to the fitness of the daughter cell; this additive change is chosen from a mutational fitness distribution which is approximated discretely by a mutational kernel M1 . The survival of the resulting mutant clone is dependent on its relative fitness , as well as any subsequent mutations it may accumulate . This phase of the methodology is designed to model the behavior of stem cells within a crypt of the colon . If a cell in the population has accumulated a sufficiently large fitness to counter the homeostatic mechanisms of the compartment , then the second phase of clonal expansion begins ( Fig 1 . ( b ) ) . The cell number in the tumor is now described by a multi-type stochastic branching process: at each time step , a cell is chosen proportional to fitness to divide ( possibly with mutation ) or chosen at random to die . This initiated cancer cell carries one or more driver mutations that confer a larger growth than death rate; the population of cells thus grows on average exponentially . Each time a cell in this population divides , a mutation may again arise with probability u . Once again , mutations confer an additive change to the fitness of the daughter cell; this additive change is chosen from a mutational fitness distribution which is approximated discretely by a mutational kernel M2 . This mutational kernel is not , in general , the same as the mutational kernel from the pre-initiation phase ( M1 ) , since there is no reason to assume that the mutational fitness distribution in a normal compartment of healthy cells during the pre-initiation phase should be the same as the distribution in a rapidly expanding cancer clone . However , we utilize the same family of mutation kernels , noting that the shape parameters may differ between phases . As mutations accumulate and undergo clonal expansion in the model , the number of cell types grows and the tumor population becomes more heterogeneous . Each additional driver mutation increases the fitness of the cell lineage , such that the rate of expansion accelerates ( Fig 1 . ( b ) ) . Clonal growth continues until the tumor reaches its detection size , Nd ( Fig 1 . ( b ) ) . We then utilize this underlying evolutionary model to determine the probability q that a particular candidate gene is found in a detectable frequency of the tumor , conditioned on the null hypothesis that alterations of this gene are selectively neutral . This probability q is then used to derive the probability of obtaining the observed frequency of alterations in the sample set , given the null hypothesis . More specifically , when sampling tumors from Y patients , we calculate the “Hitchhiking Index” , which specifies the probability of detecting a certain mutation in at least α% of Y patients , as H = ∑ k = ⌈ α Y ⌉ Y P ( X = k ) , where X is binomially distributed as Binom ( Y , q ) and ⌈αY⌉ is the smallest integer greater than αY . This index provides a tool for rejecting the null hypothesis . If for example H < 0 . 001 for a given observed α and Y , we can reject the assumption of neutrality of a mutations in a given gene of interest . The parameters of this model ( e . g . cell turnover rates and mutation kernels in both phases , population size during homeostasis and at detection ) must be specifically tuned for each cancer and sample type . In addition to the evolutionary processes within the tissue , the value of the Hitchhiking Index also depends upon the relative mutation rate of each candidate gene , detection sensitivity of the sample set , and the observed alteration frequency in the sample set . Note that the Hitchhiking Index provides a method for rejecting the null assumption of neutrality; however , failure to reject this assumption does not necessarily imply that the gene is neutral . Let us provide further details of underlying mathematical framework that models the pre-initiation phase , which was first introduced in [28] . During this phase we consider a small population of cells of constant size N which describes a homeostatic compartment of cells at risk for accumulating mutations leading to cancer initiation , described by a multi-type Moran process [29] . Each cell on average divides every D days . Thus , at rate N/D ( i . e . time between events are i . i . d exponential random variables with mean D/N ) , the process undergoes division events . During each event , a cell is chosen at random to die , and proportional to its fitness an individual is chosen to reproduce . Specifically , if there is a single cell with fitness s and N−1 cells with fitness 1 , then the cell with fitness s is chosen to reproduce with probability s/ ( s + ( N−1 ) ) . During each cell division event , an ( epi ) genetic alteration may occur with probability u < < 1; thus alterations arise in the compartment of cells at rate Nu . The fitness effects of individual alterations are random variates drawn from a mutational fitness landscape governed by the mutation kernel M1 ( ⋅ , ⋅ ) . Here M1 ( x , y ) represents the probability that a cell with fitness x produces a daughter cell with fitness y ( i . e . M ( x , y ) = f ψ x ( y - x ) ) . If y > x , then the fitness of the daughter cell is advantageous as compared to the fitness of its parent cell; if y < x , it is disadvantageous , and if y = x , it is neutral . The type space is discretized into fitness bins to aid in computational tractability , and thus the kernel M is a finite-state transition matrix . In this work we utilize a general family of mutation kernels that have exponentially decaying tails on the positive and negative sides with shape parameters α and β , respectively . Note that α = β = 0 represents the uniform distribution case , and for α , β > 0 the mutational fitness distribution has a mode at 0 ( neutral mutations ) . This process continues for as long as the fitness of all cells is within the homeostatic range [a , b] for a = 1−1/N and b = 1+1/N; these values were chosen since they signify the boundaries for neutral evolution [30] . Also note all sample paths will result in cancer initiation prior to death . In the event that a cell with a sufficiently large fitness emerges , it can escape homeostatic mechanisms in the compartment and initiate clonal expansion . It has been shown that when 3Nu ( logN + γ ) ≪ 1 , where γ is the Euler-Mascheroni constant , the time between mutational events is much larger than the time it takes for a mutation to take over or go extinct in a population of cells ( see , e . g . [28] ) . In the application of colorectal cancer considered in this work , this condition holds , supported from the parameterization discussed in the section Model parameterization . Therefore , on the timescale of interest , the population moves between various homogenous states and we can approximate this process by a Markov process Z ( ⋅ ) , where Z ( t ) represents the fitness of the homogeneous compartment at time t . The process Z jumps whenever a cell harboring a novel non-neutral mutation reaches fixation in the compartment , and takes values in the space of all possible fitness values dictated by the fitness landscape . This process closely approximates the behavior of cellular fitness values in a small compartment for the vast majority of time . Given that a mutation of fitness y arises in a population of cells with fitness x , the probability that the mutation takes over the population is given by rx , y = ( 1−x/y ) / ( 1− ( x/y ) N ) . By symmetry we have rx , x = 1/N . With this information , we define the intensity matrix for the Markov process Z , denoted by Q = Q ( x , y ) , Q ( x , y ) = { u r x , y M 1 ( x , y ) N / D , y < b u N M 1 ( x , y ) / D , y ≥ b , . Mutational events transforming a cell with fitness x to a cell with fitness y occur at rate uM ( x , y ) N/D , and of those a fraction rx , y reach fixation ( i . e . 100% frequency ) in the entire population . At the lower end of the fitness range a , there is a reflecting boundary such that any fitness below a is immediately replaced by a cell with fitness a; this behavior is implemented in the mutation kernel M . By definition , we have Q ( x , x ) = −∑y ≠ x Q ( x , y ) , and the states x ≥ b are absorbing so Q ( x , y ) = 0 for all y and x ≥ b . The summation is over all y in the discrete fitness space . The process Z represents the dynamics of the fitness of a healthy compartment of cells over time; this process is then conditioned upon the event that cancer initiation occurs during a human lifetime [28] . This is achieved by creating an additional lifetime process , L , that is run simultaneously with Z in a second dimension . The lifetime process has a single absorbing state representing death of the patient , and transition rates between intermediate stages are tuned using mortality statistics in the United States . Details of this tuning process can be found in [28] . We are then interested in the joint process ( Z , L ) conditioned on Z hitting its absorbing state ( fitness greater than b ) prior to L hitting its absorbing state ( death ) . This set of sample paths describes the cancer initiation paths which may lead to tumor diagnosis prior to death . To consider the fate of a particular candidate mutation ‘A’ in our data set , suppose that this mutation arises with probability u0 per cell division . Under our null hypothesis , we assume mutation A is neutral and confers no selective advantage/disadvantage; thus its presence does not alter the evolutionary outcome of the sample path . Conditioned on initiation prior to death , we compute the probability of mutation A arising in the initiating cancer cell . To do this , we analyze the amount of time the ( Z , L ) process spends in each state of the two-dimensional state space . Details of this derivation are provided in the Methods . Once a cell in the pre-initiation phase has acquired a fitness value greater than b , the second clonal expansion phase of the model commences . This phase is modeled by a continuous time multi-type birth and death process , initiated by the cell from the pre-initiation phase that has accumulated a sufficiently large fitness to break free from the Moran process and initiate clonal expansion . The initial branching process has birth rate b and death rate d , where b > d . During this expansion phase , each time a cell divides , it has a probability u of mutating and selecting a new random birth rate from a fitness distribution , and probability u0 of obtaining the specific candidate mutation A without any change in birth rate . The birth rate of a mutated daughter cell is the parental fitness plus a random variable selected from a distribution specified by the mutation kernel M2 . The type space is once again discretized into fitness bins to aid in computational tractability , and the kernel M2 is a finite-state transition matrix , accordingly . We then aim to determine the probability that mutation A is present in a significant fraction of the final population size at detection , Nd , conditional upon the event that the expansion process was initiated and reached detection size during a human lifetime . To study this event we first utilized analytical calculations to determine the probability of tumor initiation prior to death , in the first phase of the model , by solving the linear system described in [28] using a biconjugate gradient stabilized method . We next performed event-driven Monte-Carlo simulations of the two-dimensional Markov process describing the fitness of the crypt and the lifetime state conditional on initiation prior to death [28] . To account for the fact that sample paths of interest may be rare , the probability of initiation calculated in the previous stem was used to perform a Doob h-transform of the process ( Z , L ) conditioned on initiation prior to death . Thus we only simulated sample paths that lead to initiation , saving computational time . At the time of initiation for each sample path , the time of initiation as well as the status of mutation A in the initiating cell is recorded . These initial conditions are used to begin a stochastic simulation of the clonal expansion process . During each event in the simulation , a cell is chosen to divide based on its relative fitness and abundance . During each cell division , a mutation occurs with probability u and the outcome of that mutation is selected from the mutation kernel Mc . The tagged mutation A arises with probability u0 ≪ u . Naturally , a certain fraction of paths in the expansion phase die out at early times due to stochastic fluctuations . For the remaining paths , the simulation is halted when the total population hits the detection size Nd , and then the abundance of mutation A in the total population of tumor cells is recorded .
To apply this framework to analyze genomic data from any specific cancer type , the main challenge is to determine the probability q that a particular mutation of interest , mutation A , arises during the clonal expansion phase and eventually makes up a significant fraction of the final population size , M . This outcome can occur via two possible scenarios: ( 1 ) mutation A arises and reaches 100% frequency during the pre-initiation phase , so that all cells of the resulting tumor have mutation A ( Fig 1 . ( c ) ) , and ( 2 ) mutation A is not present in the initiating cell but arises during clonal expansion ( Fig 1 . ( d ) ) . Recognizing these two mutually exclusive possibilities suggests an interesting question: which is the more likely path out of these two scenarios ? The answer to this question depends on the parameters of the evolutionary model , which might vary from cancer type to cancer type . We investigated colorectal cancer in particular , through the approach outlined in the following .
In this work we have developed a novel methodology to identify driver mutations from cross-sectional tumor sequencing data , based on an evolutionary model of tumorigenesis . We developed the ‘Hitchhiking Index , ’ which represents the probability of observing alterations in a particular gene in a certain fraction of the patient sample set , under the null assumption that the gene is not a cancer driver . This index takes into account the impact of a number of important parameters on the statistical power of the conclusion: the sample detection threshold ( sensitivity of the sequencing method ) , patient sample size , and variable mutation rates across the genome . The underlying evolutionary model is designed and parameterized for colorectal tumorigenesis , but can be generalized to other cancer types . Here we have not incorporated full pathway information of the gene of interest , but the model can easily be adapted to group genes together into pathways and analyze selection dynamics at the pathway level instead of at the individual gene level . The Hitchhiking index is calculated for any particular candidate gene , using the observed patient sequencing data , and can be used to identify candidate genes as potential drivers . We applied our methodology to analyze TCGA data for colorectal cancer , considering heterogeneous mutation rates measured per cell division which are inferred from baseline mutation rate estimates and relative changes from that rate across the genome as determined by MutSigCV [9] . We built upon MutSigCV by incorporating heterogeneous mutation rate estimations into our model: ( 1 ) We specify an underlying evolutionary dynamic model to describe the processes generating mutations to calculate the probability of a mutation being a driver event; and ( 2 ) by controlling for the bias introduced by DNA replication timing , gene expression and higher-order chromatin structure , we infer the relative mutation rate per cell division compared to the cross-sectional mutation rate . Remarkably , we found that any gene that is mutated in at least 10% of cells in the tumor is most likely to have arisen prior to clonal expansion of an initiated cell clone . Utilizing the Hitchhiking Index analysis , we obtained a list of 43 genes identified as potential drivers . In comparison to a recent analysis utilizing MutSigCV [9] , our methodology identified other colorectal cancer related genes such as COL12A1 , MLL2 , FAT4m and ARID1A . Recent studies support the crucial role of these genes in the development of colon cancer [36–39] . One caveat to our approach is that it is unclear how to choose the threshold Hitchhiking Index value; similar to a statistical p-value , the choice of threshold at which to reject the null hypothesis is largely a matter of choice . Since this index depends upon the sample size and detection sensitivity of the method , it would be difficult to compare absolute values of the Hitchhiking Index across different sample sets . Note that if the Hitchhiking Index for a particular gene is above the rejection threshold , we do not conclude that the gene is necessarily a passenger—our methodology provides only the probability of observing the data , conditioned on the assumption of neutrality . Also , note we used the same u and u0 in both phases . While the precise values are unknown , it is possible that the mutation rates can be higher in the latter phase , since it is possible that the initiating mutation causes an increase in the mutation rate itself , e . g . , a mutation that reduces the effectiveness of DNA repair . This scenario has the potential to alter the creation rate of passenger mutations and will be the topic of future investigations . As such , our current work excludes consideration of colorectal cancers with microsatellite instability , a deficiency of the mismatch repair ( MMR ) pathway that leads to increased point mutation rates across the genome . Additionally , hereditary forms of colorectal cancer are also not explicitly considered and will be investigated in future work . Furthermore , the presented model does not include all possibilities for alternative initiation mechanisms . Such a model would not be very useful since it would address mutually exclusive evolutionary trajectories; instead , we have presented one possible model of the evolutionary process leading to tumorigenesis . A novel feature of this model is the formulation of the initiation event as an accumulation of a sufficiently large fitness advantage in the initiating cell through a flexible series of mutational events rather than a specific set or number of hits . Because of this flexibility , a multitude of mutational pathways can lead to initiation in our model , and in particular it can be used to consider the situation in which each of these events is disruption of a particular pathway . The approach can also modified to incorporate the more traditional view that a specific set of hits is required to initiate cancer ( by specifying instead a discrete distribution for the mutational fitness landscape ) and making this landscape dependent on the current mutation status . Here , we have utilized a specific , tunable evolutionary model of mutation accumulation in cancer to develop a novel statistical test for identifying driver mutations from cross-sectional genomic data of cancer sample sets . We have opted for a somewhat more flexible approach to modeling the process of mutation accumulation and initiation . For instance , we have considered mutational heterogeneity in a coarse manner , by grouping genes into three different categories with different baseline mutation rates per cell division . A more complete model could in principle use different baseline mutation rates for each categories of DNA replication timing , gene expressions and other genomic features , even including the difference between transitions and transversions [15 , 40] . In contrast to the model by Tomasetti et al [18] , in which all mutations prior to initiation are considered to be selectively neutral and the time of tumor initiation is set by epidemiological data , here we have assumed that mutations conferring random fitness advantages can arise during the constant population size phase , and that tumor initiation occurs as a result of accumulating sufficiently many advantageous mutations to escape homeostasis . Consequently , in our model the timing of cancer initiation is random , and correlated with the process of mutation accumulation . We have carried the same modeling framework through to the tumor growth phase , in which cells may accumulate mutations conferring a spectrum of fitness changes . Consequently , in contrast to the model by Bozic et al [10] , we assume that driver mutations may be variable in number and lead to variable fitness effects and that tumors may alternatively have many drivers with small selective advantage or a few drivers with large selective advantages . These differing modeling choices reflect a rich set of hypotheses about the underlying evolutionary dynamics of mutation accumulation in cancer; more modeling and experimental effort is needed to investigate the perspectives and relative strengths of these and many other models . Several important conclusions , however , seem robust: first , mathematical analyses of the evolutionary processes in cancer suggest that the majority of mutations found in tumor sequencing efforts arise prior to cancer initiation; and second , mathematical frameworks of evolution and mutation accumulation in cancer can be exploited to extract important biological information from genomic sequencing data .
Let m ( t ) denote the number of cells carrying mutation A that are present in the compartment at time t . We define the stopping times τ = inf{t ≥ 0 : Z ( t ) ≥ b} and σ = inf{t ≥ 0 : L ( t ) = d} . We are interested in finding μ A ( x , r ) = P ( x , r ) [ m ( τ ) > 0 | τ < σ ] , ( 1 ) where μA ( x , r ) represents the probability that mutation A is present in the compartment at the time of initiation starting from state ( x , r ) . Between jumps of the two-dimensional process ( Z , L ) , a random number of neutral mutations can reach fixation within the compartment of cells . Let Tj and Tj+1 be the jump times of ( Z , L ) , and for simplicity denote ( Z ( Tj ) , L ( Tj ) ) = Xj . During the transition from Xj to Xj+1 , the compartment can accumulate Yj ( Xj ) neutral mutations . Define ρ ( x , r ) = u M 1 ( x , x ) / D u M 1 ( x , x ) / D - Q ( x , x ) - S . The numerator represents the rate at which neutral mutations which eventually reach fixation arise within the compartment , and the denominator represents the total rate at which fixating mutations arrive and the time process changes . With this definition , Yj ( x , r ) is distributed like a geometric random variable with Prob ( Y j ( x , r ) = n ) = η ( x , r ) n ( 1 - η ( x , r ) ) , which gives Prob ( Y j ( x , r ) > 0 ) = ρ ( x , r ) . By conditioning on the first step we can see that μA ( ⋅ , ⋅ ) satisfies the following equation for each possible fitness x in [a , b] , μ A ( x , r ) = ρ ( x , r ) + ∑ y Q r ( x , y ) μ A ( y , r ) u M 1 ( x , x ) / D - Q ( x , x ) - s - Q x ( r , r + 1 ) μ ( x , r + 1 ) u M 1 ( x , x ) / D - Q ( x , x ) - S , where we note that μA ( x , r ) = 0 for those fitnesses that lie outside of [a , b] . Therefore we can find μA ( ⋅ , ⋅ ) by solving the linear system . We tested the sensitivity of the model predictions to varying parameters . First , we studied the sensitivity to the parameters for which we have no experimental data-based estimates: the shape parameters of mutational fitness distribution and the birth and death rates of the initiating cell . We also investigated the sensitivity to the background mutation rate u , and in particular studied the impact of an increased mutation rate during the clonal expansion phase . For all of these sensitivity analyses we confined our parameter variation to the ranges in which the model is consistent with the population-level epidemiological data . In particular , we required that there is a significant incidence of aberrant crypt foci over a lifetime ( 10–90 percent of the population ) , that the average age at diagnosis is between 70–79 yrs , and that the lifetime risk of colon cancer is around 6–10% . We first investigated the sensitivity of our model predictions to the shape parameters of the mutational fitness distribution during the pre-initiation phase of the model . Keeping all other parameters constant , we varied α1 and β1 . To determine the allowable ranges of these parameters , we first studied the probability of initiation during a lifetime as a function of α1 and β1 . Note that this is the probability that a single crypt leads to initiation during a lifetime , and there are 15 million crypts in the average human colon . Thus , in order to ensure that the average probability of aberrant crypt foci is between 10 and 90 percent in the population , we require that the probability of initiation from a single crypt is between 0 . 7×10−8 and 6×10−8 . In Fig 5 . a the probability of initiation for a single crypt is plotted for example ranges of parameters α1 , β1 . The intermediate colors ( between dark blue and red ) represent admissible initiation probabilities . Thus we see that for any given α1 , there is a small range of β1 that can give rise to the correct range of initiation probabilities . We then investigated the compatibility of these α1 , β1 combinations with the other incidence data . We found that only α1 in the range 170 ∼ 180 can give rise to the correct overall lifetime cancer incidence rates within the range 6–10% . Furthermore , for α1 = 170 , β1 must fall within the narrow range 115 ∼ 120; larger values of β1 result in lifetime incidence above the allowable range , and lower values of β1 result in too low an incidence of aberrant crypt foci in the population Similarly , for α1 = 180 , β1 must lie within the range 180 ∼ 185 to match the incidence data constraints . Next we used the model to determine the sensitivity of the results , as determined by the Hitchhiking Index , to variations to α1 and β1 within these allowable ranges . For example , Fig 5 . b shows the number of patients out of 100 , 000 samples in which cells harboring mutation ‘A’ make up a threshold frequency in the final tumor cell population , for varying α1 and β1 . We observed only modest differences in q , which would translate to negligible differences in the Hitchhiking Index . Therefore , within the constraints of matching the observed incidence data , the Hitchhiking Index is robust to varying the shape parameters of the mutational fitness landscape during the carcinogenesis phase . We also investigated the sensitivity of the results to the shape parameters of the mutational fitness distribution during the clonal expansion phase of the model . Fig 5c . demonstrates the impact of varying α2 and β2 up to 60 percent from the original values on the Hitchhiking Index; we found that the Hitchhiking Index is not particularly sensitive to these parameters . We then investigated the model’s sensitivity to the growth rates of the first cell initiating clonal expansion ( b , d ) . We varied b first to determine the impact of the net growth rate on the Hitchhiking Index . Variation of this parameter leads to overall lifetime cancer incidence rates that fall outside the range of our incidence data; this suggests that the net growth rate during the clonal expansion phase within our model should not vary significantly from the fitted value . There is , however , the possibility that both b and d vary in such a way that the net growth rate remains conserved , for example if both b and d are increased or decreased by the same amount . These variations might lead to small differences in the model predictions . | Evolutionary dynamic models have been intensively studied to elucidate the process of tumorigenesis . One key aspect of studying tumorigenesis is to distinguish the “driver” mutations providing a fitness advantage to cancer cells against neutral “passenger” or “hitchhiking” mutations . Many statistical models to address this question have been developed . Evolutionary models , however , add another layer of complexity by taking into account the process of mutation accumulation and selection within the tissue . Here we present a novel approach combining both statistical and evolutionary thinking to identify driver mutations in cancer genomes using cross-sectional mutation data . Our method considers the process of mutation accumulation and selection before and during colorectal cancer initiation . This work demonstrates the importance of using evolutionary population dynamic models to study driver events of tumorigenesis . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2015 | An Evolutionary Approach for Identifying Driver Mutations in Colorectal Cancer |
Cancer genomes exhibit profound somatic copy number alterations ( SCNAs ) . Studying tumor SCNAs using massively parallel sequencing provides unprecedented resolution and meanwhile gives rise to new challenges in data analysis , complicated by tumor aneuploidy and heterogeneity as well as normal cell contamination . While the majority of read depth based methods utilize total sequencing depth alone for SCNA inference , the allele specific signals are undervalued . We proposed a joint segmentation and inference approach using both signals to meet some of the challenges . Our method consists of four major steps: 1 ) extracting read depth supporting reference and alternative alleles at each SNP/Indel locus and comparing the total read depth and alternative allele proportion between tumor and matched normal sample; 2 ) performing joint segmentation on the two signal dimensions; 3 ) correcting the copy number baseline from which the SCNA state is determined; 4 ) calling SCNA state for each segment based on both signal dimensions . The method is applicable to whole exome/genome sequencing ( WES/WGS ) as well as SNP array data in a tumor-control study . We applied the method to a dataset containing no SCNAs to test the specificity , created by pairing sequencing replicates of a single HapMap sample as normal/tumor pairs , as well as a large-scale WGS dataset consisting of 88 liver tumors along with adjacent normal tissues . Compared with representative methods , our method demonstrated improved accuracy , scalability to large cancer studies , capability in handling both sequencing and SNP array data , and the potential to improve the estimation of tumor ploidy and purity .
Profound somatic copy number alternations ( SCNAs ) are present in many types of tumors [1–3] , where they affect a larger fraction of the genome than other types of somatic variations [3 , 4] . The roles of SCNAs in promoting oncogenesis and tumor progression are under intensive study . In contrast to germline copy number variations ( CNVs ) , which are sparsely distributed along the genome and of small to moderate size , tumor SCNAs are large in size and have a much wider range of magnitudes in copy number . Accurate detection and characterization of genome-wide SCNA profile are further complicated by aneuploidy and heterogeneity of tumor cells and contamination of normal cells [5] . Array comparative genomic hybridization ( array-CGH ) [6] and single nucleotide polymorphism ( SNP ) array [7] were widely used in surveying genome-wide SCNAs in the past decade . More recently , next-generation sequencing ( NGS ) technology provides unprecedented resolution to comprehensively characterize SCNAs at rapidly decreasing cost [8 , 9] . A long list of tools has been developed and successfully applied in analyzing CNV data harvested from NGS; see [2 , 10 , 11] for reviews . The available methods mainly fall into four categories: 1 ) read depth ( RD ) , 2 ) pair-end mapping ( PEM ) , 3 ) split read ( SR ) and 4 ) Assembling ( AS ) . While they take advantage of complementary information , featuring different perspectives of CNVs , each of them encounters important limitations , due to the complications from the high dynamics of cancer genome . With whole genome sequencing ( WGS ) data , the latter three methods are good at detecting CNVs of small to moderate sizes , locating CNV break points at higher resolution , and discovering copy neutral rearrangements , but they are less capable in characterizing large-size and wide-range copy number changes at the genome-wide scale . Moreover , their applicability in whole exome sequencing ( WES ) data is limited by the fact that short reads from WES are concentrated in interspersed genomic regions . Therefore , the RD-based methods are used more widely in the study of tumor CNVs with both WGS and WES data [10] . Basically , the majority of RD-based methods , such as CNV-seq [12] , SegSeq [9] , ExomeCNV [13] and PatternCNV [14] , follows a “bottom-up” procedure: short-reads mapping , normalization of read depth , copy number estimation in a local region ( usually in a window of certain size or in an exonic region ) and segmentation to merge regions with the same copy number status [10] . This strategy is sensitive in the detection of germline CNVs , but for tumor CNVs ( i . e . SCNAs ) , it is difficult for the local inference to correctly decide the baseline of ploidy and accurately discern weak signals of copy number change in the presence of aneuploidy and normal cell contamination , so that the genome-wide inference drawn from the later segmentation step is inclined to accumulate false positive findings from the earlier local inference step . Further , these methods only consider the aggregated depth of sequencing reads carrying paternal and maternal alleles , aimed at estimating the total copy number , but largely ignore allele specific read depth , while the latter contains critical information of copy number change , copy-neutral loss of heterozygosity ( CN-LOH ) , and , importantly , genome ploidy . In analogy to Illumina SNP array [7] , the allele specific signal from NGS can be quantified at heterozygote sites ( e . g . SNP loci ) and converted to the so-called B allele frequency ( BAF ) , which is defined as the proportion of the reads carrying “B” allele ( i . e . non-reference allele ) among all the mappable reads at that site . At an SCNA-free locus , the expectation of BAF is close to 1/2 , indicating equal amount of paternal and maternal alleles . With an SCNA event , the expectation of BAF might deviate from 1/2 , reflecting differential copy number changes in the two alleles . The two data dimensions , total read depth and allele-specific signal ( i . e . , BAF ) , carry complementary and consensual information of SCNA events . Incorporating both quantities could theoretically improve power and accuracy in identifying SCNA segment boundaries and characterizing alteration types ( i . e . , gains , losses , and CN-LOHs ) . Control-FREEC [15] is a representative method that incorporates both types of information . However , it uses them separately rather than jointly in the segmentation and SCNA calling , nor does it make good use of data from match normal sample to generate reliable allele-specific signal . Herein , we propose a tumor SCNA analysis method , SAAS-CNV , based on a joint segmentation algorithm [16] , to accommodate both total read depth and BAF . Using both data dimensions simultaneously , the method first takes a “top-down” strategy to partition the genome into segments with different alterations by joint segmentation , and subsequently , determines their alteration types . The method was designed for paired normal-tumor settings , in which the spatial variability from non-uniform distribution of short reads along the genome can be alleviated [13] . It is able to accommodate WGS and WES data as well as SNP array data . In this paper , we first characterized the type-I-error properties of SAAS-CNV using a carefully designed “null” dataset containing no SCNAs ( H0 ) , created by pairing sequencing replicates of a single HapMap sample as normal-tumor pairs [17] ( Materials and Methods , Dataset I ) . Further , we demonstrated its ability to analyze a real-world WGS study of 88 hepatocellular carcinoma ( HCC ) samples ( Ha ) , some of which exhibit highly complicated SCNA profiles [18 , 19] ( Materials and Methods , Dataset II ) . In both settings ( Ho and Ha ) , we compared the performance of SAAS-CNV with other existing methods . We also explored its potential extension in the estimation of ploidy and purity of tumor cells .
We used GATK variant analysis pipeline [21–23] to process raw fastq files in this study [17] ( more details in S1B Text ) . As a standard output of the pipeline , single nucleotide variants ( SNVs ) and small insertion and deletions ( Indels ) , called from mapped high-quality reads , were saved in the variant call format ( VCF ) files [24] . At each variant locus , we extracted the genotype and the depth of reads carrying reference allele and alternative allele , all informative for SCNA inference . Fig 1 overviews the workflow of our algorithm in the paired normal-tumor setting . For both datasets , a standard implementation of NGS analysis pipeline following the GATK best practices for variant detection [17 , 21 , 22] was applied to the raw FASTQ files to generate recalibrated and deduplicated high-quality BAM files [27] , as well as VCF files containing detected SNVs and Indels . Information retrieved from biallelic heterozygous sites were processed by our SCNA analysis pipeline ( Fig 1 ) . Illumina SNP array data was processed to generate log2ratio and log2mBAF signals in a similar way for downstream analysis . Detailed data analysis steps are described in S1B Text .
First , we present a visualization of the processed signals and the results from SAAS-CNV . Fig 2 demonstrates a typical example taken from the analysis of Dataset II . The 2-dimensional profiles ( log2ratio and log2mBAF ) from SNP array ( Fig 2A and 2B ) and WGS ( Fig 2C and 2D ) were processed for genome-wide SCNA detection . The signals in the two dimensions are altered by copy number gains and losses , showing consistent patterns of changes , for both platforms . The alteration status assigned to each segment is highly consistent between the two platforms ( Fig 2A and 2C ) . We projected the medians of log2ratio and log2mBAF of each segment onto a 2-D space for SNP array and WGS respectively , confirming the consistency of the inference drawn from the two platforms ( Fig 2B and 2D ) . Segments with the same alteration status are clustered together . Segments with less difference in log2ratio dimension may be distinguished in log2mBAF dimension , suggesting the valuable information added from BAF for SCNA inference . It is noticed that the original baselines in both dimensions deviate substantially from zero ( Fig 2B and 2D ) , and simply using zero as baseline for SCNA inference would result in many false positive calls , underscoring the necessity of baseline adjustment . We processed the HKU HCC WGS data , consisting of 88 pairs of tumor and adjacent normal tissues , with the standard implementation of GATK pipeline [17] . The average read depths of 86 pairs are moderate , ranging from 24 . 8x to 71 . 5x , with two pairs sequenced at higher depth ( >120x ) . The average effective read depths measured at heterozygous sites range from 18 . 2x to 99 . 8x , composing 68%~90% of available read depth ( S1 Fig ) . We applied SAAS-CNV , CNVnorm and Control-FREEC on the 88 pairs of normal-HCC samples . Further , we synthesized WES data by retrieving reads located in exonic regions from WGS data . The synthesized WES data was used to test the performance of SAAS-CNV and ExomeCNV . We also applied SAAS-CNV and GAP on the SNP array data from the same samples . As stated above , the GAP results on SNP array data were used as “truth” in benchmarking other SCNA methods . Some basic metrics from these analyses are summarized in Fig 6 . WGS provides millions of data points ( loci ) per sample while SNP array and WES provides hundreds of thousands of data points ( loci ) ( Fig 6A ) . Since SAAS-CNV takes heterozygous loci as input , it utilizes less number of loci than competing methods . For example , only tens of thousands of loci were used by SAAS-SNV in WES data . On SNP array data , SAAS-CNV and GAP produced comparable number of segments per sample ( Fig 6B ) , as well as comparable segment size in terms of locus number per segment ( Fig 6C ) and physical length ( Fig 6D ) . On WES and WGS data , the competing methods tended to chop the genome into smaller segments ( Fig 6B ) than SAAS-CNV ( Fig 6C and 6D ) . We highlighted the potential usage of BAF information in improving the estimation of absolute copy number and tumor purity on the HKU HCC sample PT116 in Dataset II ( Materials and Methods ) . This sample exhibits complicated SCNA profile ( Fig 9 ) . We applied SAAS-CNV and CNAnorm together to analyze this sample . In CNAnorm analysis , the ratio of tumor versus normal read depth was calculated per 1kb window along the genome ( gray dots in Fig 10A ) . A smoothing approach was then employed by CNAnorm [5] to reduce the random error variability ( fitted black curve in Fig 10A ) . The distribution of smoothed ratio signal clearly showed seven major peaks corresponding to different copy numbers ( Fig 10B ) . CNAnorm attempted to fit a Gaussian mixture model along with Akaike’s information criterion ( AIC ) onto the distribution in order to identify the number of components ( peaks ) and their locations . However , CNAnorm was only able to identify five out of the seven peaks ( Fig 10C ) . It then searched different configurations , where plausible copy numbers and the five identified peak centers were aligned in different ways , and chose the most likely configuration that resulted in the best correspondance . Here , the correspondance was measured by the goodness-of-fit ( R2 value ) of the linear regression model of peak centers on estimated copy numbers ( Fig 10C ) . In this case , CNAnorm assigned the most common component ( the highest peak in Fig 10A ) to copy number 2 ( i . e . tumor-normal ratio 1 ) . Finally , CNAnorm estimated tumor purity ( ρ ) based on the relationship: μiμCN=2−1= ( CNi2−1 ) ⋅ρ ( 5 ) where μi is the identified peak center associated with copy number CNi and μCN = 2 corresponds to peak center associated with copy number 2 . With the estimated tumor purity , the absolute copy number of each segment was obtained ( S13A Fig ) . By a visual check of Fig 10B and 10C , it is obvious that CNAnorm failed to assign copy number correctly . Without accounting for the allelic imbalance pattern information ( Fig 9 ) , CNAnorm was not able to correctly infer absolute copy number in such a complicated cancer genome . For example , Chromosomes 3 , 9 , 11 , 12 , 14 and 15 were estimated to be diploid ( S13A Fig ) , contradictory to the strong pattern of allelic imbalance manifested in log2mBAF dimension ( Fig 9 ) ; Chromosomes 2 , 6 and 18 were estimated to be approximately triploid , also contradictory to the log2mBAF pattern showing allelic balance ( Fig 9 ) . These observations motivated us to manually correct the inference from CNAnorm . We adjusted the correspondence between peak centers and copy numbers and as a result of the correction , the R2 was substantially improved ( Fig 10D ) . The tumor purity was re-estimated with the corrected correspondence between μi and CNi in Eq ( 5 ) , and revised from 64 . 92% to 80 . 88% . The re-estimated copy number profile was improved in terms of better fit to integer copy numbers ( horizontal black segments overlap more with the horizontal gray lines in S13B Fig ) . The correction also lead to strikingly better fit of the theoretical mBAF to the observed mBAF values ( Fig 11 ) . The theoretical mBAF was calculated based on the estimated copy number and tumor purity ( detailed in S1K Text ) . Interestingly , this tumor genome likely underwent a whole-genome doubling event [31] and was estimated to be tetraploid , based on which the baseline of “normal” copy number was anchored . In summary , this case study illustrated the joint inference from BAF along with total copy number could result in more reliable estimation of tumor ploidy and purity .
We have developed a joint segmentation and inference approach for SCNA analysis using both total and allele-specific sequencing depth and investigated its performance with a “null” dataset and a real-world large-scale dataset . Compared with existing methods: ExomeCNV , PatternCNV , CNAnorm and Control-FREEC , our method demonstrated improved accuracy , scalability to large cancer sequencing studies , and the potential in improving the estimation of tumor ploidy and purity . Our approach exhibits flexibility and applicability in a wide range of platforms , including both deep sequencing and SNP array . These good properties can facilitate integrative cancer genomics study using multiple platforms . An R package called saasCNV , which implements our proposed appoach , is avaiable at https://zhangz05 . u . hpc . mssm . edu/saasCNV/index . htm . In contrast with germline CNV detection , characterization of SCNA in cancer genome gives rise to particular challenges , including ambiguous baseline due to aneuploidy and diluted signal pattern due to heterogeneity and normal cell contamination . For this regard , allelic specific information adds valuable input . Our method takes advantage of this information in both deep sequencing and SNP array data and achieves better capability in the correct identification of copy number baseline . We have also demonstrated that incorporating BAF could substantially improve the inference of tumor ploidy and purity . An important future work is the development of an integrated statistical model based on segment-level total sequencing depth and BAF to infer absolute copy number and tumor purity simultaneously . SAAS-CNV features good efficiency and scalability by adopting the “top-down” strategy , which reduces the number of statistical inferences from millions to hundreds , and by taking advantage of condensed information from VCF file , which is about 1% of the size of BAM file . On Dataset II , we showed WES of moderate sequencing depth can still provide comparable specificity and sensitivity as WGS in large SCNA detection . At the same time , we acknowledge that quantifying SCNA at heterozygous loci has limitations in that not all information from mapped reads is fully utilized . While this limitation has less influence in WGS , where SNPs and indels are densely distributed on the genome , it affects the resolution in a greater degree on WES data . A possible improvement is to leverage BAMs to boost the SNR of log2ratio signal and the resolution by averaging over read depths within the windows surrounding or nearby heterozygous sites . However , it is quite time-comsumig to manipulate large BAM files . An alternative option is to utilize genomic VCF ( GVCF ) file , which is in the similar format as VCF , but record both variant sites and non-variant blocks . Inspired by the normalization procedure of Illumina SNP array data [7] , the data processing step in our method can be further improved by incorparating multiple samples simultaneously , especially when manipulating GVCF makes it computationally feasible . The matched tumor-normal design provides several desired features for the identification of SCNA: 1 ) it is biologically sensible to take matched normal genome as reference to define somatic alterations in tumor genome; 2 ) it is helpful to reduce the bias induced from the spatially non-uniform distribution of short reads across the genome , due to variability in GC content , exon capture efficiency , mappability of complex regions and so forth ( also see S1L Text ) ; 3 ) it is able to improve the normality of signals ( S2 Fig ) , which is desirable for joint segmentation step; 4 ) it can help alleviate allelic signal bias commonly observed in SNP array data ( S10 Fig ) [30] . Lastly , our method can be used in accompany with paired-end mapping or split read methods , for example CREST [28] , to refine the resolution of break points up to base pair level and verify other types of genomic rearrangements , such as intra-chromosomal and inter-chromosomal translocations , which are commonly associated with SCNAs [32] . | Somatic copy number alterations ( SCNAs ) are essential in oncogensis and progression of a variety of cancers . Accurate identification and quatification of SCNAs are fundamental in the effort of cataloging different variants in cancer genome . This task has its own challenges due to complex nature of tumor SCNA profile and is further complicated by the heterogeneity of the cells collected from a tumor tissue and the contamination from adjacent normal cells , making it difficult for the methods well tailored for the detection of germline copy number variation ( CNV ) to fit in tumor SCNA detection . Next generation sequencing provides an opportunity to comprehensively characterize SCNA at unprecedent resolution . While total read depth information is commonly used in SCNA detection methods , the allele-specific read depth is less often considered , leading to sub-optimal solution . By incorparating both pieces of information , we developed a segmentation-based pipeline to address aforementioned issues in SCNA detection . This tool is applicable on both deep sequencing data as well as SNP array data and enables accurate and efficient characterization of genome-wide SCNA profile to facilitate large-scale cancer studies . | [
"Abstract",
"Introduction",
"Methods",
"and",
"Materials",
"Results",
"Discussion"
] | [] | 2015 | SAAS-CNV: A Joint Segmentation Approach on Aggregated and Allele Specific Signals for the Identification of Somatic Copy Number Alterations with Next-Generation Sequencing Data |
NKT cells play an important role in autoimmune diseases , tumor surveillance , and infectious diseases , providing in most cases protection against infection . NKT cells are reactive to CD1d presented glycolipid antigens . They can modulate immune responses by promoting the secretion of type 1 , type 2 , or immune regulatory cytokines . Pathogen-derived signals to dendritic cells mediated via Toll like Receptors ( TLR ) can be modulated by activated invariant Natural Killer T ( iNKT ) cells . The terminal β- ( 1–4 ) -galactose residues of glycans can modulate host responsiveness in a T helper type-1 direction via IFN-γ and TLRs . We have attempted to develop a defined immunotherapeutic , based on the cooperative action of a TLR ligand and iNKT cell using a mouse model of visceral leishmaniasis . We evaluated the anti-Leishmania immune responses and the protective efficacy of the β- ( 1–4 ) -galactose terminal NKT cell ligand glycosphingophospholipid ( GSPL ) antigen of L . donovani parasites . Our results suggest that TLR4 can function as an upstream sensor for GSPL and provoke intracellular inflammatory signaling necessary for parasite killing . Treatment with GSPL was able to induce a strong effective T cell response that contributed to effective control of acute parasite burden and led to undetectable parasite persistence in the infected animals . These studies for the first time demonstrate the interactions between a TLR ligand and iNKT cell activation in visceral leishmaniasis immunotherapeutic .
Visceral leishmaniasis ( VL ) is a deadly disease caused by the parasitic protozoan Leishmania donovani ( LD ) in India , Bangladesh , China , Nepal and Sudan; by L . Infantum in N . Africa and Southern Europe , and by L . chagasi in Latin America . There is a regional variation in response to antileishmanial drugs and thus treatment regimens vary in different regions . Except for in Europe and antimony-unresponsive regions of India , pentavalent antimonials are still the drug of choice . In Europe liposomal amphotericin is used . Miltefosine is the first effective oral drug for VL . http://www . nature . com/nrmicro/journal/v5/n11/full/nrmicro1748 . html - B119#B119Most available drugs are costly , cause severe side toxicity , require long treatment regimens and are becoming more and more ineffective , necessitating the discovery of new drugs . The problem is further magnified by the emergence of drug resistance and HIV co-infection [1] , [2] . One approach that has shown promise is immunotherapy . The disease is characterized by depressed cell-mediated immunity ( CMI ) and agents which directly stimulate the macrophage ( Mφ ) to kill intracellular amastigotes and/or induce the basic T-helper type 1 ( Th1 ) -cell anti-leishmania immune response would provide a rationale for treatment in visceral infection [3] . Conventional CD4+ and CD8+ T cells of the immune system recognize specific peptide antigens ( Ag ) bound to major histocompatibility complex ( MHC ) class II or MHC class I molecules , respectively . In contrast Natural Killer T ( NKT ) cells are a unique subset of T cells that recognize glycolipid antigens presented by CD1d molecules . NKT cells have the potential to produce key type 1 and type 2 cytokines and are involved in the control of several types of immune response [4]–[6] . It has been suggested that IFN-γ production in response to glycolipid Ag stimulation is initiated after Toll-like-receptor ( TLR ) signaling of Ag-presenting-cells ( APCs ) and subsequent recruitment of NKT cells as well as other cell types [7] . The β ( 1–4 ) galactose terminal Lewis X type glycans have the ability to modulate host responses in a Th1 direction via NF-κBp65 , IFN-γ and macrophage TLRs [8] . Binding of LD immunostimulating glycosphingophospholipid ( GSPL ) Ag to the Ricinus communis agglutinin-1 ( RCA-1 ) [9] suggests that this glycolipid possesses terminal β1 , 4 linked galactosyl residues [10] . In vitro pulsing of LD infected APCs with GSPL , caused the activation of the Vα14+CD1d1-specific NKT cell hybridoma DN32 . D3 [11] . GSPL also induced ROS and RNI in addition to IFN-γ and IL-12 in PBMC from normal individuals [12] . Control of LD infection depends on early induction of an IL-12-driven expansion of Th1 cells , production of IFN-γ [13] , [14] , Mφ activation and subsequent generation of reactive nitrogen and oxygen species [15] . Because GSPL can induce type 1 cytokines and nitric oxide ( NO ) generation in Mφs , we tested its therapeutic efficacy in the mouse model of VL . Our data demonstrated that GSPL could confer complete protection to LD infection by an early triggering of IL-17 and IFN-γ responses of murine NKT cells by TLR4 activated CD11c+ APCs .
To explore whether terminal β- ( 1–4 ) -galactose residues were needed for GSPL mediated protection , GSPL was treated with α or β galactosidase . The purity of these preparations was analyzed by 1 ) TLC mobility shift and 2 ) by lectin blotting with Erythrina cristagalli lectin ( ECL ) , which detects terminal β1 , 4-galactose residues ( Figure S1 A , B ) . Bone marrow-derived-macrophages ( BMDC ) adhered on cover slips were infected with LD . Therapeutic efficacy of galactosidase treated GSPL ( 100 µg/mL ) was compared to untreated GSPL ( 100 µg/mL ) . Intracellular parasite number in control LD infected BMDC was 4310±165 . 44 parasites/1000 BMDC . α-galactosidase treated ( 100 µg/mL ) and untreated GSPL ( 100 µg/mL ) reduced the intracellular parasite number of LD infected BMDCs by 97 . 2% and 97 . 5% respectively ( 123 . 66±14 . 29 parasite/1000 BMDC and 110 . 33±21 . 59 parasite/1000 BMDC respectively ) . However , commercially available position specific β- ( 1–4 ) -galactosidase treated GSPL reduced the intracellular parasite number by only 2 . 7% ( 4237±79 . 37 parasite/1000 BMDC ) . These observations indicate that terminal β- ( 1–4 ) galactose residues play an important role in the therapeutic efficacy of GSPL against experimental VL . No obvious cytotoxicity was noted against BMDC at concentrations ranging from 50–400 µg GSPL/mL/106 cells ( Figure S1 C ) . A critical function of macrophages is their ability to phagocytose . The phagocytic ability of the BMDCs treated with GSPL remained unaltered ( Figure S1 E ) . GSPL binds to CD1d , restores the defective APC function in LD-infected cells , and stimulates robust IL-2 production in Vα14Jα18 NKT hybridoma cells [16] . The evident immunomodulatory property of GSPL paved the way for assessing its antileishmanial potency in the murine model of VL . Treatment of LD-infected BALB/c mice was initiated 8 wk post-infection ( p . i . ) , time when the infection was already established . LD infected animals were divided into five groups of 20 animals each . Group I mice received 100 µL of vehicle only . Mice in groups II received β1 , 4-galactosidase treated GSPL twice at 15 days interval ( 100 µg/100 µL vehicle , subcutaneous [s . c . ] ) , Group III mice received 100 µL of αgalactosidase treated GSPL twice at 15 days interval ( 100 µg/100 µL vehicle , s . c . ) , Group IV received GSPL only while group V received 100 ng/mL polymyxin/dose along with GSPL . Fifteen days post treatment , parasite burden in the liver , spleen , and bone marrow of each BALB/c mice was determined . The effect of GSPL was dose dependent , being effective at 10 , 25 , 50 µg/dose , and optimal at 100 µg/dose ( Figure 1 A ) . At 100 µg/dose , there was complete absence of amastigotes in the impressions of stamp smears of transverse sections of spleens and livers of 90% , and 100% animals respectively . There was also complete absence of amastigotes in the bone marrow smears of 90% animals ( Figure 1 B ) . Rest of the animals showed ∼99% reduction in splenic and bone marrow parasite burden . The reduction in parasite number was sustained up-to 12 months post treatment , until the end of the experiment . Up-to 12 months later , we still could not detect any liver parasite burden , while spleen and bone marrow parasite burden was still almost absent ( Figure S2 ) . At the end of treatment , the average body weight in animals treated with GSPL was equivalent to values for the control vehicle treated group ( control group of mice: 25 . 9±1 . 1 g; treated group of mice: 26 . 1±1 . 2 g ) . There was no appreciable change in the protective efficacy of GSPL in presence of polymyxin B ( 100 ng/mL , Figure 1 A ) . This result rules out the possibility that there was any LPS ( lipopolysaccharide ) contamination in GSPL . The LAL endpoint assay further confirmed that the GSPL preparations did not contain endotoxin ( data not shown ) . To further ascertain whether GSPL had conferred long-lasting immunity , cured mice were later re-infected via the intracardiac ( i . c . ) route , 8 wk after the last GSPL dose . Parasite burden in the re-infected animals progressed rapidly in vehicle-treated BALB/c mice , whereas GSPL-treated mice remained resistant , as observed up to 60 days ( Figure 1 C ) . Thus , GSPL therapy might exert an acquired protective immunity against VL . In order to confirm the association of terminal galactosylation with the protective efficacy of GSPL , LD-infected BALB/c mice ( n=20 ) were treated with β1–4 galactosidase treated GSPL ( 100 µg/dose ) or α-galactosidase treated GSPL ( 100 µg/dose ) 8 wk p . i . As shown in Figure 1 A and 1 B , treatment of GSPL with β1–4 galactosidase resulted in only ∼4 . 76% , 4 . 8% and 3 . 7% reduction of hepatic , splenic and bone marrow parasite burden respectively , while α-galactosidase treated GSPL was as effective as untreated GSPL . It has been reported that the terminal β- ( 1–4 ) -galactose linked glycans can modulate host responses in a T-helper type 1 direction via NF-κB p65 , IFNγ and macrophage TLRs [8] . As GSPL expresses β1 , 4-terminal galactose residues , we designed experiments to determine whether TLR expression on APCs was important for intracellular parasite killing by GSPL . Treatment of infected BMDCs with 100 µg/mL GSPL but not β-galactosidase treated GSPL resulted in a significant reduction in intracellular amastigotes ( 96% reduction ) and this reduction was notably negated by prior treatment with TLR4/MD2 Ab ( 10 µg/mL ) , but not TLR2 Ab ( 10 µg/mL ) ( 2% and 92% reduction in intracellular parasites respectively ) ( Figure S3 A ) . The viability ( Figure S3 B ) and phagocytic ability ( Figure S3 D ) of the BMDCs treated with GSPL remained unaltered . To further assess the possible role of TLR2 and 4 in GSPL-mediated anti-leishmanial effector response , siRNA mediated knock-down system was used . TLR silencing was confirmed by western blot assays ( Figure 2 A ) . As shown in Figure 2B , transfection of splenic adherent cells with siRNA directed towards TLR4 but not TLR2 significantly abrogated GSPL-mediated- parasite suppressive effect . The addition of polymyxin B ( 100 ng/mL ) did not alter the GSPL mediated killing of the parasite ( Figure 2 B ) . Since GSPL did not confer protection in presence of TLR4 Ab or siRNA , we evaluated therapeutic efficacy of GSPL in LD infected TLR4 defective C3H/HeJ mice . Sixty days post-infection , LD infected C3H/HeJ mice were treated twice at 15 days interval with GSPL ( 100 µg/dose ) . GSPL elicited very little protection in the C3H/HeJ mice compared with the vehicle treated mice and the parasite load remained high in the treated mice ( Figure 2 C , D ) . Our findings demonstrate that TLR4 is essential for protective immunity elicited by GSPL . To ascertain if host responses mediated through TLR4 contribute to parasite clearance in an iNKT independent manner , we used LPS to examine the effect of TLR4 activation on intracellular parasite replication . Sixty days post-infection , LD infected BALB/c mice were treated with LPS thrice ( 5 µg/dose or 10 µg/dose; intraperitoneal injection ) on alternate days and animals were sacrificed on days 1 , 3 and 12 after the last treatment . LPS treatment did not lead to reduced parasite burden ( Figure 2 E , F ) . TLR/MyD88-dependent signaling has been implicated as essential for the immune responses against Leishmania parasites [17] . To determine the effect of MyD88 on TLR4 mediated protection , BMDCs were either mock transfected or transfected with MyD88 siRNA , infected with LD parasites followed by GSPL treatment . GSPL-induced inhibition of amastigote multiplication was also found to be markedly attenuated by MyD88 gene silencing ( Figure 2 B ) . MyD88 silencing was confirmed by western blot assays ( Figure 2 A ) . To determine if GSPL could modulate TLR expression , we analysed the expression of TLR2 , and 4 in LD infected GSPL treated APCs by real-time-PCR . Stimulation with the TLR ligands Zymosan ( TLR2 ) and LPS ( TLR4 ) induced increased expression of the corresponding TLRs . GSPL treatment significantly increased TLR4 expression but not TLR2 expression , in both LD infected and uninfected APCs ( Figure 3 A ) . We next assessed the expression of TLR proteins by flow-cytometry . Significant numbers of CD11c+ spleen cells expressed TLR2 and 4 . Compared to LD infected cells , there was a 2 . 45-fold increase in the number of CD11c+TLR4+ cells on GSPL treatment in LD infected APCs . There was also a 1 . 63-fold increase in the number of CD11c+TLR4+ cells on GSPL treatment in the uninfected APCs , where as there was no significant change in the expression of CD11c+TLR2+ cells ( Figure 3 B , C ) . Stimulation of TLR4 induces DC maturation and strong Th1-type responses through release of IL-12 [18] . iNKT cells isolated from experimental animals were adjusted to 1×105 cells/mL and mixed with 1∶10 of autologous splenic adherent cells ( 1×106 adherent cells ) . To determine the pattern of adherent cell composition , we performed flow cytometric analysis ( Figure S4 ) . Adherent cells were composed of 72% Cd11b+ cells , 7% Cd11c+ cells and 18% Cd11b+Cd11c+ cell populations . The purity of the sorted NKT cells averaged greater than 99 percent ( Figure S5 Aiii ) . To determine whether IL-12 secretion following iNKT cell activation correlates to in vivo induction of Th1 and Th2 cells , APC were cultured with purified iNKT cells as described by Maruo et . al . [19] . Culture supernatants were collected after 24 h and IL-12p70 and IL-12p40 was measured by ELISA . There was ∼10 . 9 and 10 fold increase in the production of IL-12p70 and IL-12p40 respectively in the GSPL treated LD infected BALB/c mice compared to the infected control animals ( Figure 4 A ) . IL-12p70 and IL-12p40 secretion in the GSPL treated C3H/HeJ mice were appreciably lower compared to the cytokine levels in wild type ( WT ) BALB/c mice . There was no appreciable change in IL-12p70 or IL-12p40 expression in presence of polymyxin B ( 100 ng/mL ) . This result rules out the possibility that there was any LPS contamination in GSPL . Release of IL-12 by dendritic cells ( DC ) activated by TLR ligation is dependent on MyD88 signaling [20] . GSPL-induced IL-12 production was found to be markedly attenuated by MyD88 gene silencing ( Figure 4 A ) . There was 11 and 10 fold increase in the production of IL-12p70 and IL-12p40 respectively by APCs that were transfected with non-silencing siRNA control ( Figure 4 A ) . Since there was an increase in IL-12p40 , we assessed the expression of the IL-12 and IL-23 specific chains IL-12p35 and IL-23p19 by real-time PCR . There was significant increase in the expression of both IL-12p35 and IL-23p19 in GSPL treated BALB/c mice ( Figure 4 B ) . For IFN-γ , IL-18 , TNF-α and NO estimation , iNKT cells isolated from experimental BALB/c mice were mixed with autologous splenic adherent cells as described above . Supernatants from GSPL stimulated and un-stimulated cultures were collected after 48 h for IL-18 and 72 h for IFN-γ , TNF-α and nitric oxide ( NO ) , and the levels of cytokines were measured by ELISA and NO by the Griess Reagent . Cells were also analyzed by intracellular cytokine staining and NO staining with 4 , 5-diaminofluorescein diacetate . No cytokines were detected without antigenic stimulation of spleen cell cultures . Cytokine profile at the protein level was assessed by ELISA . Compared to the LD infected animals , there was a 10 , 4 . 5 , 10 . 3 and 11 . 4 fold up-regulation of IFN-γ , IL-18 , TNF-α , and NO respectively in the cured BALB/c mice , where as the cytokine levels were appreciably lower in the GSPL treated C3H/HeJ mice ( Figure 5 , C–F ) . Intracellular cytokine levels as measured by flow cytometry revealed a similar increase of IFN-γ , IL-18 , and TNF-α in spleen cells of GSPL-treated infected animals ( Figure S5 ) . To specifically identify iNKT cells , we gated on cells that doubly stained with fluorescent PE–CD1d–α-GalCer tetramers and PE-Cy5-anti-TCRβ ( Figure S5 , C , gate R5 ) and analyzed intracellular FITC-labeled cytokines in the gated populations . Consistent with our ELISA studies , and intracellular cytokine profiles , transcript levels for IFN-γ , IL-18 , TNF-α , and iNOS in spleen cells of GSPL-treated infected animals showed similar results as assessed by real-time PCR ( Figure S6 , A-D ) . IFN-γ and NO are important mediators of antileishmanial immunity . IL-18 has been demonstrated to act in concert with both IL-12 and IL-23 to enhance IFN-γ production mediated by either NKT or NK cells [21]–[23] . To investigate if IL-12 , IL-18 , or Il-23 enhanced IFNγ production , we examined the effect of neutralizing anti IL-12 , anti IL-18 or anti IL-23 Abs on IFNγ production . Significantly lower levels of IFN-γ were produced when cultures of BALB/c cells were incubated with either anti IL-12p70 , anti IL-18 or anti IL-23p19 antibody ( 10 µg/mL ) ( Figure 5 C , Figure S5 ) . Treatment with control IgG did not have any effect on IFN-γ secretion ( data not shown ) . To define the physiologic role of IL-12 , IL-18 and IL-23 in GSPL mediated protection , we further examined the effect of neutralizing anti-IL-12 , anti IL-18 and anti IL-23 Abs on LD infected mice . Administration of the Abs significantly abrogated the protective efficacy of GSPL ( Figure S7 ) . Since , the C17 . 8 antibody has the potential to recognize the p40 subunit of both IL-12 and IL-23 ( composed of p19 and p40 ) we knocked down IL-12p35 , and IL-23p19 in BMDC with IL-12p35 and IL-23p19 specific siRNAs respectively , infected the BMDCs in vitro for 48 h before treating with 100 µg/mL GSPL . Cytokine silencing was confirmed by real-time-PCR ( Figure 5 A ) . The data were normalized using the housekeeping gene GAPDH . IFN-γ produced by LD-infected GSPL treated control BMDC ( un-transfected ) was 1039 . 7±123 . 10 pg/mL . Transfection of BMDC with IL-12p35 or IL-23p19 specific siRNAs resulted in a significant reduction in IFNγ production ( Figure 5 C , insert ) . Transfection with scrambled siRNA did not have any effect on IFN-γ secretion . Transfection efficacy was determined using FITC-labeled scrambled siRNA control . The transfection efficiency was quantified by flow-cytometry . As shown in Figure 5B , FITC-labeled control siRNA successfully transfected >93% of the cells . Since IFN-γ is needed for optimal induction of NO , a predicted consequence of IFNγ down-regulation was a significant reduction in NO production ( data not shown ) . GSPL treatment significantly enhanced the expression of IL-17A , TGF-β and IL-6 in the LD-infected BALB/c mice . This was reflected in a 4 . 2 , 4 . 7 and 2 . 4 fold increases in IL-17A , TGF-β and IL-6 protein detected by ELISA in culture supernatants ( Figure 5 G–I ) . Though IL-4 expression was comparable in the infected and cured groups ( Figure 5 J , IL-13 level was higher in the cured group of BALB/c mice ( Figure 5 K ) . The elevated IL-10 expression in the infected BALB/c mice declined in the cured group of mice ( Figure 5 L ) . There was no appreciable change in cytokine expression in the GSPL treated and untreated LD-infected C3H/HeJ mice ( Figure 5 , G–L ) . Similar results were obtained when the intracellular cytokine and mRNA transcripts levels of the infected and cured groups was compared ( Figure S5 , S6 ) . It has been suggested that a rapid innate IL-17 production by NKT cells precedes the adaptive IL-17 response [24] . To explore this probability , GSPL stimulated spleen cells from infected and GSPL treated cured animals were fractionated into CD1d-αGalCer tetramer+ iNKT cells and iNKT cell depleted populations . iNKT cells were purified using MACS beads followed by FACS sorting . To specifically identify iNKT cells , we gated on cells that doubly stained with fluorescent PE–CD1d–α-GalCer tetramers and PE-Cy5-anti-TCRβ ( Figure 6 A–C ) . We gated further on the CD1d-α-GalCer− PE-Cy5-anti-TCRβ+ PE-CD4+ ( Figure 6 D ) and analysed IL-17A production in both the gated populations . Kinetics of IL-17A production in cultured supernatants of iNKT cells or iNKT− CD4+ TCRβ+ cell populations ( 1×105 cells/mL ) co-cultured with 1∶10 of autologous splenic adherent cells revealed that there was significant IL-17A production by NKT cells from cured mice within 6 h that reached a plateau by 24 h ( Figure 6 E ) , while within iNKT−CD4+TCRβ+ populations in these mice , though there was very little IL-17A production up to 24 h , IL-17 production markedly increased thereafter ( Figure 6 F ) . Though mouse TH17 cells have been reported to require IL-6 and transforming growth factor-β ( TGF-β ) for lineage commitment and IL-23 for maintenance , the early production of IL-17 by NKT cells is independent of IL-6 [24] . Consistent with an expandable role for IL-6 in the expression of IL-17 by NKT cells , IL-6 neutralization ( 20 ng/200 µL ) did not effect the expression of IL-17A in NKT cells ( Figure 6E ) , though IL-17A secretion from iNKT− CD4+ cells was almost completely inhibited when the cells were pretreated with an anti-mouse IL-6R Ab ( 1 µg/mL ) ( Figure 6 F ) . Treatment with isotype control Abs did not have any effect on IL-17A production by T cells ( data not shown ) . A similar IL-17A expression profile of iNKT and iNKT−CD4+ TCRβ+ cells was also reflected in terms of protein secretion as determined by ELISA ( Figure S 8 ) . Based on the knowledge regarding the role of TLR4 in IL-23p19 mediated IL-17 expression by iNKT cells [25] , we wanted to assess whether TLR4 signaling was critical for IL-23p19 mediated IL-17A production in GSPL treated LD-infected BALB/c mice . iNKT cells ( 1×105 cells/ml , isolated from experimental animals ) co-cultured with 1∶10 of autologous splenic adherent cells were stimulated with GSPL ( 100 µg/mL ) . There was an up-regulation of IL-17A protein expression from 603 . 67±22 . 94 pg/mL in infected group of animals to 5209 . 33±316 . 09 pg/mL in the cured group . Ab neutralizing anti-IL-23p19 ( 10 µg/mL ) abrogated IL-17A expression in GSPL-pulsed iNKT cell-autologous adherent cell co-culture from cured group of animals ( Figure S9 ) . Silencing of IL-23p19 expression in adherent cells by IL-23p19 specific siRNA transfection before setting up adherent cell-autologous non-adherent cell ( iNKT cells ) co-culture also abated IL-17A secretion ( Figure S9 ) . Isotype control or scrambled siRNA transfection did not have any effect on IL-17A expression . These results suggested that IL-17A may play a role in GSPL therapy against experimental VL . We therefore examined the effects of IL-17A depletion on the development of GSPL mediated cure in mice given an experimental LD infection . LD infected animals were treated with neutralizing antibodies against IL-17A . IL-17A depleted animals had significantly higher organ parasite burden compared to mice treated with the isotype control antibody ( p<0 . 0001 ) at day 15 post-treatment ( Figure 7 A , C ) . These results suggest that IL-17 may be important for optimal protective immune responsiveness during GSPL therapy . From the results it appeared that GSPL conditioned spleen cells drive concurrent IFN-γ and IL-17A . To assess the direct role of IFN-γ in GSPL mediated protection , LD infected BALB/c splenic macrophages adhered on cover-slips ( 4578±115 . 53 parasites/1000 adherent cells ) were treated with GSPL in presence and absence of anti IFN-γ Ab . While single exposure to 100 µg/mL GSPL for 24 h post-infection , reduced the intracellular parasite load to virtually zero ( 4 . 52±1 . 0 parasites/1000 adherent cells ) , 1 h pre-treatment with 20 ng anti IFN-γ significantly abrogated the GSPL mediated protection ( 4342 . 6±173 . 44 parasites/1000 adherent cells ) . Further evidence for the critical role for IFN-γ in the control of LD infection comes from the demonstration that GSPL treatment failed to cure infection in IFN-γ knockout ( KO ) mice ( Figure 7 B , D ) . Treatment of LD infected C57BL/6 WT mice with GSPL ( 100 µg/dose ) resulted in complete absence of promastigotes in the serially diluted spleen cell cultures till 21 days of observation ( Figure 7 B ) in 4 out of 5 animals , while there was no parasite detected in the liver of the animals treated with GSPL . Thus , IFN-γ deficiency completely abrogated the GSPL mediated protection . IL-12 production as a result of DC-iNKT cell interaction requires ligation of CD40 by CD40L . To determine the involvement of CD40-CD40L ( CD154 ) ligation , we analyzed the expression of CD40 and CD40L on DC and T cell surface respectively from GSPL treated and untreated LD infected BALB/c mice . On GSPL treatment , CD40 and CD154 expression increased on the surface of DC ( 36 . 19% ) and T ( 27 . 63% ) cells respectively , as compared to the LD infected animals ( 2 . 1% and 5 . 4% respectively ) ( Figure 8A ) . As shown earlier , GSPL treatment resulted in an increase in IL-12 production . Blocking CD40-CD40L interactions with anti CD40L mAb inhibited IL-12 production by splenocytes from cured animals ( Figure 8 B ) . Isotype matched control antibody showed no effect on IL-12 production . Adherent cells lacking T cells isolated from the cured animals did not produce IL-12p40 ( <45 pg/ml ) on in vitro stimulation with 100 µg/mL GSPL . These results indicated that the CD40-CD40L dependent IL-12 production in splenocytes occurred as a result of a direct cognate interaction between T cells and adherent cells .
Glycolipid activated iNKT cells are capable of producing both Th1 and Th2 cytokine responses . The stimulatory Th1/Th2 balance is dictated by the presence of other maturation stimuli simultaneously acting on DCs [26] . Although , interaction of the same iNKT cells with the DCs in the presence of simultaneous TLR4 stimulation significantly enhances proinflammatory DC maturation and IL-12 secretion [26] , the therapeutic implication of this phenomenon has not been exploited . Previous studies have indicated a rather controversial role of the CD1d-restricted NKT cells in VL . Studies in CD1d-deficient BALB/c mice suggested that NKT cells were required for efficient control of hepatic LD infection [27] , while , CD1d-restricted NKT cells have been reported to play only a minor physiological role in experimental VL in C57BL/6 mice [28] . Previous work from our group has shown that stimulation of NKT cells by GSPL requires the presence of CD1d [11] . Here we show that stimulation of NKT cells and TLR4 by Leishmania glycolipid Ag GSPL leads to the production of IFN-γ and IL-17A and the subsequent clearance of organ parasite burdens in a mouse model of experimental VL ( Figure 9 ) . In the BALB/c mouse model infected i . c . with L . donovani it has been reported that the liver parasite load reaches a maximum around 2-month post-infection period following which it starts declining , while parasite load in the spleen increases up to 4 months and thereafter the animals maintain chronic infection with decreased parasite load for many months , if not for life . [29] . On the other hand , VL induced by intravenous inoculation of parasites results in a faster clearance of liver parasite burden that peaks at 4 wks p . i . [29] . In the early stage of infection , in absence of activated T cells , number of parasites in the liver reaches a peak . With the acquisition of a granulomatous response at the later stage of infection , the liver parasite burden decreases [30] . In contrast to the liver , 50% of the ingested parasite inoculum is killed by the marginal zone macrophages within the first 24 h . Failure to activate intrinsic leishminicidal mechanism together with the inability to develop granulomatous immune effector responses contribute to the failure of the spleen to resolve VL [30] . The sixty days infected murine model of leishmaniasis was used since we reasoned that an efficient therapeutic agent should be able to control the splenic parasite burden . The importance of the membrane-associated LD glycolipid Ag GSPL comes from previous results , demonstrating that GSPL 1 ) can induce Th1 cytokines and NO generation in Mφ [12] , and 2 ) can stimulate robust IL-2 production in Vα14Jα18 NKT hybridoma cells [11] . Since lack of anti-Leishmania CMI has been considered a hallmark of VL [31] , parasite antigens that can control Th2 expansion and promote the predominance of a Th1 type of response should be potential candidates for specific immunotherapy . Two subcutaneous ( s . c . ) inoculations of GSPL ( 100 µg each ) 15 days apart completely cleared intracellular LD parasites in BALB/c mice . Successful therapy should not only kill the intracellular amastigotes , but should also prevent post-treatment relapse . GSPL treated animals remained parasite-free up-to 12 months post-treatment . The ability to switch between type1 ( IFN-γ ) and type 2 ( IL-4 ) cytokines emphasizes the immunological regulatory role that the NKT cells play . The mechanism by which NKT cells select the cytokines they secret are not well characterized . Cytokine response induced by stimulated DCs is influenced by pattern recognition [32] . Pathogen-associated molecular patterns ( PAMP ) that directly stimulate DCs via TLRs act together with signals from activated iNKT cells to influence the quality of immune responses induced [33] . Terminal β- ( 1–4 ) -galactose residues in glycans have been identified as the ligand that can induce IFN-γ via TLR signaling [8] , [34] . Presence of terminal β1 , 4 linked galactosyl residues in GSPL has been previously reported [9] . We have reported previously that GSPL stimulates iNKT cells [11] . Conclusive evidence that terminal β- ( 1–4 ) -galactose residues are involved in GSPL mediated protection was provided by the observation that enzymatic removal of the terminal galactose completely abrogated protective efficacy of GSPL . TLR4 expression is low in the macrophages and DCs and has been shown to be regulated by inflammatory cytokines [35] . As TLRs are instrumental in both launching innate immune responses and influencing adaptive immunity [36] , regulation of TLR expression may be important in the pathophysiology of VL . Gene knockout studies in mice have suggested that TLR signalling is essential for the immune responses against Leishmania parasites [17] . A number of in vitro and in vivo studies have already documented the importance of various TLRs in host defence against different forms of leishmaniasis [37]–[43] . Further , TLRs have the potential to act as therapeutic targets . In the recent years , TLR agonists are being developed for the treatment of cancer , allergies and viral infections and as adjuvants to enhance immune responses against tumors and infectious diseases [44] . GSPL induced the expression of TLR4 but not 2 in infected BMDCs and silencing of TLR4 markedly attenuated the leishmanicidal activity of GSPL , thereby suggesting the importance of downstream TLR4-dependent signaling in anti-leishmanial effector response . To further substantiate the role of TLR4 in protection against VL , we compared the protective efficacy of GSPL in LD infected TLR4 defective C3H/HeJ and WT BALB/c mice . GSPL mediated protection was completely abrogated in C3H/HeJ TLR4 mutant mice . To address the question whether iNKT independent TLR4 dependent immune response is essential for parasite clearance , LD infected BALB/c mice were treated with LPS . However , since LPS could not substitute for GSPL , it appears that GSPL mediated activation of TLR4 is required to bring about the protection seen . Resistance against Leishmania infection remains largely associated with an IL-12 induced type-1 response [45] . TLR signals APCs to produce high levels of IL-12 [46] , [47] . In our study IL-12 was completely down-regulated in the AG83- infected mice , whereas high levels of IL-12p40 and IL-12p35 mRNA transcripts and IL-12p70 and IL-12p40 proteins were found in GSPL treated cured BALB/c mice . Very little IL-12 could be detected in the C3H/HeJ mice . Release of IL-12 by DCs activated by TLR ligation is dependent on MyD88 signaling [20] . Inhibition of MyD88 , strongly inhibited GSPL mediated parasite suppressive effect in infected BMDCS . Interaction of PAMPs with Mφs and DCs via TLRs results in a type 1 like response [30] , [48] , [49] . The cooperative stimulation of TLR and iNKT cells resulted in Th1 skewing on GSPL immunotherapy . There was an up-regulation of the type 1 cytokine IFN-γ , IL-12 , IL-18 and IL-23 with a concomitant decrease in the disease promoting IL-10 . Besides IL-12 , cure is associated with strong IFNγ responses in the absence of IL-10 [50] . Evidence for the critical role for IFN-γ in the GSPL mediated control of LD infection came from the demonstration that IFN-γ knockout ( KO ) mice failed to cure infection . IFN-γ production by NKT cells is a consequence of the synergistic action of IL-18 with IL-12 , or IL-23 produced by PAMP stimulated APC [23] . Our results indicated that APCs after binding of PAMPs became primed to subsequently produce large amounts of IL-12 , IL-18 , and IL-23 and thus amplified IFN-γ production . This conclusion is supported by the finding that IFN-γ production decreased in presence of anti IL-12 , anti IL-18 and anti IL-23 Ab . IFN-γ derived from iNKT cells inhibits the growth of intracellular microbes by stimulating infected APCs to synthesize NO [51] . IL-12 and IL-18 augment this response . There was 11 . 4 and 13 . 5 fold increase in the expression of NO and iNOS transcript respectively in the cured BALB/c mice in comparison to the infected animals . Although IL-4 and IL-13 are associated with development of type 2 immune responses in models of cutaneous leishmaniasis [52] , [53] , there are many conflicting reports of both IL-4 and IL-13 having opposite roles to play in VL [54]–[56] . Though IL-4 production was comparable in the infected and cured mice , there was a 2 fold increase in IL-13 production in the GSPL treated BALB/c mice . In addition to classical Th17 cells , NKT cells also produce IL-17 [24] , [57] . Early production of IL-17A by CD1d-αGalCer-tetramer+TCR-β+ iNKT cells from cured animals validated the earlier findings that innate IL-17 production by NKT cells is rapid and precedes the adaptive IL-17 response [24] . In addition , in vivo GSPL treatment also produced TCR-β+CD4+ T cells capable of producing IL-17A . Early production of IL-17A by iNKT cells was independent of IL-6 , while CD4+ T cells produced IL-6 dependent IL-17A . This was in agreement with the previous finding that alpha-galactosylceramide stimulated naive IL-6 ( −/− ) splenocytes produces normal amounts of IL-17 during the first 24 h of culture [24] . IL-23 is known to promote the production of IL-17 by NKT cells mainly in a TLR2/4-dependent manner [25] . IL-17A production by iNKT cells from cured animals decreased in presence of anti IL-23p19 Ab or IL-23p19 specific siRNA . Interaction of iNKT cells with DCs , in the presence of simultaneous TLR4 stimulation , enhances IL-12 secretion through CD40–CD40L interaction [26] . In the present study we observed that IL-12 production in vivo is dependent on CD40∶CD40L ligation in GSPL-treated mice . Together , these results indicated that TLR4-NKT cell synergism mediated GSPL induced host-protective immunological response in experimental VL . The innate immune component of GSPL immunotherapy required dual activation of the IL-12/IFN-γ and IL-23/IL-17 signaling pathways . IL-23 driven NKT cells induced IL-17A , while GSPL induced IFN-γ production by NKT cells required the simultaneous TLR receptor signaling through MyD88/CD40-CD40L , and secretion of IL-12 .
Use of both mice and hamsters was approved by the Institutional Animal Ethics Committee of Indian Institute of Chemical Biology , India ( Accreditation Number 147/1999/CPCSEA ) . All animal experimentations were performed according to the National Regulatory Guidelines issued by CPSEA ( Committee for the Purpose of Supervision of Experiments on Animals ) , Ministry of Environment and Forest , Govt . of India . Four to 6 wk old BALB/c mice or C57Bl/6 mice ( irrespective of sex , originally bought from Jackson Laboratory , Bar Harbour , Maine ) , reared in the Indian Institute of Chemical Biology facility were used , with prior approval of the animal ethics committee of the Institute . C57Bl/6-background IFN-γ KO mice and TLR4 defective C3H/HeJ mice were a kind gift of Prof . A . Surolia ( National Institute of Immunology , New Delhi ) . The cells of IFN-γ KO mice reproducibly did not produce detectable IFN-γ under optimal stimulatory conditions ( not shown ) . Pentavalent antimony-responsive AG83 ( MHOM/IN/83/AG83 ) was used for experimental infection [11] . Parasites were maintained in golden hamsters as previously described [11] . Promastigotes obtained after transforming amastigotes from infected spleen , were maintained in M199 [11] . Animals were infected via the intracardiac ( i . c . ) inoculation of LD promastigotes [16] . Splenic and hepatic parasite burden in infected animals were determined as described [16] , and results were expressed as mean parasite number ± standard deviation . For bone marrow , the parasite burden was determined microscopically , as the number of parasites per 1 , 000 host nuclei in smears . GSPL was purified from AG83 promastigote membranes as described [11] . In short , late log phase promastigote membrane ( 1 g wet weight ) was extracted with 19 vol of chloroform∶methanol∶ethyl acetate∶pyridine∶4 . 5N ammonia∶water ( 15∶15∶5∶0 . 5∶0 . 5∶0 . 5 , v/v; Solvent A ) . Anionic glycolipids were eluted from a DEAE-Sephadex A-25 column with a gradient of KCl in 0 . 01 M phosphate buffer , pH 6 . 4 , containing 0 . 05 N ammonium hydroxide and 0 . 1% sodium salt of taurodeoxycholic acid . The anionic glycolipids were loaded onto a silicic acid column and the glycophosphosphingolipid ( GSPL ) was eluted with C∶M ( 4∶6 , v/v ) and further purified on a RCA-1-Sepharose 4B affinity column . GSPL was eluted with 0 . 1 M Galactose in Solvent A . Purity of GSPL was checked by HPTLC developed in three different solvent systems , Solvent B , C and D . ( Solvent B , chloroform ∶ methanol∶ 0 . 25N ammonia in 0 . 25% KCL ( 65∶45∶9 ) ; Solvent C , pyridine∶ethyl acetate∶acetic acid∶0 . 25% KCl ( 36∶36∶7∶21 , v/v ) , Solvent D , 1-butanol∶pyridine∶0 . 25% KCl ( 3∶2∶1 , v/v ) . Plates were sprayed with either the diphenyl amine reagent for glycolipids [58] or Dittmer and Lester reagent for phospholipids [59] . The endotoxin level of 100 mg/L GSPL preparation was less than 0 . 1 endotoxin units ( EU ) /mL as measured by chromogenic Limulus amoebocyte lysate ( ‘LAL’ ) endpoint assay ( QCL-1000; BioWhittaker , MD , USA ) following the manufacturer's manual . BMDC was generated from bone marrow progenitors in the presence of rmGM-CSF and rmIL-4 [11] . A total of 106 non-adherent bone marrow cells/ml , collected after passage of marrow from the tibias and femurs of BALB/c mice , were seeded in a 24-well plate in the presence of rmGM-CSF ( 150 U/ml ) and rmIL-4 ( 75 U/ml ) and then cultured for 3 days in a 37°C incubator with a 5% CO2 supply . On day 3 , nonadherent cells ( 2 . 5×106/2 ml/well ) were again transferred and supplemented with complete medium and cytokines , and subsequently cultures were fed with rmGM-CSF and rmIL-4 on days 5 and 7 . After 10 days , the nonadherent cells expressing CD11c assessed by flow cytometry ( data not shown ) were collected . During the last 24 h of BMDC culture , the cells were grown in the presence of rmTNF-α ( 20 ng/ml ) , providing DC maturation stimulus . For in vitro infection of BMDCs , cells were seeded on glass coverslips inside 6-well culture plates to a final number of 2×105 cells per well . Cells were infected with stationary phase 2nd passage LD promastigotes at a parasite/APC ratio of 20∶1 . The cells were incubated at 37°C with 5% CO2 . Following 12 h incubation , non-internalized promastigotes were removed by washing with PBS and cells were incubated for another 36 hours using the same culture media . Forty eight hours parasitized APCs are used throughout the study . Cell viability was assessed using an MTT ( 3- ( 4 , 5-Dimethylthiazol-2-yl ) -2 , 5-diphenyltetrazolium bromide ) -based colorimetric assay kit ( Roche Applied Science , Indianapolis , IN , USA ) according to the manufacturer's instructions . GSPL was dried from solution in organic solvent by rotary-evaporation under reduced pressure and re-dissolved in PBS . After brief sonication , the sample micellar solution was added to the culture medium at the selected concentrations for in vitro work . Infected BMDCs were treated with the indicated concentration of GSPL for 24 h , the coverslips with the attached infected cells were removed , washed with saline solution , fixed and stained with Giemsa . The average number of intracellular parasites per 1000 BMDC was calculated by counting the cells per coverslip . The macrophages were tested for their ability to ingest fluorescein isothiocyanate-labelled Latex beads using the Phagocytosis Assay Kit ( Cayman , Ann Arbor , Michigan , USA ) according to the manufacturer's instructions . The fluorescence was determined using a fluorescence microscope equipped with filters for detecting excitation and emission at 483 nm and 535 nm , respectively . We ensured that the fluorescence measured accounted exclusively for ingested particles , as the signal potentially generated from any non-internalized bioparticles was quenched by the addition of trypan blue , as supplied by the manufacturer . Infected BMDCs were pretreated with anti-TLR4 monoclonal antibody ( with a final concentration of 10 µg/mL; clone MTS510 , eBioscience ) , or an isotype matched control ( rat IgG2aκ , eBioscience ) , for 1 h , then washed , three times , with PBS solution . Subsequent , identical steps were taken with the GSPL treated groups . The anti TLR2 monoclonal antibody group was similarly pretreated with anti TLR2 antibody ( 10 µg/mL , clone mT2 . 7 , eBioscience ) or an isotype matched control ( mouse IgG2aκ , eBioscience ) for 1 h , prior to treatment with GSPL . BALB/c mice were infected with 1×107 , 2nd passage LD promastigotes in saline through the i . c . route . LD infected animals were divided into five groups of 20 animals each . Sixty days p . i . , animals were injected twice at 15 days interval with 100 µg GSPL/100 µL PBS through the s . c route . Group I mice received 100 µL of vehicle only . Mice in groups II received β1 , 4-galactosidase treated GSPL twice at 15 days interval ( 100 µg/100 µL vehicle , subcutaneous ) , Group III mice received 100 µL of αgalactosidase treated GSPL twice at 15 days interval ( 100 µg/100 µL vehicle , s . c . ) , Group IV received GSPL only while group V received 100 ng/mL polymyxin/dose along with GSPL . Fifteen days after the last injection , animals were sacrificed and hepatic and splenic parasite burden was determined from impression smears following methanol fixation and Giemsa staining . For the spleen and liver , the parasite burden was expressed as Leishman-Donovan units ( LDU ) , of Stauber on Giemsa-stained imprints ( LDU=number of amastigotes/1000 cells nuclei x mg organ weight ) , as well as by the limiting dilution method [11] . A weighed piece of spleen or liver from experimental animal was first homogenized between two sterile frosted glass slides in complete M199 medium and diluted with the same medium to a final concentration of 1 mg/mL . Ten-fold serial dilutions of the homogenized tissue suspensions were then plated in 96-well plates and incubated at 22°C for 2–3 wk . Wells were examined for viable and motile promastigotes at a 3-day interval , and the reciprocal of the highest dilution that was positive for parasites was considered to be the parasite concentration per milligram of tissue . The total organ parasite burden was calculated using the weight of the respective organs . Sixty days LD infected mice were treated with 5 µg/dose or 10 µg/dose LPS ( intraperitoneal , Escherichia . coli 055:B5 , Sigma ) on days 0 , 2 and 4 and animals were sacrificed on days 1 , 3 and 12 after the last treatment . Parasite loads of liver , spleen , and bone marrow of individual animals were determined as mentioned above . Galactosidase treatment was performed using 500 µg GSPL . Treatment with α-galactosidase ( G8507 , Sigma-Aldrich , St . Louis , ) was carried out using 90 mU of enzyme and p-nitrophenyl α-D-galactopyranoside as a positive control . Treatment with positionaly specific β1–4 galactosidase ( G-0413 , Sigma-Aldrich , St . Louis , ) , was carried out using 9 mU of enzyme and p- nitrophenyl β-D-galactopyranoside as the positive control . For all enzyme treatments both the treated sample and an identical negative control were worked up in tandem using the manufacturer's recommended conditions . The hydrolysates were centrifuged ( 20000 g , 4°C , 15 min ) , and the glycosphingolipids ( GSL ) were precipitated in cold acetone for 24 h at 4°C . The resulting GSL pellets were further purified by preparative TLC and purified compounds were confirmed by TLC . 10 µg of α/β-galactosidase-treated and untreated samples were run on TLC . Plates were resolved using a solvent system of chloroform∶methanol∶0 . 25N ammonia in 0 . 25% KCL ( 65∶45∶9 ) ( v/v ) . Typically , two chromatograms were developed in parallel on the same HPTLC plate . One was sprayed with the diphenylamine-aniline-phosphoric acid ( DPA ) spray reagent [60] and the other was transferred to polyvinylidiene difluoride membrane ( PVDF ) by TLC blotting [61] and overlaid with biotinylated Erythrina cristagalli lectin . The plates were then immersed in buffer B ( 0 . 01 M Na2HPO4 , 0 . 14 M NaCl , 2% polyvinylpyrrolidone-40 [pH 7 . 2] ) for 1 . 5 h at room temperature with gentle stirring ( 20 rpm ) . Plates were then incubated with biotinylated E . cristagalli lectin ( 10 µg/ml , 24 h , Vector Laboratory ) , followed by incubation with streptavidin-HRP ( 2 h , GE Biosciences ) and visualized using a luminol-based , light-producing reaction generated with the enhanced chemiluminescent detection reagent for horse-raddish peroxidase ( Pierce ) . A shift in the chromatographic mobility of β-galactosidase-treated GSPL was observed ( Figure S1 A , lane3 ) . Total RNA was isolated from splenic lymphocytes of BALB/c mice according to the RNeasy minikit isolation procedure ( QIAGEN ) , and was individually analysed ( 5 animals/group ) by real-time reverse transcription PCR . Two µg samples of RNA from different experimental groups of mice were first utilized for cDNA synthesis by random hexamers ( Invitrogen ) using Superscript II ( Invitrogen ) . The synthesized cDNA was subjected to real-time PCR with SYBR Green JumpStart Taq Ready Mix ( Sigma ) and gene-specific primers in an iCycler PCR detector ( Bio-rad ) according to the manufacturer's instructions . The primers used for amplification of IL-10 , IL-4 , IFNγ , TNFα , TGFβ and iNOS were described previously [11] . The primer sequences for p19 , p35 , IL-6 , IL-17 , TLR2 , TLR4 and GAPDH are given in Table SI . The relative quantization of products was determined by the comparative ΔΔCT method . Each gene of interest was normalized to the ß-actin gene and the fold change was compared relative to the normal control . iNKT cells from mouse spleen were isolated according to the method of Benlagha et al . [62] . Mononuclear cells were obtained from spleens of mice by purification over 35% Percoll gradient centrifugation ( Sigma-Aldrich ) . B-cells were depleted prior to iNKT cell enrichment by using CD45R ( B220 ) Microbeads ( Miltenyi Biotec ) . Subsequent isolation of iNKT cell was carried out using a PE-conjugated CD1d tetramer loaded with α-GalCer ( Miltenyi Biotec ) and anti PE-Microbeads at ice cold temperature following the manufacturer's instructions . The CD1d-tetramer+ cells were labelled with PE-αGalCer-Cd1d-tetramer ( Proimmune ) and PE-Cy5-conjugated anti-mouse TCRβ ( clone: H57-597 , hamster IgG2a; BD Biosciences ) for further αGalCer-Cd1d-tetramer+ TCRβ+ enrichment by FACS , and are referred to as NKT cells throughout the study . We gated further on the CD1d-α-GalCer− PE-Cy5-anti-TCRβ+ PE-CD4+ and are referred as the iNKT depleted populations . In a final volume of 0 . 2 mL , iNKT cells isolated from experimental animals were adjusted to 1×105 cells/mL and mixed with 1∶10 of autologous splenic adherent cells ( 1×106 adherent cells ) from individual mice ( 5 animals/group ) of different groups of experimental mice and were incubated for 24 h at 37°C with or without 100 µg/mL GSPL . Adherent cells were obtained by incubating 5×l07 spleen cells in 90-mm glass petri dishes in RPMl 1640 supplemented with penicillin/streptomycin , 10% fetal calf serum , and 5×M 2-mercaptoethanol for 3 hr at 37°C . Non-adherent cells were removed , the adherent cells were washed ( ×2 ) with warm RPMI-1640 with gentle swirling and adherent cells were gently detached using a rubber policeman . The release of cytokines was measured in the supernatants by commercial ELISA kits ( Quantikine M; R&D Systems , Minneapolis , MN , USA; IL-18 , e-Bioscience ) . The detection limit of these assays was <2 . 5 , <4 . 0 , <5 . 1 , <2 . 0 , <2 . 0 , <1 . 5 , <2 . 5 , <4 . 0 , <5 . 0 , <4 . 6 , <1 . 6 , and 10 . 0 pg/mL for IL-12p70 , IL-12p40 , TNF-α , IL-4 , IL-13 , IL-10 , IL-17A , TGF-α , IL-6 , and IL-18 respectively . Stimulation with PMA ( 250 ng/mL; Sigma-Aldrich ) and anti-mouse CD3 ( 1 µg/mL; BD Biosciences ) or medium only was used as positive and negative controls , respectively . Appropriate isotype controls were also analyzed . The data are represented as the mean ± SD of all the five individual animals per group under consideration . Blockade of IL-18 , IL-23 , IL-12 , and IL-17 was carried with 100 µg of anti-mouse IL-18 , IL-23 , IL-12 , and IL-17 Abs , clone 93-10 ( R&D Systems ) , G23-8 and C17 . 8 ( eBioscience ) and TC11-18H10 ( Southern Biotech ) respectively or an isotype matched control on day −1 , 0 and +1 of treatment with GSPL . For CD40L blockade , anti-CD40L mAb MR1 ( eBioscience ) was added to the cultures . IL-6 was blocked with 20 ng/200 µL anti IL-6 Ab , clone MP5-20F3 ( R & D Systems , rat IgG1 ) and IL-6R was blocked with mouse IL-6R Ab ( 1 µg/mL , clone D7715A7 , eBioscience , rat IgG2b ) . For Ag-specific cytokine responses , splenocytes cultured with either GSPL at 100 µg/mL concentration or no antigen ( as a negative control ) were stained for intra-cytoplasmic cytokine , or surfaced stained for TLR as described [16] . For siRNA transfection , cells were transfected with 1 µg of appropriate siRNA or control siRNA according to the manufacturer's instructions ( Santa Cruz Biotechnology ) . | Kala azar ( visceral leishmaniasis ) is a deadly disease caused by the parasitic protozoa Leishmania donovani . In absence of a suitable vaccine , the incidence of leishmaniasis has increased . The World Health Organization observes that , if the disease is not treated , the fatality rate in developing countries can be as high as 100% within 2 years . Therapy of visceral leishmaniasis can be complicated by toxic side effects , drug resistance , and the need for prolonged treatment regimens . Therefore , improved therapy for leishmaniasis remains desirable . Immunotherapy to selectively induce type 1 immune responses considered essential for resistance to leishmaniasis has shown great promise . CD1d-binding glycolipids stimulate TCR signaling and activation of invariant natural killer T ( iNKT ) cells . Terminal β- ( 1–4 ) -galactose residues in glycoconjugates have been identified as the TLR ligand that induces IFN-γ via TLR signaling . We have used the β- ( 1–4 ) -galactose terminal glycosphingophospholipid ( GSPL ) antigen from L . donovani parasites to treat infected BALB/c mice . We report that immunotherapy with GSPL induced IFN-γ , a type 1 cytokine , through the cooperative action of TLR4 and NKT-cells that contributed to effective control of acute parasite burden in the infected animals . | [
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"biochemist... | 2012 | TLR4 and NKT Cell Synergy in Immunotherapy against Visceral Leishmaniasis |
Interpreting the impact of human genome variation on phenotype is challenging . The functional effect of protein-coding variants is often predicted using sequence conservation and population frequency data , however other factors are likely relevant . We hypothesized that variants in protein post-translational modification ( PTM ) sites contribute to phenotype variation and disease . We analyzed fraction of rare variants and non-synonymous to synonymous variant ratio ( Ka/Ks ) in 7 , 500 human genomes and found a significant negative selection signal in PTM regions independent of six factors , including conservation , codon usage , and GC-content , that is widely distributed across tissue-specific genes and function classes . PTM regions are also enriched in known disease mutations , suggesting that PTM variation is more likely deleterious . PTM constraint also affects flanking sequence around modified residues and increases around clustered sites , indicating presence of functionally important short linear motifs . Using target site motifs of 124 kinases , we predict that at least ∼180 , 000 motif-breaker amino acid residues that disrupt PTM sites when substituted , and highlight kinase motifs that show specific negative selection and enrichment of disease mutations . We provide this dataset with corresponding hypothesized mechanisms as a community resource . As an example of our integrative approach , we propose that PTPN11 variants in Noonan syndrome aberrantly activate the protein by disrupting an uncharacterized cluster of phosphorylation sites . Further , as PTMs are molecular switches that are modulated by drugs , we study mutated binding sites of PTM enzymes in disease genes and define a drug-disease network containing 413 novel predicted disease-gene links .
Decreasing sequencing costs have led to unprecedented opportunities to explore human genomes [1 , 2] . Linking genome information to molecular mechanism and resulting phenotype , including disease , is a key aim of human genetics that is hindered by complex patterns of inter-individual variation [3] . Protein-coding variants found in genome-wide sequencing and association studies are often scored for functional impact using population frequency , evolutionary sequence conservation and physicochemical amino acid properties [4] . However other intrinsic protein features are functionally important . For example , physical interfaces of protein-protein interactions harbor disease mutations [5] . Post-translational modifications ( PTMs ) are biochemical alterations of amino acids that extend the functional repertoire of proteins . PTMs regulate structural confirmations of proteins , protein-protein interactions and cellular signal transduction central in development and cancer . PTMs are specific to types of amino acids . For instance , phosphorylation affects serines ( S ) , threonines ( T ) , and tyrosines ( Y ) , acetylation and ubiquitination occur on lysines ( K ) and methylation occurs on lysines ( K ) and arginines ( R ) . Often PTMs involve reversible reactions mediated by systems of reader-writer-eraser enzymes that recognize short linear motifs in substrate proteins [6 , 7] . We hypothesize that genetic variants in PTM regions add and remove molecular interaction sites and cause rewiring of protein networks that impact phenotype with potentially deleterious outcome . To investigate this hypothesis , we integrated human genome variation data and experimentally confirmed human protein PTMs . We show that PTM-associated protein-coding regions are significantly less variable among humans , independent of major known sources of variation , and also are more likely to harbor disease mutations . Genomic , pathway and network analyses support this observation across a diverse collection of sites , proteins , and processes , demonstrating the value of PTM site integration in discovery of functional genome variation .
We first investigated global trends of PTM-associated variation and focused on four modification types with the most experimental data for human proteins . Phosphorylation is the best-described PTM with important roles in core cellular processes such as cell cycle , as well as developmental and cancer pathways [8] . Methylation and acetylation are modifications primarily involved in epigenetics and regulation of chromatin state [9] , and ubiquitination is most commonly known as the signal for protein degradation [10] . We collected 130 , 439 experimentally verified PTM sites from public databases [11–13] with phosphorylation representing 72% of all sites ( Fig . 1A ) . We added ±7 flanking residues around PTM sites to account for short linear motifs and merged overlapping sequence into 55 , 543 PTM regions . PTM regions are abundant in the proteome , representing 11% of total protein sequence and involving ∼66% of proteins , with more than 25% of proteins having five or more PTM regions ( S1–S2 Fig . ) . To evaluate the importance of PTM regions , we sought region-specific signs of selection in two population genomics projects , the Exome Sequencing Project [1] ( ESP6500 ) and the 1000 Genomes Project [2] ( G1000 ) . We studied non-synonymous single nucleotide variants ( SNVs ) resulting in amino acid substitutions . Selection was inferred with two complementary criteria , proportion of rare substitutions ( Derived Allele Frequency , DAF≤0 . 5% ) , and ratio of non-synonymous to synonymous variants ( Ka/Ks ) . We carried out paired comparisons of PTM regions and non-PTM sequence in 100 bins of proteins with matched substitution rates . We found that proteins with PTMs are significantly less variable in general ( S3 Fig . ) , and thus we restricted our analysis to 12 , 495 proteins with PTMs to avoid systematic biases . Variation in PTM regions and non-PTM sequence comprised 77 , 819 and 493 , 619 unique substitutions , respectively . We found that PTM regions have significantly more rare substitutions compared to non-modified protein sequence ( p<10−10 , paired Wilcoxon test , Fig . 1B ) . PTM regions also have lower Ka/Ks ratio compared to matched non-PTM sequence ( Fig . 1C ) . Significantly lower Ka/Ks ratio is apparent in disordered protein sequence ( p<10−10 ) , while structured protein sequence shows a mildly significant difference ( p = 3 . 9×10−3 for G1000 , p = 0 . 32 for ESP6500 ) . To validate the robustness of our observations , we repeated the analyses with adjusted parameters , and subsets of sites and proteins . First , PTM regions are also constrained when all 18 , 671 proteins are considered ( S4 Fig . ) , and when PTM sites are restricted to high-confidence findings with multiple independent publications ( S5 Fig . ) . Negative selection of PTM regions is observed across bins of proteins with similar substitution frequencies , indicating that PTM constraint is independent of local variation rate ( S6–S7 Fig . ) . Disordered PTM regions are particularly significant , highlighting areas of constraint in less conserved sequence regions . Rare PTM enrichment is apparent across the gene expression intensity spectrum of the Human Tissue Expression Atlas [14] ( S8 Fig . ) , suggesting that our results are not influenced by increased sensitivity of PTM mapping experiments to abundant proteins . PTM regions also show increased proportion of rare variants when different DAF thresholds are considered , and single alleles show the strongest enrichment ( p = 6 . 3×10−16 , S9 Fig . ) . Rare variant enrichment and lower Ka/Ks ratio are confirmed in African and European cohorts of the ESP6500 dataset ( S10 Fig . ) . PTM-specific enrichment of rare substitutions is also significant in protein residues recently diverged between human and chimp ( S11 Fig . ) . Thus , analysis of ∼7 , 500 protein-coding genomes shows that PTM regions are less variable than variation-matched protein sequence and undergo specific negative selection . We next sought to verify that PTM-specific negative selection is independent of potentially confounding factors . We used logistic regression models to estimate regional probabilities of rare substitutions given PTM regions as well as six additional sources of variation as predictors , including conservation scores from protein alignment of 100 vertebrates [15] , GC nucleotide content , sequencing depth at detected substitutions , predicted protein disorder by DISOPRED2 software [16] , recombination rate from the IMPUTE2 software [17] , codon usage , and all statistical interactions of these variables . Deviance analysis confirms that PTM regions are significant positive predictors of rare substitutions that cannot be replicated by any combination of other factors ( Fig . 1D ) . In the ESP6500 dataset , PTM regions represent the third strongest predictor of rare substitutions after conservation and codon bias ( p<6 . 1×10−11; Chi-square test ) , and in the G1000 dataset as the fourth strongest after GC content ( p<2 . 3×10−15; S12 Fig . ) . Other factors support our models: for instance , while higher conservation positively associates with rare substitutions , PTM regions have relatively more rare variants than matched non-PTM protein sequence across the conservation spectrum ( S13 Fig . ) . Thus our analysis highlights specific evolutionary forces on PTM regions that cannot be estimated from conservation and major known factors relevant to genome variation . The relative depletion of inter-individual variation in PTM regions suggests that corresponding substitutions are often deleterious . In agreement with this , analysis of ∼51 , 000 disease-associated non-synonymous SNVs collected in the Human Gene Mutation Database [18] ( HGMD ) shows their over-representation in PTM regions . PTM regions are affected in 913 disease genes with substitutions in 4 , 696 protein residues ( 4 , 055±88 expected ) , comprising a significant mutation enrichment in structured as well as disordered protein regions ( p = 7 . 1×10−24 and p = 7 . 3×10−5 respectively , Fisher’s exact test , Fig . 1E ) . PTM-associated disease mutations are also over-represented when substitutions with multiple disease annotations are considered ( S14 Fig . ) . This confirms earlier analyses of PTM mutations in inherited disease and cancer by our group and others [19–21] . Our dataset includes 503 substitutions that replace 418 central modified residues , leading to hypotheses of disrupted PTM signaling in disease . The number of direct PTM substitutions affecting modified residues is statistically expected given all protein sequence ( Fig . 1E ) , potentially due to small number of such substitutions . However , substitutions in central modified residues are more frequent relative to residues of matched amino acids ( S15 Fig . ) , indicating their importance in disease . For example , phosphorylated residues are more often substituted than serines , threonines , and tyrosines in general ( p≤1 . 3×10−7 ) . The remaining PTM-associated ( flanking region ) mutations may function via other mechanisms such as interference with functional short linear motifs involved in signal transduction , studied below . To further evaluate the functional impact of PTM-associated disease mutations , we characterized corresponding protein substitutions using six state-of-the-art computational methods [22–27] . Between 15–30% of known disease mutations in PTM sites are not scored , or are predicted benign by tools such as PolyPhen2 , SIFT , and CADD ( Fig . 1F ) . As sequence conservation is an important variable in these methods , such predictions tend to underestimate the functional importance of disordered protein sequence that is less conserved ( S16 Fig . ) and enriched in PTMs [28–30] ( p<10−300 , OR = 2 . 24 , S17 Fig . ) . Comparison of functional predictions from PolyPhen2 , SIFT and CADD software shows that population variants and disease mutations in disordered regions are systematically less likely to be predicted deleterious than in non-disordered regions ( S18 Fig . ) . Predicting impact of coding variants will therefore benefit from integration of PTM region information . To understand the extent of evolutionary constraint of PTM regions , we next analyzed groups of proteins with diverse functional annotations . In each group we compared PTM regions with non-PTM protein sequence of that group , thus considering regional and process-specific differences in variation . To reliably estimate expected substitutions in structured and disordered sequences , we used logistic regression models with protein disorder as a confounding factor . First we evaluated PTM constraint across human tissues by comparing proportions of rare substitutions in PTM and non-PTM regions in the ESP6500 dataset . We retrieved 44 tissue-specific signatures of protein expression from the Human Protein Atlas [31] and defined a category of ubiquitous proteins ( expressed in ≥18 tissues ) . We found that ubiquitous proteins and 90% of tissue-specific protein groups are enriched in rare substitutions in PTM regions ( FDR p<0 . 05 , likelihood ratio test , Fig . 2A , S19 Fig . ) . Top-ranking tissues comprise human reproductive organs such as testis and placenta where gene expression is rapidly evolving [32] . Specific selection against PTM substitutions in the background of rapid evolution suggests that PTM regions control early and central aspects of tissue development and homeostasis [33] . To investigate the physiological function of PTM constraint , we studied groups of proteins annotated to 9 , 084 biological processes and pathways . Analysis of rare substitutions revealed 400 processes and pathways with significant variation bias in PTM regions , whereas 90% of these processes ( 359 ) are enriched in rare variants ( FDR p<0 . 05 , Fig . 2B , S20–S21 Fig . ) . The major functional themes with PTM-specific constraint include both rapidly evolving and conserved processes: immune response , embryonic development , brain and nervous system development , heart and renal function , lipid and carbohydrate metabolism , as well as multifunctional signal transduction pathways ( e . g . PI3K-AKT , MAPK , FGF ) . While the immune system is generally constrained in PTM regions , we find a few immune-related processes with positive selection of PTM regions , including proteins related to bacterial response , T-cells and the major histocompatibility complex . Interestingly , ∼75% of processes with significant PTM-specific selection are also enriched in disease genes with PTM mutations ( FDR p<0 . 01 , n = 287; Fisher’s exact test ) . For instance , proteins annotated to the Gene Ontology term for post-translational modifications are enriched in PTM-associated disease mutations , including cardiomyopathies , diabetes , sclerosis , and cancer ( Fig . 2C ) . As the ESP6500 dataset comprises patient cohorts of heart , lung and blood disorders , phenotypic analysis of rare PTM substitutions in these pathways may reveal novel disease genes and risk modifier variants . Together , these analyses show that PTM constraint and associated disease mutations are widely distributed in genomic and functional context . Next we studied substitutions in PTM regions relative to their potential biochemical outcome by measuring selection of distinct types of PTMs and different regions around PTM sites . To account for potential biases arising from variable codon redundancy of specific amino acids modified in different PTMs , we implemented a permutation strategy that proportionately samples relevant amino acids from background protein sequences . Analysis of rare substitutions in PTM regions indicates that central post-translationally modified residues are under the strongest negative selection ( Fig . 3A , S22 Fig . ) . This is expected as such substitutions clearly disrupt PTMs and potentially impact PTM-dependent pathways . Selection is also apparent in the flanking sequence even when central amino acids are not considered , suggesting involvement of the electro-chemical environment and short linear motifs of reader-writer-eraser enzymes . This holds true for phosphorylation as well as other PTM types . Although the statistical significance of selection in acetylation and methylation sites is weaker due to fewer sites and substitutions , constraint in their flanking sequence indicates the presence of functionally important residues . As less is known about the sequence specificity of non-phosphorylation PTMs , deeper analysis of constrained PTM regions is needed . More than 50% of PTM regions comprise multiple modified residues and regions with different PTM types are not uncommon ( Fig . 3B , S23 Fig . ) . We binned residues in PTM regions according to the number of consecutive PTM sites in adjacent sequence . We found that PTM regions with a higher concentration of modified residues are under stronger negative selection according to enrichment of rare substitutions ( Fig . 3C , S24 Fig . ) . Such PTM clusters may reflect complex signaling cascades , for example multi-phosphorylation switches involved in cell cycle control [34] , or histone tail modifications where a combinatorial PTM code determines open and closed chromatin states [9] . Substitutions in PTM clusters are more likely to disrupt existing PTM sites or create new sites by adding or removing modifiable residues or critical components of motifs . Together , these data highlight the importance of flanking sequence and suggest the presence of functional elements that regulate PTM interactions . To further investigate the variation in PTM regions , we focused on kinase signaling , as the human kinome has the most reliable information on substrate specificity [35 , 36] . Kinases are known to recognize short linear motifs in flanking sequence of approximately ±7 residues around phosphorylated residues [37] . We scanned 95 , 021 experimentally confirmed phosphosites for 124 human kinase ligand motifs and predicted a high-confidence set of kinase target sites in flanking sequence using our MIMP software [28][Wagih , Reimand , Bader , submitted] . Simulated mutations of these sites identified 61 , 178 amino acid residues in 81% of phosphoproteins that would dramatically disrupt motifs and lead to loss of signaling when substituted ( ≥4-fold decrease in binding score ) . These high-confidence motif-breaker sites cover 7% of PTM regions and contain 366 substitutions annotated to diverse human diseases ( S25 Fig . ) . When also considering direct substitutions of modification sites for phosphorylation and all other available PTM types , we predict 186 , 704 residues important for PTMs , including mechanistic hypotheses for 863 ( 14% ) of PTM-related disease mutations . Next we performed kinase-specific analyses of motif-breaker sites by proportionally sampling equal numbers of matched residues from all protein sequence . We found that motif-breaker sites in motifs of 14 kinases are significantly constrained in the population ( p<0 . 05 , permutation test; Fig . 3D , top panel ) . Similar analysis revealed 19 kinases whose motif-breaker sites are enriched in disease mutations ( FDR p<0 . 05 , Fisher’s exact test; Fig . 3D , bottom panel , S26 Fig . ) . Nine kinases are shared between the two groups , highlighting their importance in signal transduction networks . Top-ranking kinases such as AKT1 , CHEK2 , and ABL1 regulate core cellular processes of growth and proliferation , and are well-studied cancer drivers according to the CancerGenes database [38] . Members of the calcium-dependent CAMK kinase family involved in neuronal function are also apparent . The site-specific phosphorylation network of the 24 significant kinases includes 7 , 858 proteins , 69 , 248 kinase-target interactions and 35 , 253 motif-breaker sites , whereas PTM-associated disease genes are more central to this network ( p = 1 . 6×10−4 , Wilcoxon test; Fig . 3E ) . Most of significant motif-breaker sites of the highlighted kinases occur within ±3 residues of the modified residue and involve arginines ( R ) , glutamines ( Q ) , lysines ( K ) , and glutamates ( E ) ( Fig . 3F , S27 Fig . ) . As we only cover ∼25% of the human kinome with high-confidence motifs , characterization of further kinase binding specificities is likely to reveal additional motif-breaker sites . In summary , kinase motif analysis reveals negatively selected motif-breaker sites in PTM regions that likely participate in essential cellular signaling and interaction networks . In contrast , frequent disease mutations substitute motif-breaker sites and potentially abolish kinase binding , causing network rewiring . Our dataset of predicted motif-breaker sites is a useful resource for integration into variant interpretation software . To identify hotspots of disease mutation in PTM regions , we used our ActiveDriver mutational significance model [19] that evaluates enrichment of mutations in protein active sites . This analysis assumes that unexpected co-occurrence of mutations in PTM regions suggests a mechanism involved in disease . We found 152 high-confidence genes with evidence of PTM-associated disease ( PAD ) where 2 , 282 disease-annotated substitutions in corresponding proteins are significantly enriched in PTM regions ( FDR p<0 . 05 from ActiveDriver , Fig . 4A ) . Although phosphorylation is the most abundant PTM in our dataset , mutations in 47% of PAD genes affect multiple PTM types , suggesting complex modification mechanisms . The PAD gene list relates to a diverse set of human diseases , including cardiovascular ( LMNA , MYH7 ) , cystic fibrosis ( CFTR ) , diabetes ( HNF4A , IRS1 ) and migraine ( ATP1A2 ) ( S28 Fig . ) . Cancer genes are also over-represented in agreement with our pan-cancer mutation analyses [19 , 28] ( 31 genes , p = 1×10−19 ) . Several genes are known to have PTM-associated disease mechanisms and thus support our analysis . For example , hyper-phosphorylation of Tau proteins is implicated in Alzheimer’s disease [39] , and ActiveDriver predicts the corresponding MAPT gene as significantly enriched in PTM-related substitutions ( FDR p = 0 . 0011 ) . Our predicted list of PAD genes serves as a good starting point for investigating PTM mechanisms in disease . To exemplify the PAD gene list , we studied the tyrosine phosphatase PTPN11 where substitutions lead to the congenital Noonan Syndrome , a developmental disorder [40] . Half of these substitutions affect an SH2 domain and aberrantly activate the protein by disrupting its auto-inhibitory interaction [41] . We predict that these disease mutations significantly coincide with a phosphorylation cluster ( FDR p = 0 . 010 from ActiveDriver , 23 substitutions; Fig . 4B ) . While no detailed studies exist , ∼30 proteomics screens indicate PTMs in the region according to the PhosphositePlus database [13] ( e . g . ref . [42] ) . Although SH2 is known as a reader domain that interacts with phosphorylated sites [6] , phosphorylation of the domain may inhibit its interactions [43] . Thus , we propose that substitutions in Noonan syndrome aberrantly activate the PTPN11 phosphatase by disrupting a phosphorylation-mediated auto-inhibitory loop of the SH2 domain . This example illustrates the integration of PTM information and genetic mutations to predict novel experimentally testable hypotheses of disease mechanisms . PTM enzymes are well-established drug targets [44 , 45] . To investigate novel interactions between PTM-associated drugs and diverse human diseases , we studied PTM mutations in the significant gene list predicted by ActiveDriver . We aimed to discover secondary drug-gene interactions that are not apparent when analyzing drug interactions with known disease genes , but become apparent when studying the post-translational modification networks of disease genes . In particular , many disease mutations in this list affect PTM sites with experimentally verified upstream PTM enzymes , suggesting that disease mutations specifically alter enzyme activity in these sites . We found that 25% of PAD proteins are post-translationally modified by known enzymes that are also targetable with approved drugs according to the DrugBank database [46] . In such cases , pharmacological targeting with known drugs may modulate the aberrant interaction between the upstream enzyme and the substrate protein with PTM-specific disease substitutions . We summarized this as a network of 413 candidate interactions between 47 drugs and 110 diseases where interactions are mediated by PTM enzymes and site-specific substitutions in their substrates encoded by disease genes ( Fig . 4C ) . Systematic queries of drug-disease pairs in the Europe PubMed Central literature database revealed no publications for 79% of pairs ( 9% with >10 PMIDs ) , suggesting that most predicted interactions represent novel hypotheses potentially useful for drug repurposing screens . Thus , incorporation of PTM information can help identify information about potentially targetable mechanisms of genetic variant function .
The general and independent signal of mutational constraint in PTM regions establishes these as important factors to consider in variant interpretation . Abundant mutations of monogenic and complex inherited disease as well as cancer [19] emphasize the extent of pathogenic rewiring of PTM-mediated cellular interaction networks . PTM-specific constraint is distinct across the sequence conservation spectrum of human genes , and PTM regions are particularly enriched in disordered sequence that is generally less constrained . Signaling networks are thought to evolve through rapid PTM turnover in clusters such that sequence positions of individual PTM residues are often not conserved [47 , 48] . This suggests a model where mutations in PTM sites would be functionally masked by compensation from adjacent sites , however our data indicate that PTM clusters are relatively less tolerant to variation in the population and thus highly functional . Negative selection of PTM regions is also apparent in the sequence sites diverged between human and chimp , highlighting their importance in recent evolution . Therefore , variant function prediction tools are underpowered to evaluate PTM sites solely based on conservation . PTM data integration will improve predictions and provide mechanistic hypotheses . Integrated statistical modeling of population variation shows that PTM regions are significant predictors of rare substitutions regardless of several well-recognized determinants of variation . We tested six confounding factors with major impact on variation and included all potential interactions to account for complex correlations . The list of confounders is not final and other relevant factors should be further studied . For instance , chromatin state correlates with regional mutation rates in cancer cells [49] , and coding sequence variation is impacted by transcription factor binding sites in exonic DNA [50] . Future studies of population variation need to consider variable chromatin state and gene expression in tissue context . Integrated variation analysis of PTM regions , transcription factor binding sites , tissue-specific gene expression , and chromatin state may improve our understanding of the co-evolution of transcriptional and post-translational networks . Here we focused on a restricted set of post-translational modifications for which most experimental data are available for human proteins , however more than 400 post-translational modifications are known [51] . The proteomics community is mapping PTM sites across a wide range of organisms and disease states [52] , thus we expect substantial growth in this area . For example , protein glycosylation is a wide-spread modification with implications in neurological and developmental deficiencies [53] , and large-scale experimental data for human proteins are emerging . Further , whole-genome sequencing creates opportunities to evaluate variation of non-coding regulatory elements [54] . Incorporation of functional site-specific information to analyze genome variation can thus help improve associations to phenotype and decipher genetic disease .
Experimentally derived post-translational modification ( PTM ) sites were retrieved from three proteomics databases ( PhosphositePlus [13] , HPRD [11] , PhosphoELM [12] ) as 15-mer peptides and matched to longest isoforms of 18 , 671 completed human RefSeq genes ( hg19 ) allowing multiple matches per sequence , similarly to our earlier analysis [28] . Four modification types with most sites in human proteins were studied ( phosphorylation , ubiquitination , acetylation , methylation ) . Gene and protein IDs were translated to HGNC symbols with g:Profiler [55] software . Disordered and structured protein sequence regions were predicted with DISOPRED2 software [16] version 2 . 4 using default parameters . PTM regions were defined with seven amino acids of sequence flanking both sides of post-translationally modified protein residue ( PTM site ) . Partially overlapping regions with multiple adjacent PTM residues were merged . Human genome variation data were retrieved as chromosomal nucleotide annotations from online resources . Only missense single nucleotide variants ( SNVs ) were used while stop codon mutations , small indels , and structural variations were discarded . Protein-level annotations were also discarded from original datasets . Allele frequencies of the Exome Sequencing Project [1] ( ESP6500 ) for 6 , 503 individuals were downloaded from the Exome Variant Server . Allele frequencies of the 1000 Genomes Project [2] ( G1000 , Phase 1 , Release v3 ) for 1 , 092 individuals were downloaded for all Ensembl Gene ( ENSG ) coordinates from remote VCF files using the Tabix software [56] . We retrieved Derived Allele Frequencies ( DAF ) relative to the reference human genome to ensure compatibility with our mapping of PTM sites in protein isoforms . Variants with DAF = 0 were removed . Human disease mutations were collected as chromosomal nucleotide annotations from the Human Gene Mutation Database[18] ( HGMD ) after removing variants with dubious disease association ( “DM ? ” ) . Single nucleotide variants ( SNVs ) from population genome sequencing projects and the HGMD database were mapped to substitutions in human proteins ( hg19 ) using the Annovar [57] software . Non-synonymous variants affecting the same codon were filtered due to ambiguous interpretation of allele frequencies at the protein level . We used a non-redundant set of substitutions by retaining only the longest isoform of each protein . We compared our annotations of protein substitutions with publicly available annotations of the ESP6500 dataset and found and agreement of 97 . 7% . ActiveDriver software [19] was used to analyze PTM-related substitutions . Functional impact predictions of substitutions of the ESP6500 dataset and the HGMD database were retrieved from five tools ( PhyloP [22] , SIFT [23] , PolyPhen2 [24] , LRT [25] , MutationTaster [26] ) through the Annovar annotation pipeline , using the cutoff criteria as defined in the dbNSFP database of human non-synonymous SNPs [58] . Functional predictions from the CADD software [27] were retrieved from its website using the Tabix software [56] and classified according to recommended thresholds ( score<15 for benign; score≥15 for deleterious ) . First we evaluated global distribution of substitutions in PTM regions ( modified site ±7 amino acids ) relative to substitutions in matched non-PTM protein sequence using two metrics of evolutionary selection: a ) proportion of rare substitutions in all substitutions , b ) ratio of non-synonymous variants per non-synonymous site to synonymous variants per synonymous site ( Ka/Ks ) . We found that proteins with one or more PTM sites are significantly less variable than proteins without any PTM sites , and thus we filtered all non-PTM proteins from all further analyses to avoid systematic biases . The two selection metrics were computed separately for the ESP6500 and G1000 datasets and evaluated with paired one-sided non-parametric tests ( Wilcoxon signed rank tests ) to estimate statistical significance . To account for variation relative to tolerance to mutations characteristic to different protein groups , we binned all proteins into 100 non-overlapping sets with matched variation such that each set represented one percentile of proteins with similar mean substitution rate per protein sequence length . The paired tests compared PTM-associated substitutions to non-PTM substitutions across the 100 protein sets . For proportion of rare substitutions , Derived Allele Frequency ( DAF ) cutoff DAF≤0 . 5% was used to define rare substitutions . The Ka/Ks ratio was computed by accounting for all possible synonymous and non-synonymous sites in protein sequence . To further validate the three global trends of variation in PTM regions , we repeated the analyses with different subsets of genes , PTM sites and variants . To confirm that the observed PTM constraint is also apparent in the entire proteome , we replicated the analysis on all proteins . We also validated negative selection in more stringent collections of PTM sites by only retaining sites and proteins that were seen in several independent proteomics datasets ( 2+; 3+; 4+ datasets ) . To validate robustness of our observations relative to definition of rare substitutions , we tested different DAF values ( single allele , two alleles , 1% , 2% of DAF ) . To check that our observations are not biased by highly expressed proteins that are easier to capture in mass spectrometry , we binned proteins according to median gene expression value in the Human Tissue Expression Atlas of >5 , 000 microarrays [14] . We also computed the two statistics separately for populations of African and European ancestry of the ESP6500 dataset . We also separately studied the subset of ∼3 . 5% protein residues diverged between human and chimp from the 100 vertebrate protein alignments of the UCSC Genome Browser [15] . Enrichment of disease mutations in PTM regions was evaluated with two strategies . First , we computed the significance of any disease annotations in protein residues in PTM regions with Fisher’s exact tests . As many protein residues are associated to multiple disease annotations and/or substituted residues , we also conducted Poisson exact tests on the total number of disease annotations in PTM regions . Expected values were sampled from the corresponding distributions and shown with ±1 s . d . This analysis was carried out separately for structured and disordered protein sequence due to different variation rates and ascertainment bias of functional predictions . To further study disease mutations of central post-translationally modified residues , we carried out Fisher’s exact tests on each PTM type separately , restricting the background protein sequence to matched types of amino acids to avoid codon bias ( S , T , Y for phosphorylation; K for ubiquitination and acetylation; K , R for methylation ) . The latter analysis was restricted to proteins with specific types of PTMs . To further investigate the functional prediction bias of disease mutations in PTM regions and disordered sequence , we confirmed that disordered regions are less conserved in ancient as well as recent genes ( exclusive sets of genes conserved up to S . cerevisiae , D . melanogaster , D . rerio , G . gallus , M . musculus , P . troglodytes retrieved from the Ensembl database [59] ) . We measured the proportion of deleterious and benign variants predicted by SIFT [23] , PolyPhen2 [24] and CADD [27] in structured and disordered regions in both disease variants ( HGMD ) and population variants ( ESP6500 ) and estimated the significance of under-representation of deleterious variants in disordered regions with Fisher’s exact tests . Next we confirmed that observations of negative selection in PTM regions are not confounded by other factors in the ESP6500 and the G1000 datasets . We fitted binomial logistic regression models to test the contribution of PTM regions to rare substitutions relative to all substitutions in the presence of six potentially confounding factors contributing to coding genome variation: a ) GC content , b ) codon usage , c ) average sequencing depth ( ESP6500 only ) ; d ) recombination rate; e ) sequence conservation; f ) protein disorder . Our null model contained all substitutions as samples , the substitution class as response variable ( 1 as rare , 0 as common; according to DAF ) , and as predictive variables the confounding factors and all possible binary and higher-order interactions to account for complex correlations between variables . Our alternative model additionally contained the binary PTM variable and its potential interactions , indicating substitutions in PTM regions . The alternative model was further challenged with a backwards step selection procedure that discarded uninformative predictor variables . The statistical significance of PTM regions in contributing to variation patterns was assessed with an ANOVA procedure with a chi-square test , in which the difference in fits of the null and alternative model was quantified by log likelihood ( deviance ) and compared relative to change in model complexity ( degrees of freedom ) . The relative contribution of other factors and interactions was also assessed with chi-square tests . Effect directions were estimated from signs of corresponding coefficients . Confounding factors were defined as follows . GC content was retrieved for every sample ( substitution or protein residue ) as the percentage of GC nucleotides in the genomic window of 35bp around the SNV ( variant location ±17bp ) . Codon structure was coded as number of nucleotide triplets corresponding to a given amino acid . Average sequence read depth per substitution was retrieved for the ESP6500 dataset while no corresponding values were available for G1000 . Recombination rates from the 1000 Genome Project computed by IMPUTE2 software [17] ( Phase1 integrated , v3 ) were matched to every substituted protein residue by retrieving the rate of closest locus with measured recombination rate . Sequence conservation was computed from the 100 vertebrate protein alignments of the UCSC Genome Browser [15] and scored with the BLOSUM62 scores of amino acid substitution ( gaps were scored with −10 as used by the BLAST website [60] ) . Disordered sequences of proteins were predicted with the DISOPRED software [16] . Having established the global significance of PTM-related variation relative to all coding sequence , we studied PTM-related variation in different contexts including tissue-specific expression , biological processes , and pathways . To account for different mutation rates of structured and disordered protein sequences , we implemented a statistical test based on logistic regression models where the null model classified rare and common variants with disorder as a binary confounding variable , and the alternative model included an additional binary term for PTM regions . Log likelihood ratio test with chi-square statistic was used to compare the alternative and null models , and p-values were corrected with Benjamini-Hochberg False Discovery Rate ( FDR ) . Groups of highly expressed tissue-specific proteins originate from the Human Protein Atlas [31] . Tissues with numbered subsets were merged ( skin , uterus , stomach , soft tissue ) . An additional group of ubiquitous proteins was defined to include proteins with high expression in 18 or more tissues . This corresponds to robust z-score Z≥2 of tissues per gene . Ubiquitous proteins were removed from tissue-specific categories . Protein lists corresponding to pathways and processes were retrieved from g:Profiler [55] . We selected biological processes from Gene Ontology [61] , pathways from Reactome [62] and KEGG [63] , and protein complexes from the CORUM database [64] , restricting the analysis to sets with at least five and no more than 1 , 000 proteins . Pathways were assessed with the permutation-based estimation of rare substitutions and substitution density as described above . Resulting pathways were filtered for significance ( FDR p<0 . 05 ) and subsequently evaluated for enrichment of disease genes using Fisher’s exact test ( FDR p<0 . 01 ) and disease genes with PTM-related substitutions as test set . These pathways and processes were visualized as an enrichment map [65] where shades of light and dark blue represent negative selection , shades of orange and red represent positive selection , node fillings indicate significant selection in the ESP6500 dataset , and node edges show selection in the G1000 dataset . Darker nodes ( dark blue , red ) are pathways where disease genes with PTM-associated substitutions are enriched . Disease associations of pathways were further explored with word clouds using the R WordCloud package . In word clouds , text sizes correspond to numbers of disease annotations in HGMD that link to PTM-associated substitutions in pathway proteins . The same strategy was applied to evaluate variation in PTM regions across the spectra of gene expression and evolutionary sequence conservation . Proteins were binned into 100 non-overlapping groups of equal size , based on median expression across 5 , 000 tissues in the Human Tissue Expression Atlas [14] , and median protein residue conservation scores across 100 vertebrates from the UCSC Genome Browser database [15] , respectively . Each set was tested with the logistic regression models shown above . Finally , Pearson correlation scores and corresponding p-values were computed between per-bin median expression ( conservation ) values and relative enrichments/depletions of PTM-related rare substitutions across the 100 bins . Expected values were derived from predicted model responses given estimated model coefficients ( mean±standard error of responses ) . Gene expression analysis was restricted to 9 , 500 genes encoding PTM proteins that are represented in the microarrays . Codon structure appeared as an important factor in determining the extent of variation of protein residues , and this was particularly apparent when focusing on single residues such those directly modified by PTMs . To better dissect the biochemical structure of PTM regions and to correctly account for specific variation patterns in particular types of protein residues , we designed an amino acid adjusted permutation strategy . For the PTM regions within a given set of proteins , we computed the observed value as the ratio of rare PTM substitutions over all PTM substitutions . Expected values were derived by 1 , 000-fold sampling equal numbers of protein residues without replacement , accounting for amino acid frequencies in tested PTM regions or residues . First , we evaluated variation in central post-translationally modified protein residues as well as proximal ( ±1–2 amino acids ) and distal flanking residues ( ±3–7 amino acids ) using the closest PTM residue as reference . Flanking regions excluded central residues , and wide flanking regions excluded narrow ones . This analysis was performed for all PTMs together and also for different types of PTMs separately . For each comparison , only proteins with the specific type of PTM sites were considered for computing expected values . Second , we studied variation patterns in clustered PTM sites for the combined set of all PTMs . All protein residues in PTM regions were grouped into five bins based on the number of adjacent PTM sites within ±7 residues ( residue adjacent to a single site , two sites , three sites , four sites , five or more sites ) . Observed and expected substitutions were derived as described above . Using our previously developed computational strategy MIMP [28] [Wagih , Reimand , Bader , submitted] , we predicted high-confidence binding sites of 124 kinases using a reliable subset of kinase binding models ( position weight matrices ) we collected earlier . To increase the confidence of our kinase-substrate network , we only predicted motifs in protein sequence that flanked experimentally verified phosphorylation sites . In brief , a kinase was considered to bind a phosphorylation site if its binding score exceeded the bottom 10% of positive control sequences and was above 90% of negative control sequences sampled from non-phosphorylated sites with central S , T , Y residues . Using this set of predicted kinase sites , we performed all exhaustive mutations of predicted sites and selected residues that would lead to strong loss of binding motif if substituted ( ≥4-fold reduction of binding score ) . These residues are referred to as motif-breakers . Motif-breaker sites were grouped by kinases ( corresponding motifs ) and analyzed separately for enrichment of rare variants in the ESP6500 dataset . We used the amino acid-weighted permutation strategy shown above to compute expected values of proportions of rare substitutions , where amino acids corresponding to motif-breaker sites were sampled without replacement from all proteins with phosphorylation sites . Kinase-specific motif-breaker sites were also subjected to enrichment analysis of disease mutations with Fisher’s exact tests and resulting p-values were corrected with FDR . The set of 24 kinases with motif-breaker site-specific negative selection , disease mutation enrichment or both signals were selected for further analysis . Motif-breaker sites of selected kinases were collected and assembled into a network of interactions between kinases and predicted substrate proteins . Disease genes with known PTM mutations from HGMD were highlighted separately . The network was visualized with the Cytoscape software [66] . Network node degree ( i . e . , number of bound kinases ) of disease genes and other genes was assessed with the Wilcoxon test . Amino acid types and positions of all motif-breaker sites relative to central phosphorylated residues were assembled into a summarized position weight matrix and visualized as a logo using the WebLogo software [67] . We used our previously developed ActiveDriver method [19] to evaluate HGMD disease mutations in PTM sites using the entire collection of PTMs . In brief , a Poisson regression model with protein disorder as a confounding factor was used to decide whether a particular PTM site contains more mutations than expected from protein-wide average . Protein-wide significance score was estimated as an aggregate of site-specific p-values , and the results were corrected for multiple testing ( results with FDR p<0 . 05 were selected as significant genes ) . The number of independent records per amino acid position in the HGMD database was used as proxy of mutation frequency , reflecting different underlying diseases and certainty in particular disease variants . High-confidence cancer genes were retrieved from earlier review papers [68–71] via the CancerGenes database [38] . Enrichment of cancer genes was conducted with Fisher’s exact test . Using approved pharmaceutical drugs known to target these enzymes the DrugBank database [46] , we constructed a directed network of drug-disease interactions . The network contains hierarchical associations of the following components: a ) pharmaceutical compound ( drug ) acting on a PTM enzyme , b ) druggable PTM enzyme binding a disease gene according to experimental evidence from proteomics databases , c ) confirmed disease gene with enriched PTM mutations from ActiveDriver analysis , where gene mutations from HGMD specifically affect PTM sites bound by the above PTM enzyme , and d ) disease annotations from the HGMD database that associate human diseases to gene mutations that occur in the binding site of the PTM enzyme . We applied ActiveDriver analysis to pre-select disease genes with PTM-specific mutational enrichment to de-convolute the drug-gene network and focus on abundant and PTM-specific disease annotations . | Individual human genomes differ in numerous and infrequent small-scale changes such as single nucleotide variants . Understanding the biological role of variation and impact on phenotypes such as physical appearance or disease risk is an important challenge . We studied human variation of post-translational modification ( PTM ) sites spanning >11% of protein sequence . PTMs are chemical modifications of protein residues that extend protein functions and regulate many cellular processes . We found that PTM sites are specifically conserved among humans , indicating that these sequence regions are particularly important for human physiology . We confirm this observation by carefully studying other factors of genome variability , concluding that human PTM sites are broadly constrained in biological contexts . PTM sites are also significantly enriched in disease mutations , thus we can better understand disease genetics by analysing PTMs . We highlight 152 genes where disease mutations significantly accumulate in PTM regions , and integrate these with pharmacological information of PTM enzymes to predict new drug candidates to diseases . As an example , we propose a novel mechanism to PTPN11 mutations implicated in Noonan syndrome . This work aids understanding of the selective forces acting on protein-coding genome sequence and provides an integrative framework for predicting variant function in population and disease . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2015 | Evolutionary Constraint and Disease Associations of Post-Translational Modification Sites in Human Genomes |
Pneumococcal neuraminidase is a key enzyme for sequential deglycosylation of host glycans , and plays an important role in host survival , colonization , and pathogenesis of infections caused by Streptococcus pneumoniae . One of the factors that can affect the activity of neuraminidase is the amount and position of acetylation present in its substrate sialic acid . We hypothesised that pneumococcal esterases potentiate neuraminidase activity by removing acetylation from sialic acid , and that will have a major effect on pneumococcal survival on mucin , colonization , and virulence . These hypotheses were tested using isogenic mutants and recombinant esterases in microbiological , biochemical and in vivo assays . We found that pneumococcal esterase activity is encoded by at least four genes , SPD_0534 ( EstA ) was found to be responsible for the main esterase activity , and the pneumococcal esterases are specific for short acyl chains . Assay of esterase activity by using natural substrates showed that both the Axe and EstA esterases could use acetylated xylan and Bovine Sub-maxillary Mucin ( BSM ) , a highly acetylated substrate , but only EstA was active against tributyrin ( triglyceride ) . Incubation of BSM with either Axe or EstA led to the acetate release in a time and concentration dependent manner , and pre-treatment of BSM with either enzyme increased sialic acid release on subsequent exposure to neuraminidase A . qRT-PCR results showed that the expression level of estA and axe increased when exposed to BSM and in respiratory tissues . Mutation of estA alone or in combination with nanA ( codes for neuraminidase A ) , or the replacement of its putative serine active site to alanine , reduced the pneumococcal ability to utilise BSM as a sole carbon source , sialic acid release , colonization , and virulence in a mouse model of pneumococcal pneumonia .
Glycosylation is the most common posttranslational modification for proteins and lipids [1 , 2] . Given the abundance of the glycans , microbes have evolved mechanisms to take advantage of the carbohydrate-rich environment in the human body during colonization and , invasive infections [2] . Streptococcus pneumoniae is a good example in case , which has been used as a model organism for the study of microbial interaction with the glycosylated host molecules [3–5] . This microbe is a commensal of the nasopharyngeal microbiota but it also causes serious life-threatening infections with a high morbidity and mortality , such as meningitis , bacteraemia , and pneumonia [6 , 7] . The pneumococcus encounters glycosylated host molecules both during colonization and infection of various tissues , and these molecules are very relevant for pneumococcal in vivo survival , attachment , and invasiveness [4 , 8–10] . Therefore , a detailed understanding of pneumococcal interaction with host glycoconjugates can offer strategies to combat this pathogen . The pneumococcus generates energy through fermentative metabolism of sugars . However , in the respiratory tract the concentration of free sugars is limited [11] . Therefore , the pneumococcus relies on glycosylated host molecules for its nutrient requirements [11] . The hydrolysis of sugars from the complex host glycans is achieved by a collection of glycosidases [12–15] . Not surprisingly , the pneumococcus has at least 10 exo-glycosidases , including galactosidase , heaxosaminidase , and neuraminidases [8] . By its glycosidases , S . pneumoniae has capability to reduce complex glycans with the exo-glycosidases neuraminidase , galactosidase , and hexosaminidase to remove the terminal sialic acid , galactose , and heaxosaminidase , respectively . Cleavage of sugars from host glycoconjugates does not only provide in vivo source of nutrients , but also it allows the pneumococcus to infiltrate into deeper tissue sites and translocate from one niche to another [8 , 16] . Neuraminidase , also known as sialidase , cleaves terminal sialic acid from glycoconjugates , and is the most crucial sugar hydrolase since the cleavage of terminal sialic acid is required for sequential degradation of host glycoconjugates [3 , 16] . Unless it is removed , the other sugar hydrolases cannot ‘see’ their substrate . In S . pneumoniae the total neuraminidase activity is encoded by three different genes , nanA , nanB and nanC [16–20] . nanA is present in all pneumococcal strains , while nanB and nanC are reported to be in 97% and 51% of isolates , respectively [21] . The major pneumococcal neuraminidase activity is NanA , which has cell surface localization , and cleaves α2-3- and α2-6-linked sialic acid [15 , 20 , 22] . Neuraminidase A ( NanA ) plays an important role in pneumococcal colonization and virulence as it decreases the density of mucin , which mediates the pneumococcal infiltration into deeper tissue sites [15] , and exposes the host receptors for the pneumococcal attachment [23] . By using different animal models of pneumococcal infection , neuraminidase A has been linked to otitis media , meningitis , and upper and lower respiratory tract infection , and sepsis [15 , 24 , 25] . Therefore , the study of factors important for potency of NanA provides a better understanding of pneumococcal colonization and virulence . The activity of neuraminidase can be hindered due to the modifications present in sialic acid [26] . The most frequent derivatives of sialic acid in human cells is N-acetyl-9-O-acetylneuraminic acid ( Neu5 , 9Ac2 ) , N-acetylneuraminic acid ( Neu5Ac ) and N-glycolylneuraminic acid ( Neu5Gc ) [27–29] . After synthesis , sialic acid is modified in the Golgi apparatus of eukaryotic cells [26 , 30] , and the modifications include O-acetylation , N-acetylation , methylation , hydroxylation and lactylation [26] . Among these O-acetylation is the most common modification , and one sialic acid molecule may carry up to four acetyl residues at 4- , 7- , 8- and 9-C [31] . O-acetyl groups can influence sialic acid metabolism by increasing sialic acid synthesis and critically by preventing sialic acid cleavage from glycoconjugates [32] . It has been reported that the activity of sialidases is increased by 50–80% when all O-acetyl group are removed from sialic acid [33 , 34] . Given that up to 10% of human nasal mucin is acetylated [35] , the pneumococcal ability to deal with acetylation must be important for the infections caused by the microbe . Removal of acetylation by esterases could be a main step of sialic acid removal by neuraminidase A . However , how the acetylation affects neuraminidase A activity , and more widely , what impact this modification may have in pneumococcal colonization and virulence are not known . Esterases are hydrolytic enzymes that can liberate acetyl groups from partially acetylated substrates . Evidence regarding esterase activity against O-acetyl groups on terminal sialic acid has been studied with viruses [36] . But very little is known about how bacterial pathogens de-acetylate sialic acid , and how this may effect host-pathogen interaction . We hypothesised that esterases are important for pneumococcal growth on host glycoconjugates , and virulence through potentiation of neuraminidase activity . In this study , the pneumococcal esterases have been characterised by biochemical assays , and their role in augmentation of neuraminidase activity was tested using genetically modified strains in vitro and in vivo .
In silico analysis showed that the sequenced S . pneumoniae strain D39 ( serotype 2 ) has four putative esterases: acyl-ACP thioesterase ( SPD_1239 ) , tributyrin esterase ( estA , SPD_0534 ) , phosphoesterase ( SPD_0932 ) , and acetyl-xylan esterase ( axe , SPD_1506 ) [37] . To determine the contribution of each of them to the total pneumococcal esterase activity , isogenic mutants were constructed and tested using chromogenic substrates . Analysis indicated that all isogenic mutants ( 42 . 5±1 . 6 , 16 . 9±0 . 7 , 42 . 8±1 . 2 and 33 . 8±1 . 2 mU for ΔSPD1239 , ΔestA , ΔSPD0932 and Δaxe , respectively , n = 6 for all strains ) had significantly lower esterase activity than the wild type ( 50 . 0±1 . 6 mU , n = 6 ) when 4-Nitrophenyl acetate ( pNPA ) was used as a substrate ( p<0 . 01 ) . The largest reduction in activity was observed in ΔestA , indicating that EstA is the major pneumococcal esterase . Mutation of both estA and axe in ΔestAaxe ( 4 . 1±0 . 76 mU , n = 6 ) further reduced the activity ( p<0 . 05 ) . When 4-Nitrophenyl butyrate ( pNPB ) was used as a substrate , which is a 4-carbon acyl ester , while the activity in ΔSPD1239 ( 19 . 8±0 . 3 mU , n = 6 ) and ΔSPD0932 ( 19 . 1±0 . 2 mU n = 6 ) was similar to the wild type ( 21 . 2 mU±1 . 1 , n = 6 ) ( p>0 . 05 ) , ΔestA ( 7 . 3±0 . 4 mU , n = 6 ) and Δaxe ( 14 . 6±0 . 6 mU , n = 6 ) had significantly lower activity than the wild type ( p<0 . 01 for both ) . These results show that esterase encoded by SPD_1239 and SPD_0932 , are specific for short-chain acyl esters , while estA and axe encode esterases that are active against both 2- and 4-carbon acyl esters . In the genetically complemented strains estAComp ( 44 . 2±2 . 3 mU , n = 6 ) and axeComp ( 48 . 2±1 . 1 mU , n = 6 ) , the esterase activity was similar to the wild type level when assayed with pNPA ( p>0 . 05 ) , indicating that the mutation of these genes did not cause polar effects . Homology comparison of EstA and Axe with known esterases indicated that they are serine dependent enzymes because both contained a typical catalytic triad of Ser-His-Asp , and have a characteristic consensus sequence of Gly-X-Ser-X-Gly ( where X represents an arbitrary amino acid residue ) around the active site serine ( S1 Fig ) . Serine residue within a catalytic triad of Ser-His-Asp has been reported to play a key role for enzymatic activity of esterases [38] . The importance of this site has been studied neither in EstA nor in Axe . Hence , the putative EstA and Axe as well as their genetically modified versions with putative serine active site replacement were purified to test their substrate specificity in detail , to determine the kinetic properties , and to study the importance of serine within catalytic triad for the activity . To verify whether EstA and Axe are serine dependent enzymes , we introduced mutations to replace the serine at positions 121 and 181 in EstA and Axe , respectively , with alanine . The results showed that the strains carrying these modifications in EstA and Axe , estACompS121A ( 14 . 8±0 . 5 mU , n = 6 ) and axeCompS181A ( 28 . 4±0 . 65 mU , n = 6 ) , had lower esterase activity than the wild type ( 50 . 02±1 . 56 mU , n = 6 ) ( p<0 . 01 ) . These results indicate that the serine is important for the catalytic activity of both EstA and Axe , consistent with other serine esterases [38] . To study , the substrate specificity in further detail , the activity of recombinant Axe and EstA was tested against a wider range of chromogenic substrates . The activity of both enzymes against different chromogenic substrates of varying acyl chain length ( pNPA , pNPB , pNPH , pNPO and pNPD , with 2 , 4 , 6 , 8 , and 10 C , respectively ) was determined at their optimal pH of 7 . 5 . It was found that there was a reverse correlation between the activity level and carbon chain length ( Fig 1 ) . For example , the specific activity against pNPA was 426 . 1± 4 . 1 mU and 164 . 3±3 . 2 mU for EstA and Axe , respectively , and for pPND it was 7 . 2 ± 0 . 9 mU , and 9 . 5 ± 1 . 848 mU ( n = 6 for all ) , for EstA and Axe , respectively . Using pNPA , the Vmax and Km values for EstA were calculated to be 364 . 1±13 . 9 mU and 4 . 7±0 . 6 mM , respectively , and for Axe the values were 220±7 . 94 mU and 4 . 4±0 . 58 mM , respectively . Although both Axe and EstA could use chromogenic substrates , we determined whether EstA and Axe could use tributyrin ( triglyceride ) and acetylated xylan as substrates , respectively , as the genome annotation suggested . EstA and Axe were tested on tributyrin , a substrate for carboxylesterases [39] , and acetylated xylan , a substrate for carbohydrate esterases that can utilise O-acetyl substituents within the substrate [40] . As shown qualitatively in Fig 2A , the recombinant EstA could use tributyrin as a substrate . A zone of clearance representing a positive reaction can be seen , and this was comparable to the zone of clearance ( 26 . 8 mm ) obtained with 300 U of commercial lipase from Staphylococcus aureus , which can also use tributyrin as a substrate [39] . On the other hand , neither EstAS121A nor Axe was active against tributyrin . This suggests that EstA belongs to carboxylesterase family and that serine S121 is important for EstA catalytic activity ( Fig 2A ) . The specific activity of Axe and EstA against tributyrin was also measured spectrophotometrically . It was found that the specific activity of EstA was 198 . 3±1 . 7 U per mg activity when tributyrin was used as a substrate . As before , Axe did not display any activity towards tributyrin ( Fig 2B ) . The specific activity of EstA and Axe against acetylated xylan was also determined . The results are shown in Fig 3A and 3B . It was found that when 400 μg of either enzyme was used , the amount of acetate release was 15 . 5±0 . 7- and 16±0 . 6 μM for EstA and Axe , respectively , indicating that both EstA and Axe are equally effective in utilization of acetylated xylan as substrate . Replacement of the serine 121 of EstA and 181 of Axe abolished the catalytic activity ( Fig 3 ) . BSM contains up to 17% sialic acid , and 22 . 5% of this is reported to be acetylated [41] . Esterase activity on BSM was measured by quantifying the acetate released from the substrate . The results showed a positive correlation between acetate release and incubation time for both esterases ( S2 Fig ) . The released acetate concentration at 120 min was significantly higher than that of at 30 , 60 and 90 min ( p<0 . 01 ) . The replacement of S121 and S181 in EstA ( 13±1 . 5 μg/ml , n = 5 ) and Axe ( 12 . 8±2 . 4 μg/ml , n = 5 ) , respectively , abolished acetate release . Synergism between esterases and NanA was investigated . Firstly , BSM was incubated with 250 U of recombinant Axe or EstA for 90 min , and then incubated for an additional 90 min with 250 U recombinant NanA . The result showed higher sialic acid release by NanA when BSM had been pre-treated either with Axe ( 2 . 81±0 . 34 mM/mg , n = 5 ) or EstA ( 2 . 78±0 . 44 mM/mg , n = 5 ) , than NanA treatment alone ( 1 . 78±0 . 33 mM/mg , n = 5 ) ( p<0 . 01 for both ) . However , treatment of BSM either with EstAS121A ( 1 . 83±0 . 75 mM/mg , n = 5 ) or AxeS181A ( 1 . 71±0 . 65 mM/mg , n = 5 ) did not have any effect on sialic acid release , indicating the importance of catalytic activity rather than the absence of protein . Treatment with EstA ( 0 . 81±0 . 13 mM/mg , n = 5 ) or Axe ( 0 . 75±0 . 21 mM/mg , n = 5 ) alone did not result in detectable sialic acid release . Moreover , the increase in pre-treatment time with esterases enhances the release of sialic acid from BSM by NanA as shown in Fig 4A and 4B . For example , while the amount of released sialic acid was 1 . 5±0 . 4 mM/mg and 1 . 5±0 . 1 mM/mg after 30 min pre-treatment , at 120 min the released sialic acid concentration increased to 3 . 0±0 . 3 mM/mg and 2 . 9±0 . 6 mM/mg for EstA and Axe , respectively ( p<0 . 001 ) . Having established the esterases’ role in potentiation of neuraminidase activity in cell free system , next , we determined whether esterases could potentiate NanA activity in intact cell . For this , the growth of ΔestA , Δaxe and ΔnanA mutants were compared to those of ΔestAnanA and ΔaxenanA mutants on BSM as the sole carbon source . In addition to growth , the released sialic acid in culture supernates was determined . This was possible because D39 is defective in utilisation of sialic acid [42] , hence any released sialic acid remains in culture supernatant . It was found that 0 . 071% ( w/v ) BSM was the optimal for pneumococcal growth . The growth profiles of pneumococcal strains on 0 . 071% BSM are shown in the Fig 5A and 5B . After 6 h growth the colony counts for ΔestA ( Log10 7 . 4±0 . 27 CFU/ml , n = 5 ) , estACompS121A ( Log10 7 . 7±0 . 55 CFU/ml , n = 5 ) , and ΔnanA ( Log10 6 . 9±0 . 13 CFU/ml , n = 5 ) were significantly lower than that of wild type ( Log10 9 . 1±0 . 87 CFU/ml , n = 5 ) and estAComp ( Log10 8 . 85±0 . 34 CFU/ml , n = 5 ) ( p<0 . 0001 ) . As expected , there was no significant difference in growth profiles of D39 and estAComp ( p>0 . 05 ) . The major attenuation in growth , however , was seen in ΔestAnanA as the Log10 CFU/ml of double mutant decreased progressively during the time course of growth . Furthermore , the growth rate of double ΔestAnanA mutant was significantly lower than ΔestA and ΔnanA mutants ( p<0 . 01 ) . On the other hand , Δaxe grew as well as the wild type on BSM , and there was no difference in bacterial counts between ΔaxenanA and ΔnanA ( p>0 . 05 ) ( Fig 5B ) . The sialic acid concentration in culture supernatants was consistent with the growth profiles of pneumococcal strains . The highest level of sialic acid was released by D39 ( 212±10 . 3 μM/mg , n = 5 ) and estAComp ( 195 . 4±5 . 3 μM/mg , n = 5 ) strains , and the lowest was with the ΔestAnanA ( 25 . 3±1 . 3 μM/mg , n = 5 ) compared to D39 ( p<0 . 0001 ) ( Fig 5C ) . Moreover , the released sialic acid concentration from ΔestA ( 143 . 5±6 . 3 μM/mg , n = 5 ) , estACompS121A ( 138 . 7±4 . 5 μM/mg , n = 5 ) and ΔnanA ( 65 . 8±7 . 5 μM/mg , n = 5 ) culture supernates was significantly lower than the wild type , but higher than ΔestAnanA ( p<0 . 01 ) . On the other hand , the level of sialic acid from Δaxe ( 209 . 2±8 . 3 μM/mg , n = 5 ) culture supernate was as high as the wild type ( p>0 . 05 ) ( Fig 5 ) . Moreover , there was no significant difference in sialic acid release between ΔestAaxe and ΔestA ( p>0 . 05 ) , ruling out compensation of esterase activity by Axe in the ΔestA cohort . To determine the subcellular localization of pneumococcal esterases , pneumococci were fractionated and hybridised with anti-EstA and anti-Axe polyclonal antibodies . To determine the efficiency of cell fractionation , lactate dehydrogenase ( LDH ) activity , known to have intracellular localization , was determined in different cell fractions and culture supernatant . It was found that the soluble intracellular fraction contained the highest LDH activity ( 357 . 5±21 mU/mg ) compared to cell membrane ( 20 . 8±11 . 4 mU/mg ) and cell wall fractions ( 9 . 4±3 . 6 mU/mg ) , indicating the quality of fractionation . Both polyclonal antibodies reacted strongly with the recombinant EstA and Axe regardless of pneumococcal growth on BSM or glucose ( S3 Fig ) . Each antibody reacted well with the soluble intracellular fraction , whereas there was no binding to the cell membrane and cell wall fractions . In addition , esterase activity was also detected in culture supernatant of S . pneumoniae ( 18 . 1± 1 . 3 mU , n = 5 ) ( S3 Fig ) , indicating that pneumococcal esterases can be released outside cells . To determine whether esterase release is due to autolysin activity , esterase activity was tested in clear cell lysates and culture supernatants of an autolysin mutant and wild type pneumococcus . No difference was found in esterase activity in the autolysin mutant ( clear cell lysate: 41±3 . 4 mU and culture supernate: 15±2 . 7 mU , n = 4 ) compared to the wild type ( clear cell lysate: 42 . 6±2 . 3 mU and culture supernate: 17±1 . 8 mU , n = 4 ) ( p>0 . 05 ) . This means that the esterase activity in culture supernatant is not due to autolysin activity but due to an unknown export mechanism . After biochemical assays , and growth studies , next , we evaluated esterases’ role in pneumococcal colonization and virulence , and also assessed whether esterase activity would potentiate neuraminidase A activity in vivo . In asymptomatic colonization model , it was found that the number of D39 recovered from nasopharyngeal tissues remained constant over 7 days ( 3 . 16 ±0 . 23 CFU/mg , 3 . 08 ±0 . 16 CFU/mg , 3 . 48 ±0 . 27 CFU/mg and 3 . 36 ±0 . 06 CFU/mg , n = 10 for all at day 0 , 1 , 3 , and 7 days post-infection , respectively ) ( Fig 6 ) . However , at 3 and 7 days post-infection , the numbers of ΔestA ( Log10 2 . 73±0 . 04 and 2 . 36±0 . 03 CFU/mg n = 10 , respectively ) and ΔnanA ( Log10 2 . 34±0 . 05 CFU/mg and 1 . 5±0 . 07 CFU/mg n = 10 ) were significantly lower than D39 ( p<0 . 01 ) . However , the biggest attenuation in colony counts was detected with ΔestAnanA ( Log10 1 . 46±0 . 05 CFU/mg and 0 . 61 ±0 . 04 CFU/mg ( n = 10 ) at 3 and 7 days post-infection , respectively ) . The numbers of ΔestAnanA at 3 and 7 days post-infection were significantly lower than those of ΔestA and ΔnanA ( p<0 . 0001 ) . There was no significant difference in colony counts of genetically complemented strain and the wild type ( p>0 . 05 ) . On the other hand , Axe did not contribute in colonization ( Fig 6 ) . The contribution of EstA and Axe to pneumococcal virulence was determined in a mouse model of pneumococcal pneumonia with bacteraemia that develops after intranasal infection . The results showed that the median survival time of cohorts infected either with ΔestA ( 65 h ±19 . 6 , n = 20 ) and ΔnanA ( 75 h ±17 . 4 , n = 20 ) was significantly higher than mice infected with D39 ( 35 h ±15 . 2 , n = 20 ) or estAComp ( 30 h ±16 . 9 , n = 20 ) ( p<0 . 0001 ) ( Fig 7A ) . Moreover , the median survival time of mice infected with ΔestAnanA ( 90 h ±7 . 2 , n = 20 ) was significantly higher than either ΔestA or ΔnanA ( p<0 . 01 ) . On the other hand , there was no difference in survival time of the cohort infected with Δaxe ( 39 h ±15 . 3 , n = 20 ) and D39 ( p>0 . 05 ) , and no difference was detected in survival time of ΔnanA ( 75 h ±17 . 4 , n = 20 ) and ΔaxenanA ( 75 h ±20 . 1 , n = 20 ) infected mice ( p>0 . 05 ) ( Fig 7A ) . In addition , the progression of bacteraemia was determined after intranasal infection ( Fig 7B ) . The numbers of ΔestA at 24 and 36 h post-infection ( Log10 3 . 9±0 . 53 CFU/ml blood and 5 . 94±0 . 52 CFU/ml blood , n = 20 , respectively ) was significantly lower than the numbers of D39 at 24 and 36 h ( Log10 6 . 9±0 . 13 CFU/ml and 9 . 4±0 . 11 CFU/ml , respectively ) ( p<0 . 0001 ) . There was no significant difference between estAComp and D39 wild type ( p>0 . 05 ) indicating that the mutation of estA did not cause a polar effect . Similarly , ΔnanA ( 3 . 56±0 . 2 CFU/ml and 4 . 44±0 . 6 CFU/ml , n = 20 , at 24 and 36 h , respectively ) and ΔestAnanA ( 2 . 53±0 . 48 CFU/ml and 3 . 1±0 . 59 CFU/ml , n = 20 , at 24 and 36 h , respectively ) had lower blood counts than the wild type ( p<0 . 0001 ) ( Fig 7B ) . However , no significant difference in bacterial count between ΔnanA and ΔestAnanA was seen , suggesting that removal of acetylation may not be critical for pneumococcal growth in blood . Despite Axe’s ability to potentiate sialic acid release by NanA in cell free system , Axe neither contributed in pneumococcal growth on BSM ( Fig 5B ) nor sialic acid release from BSM ( Fig 5C ) . We therefore hypothesised that this could be due to the induction of estA expression in Δaxe . The increased expression of estA in Δaxe is plausible because we demonstrated that the expression of both estA ( 4 . 5±0 . 66 fold ) and axe ( 2 . 07±0 . 52 fold ) were up-regulated in CDM supplemented with BSM relative to their expression on glucose . In vivo expression of estA and axe was also determined in pneumococci recovered from infected mouse tissues . The result showed that both estA and axe expression was significantly higher in the mucin rich nasopharynx ( 12 . 6±0 . 7 and 6 . 5±0 . 5 folds , respectively ) and in the lungs ( 6 . 3±0 . 7 and 1 . 6±0 . 5 folds , respectively ) relative to their expression in blood ( p<0 . 05 ) . When the expression of estA was evaluated in Δaxe exposed to BSM , the expression of estA increased ( 7 . 73±1 . 21 folds ) relative to parental strain exposed to BSM , implying that the loss of axe is compensated by estA expression . Similarly , axe expression in Δaxe was also investigated . The analysis revealed that that axe expression was not significantly different , 1 . 3±0 . 21 fold ( n = 3 ) , in ΔestA relative to the wild type ( p>0 . 05 ) . To determine whether EstA and NanA are involved in release of sialic acid in vivo , the level of bound sialic acid in nasopharyngeal lavage of mice colonised with different isogenic mutants was assessed ( Fig 8 ) . As can be seen from Fig 8 , the level of bound sialic acid in mice colonised with wild type D39 ( 35±3 . 7 μM/mg , n = 5 ) was significantly higher than the control mice that had received PBS alone ( 6 . 09±4 . 1 μM/mg , n = 5 ) ( p<0 . 01 ) , while those colonised with ΔestA and ΔnanA ( 23 . 12±8 . 1 μM/mg , and 11 . 65±6 . 4 μM/mg , n = 5 , respectively ) had significantly decreased sialic acid concentration compared to the wild type infected cohort ( p<0 . 01 and p<0 . 0001 , for ΔestA and ΔnanA , respectively ) . There was no difference in released sialic acid level in the nasopharyngeal washings of mice colonised either with ΔestAnanA or ΔnanA ( p>0 . 05 ) ( Fig 8 ) . In addition , the released sialic acid level in the nasopharyngeal wash of mice colonised with Δaxe ( 25 . 3±3 . 6 μM/mg , n = 5 ) was similar to that of the wild type ( Fig 8 ) ( p>0 . 05 ) . As the bound sialic acid level was higher in the nasopharyngeal wash of mice than in the PBS administered cohort , these data suggest that an intact pneumococcal deglycolysation system is required for the sialylated glycoconjugate synthesis . As colonization experiments indicated that the nasopharyngeal bacterial load differ among the study strains after 3 and 7 days post-infection ( Fig 6 ) , sialylated glycoconjugate content was also determined at 24 h post-infection to exclude the possibility that observed differences are due to bacterial load . Our results showed that the bacterial load at 24 h post-infection in the nasopharynx was similar among the strains ( Fig 6 ) , and sialylated glycoconjugate content displayed the same pattern as detected 7 days post-infection ( Fig 8 ) , ruling out possibility that the release was due to bacterial burden .
O-acetylation hinders the cleavage of sialic acids from glycoconjugates . However , the microbes posses esterase activity to remove acetylation from sialic acids . Our results showed that the pneumococcal esterase activity is coded by multiple genes , indicating that the pneumococcus encounters with acetylated compounds during infection at different tissue sites , and/or during its metabolic processes . EstA is responsible for the main pneumococcal esterase activity , and its expression is induced by BSM , and in respiratory tract . This strongly indicates that EstA is involved in deacetylation of host glycoconjugates . All four pneumococcal esterases studied were found to be specific for short acyl chain esters ( Fig 1 ) . This preference for esters of short-chain fatty acids is characteristic of esterases , and it is found to be common among other lactococcal esterases [43] . Among pneumococcal putative esterases , tributyrin esterase ( SPD_0534 , EstA ) , part of the core pneumococcal genome [44] , has been structurally characterised [45] . Screening of a mutant library indicated that EstA is required for pneumococcal lung infection [46] , and it can induce nitric oxide and pro-inflammatory cytokine production in macrophages [47] . However , how EstA contributes to pneumococcal virulence and pathogenesis of disease is not known in detail . By biochemical assays , and in vitro growth studies we demonstrated synergistic action of EstA and NanA on acetylated sialic acid , and their involvement in pneumococcal colonization and virulence . We found that ΔestAnanA cleaved less sialic acid from BSM ( Fig 5 ) and had lower colony counts in nasopharynx than ΔnanA ( Fig 6 ) , reflecting synergistic action of these enzymes . The combined action of these two enzymes results in higher acetate release ( Fig 4 ) , and augments the utilization of mucin ( Fig 5 ) probably due to an increase in utilizable sugar availability through the exposure of glycosidic bonds to the activity of pneumococcal glycosidases , which ensures a steady supply of various utilizable sugars such as mannose , N-acetyl glucosamine , and , critically , galactose . Galactose is rich in respiratory mucin , and is one of the crucial sugars for pneumococcal metabolism , colonization and virulence [11 , 48] . The pneumococcus requires high concentration of galactose in extracellular milieu due to inefficiency of galactose transporters [11] . Therefore , the effective release of sialic acid ensures continuous provision of galactose for pneumococcus . In addition , the concerted action of EstA and NanA leads to increased cleavage of sialic acid , which is known to be important for pneumococcal biofilm formation , and whose intranasal inoculation significantly increases pneumococcal counts in the nasopharynx and instigates translocation of pneumococci to the lungs [22] . In addition to a nutritional impact of EstA-NanA synergism , the potentiation of neuraminidase efficacy by esterase enhances the microbe’s capacity to colonize and invade respiratory tissues , as we demonstrated in this study ( Figs 6 and 7 ) . Neuraminidase activity has been shown to be required for biofilm formation [49] , for resistance to opsonophagocytic killing by human neutrophils [50] , and for cell attachment by exposing potential ligands for bacterial receptors [51] . The comprehensive impact of neuraminidase is linked to the ubiquitous presence and abundance of sialic acid on the surface of all mammalian cell types . The typical cell is reported to exhibit tens of millions of sialic acid molecules , and it is estimated that the local concentrations on the cell surface glycocalyx can reach up to 100 mM [52] . In this study we selected NanA to test our hypothesis as it has broader substrate specificity than other pneumococcal neuraminidases , and it provides the main pneumococcal neuraminidase activity . It is likely that esterases potentiate the activity of the other neuraminidases coded by nanB and nanC because esterase will remove acetylation independent of neuraminidase activity . EstA and NanA activity is important not only for efficient cleavage of acetylated sialic acid but also , as we demonstrated , they are required for sialylated glycoconjugate synthesis in respiratory mucosa ( Fig 8 ) . This paradox very likely ensures a steady supply of host glycoconjugates for mucosal commensals such as S . pneumoniae during nasopharyngeal colonization , while replenishing surfaces of respiratory mucosa with the sialylated glycans to prevent the infiltration of obligate pathogens [8] . The level of sialylated glycan was lower in mice colonised with ΔnanA isogenic mutant than those with the wild type , indicating that cleavage of sialic acid is recognised as a signal for respiratory mucin secretion [4 , 53] . We demonstrated that Axe could utilise acetylated xylan as a substrate ( Fig 3 ) . Therefore , by definition Axe is an acetyl xylan esterase . Acetyl xylan esterases hydrolyse the ester linkages of the acetyl groups in position 2 and/or 3 of the xylose moieties of natural acetylated xylan from hardwood [40] . Xylan is not found in mammalian host . However , structural analogues of acetylated xylan are present in extracellular matrix [54] . Therefore , it is plausible that Axe may have a role in removal of acetylation in mammalian xylan analogues . Contrary to the biochemical assay results , in which Axe could utilize acetylated substrates , no measurable contribution of Axe could be detected in pneumococcal mucin utilization , colonization or virulence in our experimental models . This is very likely to be due to the absence of specific substrates for Axe in BSM , and in mouse tissues . Subcellular localization assay identified EstA and Axe as intracellular proteins though esterase activity could also be detected in the spent culture supernates as well ( S3 Fig ) . Detection of esterase activity in extracellular milieu is consistent with the reduced growth yield and the released sialic acid concentration by ΔestA and ΔestAnanA on BSM ( Fig 5 ) . Currently , the mechanism of esterase release by pneumococcus is not known . It has been reported that certain pneumococcal intracellular proteins , such as α-enolase and pneumolysin , which do not have a signal peptide , similar to EstA and Axe , can have a cell surface localization [55 , 56] . In addition , it is known that the pneumococcal proteins can be released due to autolytic activity [57] . However , there was no difference in esterase activity between autolysin mutant and the wild type , suggesting strongly the presence of an unknown export mechanism . We investigated the functional importance of putative serine active sites in Axe and EstA . The active site of esterases is usually within the typical catalytic triad of the nucleophile serine , proton carrier histidine and aspartate/glutamate [38] . This typical triad forms a sophisticated pocket responsible for esterases’ catalytic activity [58] . In the majority of esterases , serine and histidine residues are usually present , whereas aspartate/glutamate residues might be absent in some of them [58] . The mutation of these putative sites in EstA and Axe abolished esterase activity , confirming that EstA and Axe activity requires serine active site . In this study we demonstrated that removal of O-acetylation by esterase potentiates pneumococcal neuraminidase activity , and this process contributes to pneumococcal colonization and virulence . Given that there are posttranslational modifications other than acetylation in host glycoconjugates such as sulfations and methylation [32] , further work is required to understand their impact on pneumococcal biology . We only investigated esterases role in potentiation of neuraminidase efficacy in this study , hence we cannot rule out their involvement in other processes . In other bacteria , esterases are implicated in other functions such as lipid production , cell attachment and biofilm formation in P . aeruginosa [59] , and conversion of an inactive bacterial toxin into an active form in Bordetella pertussis [60] . Therefore , we intend to study esterases’ role in wider aspects of pneumococcal biology .
The list of strains used in this study is given in S1 Table . S . pneumoniae D39 was grown either in brain heart infusion broth ( BHI ) , blood agar base ( Oxoid , UK ) supplemented with 5% ( v/v ) defibrinated horse blood ( Oxoid ) , or in Chemically Defined Medium ( CDM ) supplemented with 55 mM glucose or different concentrations of dialysed bovine sub-maxillary mucin ( Sigma ) [9 , 11 , 61] . Escherichia coli cultures were grown on Luria broth , or Luria agar ( Oxoid , UK ) . Spectinomycin and kanamycin were added at 100- and 250 μg/mL , respectively , for pneumococcal cultures , and for E . coli ampicillin and kanamycin were used at 100- and 150 μg/mL , respectively . In vitro mariner mutagenesis was used for the construction of pneumococcal mutants as previously described using the primers and plasmids listed in S2 Table and S3 Table , respectively [61 , 62] . Successful mutation was confirmed by PCR analysis of transformants using transposon-specific primers , MP127 or MP128 , with appropriate chromosomal primers , and by sequencing . A representative strain for each mutation was selected , and these were designated as ΔestA , Δaxe , ΔSPD1239 and ΔSPD0932 . To construct the double ΔestAnanA and ΔaxenanA , the mutated regions from ΔestA and Δaxe were amplified , and transformed into ΔnanA . pCEP plasmid [63] , which is non-replicative in S . pneumoniae , was used for the introduction of an intact copy of estA and axe into a transcriptionally silent site in the pneumococcal chromosome as we described previously [61] . The chromosomal integration of intact copies of genes into isogenic mutants was confirmed using malF2 and pCEPR2 primers . One of the transformants for each genetic complementation was designated as estAComp and axeComp , respectively , for further analysis . The replacement of a predicted catalytic site serine 121 and 181 in EstA and Axe , respectively , to alanine were achieved by splicing overlap extension PCR ( SOEing PCR ) [64] . For this , two-step PCR was used: the first reaction amplified the left and right-flanking regions of mutagenic site using modified primers ( S2 Table ) , and the second reaction joined the left and the right flanks . The joined PCR product then was digested and ligated into pCEP . The recombinant plasmid was sequenced , and transformed into ΔestA and Δaxe . The resulting strains were designated as estACompS121A and axeCompS181A . Trizol reagent kit ( Invitrogen , UK ) was used to isolate the total RNA [65] . SuperScript III reverse transcriptase ( Invitrogen , UK ) was used to synthesise first strand cDNA using random hexamers at 42°C for 50 min according to the manufacturer’s instructions . cDNA ( 15 ng ) was amplified in a 20 μl reaction volume that contained 1X SensiMix SYBR Master mix ( Bioline , UK ) and 3 pmol of each primer ( S2 Table ) . The transcription level of specific genes was normalized to gyrB transcription , which was amplified in parallel with SPD0709F and SP0709R primers . The results were analyzed by the comparative CT method [66] . Outbred 8-10-week-old female MF1 mice ( Charles River , UK ) were intranasally infected with 50 μl PBS containing 1x106 D39 pneumococci [15 , 67] . When the mice became severely lethargic they were anesthetized and blood was obtained by cardiac puncture [62] . Subsequently , mice were killed by cervical dislocation , and tissues were dissected and homogenized on ice in 10 ml of sterile PBS . Then homogenates were centrifuged to obtain the bacterial pellet as described previously [62] . RNA extraction and purification was done as above . The estA , axe , estAS121A , and axeS181A were PCR amplified , and the amplicons were cloned into pLEICS-01 ( S2 Table ) . The recombinant plasmids were sequenced to rule out any unintended mutational events . The recombinant plasmids were transformed into E . coli BL21 ( DE3 ) for expression . The protein expression was done at 25°C , and induced with 0 . 5 mM IPTG . Recombinant proteins were then purified using Talon Metal Affinity resin ( Clontech Inc . , UK ) as previously described [9] . The definitive identity of the purified recombinant proteins were verified by matrix-assisted laser desorption ionization–time of flight ( MALDI-TOF ) by PNACL ( University of Leicester ) . Esterase and neuraminidase activities were assayed using chromogenic substrates . Esterase activity assay was done as previously described using five different p-nitrophenyl esters including p-Nitrophenyl acetate C2 ( p-NPA ) , p-Nitrophenyl butyrate C4 ( p-NPB ) , p- Nitrophenyl hexanoate C6 ( p-NPH ) , p-Nitrophenyl octanoate C8 ( p-NPO ) and p-Nitrophenyl decanoate C10 ( p-NPD ) [68] . 2-O- ( p-nitrophenyl ) -α-d-N-acetylneuraminic acid ( p-NP-NANA ) was used for neuraminidase activity [15] . The absorbance of released p-nitrophenol was measured photometrically at 405 nm . The protein concentration was measured according to the method described by Bradford , 1976 [69] . One unit of enzyme activity was defined as 1 μmol p-nitrophenol per min per milligram of protein under standard assay conditions . In addition , esterase activity was assayed using BSM , tributyrin , and acetylated xylan . To determine esterase activity on BSM , 5 mg of BSM was incubated with different concentrations of recombinant esterases in a total reaction volume of 200 μl in PBS , pH 7 . 5 at 37°C , and the released acetate was assayed enzymatically using a commercial test kit ( Megazyme , Ltd . , Ireland ) . A sample of BSM incubated with esterase at 4°C served as a control [36] . To determine the synergistic interaction between esterases and neuraminidase in sialic acid release , 5 mg of BSM ( Sigma Aldrich , UK ) was dissolved in 200 μ1 of PBS at pH 7 . 5 , and was incubated with 250 U of each recombinant esterase at 37°C for 30 to 120 min . Then , 250 U of recombinant NanA was added into the reaction mixture and further incubated for a pre-determined time . To stop the reaction , the reaction mixture was placed on ice [70] . The released sialic acid was measured using 0 . 2 M periodate reagent ( 0 . 2 M of sodium periodate in 0 . 1 M H3PO4 , pH 7 . 4 ) as previously described [71] . The activity of esterases on tributyrin was determined as described previously [39 , 40] . For qualitative determination , 0 . 5% ( v/v ) tributyrin was suspended in 50 mM Tris ( pH 8 . 8 ) and 25 mM CaCl2 and then embedded into 2% ( w/v ) standard agarose . Commercial lipase from Staphylococcus aureus was used as a positive control ( Sigma Aldrich , UK ) . The zone of clearance indicated the presence of tributyrin esterase activity . For quantitative assay , 250 U of recombinant esterase was mixed with 0 . 5% ( w/v ) tributyrin suspension embedded in 0 . 8% ( w/v ) low melting point agarose . The decrease in absorbance at 450 mm was recorded over time . The activity of tributyrin esterase in the sample was quantified using a standard curve generated with known concentrations of S . aureus lipase . Acetyl xylan activity was assayed using birchwood xylan as substrate as previously described [72] . Birchwood xylan was prepared by dissolving in dimethyl sulfoxide , and K3BO3 . The mixture was dialysed against running water , and lyophilized . Different concentrations of recombinant esterases was mixed with 150 μl of substrate solution , which was prepared by mixing 30 mM of acetylated xylan with 0 . 01% ( w/v ) bromothymol blue as indicator , and 5 mM of sodium phosphate buffer ( pH 7 . 3 ) . The reaction mixture was incubated at 37°C for 20 min , and the absorbance of supernate at 616 nm was recorded . The decrease in absorbance indicated enzyme activity , which was calculated by generating a standard curve using acetic acid . One unit of activity was defined as the formation of 1 μM of acetic acid per minute under the standard reaction medium . To determine the cellular localization of esterases , the pneumococcus was grown in CDM supplemented with either 0 . 071% ( w/v ) BSM or 55 mM glucose until late exponential phase , when the cell pellets were harvested . The pneumococcal whole cell lysate was separated into cell wall , membrane and cytoplasmic fractions as described before [73] . The recombinant proteins or cellular fractions were separated by SDS PAGE gel , and western blotting was done using polyclonal antibody as previously described [9] . Briefly , ten weeks old female CD1 outbred mice ( Charles River , UK ) were injected intraperitoneally with a 25 μg of recombinant proteins and 33 μl of Imject Alum adjuvant ( Perbio Science , Cramlington , UK ) and 67 μl of PBS , while the control group received only adjuvant and PBS . Injections were repeated three times at fortnightly intervals . Two weeks after the last injection , mice were anesthetized with 3% ( v/v ) isoflurane ( Astra Zeneca , Macclesfield , UK ) over oxygen ( 1 . 5 to 2 liters/min ) and blood was collected by cardiac puncture . The blood was left at room temperature for one hour to clot and the serum was recovered by centrifugation , and was kept at -8°C until needed . Female 8 to 10 week old MF1 mice weighing approximately 30 to 35 g ( Charles River , UK ) , were anesthetized with 2 . 5% isoflurane over oxygen ( 1 . 5 to 2 litre/min ) . For carriage model , each mouse was infected intranasally by administering approximately 1x105 CFU of pneumococci in 20 μl PBS . Pneumococcal numbers in nasopharyngeal tissues were determined by plating out the serial dilutions of nasopharyngeal tissue homogenates . Dissection and homogenization of nasopharyngeal tissues were done as described previously [9 , 62] . For the pneumonia model , the anesthetized mice were infected intranasally with approximately 1X106 CFU in 50 μl PBS [9 , 62] . The inoculum was administered drop-wise . Mice were scored for signs of disease ( starry coat , hunched and lethargic ) for 7 days [74] . At 24 and 36 hours post-infection , a sample of blood was collected from the tail vein , diluted serially in PBS , and dilutions were plated out to determine bacterial load in the blood . Mice were culled when they manifested lethargic signs , and the time to this point was considered as the survival time . Mouse experiments at the University of Leicester were performed under appropriate project ( permit no . 60/4327 ) and personal ( permit no . 80/10279 ) licenses according to the United Kingdom Home Office guidelines under the Animals Scientific Procedures Act 1986 , and the University of Leicester ethics committee approval . The protocol was approved by both the U . K . Home Office and the University of Leicester ethics committee . Where indicated , the procedures were carried out under anesthetic with isoflurone . Animals were housed in individually ventilated cages in a controlled environment , and were frequently monitored after infection to minimize suffering . Every effort was made to minimize suffering and in bacterial infection experiments mice were humanely culled if they became lethargic . Statistical analysis was determined using Graphpad Prism software 6 . 0f ( Graphpad , California , USA ) . Data were expressed as means ± standard error of the mean ( SEM ) . One- and two-way analysis of variance ( ANOVA ) followed by Dunnett's multiple comparison tests were used to compare the groups for enzyme assays and growth analysis . The Mann Whitney test was used for in vivo survival experiment whereas one-way ANOVA followed by Tukey's multiple comparisons test was used to compare the groups for bacteremia development and colonization experiment . | Neuraminidase activity is critical for pneumococcal colonization and virulence as it is required for efficient cleavage of host glycans for nutritional requirements , attachment , and translocation of the microbe through biological membranes . Modifications , such as O-acetylation , in terminal sialic acid can affect the potency of neuraminidase . In this study we investigated whether pneumococcal esterases could potentiate neuraminidase activity by de-acetylating sialic acid . We found that the pneumococcal esterase activity is coded by at least four genes , specific for short acyl chain esters , and the removal of acetylation by esterases potentiates pneumococcal neuraminidase activity for mucin utilisation , colonization and virulence . Hence , this study elucidates the complexity and importance of host de-glycosylation for pneumococcal colonization and virulence , and reveals a potential target for therapeutic intervention . | [
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"carbo... | 2017 | Deacetylation of sialic acid by esterases potentiates pneumococcal neuraminidase activity for mucin utilization, colonization and virulence |
In non-alcoholic fatty liver disease ( NAFLD ) , lipid build-up and the resulting damage is known to occur more severely in pericentral cells . Due to the complexity of studying individual regions of the sinusoid , the causes of this zone specificity and its implications on treatment are largely ignored . In this study , a computational model of liver glucose and lipid metabolism is presented which treats the sinusoid as the repeating unit of the liver rather than the single hepatocyte . This allows for inclusion of zonated enzyme expression by splitting the sinusoid into periportal to pericentral compartments . By simulating insulin resistance ( IR ) and high intake diets leading to the development of steatosis in the model , we identify key differences between periportal and pericentral cells accounting for higher susceptibility to pericentral steatosis . Secondly , variation between individuals is seen in both susceptibility to steatosis and in its development across the sinusoid . Around 25% of obese individuals do not show excess liver fat , whilst 16% of lean individuals develop NAFLD . Furthermore , whilst pericentral cells tend to show higher lipid levels , variation is seen in the predominant location of steatosis from pericentral to pan-sinusoidal or azonal . Sensitivity analysis was used to identify the processes which have the largest effect on both total hepatic triglyceride levels and on the sinusoidal location of steatosis . As is seen in vivo , steatosis occurs when simulating IR in the model , predominantly due to increased uptake , along with an increase in de novo lipogenesis . Additionally , concentrations of glucose intermediates including glycerol-3-phosphate increased when simulating IR due to inhibited glycogen synthesis . Several differences between zones contributed to a higher susceptibility to steatosis in pericentral cells in the model simulations . Firstly , the periportal zonation of both glycogen synthase and the oxidative phosphorylation enzymes meant that the build-up of glucose intermediates was less severe in the periportal hepatocyte compartments . Secondly , the periportal zonation of the enzymes mediating β-oxidation and oxidative phosphorylation resulted in excess fats being metabolised more rapidly in the periportal hepatocyte compartments . Finally , the pericentral expression of de novo lipogenesis contributed to pericentral steatosis when additionally simulating the increase in sterol-regulatory element binding protein 1c ( SREBP-1c ) seen in NAFLD patients in vivo . The hepatic triglyceride concentration was predicted to be most sensitive to inter-individual variation in the activity of enzymes which , either directly or indirectly , determine the rate of free fatty acid ( FFA ) oxidation . The concentration was most strongly dependent on the rate constants for β-oxidation and oxidative phosphorylation . It also showed moderate sensitivity to the rate constants for processes which alter the allosteric inhibition of β-oxidation by acetyl-CoA . The predominant sinusoidal location of steatosis meanwhile was most sensitive variations in the zonation of proteins mediating FFA uptake or triglyceride release as very low density lipoproteins ( VLDL ) . Neither the total hepatic concentration nor the location of steatosis showed strong sensitivity to variations in the lipogenic rate constants .
Previous computational models of glucose homeostasis and liver energy metabolism have varied from those studying liver metabolism within the body as a whole to those focussed in detail on the regulation of specific enzymes depending upon the particular purpose of the study . For example , Kim et al . [41] and Xu et al . [42] developed whole body models to study the hormonal regulation of glucose homeostasis during exercise ( Kim et al . [41] ) and under varying feeding conditions ( Xu et al . [42] ) , whilst Liu et al . [43] developed a model focussed in detail on the GLUT proteins responsible for glucose uptake and output . Additionally , representations of glucose regulation vary from black box models , for example with the purpose of calculating optimal insulin input for insulin responsive diabetic patients [44] , to mechanistic models aiming to understand the metabolic changes occurring in disease in detail [45–50] . Of these mechanistic models , the vast majority of existing models of the liver are single hepatic compartment models , and zonation in the context of hepatic energy metabolism has yet to be addressed . Numerous models of glucose regulation and other processes in liver have been presented since as early as 1965 [44] . As a result , the following review will focus on key recent mechanistic models . For a more detailed review of liver metabolism and diabetes modelling , see the reviews of Bogle et al . and Balakrishnan et al . [44 , 51] . Most recently , Somvanshi et al . published a detailed single compartment model of glucose , lipid and amino acid metabolism , including regulation at both a signalling and transcription level [45] . This model , which incorporated a number of previously published sub-models , was used to understand the effects of varying dietary compositions on metabolism . König et al . developed a detailed model of enzymatic conversions in glucose homeostasis including much of the allosteric regulation known to occur in glucose regulation [46] . This model ( a set of ordinary differential equations with 49 localised metabolites and 36 reactions ) included all of the enzymatic conversions involved in gluconeogenesis and glycolysis , with separate cytoplasmic and mitochondrial compartments within hepatocytes . Hetherington et al . developed a composite model of hormone signalling in glycogen storage , comprising of established and ab initio developed sub-models [47] . This was used to understand key features of insulin signalling including ultradian oscillations [47 , 48] . Chalhoub et al . developed a single hepatic compartment model focussing on gluconeogenesis and lipid metabolism in the hepatocyte [49] . The model was adapted to give results corresponding to the liver in either in vivo or in an ex vivo perfusion system to allow comparison with different experimental data . It was then used to simulate concentrations of various molecules and fluxes of different reactions in response to changes in the composition of the perfusion medium . Calvetti et al . developed a sophisticated spatially distributed model of glucose regulation in liver [50] . This used a set of grid points to measure fluxes of various molecules . However , despite the inclusion of spatial distribution , this model did not include zonation in enzyme expression . Whilst single compartment , homogenous hepatocyte models are useful for studying the function of liver as a whole , they do not allow simulation of changes within specific regions of the sinusoid , and exclude the implications of zonation in disease progression . Only a few models have included a representation of hepatic zonation in enzyme expression , and none of these have focused on liver energy metabolism . Ohno et al . investigated heterogeneity in ammonia detoxification [52] . They set up a model in which substances enter the sinusoids from the periportal tract , pass through 8 compartments ( hepatocytes ) in series , before exiting the sinusoid in the central vein . The model was tested with homogenous enzyme expression in all compartments and with zonated expression of carbonyl phosphate synthase , glutamine synthase and ornithine aminotransferase to understand the roles of zonated enzyme expression . Anissimov et al . created a similar model with 8 compartments to study hepatic availability and clearance [53] . A model by Sheikh-Bahaei et al . also studied hepatic zonation xenobiotic toxicity focussing on the development of zonation in key enzymes , rather than the effects of this zonation on metabolite concentrations [54] . Pang et al . looked at the various different ways of modelling heterogeneity for studying pharmacokinetics , starting with a simple compartmental PBPK model , followed by a zonal model and moving on to more complex circulatory and fractal models [55] . The compartmental and zonal models were compared with clinical data for digoxin and estradiol 17β D-glucuronide ( E217G ) . For digoxin , the zonal model provided little improvement over the compartmental model . However , for E217G results improved significantly if Sult1e1 was expressed heterogeneously , suggesting that zonation is a more important for some drugs than others . Other than this , models have been developed including zonation of closely related non-liver metabolism . König et al . published a model of glucose metabolism in cancel cells including localized gradients in metabolites and oxygen and regional hypoxia across a tumour [56] . Although this is for metabolism in cancer cells rather than liver , many similarities are seen including increased glycolysis and reduced oxidative phosphorylation in the most hypoxic cells . Davidson et al . studied the development of zonation within a bioartificial device [57] . This study did not model the metabolism in cells but instead focused on optimisation of oxygen input , blood flow and the dimensions of the device to try set-up a liver-like gradient in oxygen expression across the cells to promote a zonated phenotype .
Due to the size of the model , a full description of the metabolic processes and the equations used to represent them is provided in S1 Text . A summary is presented in the following sections . Quantitative comparison of the simulated data for the concentrations of the various plasma and hepatic molecules in both metabolically healthy ( MH ) and insulin resistant individuals with experimental data is provided in S2 Text . To include zonation , a computational model of liver function must be able to represent changes in concentrations of metabolites and hormones as blood passes through the sinusoid , as well as the variation in sinusoidal hepatic enzyme expression . Conventional two compartment ( blood/hepatocyte ) models , which treat the hepatocyte as the repeating unit of the liver , are unable to do this . Instead , following the structure suggested by Anissimov et al . [53] and Ohno et al . [52] , we treat the porto-central axis of the sinusoid as the repeating unit . The blood and surrounding hepatocytes in the sinusoid are split into compartments according to their position along this axis ( proximal periportal -> distal pericentral ) ( Fig 1 ) . 8 compartments were used when simulating the data in this article to allow simple comparison with experimental studies which tend to split the sinusoid into 2–4 zones , up to a maximum of 8 ( e . g . [58] ) . This is also consistent with previous computational models of zonation in liver [52 , 53] . No under-sampling effects were noted when using 8 compartments compared with simulations run using higher compartment numbers . Blood flows from the periportal to the pericentral end of the sinusoid . After leaving the distal pericentral compartment , it enters a larger body compartment where it interacts with simple representations of the pancreas ( hormone input ) , adipose tissue ( FFA and triglyceride regulation ) and with glucose and FFA inputs/outputs in the rest of the body ( Fig 1 ) . The model simulates an individual at rest and does not include the blood flow , blood oxygenation and hormonal changes occurring during exercise . Although this representation of the sinusoid allows inclusion of zonated enzyme expression , it remains a simplification . Several sinusoids extend between each portal triad and central vein passing following indirect paths through the cells , and the number of cells fed by each sinusoid will vary . Due to the hexagonal shape of the lobule , each sinusoid is likely to be supplying a larger number of cells nearer the portal triad than the central vein . Additionally , given that oxygen concentration is the signal promoting zonated expression [59] , cells further from the capillary ( but in the outer periportal region of the lobule ) are likely to show more pericentral expression than those neighbouring the capillary . Therefore , there is scope for development of models to refine predictions in the future by representing distributed effects across 2D and 3D representations of the lobule . Fig 2 shows the variables and processes included in each hepatic compartment of the model . The model focusses on the storage of glucose as glycogen , the cycling between glucose and lactate , adenosine triphosphate ( ATP ) production , FA production and the storage of FFAs as triglycerides . A reduced description of the representation of metabolism in each hepatic compartment in the model is provided in Tables 1 and 2 . Table 1 contains the differential equations for each hepatic variable in terms of the metabolic processes . Table 2 defines the metabolic processes included in the model and references the sections of the supplementary material in which the full equations can be found . A detailed description of all equations in the model , along with the values of each constant and references used to set them is provided in S1 Text . Since the focus of this study is on liver metabolism , the model includes only very simple representations of essential processes occurring elsewhere in the body . The representation of pancreatic hormone release developed by Hetherington et al . was used to calculate the rate of release of glucagon and insulin into the blood [47] . The two hormones are then degraded at constant rates ( per unit of hormone ) as blood passes through the sinusoid to match the experimentally measured changes in concentrations [59 , 60] . A constant oxygen input was added to the body compartment along with a constant rate of consumption across the sinusoid . These were set such that the oxygen concentration falls from 65mmHg in the blood entering the proximal periportal compartment to 35mmHg in the blood leaving the distal pericentral compartment [59] . Since no changes in oxygen input or blood flow were simulated in this study , the oxygen concentrations across the sinusoid remained fixed at the experimentally measured gradient for a healthy liver . However , the inclusion of a dynamic oxygen calculation may allow the model to be used to study changes in oxygen supply and blood flow in developing liver disease in the future . The rate of oxidative phosphorylation is oxygen dependent in the model , with a KM value based on the measurements by Matsumura et al . [61] . In addition to the liver , organs including the intestine , adipose tissue and muscle play important roles in FFA and glucose metabolism and consumption . Since the focus of this study is on liver metabolism , rather than including separate compartments with complex sets of pathways to represent these , single equation calculations for each key process dependent upon the plasma substrate and hormone concentrations were used . These retain the ability to calculate acceptable concentrations for the various plasma molecules entering the sinusoid in both metabolically healthy and insulin resistant individuals as discussed in S1 Text . The processes represented in the body compartment are adipose tissue FFA synthesis , adipose lipolysis , triglyceride production in organs other than liver ( primarily intestine ) , and glucose and FFA uptake by muscle and other body cells .
In the following section , IR with and without increased SREBP-1c expression and varying dietary intake are simulated to assess to what extent these account for the experimentally observed changes in lipid levels , glucose regulation , ATP levels and metabolic rates in NAFLD [1 , 2 , 6–9] .
A computational model of hepatic glucose and lipid metabolism across the sinusoid was presented and used to study the heterogeneity in metabolic dysfunction across the sinusoid in NAFLD . The substrate , allosteric and hormonal dependences of each process were based upon existing literature . Zonation in the model was based on measured differences in the activities of key enzymes published in the literature and validated against data for the rates of key processes in different regions of the sinusoid . Using a computational model of this form allows us to simulate for the changes in the rates of metabolic processes and concentrations of intermediates in specific regions of the sinusoid under different feeding conditions and disease states which would not be feasible experimentally . Here the model has been used to understand the initial metabolic changes occurring in NAFLD , and the causes of heterogeneity in lipid build-up across the sinusoid . These predictions will allow for more focussed future experimental study , minimising the amount of complex , time-consuming and expensive experimentation required to understand the zone specific changes in disease . When simulating increased lipid intake in a metabolically healthy , insulin sensitive individual , the hepatic triglyceride content was raised but this increase was fairly moderate . Unless a very high fat diet ( resulting in plasma lipid concentrations at the high end of those seen in obese individuals ) was simulated , the hepatic lipid concentration remained lower than the 5% cut off at which NAFLD is diagnosed . Similarly , high glucose intake primarily caused increased glycogenesis rather than steatosis in insulin sensitive individuals . For more serious steatosis to develop in the model , IR was required . Early stage steatosis arose even when simulating a healthy ( moderate intake ) diet in an insulin resistant individual . Severe build-up of lipids in the liver occurred when high lipid intake , or to a lesser extent high glucose intake , was simulated in addition to IR . Across the range of insulin sensitivities , low FFA or glucose intake returned simulated plasma triglyceride levels to a healthy range . Therefore , loss of insulin sensitivity in addition to excessive calorie intake is predicted to be required for more than early stage steatosis to arise . Given that fats in liver are known to cause both hepatic and peripheral desensitivity to insulin , sustained excessive calorie intake will strongly increase the chance of developing hepatic steatosis over time . However , these data highlight the effectiveness of a low fat diet as a treatment for NAFLD , even in insulin resistant patients . In addition to the direct effects of IR , increased expression of SREBP-1c was required for the model to fully reproduce the metabolic changes seen in the early stages of NAFLD in vivo . In particular , the inclusion of increased SREBP-1c expression was required to replicate the increases in lipogenic rates seen in vivo . Additionally , both IR and increase SREBP-1c expression contributed to mitochondrial dysfunction , increased β-oxidation and reduced ATP levels in the simulations , consistent with in vivo observation . SREBP-1c is a transcription factor which upregulates lipogenesis and triglyceride synthesis . Under normal conditions , its expression is stimulated by insulin . However , increased expression has been shown to occur in insulin resistant NAFLD patients . It is thought that insulin retains the ability to directly stimulate SREBP-1c expression , despite the loss of insulin sensitivity [13 , 92] . Alternatively , it is possible that SREBP-1c is instead stimulated by hyperglycaemia or by the fats themselves [13 , 92] , similar to the effects of carbohydrate responsive element binding protein ( ChREBP ) [32] . These results highlight the importance of the resulting increase in lipogenesis and triglyceride synthesis in the pathology of the disease . However , the presence of steatosis was seen when simulating the direct effects of IR alone . One aim of this study was to understand the metabolic changes leading to the development of steatosis in NAFLD , with a particular focus on understanding the increased susceptibility of pericentral cells to lipid build-up and the resulting damage . Changes in the rate of triglyceride synthesis , rather than output or lipolysis accounted for the pericentral zonation in steatosis in model simulations . A reduction in net triglyceride output also contributed to overall increased triglyceride levels but did not show zone specific differences . The enzymes involved in triglyceride synthesis are not zonated in the model . The pericentral increase in triglyceride synthesis instead arose due to increases in the concentrations of G3P and FFAs . Defective postprandial glycogen storage results when simulating IR and caused build-up of glucose metabolism intermediates , including G3P across the sinusoid . Pericentral cells show lower glycogen synthase activity than periportal cells [60] . When simulating insulin sensitive individuals , this was compensated by increased insulin receptor [93] and reduced glucagon receptor expression [94] in pericentral cells . However , when simulating IR , the zonation in hormone receptors no longer affects glycogen synthesis , and glycogen depletion is most severe in pericentral cells . Additionally , pericentral cells have fewer mitochondria and downregulated oxidative phosphorylation due to their low oxygen environment [60] . As a result , pericentral cells are able to metabolise glucose intermediates less rapidly . Since glucose oxidation is suppressed in insulin resistant NAFLD patients and β-oxidation is upregulated , the periportal zonation of oxidative phosphorylation enzymes , along with those mediating β-oxidation , had an even larger effect on the simulated rate of oxidation of hepatic FFAs . Excess FFAs were metabolised more rapidly in periportal cells than pericentral . Increased availability of hepatic FFAs arose from both uptake and de novo lipogenesis . The enzymes involved in glycolysis and lipogenesis show pericentral zonation and the increase in de novo lipogenesis was largest in pericentral cells . The enzymes mediating FFA uptake , meanwhile , show periportal zonation . When simulating MH individuals , FFA uptake is dominated by insulin stimulated scavenging leading to strongly periportal uptake . However , when simulating insulin resistant individuals , FFA uptake occurs due to the high plasma FFA concentration rather than insulin stimulation , and passive uptake dominates . Under these conditions , the periportal zonation of FA uptake proteins had a smaller effect on the rate of uptake . Therefore , although total uptake was still higher in periportal cells , a larger increase in rate occurred in pericentral cells . Together these results suggest that the major differences between pericentral cells and periportal cells accounting for increased pericentral susceptibility to steatosis are lower expression of oxidative phosphorylation enzymes , β-oxidation enzymes and glycogen synthase along with higher expression of lipogenic enzymes in pericentral cells . These differences across the sinusoid account for a larger increase in FFA and G3P concentrations in pericentral cells in NAFLD and , therefore , result in higher triglyceride synthesis in these cells . Future experimental validation of these simulated data could be performed through the addition of radiolabelled substrates to measure the rates of conversion within individual regions of the sinusoid . As discussed S2 Text ( section 1 . 1 . 5 ) , data of this form has previously been published for metabolically healthy individuals , and the model outputs are consistent with experimental data in this case . Similar studies comparing metabolically healthy and non-alcoholic fatty liver disease patients are required . Studies destroying specific regions of the sinusoids and measuring the remaining activity for various processes could also be used to provide insight [95] . Sensitivity analysis was performed on the rate constants in the model to determine the processes most likely to account for variation in susceptibility to steatosis seen between individuals in vivo . Sensitivity analysis on the zonation constants for each process was also used to predict the key processes accounting for differences in the predominant location of steatosis seen in vivo . The model simulations suggest that any inter-individual variations in the rate of metabolism of hepatic fats will have a large effect on susceptibility to NAFLD development . Variations in the rate constants for β-oxidation and acetyl-CoA consumption in the citrate cycle had notably larger effects on hepatic lipid levels than equivalent variations in the rate constants of other processes in the model . Furthermore , total hepatic lipid levels showed the next highest sensitivity to the rate constants for glucose uptake , glycolysis and acetyl-CoA synthesis from pyruvate all of which play a role in the allosteric suppression of β-oxidation by acetyl-CoA . Therefore , susceptibility to steatosis is predicted to be most strongly determined by the rate at which hepatocytes can metabolise fats . As validation of this prediction , there is considerable evidence to suggest mitochondrial function , aerobic capacity and capacity for β-oxidation inversely correlate with liver fat percentage and prevalence of NAFLD [67 , 96–104] . Furthermore , consistent with the high sensitivity of fat storage to oxidation rates , global knockout of ACC2 ( resulting in reduced allosteric inhibition of β-oxidation ) has been shown to reduce T2DM risk , obesity and adipose fat storage [105–107] . Liver specific knockout of ACC1 and ACC2 has been shown to reduce hepatic steatosis , although this resulted from both increased β-oxidation and reduced lipogenesis [108] . However , additional directed studies are required to determine whether higher β-oxidation and oxidative phosphorylation capacities protect against hepatic steatosis independent of confounding factors such as exercise or caloric intake . Whilst the predominant location of steatosis showed some dependence to the zonation of enzymes mediating β-oxidation , it was far more sensitive to changes in the zonation constants for FA uptake and triglyceride release as VLDL . Therefore , inter-individual variation in the distribution of steatosis is predicted to be accounted for by differences in the zonation of proteins mediating lipid uptake and triglyceride release . At present , little experimental data exists to validate this prediction due to the difficulty involved in measuring the distributed activities of large numbers of enzymes . However , the model simulations allow for targeted potential future experiments , comparing the zonation in the activities of the proteins mediating lipid uptake and triglyceride release with the distribution of steatosis across a range of samples . Due to the large heterogeneity in metabolism across the sinusoid , a clear description of the metabolic changes occurring in each zone is required to fully understand NAFLD development and to optimise potential pharmacological interventions . In this study a computational model of sinusoidal metabolism was presented and used to simulate the development of NAFLD , focussing on the metabolic changes in individual zones . Consistent with experimental observation , both IR and increased SREBP-1c expression were required for the model to fully replicate the metabolic changes seen in NAFLD in vivo . Simulations were run to identify the key differences between periportal and pericentral cells which account for higher pericentral susceptibility to steatosis . The majority of additional FFAs in NAFLD arise from fatty acid uptake rather than de novo lipogenesis both in the simulated data and experimentally [30 , 34–36] . Although fatty acid uptake enzymes show periportal zonation , the switch from predominantly insulin stimulated fatty acid scavenging to passive diffusion reduced the effect of this heterogeneity on the rate of uptake across the sinusoid in the model simulations . Instead , the model simulations highlight the periportal zonation of oxidative phosphorylation and β-oxidation enzymes , along with the pericentral expression of lipogenesis enzymes as the key differences leading to a raised FFA concentration in pericentral cells when simulating insulin resistant NAFLD patients . Additionally , reduced insulin stimulation of glycogenesis caused the build-up of glucose intermediates , including G3P across the sinusoid . A more severe increase in pericentral and intermediate cells occurred due to the periportal zonation of glycogen synthase and of oxidative phosphorylation . Sensitivity analysis was performed on the rate and zonation constants in the model to determine likely inter-individual differences in enzyme activities accounting for variation in susceptibility to NAFLD and steatosis distribution seen in vivo . Hepatic triglyceride levels are predicted to be most sensitive to inter-individual variations in the rate of FFA oxidation , either through differences in the overall rate of oxidation of acetyl-CoA or differences in the relative contribution of FFAs and glucose to oxidation . The predominant location of steatosis across the sinusoid meanwhile was most sensitive to changes in the zonation of proteins mediating FFA uptake or VLDL synthesis and release . | Fat build up in liver is known to increase the likelihood of developing numerous health problems around the body including cardiovascular problems , desensitisation to the hormone insulin leading to type 2 diabetes , and the development of fibrous tissue in liver ( fibrosis ) resulting in loss of liver function ( cirrhosis ) . Liver cells show marked differences in their metabolism depending upon their position along liver capillaries ( sinusoids ) . It has been shown previously that cells nearer the output end of the sinusoid ( pericentral ) are more susceptible to excess fat and the resulting damage than those near the input end . However , due to the micro-meter scale of the sinusoids and the large number of potential variables , it is difficult to study metabolism in individual regions of the sinusoid experimentally when considering various feeding and disease states . Here , a computational model of sinusoidal metabolism incorporating previously measured differences in enzyme activities across the sinusoid is presented and used to assess the key metabolic differences leading to pericentral susceptibility to excess fat build-up . Secondly , the model is used to assess the metabolic variations between individuals most likely to account for inter-individual variability in susceptibility to excess liver fat and its predominant location . These simulations will aid understanding of disease progression and allow for more targeted future experimental work . | [
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"gastroentero... | 2016 | A Computational Model of Hepatic Energy Metabolism: Understanding Zonated Damage and Steatosis in NAFLD |
Virulence functions of bacterial pathogens are often energetically costly and thus are subjected to intricate regulatory mechanisms . In Salmonella , invasion of the intestinal epithelium , an essential early step in virulence , requires the production of a multi-protein type III secretion apparatus . The pathogen mitigates the overall cost of invasion by inducing it in only a fraction of its population . This constitutes a successful virulence strategy as invasion by a small number is sufficient to promote the proliferation of the non-invading majority . Such a system suggests the existence of a sensitive triggering mechanism that permits only a minority of Salmonella to reach a threshold of invasion-gene induction . We show here that the secondary structure of the invasion regulator hilD message provides such a trigger . The 5’ end of the hilD mRNA is predicted to contain two mutually exclusive stem-loop structures , the first of which ( SL1 ) overlaps the ribosome-binding site and the ORF start codon . Changes that reduce its stability enhance invasion gene expression , while those that increase stability reduce invasion . Conversely , disrupting the second stem-loop ( SL2 ) represses invasion genes . Although SL2 is the energetically more favorable , repression through SL1 is enhanced by binding of the global regulator CsrA . This system thus alters the levels of hilD mRNA and is so sensitive that changing a single base pair within SL1 , predicted to augment its stability , eliminates expression of invasion genes and significantly reduces Salmonella virulence in mice . This system thus provides a possible means to rapidly and finely tune an essential virulence function .
The success of bacterial pathogens relies upon a fine balance: They must rapidly induce functions dedicated to virulence in response to signals of the host , but withstand the often immense associated fitness costs that the production of these virulence proteins entails . This need is particularly acute for enteric pathogens , including Salmonella , as they survive within the intestine in competition with a vast number and diversity of bacterial species . Pathogens thus achieve this balance by several means . They may do so by evolving to place virulence under the control of existing global regulators . They may coordinate virulence gene expression as a part of tightly controlled , integrated regulatory mechanisms that respond to host signals . They may additionally induce virulence in only a select portion of a population sufficient to cause disease . With its ability to survive within the host while also disrupting the intestinal mucosa to induce disease , Salmonella employs all of these techniques [1–3] . Invasion , the process of intestinal epithelial penetration , utilizes a type III secretion system to produce a multi-protein secretion apparatus [4] . The production of these structures , encoded within Salmonella pathogenicity island 1 ( SPI-1 ) and including some forty genes , is controlled by a cascade of transcriptional regulators within SPI-1 comprised of HilD , HilC , HilA and InvF [5–8] . A host of global regulators outside the island have additionally been enlisted to control invasion [1 , 9–14] . Regulation of invasion has further been linked to that of metabolism through the BarA/SirA/Csr system [15 , 16] . The BarA/SirA two-component regulator induces two small regulatory RNAs , CsrB and CsrC . These RNA molecules in turn bind and titrate CsrA , a post-transcriptional regulator of both invasion and central carbon metabolism [17] . CsrA binds to the 5’ end of the hilD mRNA , sequestering the ribosome-binding site and start codon , and thus is proposed to prevent HilD translation [18] . The summation of these complex controls produces a state in which only a fraction of the Salmonella population is capable of invading tissue [3] . Invasion by this sub-population , however , alters the intestinal environment to favor the growth of luminal Salmonella [19–22] , without imposing upon them the burden of virulence . How then is this fine level of control achieved ? The existence of two sub-populations , invasion-competent and -incompetent , suggests a threshold of genetic control: Any individual bacterium is capable of crossing that threshold , but only some minority of the total does . Here we show that such regulation can be achieved through post-transcriptional control of the hilD message in conjunction with the auto-induction of this central SPI-1 transcriptional activator . The hilD message employs alternative secondary structures , with mRNA stability enhanced by binding to CsrA , to control the production of HilD , and HilD in turn amplifies induction through the control of its own transcription . The sensitivity of this regulation is such that the addition of a single hydrogen bond within the message secondary structure completely prevents the expression of invasion genes and reduces virulence in an animal host . Thus , genetic and metabolic signals can be integrated to achieve the coordinated control of virulence .
The AraC-type transcriptional regulator HilD is known to be central to the control of Salmonella invasion , dictating the ability of the pathogen to penetrate the intestinal epithelium [6] . We sought to identify gain-of-function hilD mutants capable of inducing invasion gene expression under conditions in which HilD activity is greatly reduced and thus to identify hilD alleles with greater intrinsic expression or stability . We created these mutants using error-prone PCR on a plasmid construct that carried the hilD ORF and the portion of the 5’ untranslated region extending to the transcriptional start site , but replaced the native hilD promoter region with an inducible tetA promoter . The resulting plasmid library was introduced into a hilD null mutant harboring a transcriptional fusion of GFP to sipC , an invasion gene induced by HilD . Bacteria were grown in the presence of nonanoate , a fatty acid that strongly represses SPI-1 expression by reducing the stability of HilD protein ( S1 Fig ) , and mutants with aberrantly high expression of GFP were identified . This screen identified a cluster of mutations within the 5’ untranslated region and extending into the 5’ end of the hilD ORF that provided gain of hilD function . These mutations occurred at nucleotides +23 to +31 and +46 to +53 , as numbered from the transcriptional start site ( S2 Fig ) . By analyzing minimal free energy , we found that the transcript in this region is predicted to form a stem-loop secondary structure ( termed here Stem-Loop 1; SL1 ) with a free energy of -7 . 70 kcal/mol , having seven perfect nucleotide pairs at its base , and incorporating the initiating codon of HilD at position +36 ( Fig 1 ) . The mutations identified lie entirely within SL1 and largely within its predicted stem structure . To examine the importance of this region to invasion , we created chromosomal point mutations of specific bases . The resulting mutants , created using CRISPR/Cas9 , carried single , unmarked mutations without additional genetic changes to the region . The change of A25 to G , C50 to T , or T53 to C , all predicted to disrupt base pairing within the SL1 stem ( Fig 1 ) , greatly increased expression of invasion genes . We tested a single-copy chromosomal sipC::lacZY reporter fusion as a representative HilD-regulated gene within SPI-1 , and found it to be increased by 4- to 6-fold in these mutants ( Fig 2A ) . A plasmid-borne sopB::luxCDABE was additionally tested as a HilD-regulated gene outside of SPI-1 , showing peak expression of the mutants increased by >2 . 6-fold and areas under the curve increased by at least 2 . 7-fold ( Fig 2B ) . The expression of hilD itself , known to be auto-induced , was similarly affected by these mutations , with at least two-fold increases in expression in the mutants ( S3A Fig ) . Neither of the mutations created within the hilD ORF ( C50T or T53C ) altered the HilD amino acid composition , thus eliminating any possible effects by HilD protein and instead implicating the mRNA transcript as the source of these effects . To confirm the importance of this region to the regulation of invasion , we further tested a compensatory double mutant , carrying both the A25G and T53C changes . This mutant is predicted to change an A-U base pair within the message to G-C , reducing the predicted free energy to -9 . 50 kcal/mol , and thus potentially enhancing secondary structure stability . Indeed , the expression of invasion genes in this mutant , in contrast to the induction in the single mutants , was vastly repressed compared to that of the wild type , with expression of sipC reduced by 29-fold and sopB by 51-fold . ( Fig 2 ) . These mutations similarly altered the ability of mutant strains to penetrate cultured epithelial cells , demonstrating their functional significance . Mutants of A25G or T53C showed significantly increased invasion of HEp-2 cells compared to the wild type , by 1 . 4- and 1 . 6-fold respectively , using a gentamicin-protection assay ( Fig 3 ) . In contrast , the A25G , T53C double mutant invaded as poorly as did a hilD null mutant , reduced from the wild-type level by 8-fold . Thus , this single change to a predicted pair of bases within this region of the hilD message , increasing hydrogen bonding by one , fully abrogated invasion gene expression and epithelial cell invasion . To directly test the effects of these hilD mRNA point mutations on invasion in vivo , we employed the murine typhoid fever model to measure their colonization of abdominal organs , a function that requires invasion . Salmonella-susceptible BALB/c mice ( Slc11a1 negative ) were inoculated orally with similar numbers of two strains , the wild type and the hilD A25G , T53C double mutant that demonstrated greatly reduced invasion gene expression in laboratory media . Numbers of each strain in the spleens and livers of infected mice were assessed five days post-inoculation . We found that , in accordance with our gene expression studies , the A25G , T53C double mutant reached these tissues only poorly . The mean competitive index of the two strains , defined as the number of wild-type bacteria divided by that of the mutant , was 651-fold in the spleen ( P = 0 . 049 ) and 322-fold in the liver ( P = 0 . 020 ) , demonstrating a significant deficit in invasion by the mutant , and indicating the essential nature of this region to Salmonella virulence . The single mutants tested , however , ( A25G or T53C ) did not demonstrate consistent enhanced virulence but instead showed erratic behavior , with competitive indices to the wild type varying greatly among individual mice ( S4 Fig ) . This may be due to the metabolic burden caused by the over-expression of invasion genes in these mutants , manifested by reduced growth rates in vitro ( S3B Fig ) , or by the dysregulation of virulence genes once invasion has been accomplished within the host , thus compromising bacterial survival within tissues . Because these hilD mutants , predicted to affect only single base pairs within the message , manifested such profound effects on invasion , we further examined the area adjoining the SL1 secondary structure for regions that might affect its formation . mRNA structure analysis indicated the possibility of a second stem-loop in the hilD transcript ( SL2 ) , located within the 5’ region of the hilD ORF . Its stem portion consisted of nine pairs of nucleotides , of which eight form perfect base pairs , and was predicted to have a free energy of -9 . 50 kcal/mol , more favorable than that of SL1 ( Fig 1 ) . This structure , at positions +51 to +96 , would share five nucleotides with the stem portion of SL1 , making the simultaneous existence of the two impossible . We thus hypothesized that SL2 provides an alternative transcript conformation that counteracts the repression of invasion gene expression imposed by SL1 . To test this , we created mutations designed to disrupt the predicted stem portion of SL2 ( Fig 1 ) . As these exist within the hilD ORF , synonymous mutations were again created to alter nucleotides without changing the amino acid composition of HilD . The mutations A57T , G58C and T59C together disrupt three base pairs within SL2 and predict a change in the free energy of SL2 from -9 . 50 kcal/mol to -7 . 20 kcal/mol , thus potentially making this mRNA conformation less favorable to that of SL1 . We found that such a triple mutant demonstrated significantly decreased invasion gene expression when compared to the wild type ( Figs 2B and 5A and S4A Fig ) , and reduced epithelial cell invasion to a level indistinguishable from that of a hilD null mutant ( Fig 3 ) , indicating an effect opposite to that of SL1 , and suggesting the importance of alternative mRNA secondary structures within the hilD message for the control of invasion . The importance of SL1 to invasion , in spite of the presence of the competing SL2 with its predicted greater stability , suggests the existence of additional means to stabilize transcript topology . It has previously been reported that the regulatory protein CsrA binds to two sites on the hilD mRNA , at positions +26 to +33 and +35 to +42 [18] , both located within SL1 ( Fig 1 ) . CsrA is a component of the BarA/SirA/Csr regulatory cascade that integrates invasion and metabolic control , and inhibits invasion by the post-transcriptional repression of hilD . This regulator has been shown to function by preventing translation and reducing message half-life of its target genes [23] . We thus hypothesized that CsrA might recognize its binding sites in the context of SL1 , enhancing stability of this inhibitory secondary structure , and therefore that disruption of SL1 would promote the accumulation of hilD message . To test this , we expressed wild type and SL1 mutant alleles of hilD from an exogenous , inducible promoter ( PtetA ) , removing its native transcriptional control , and assessed message levels using RT-PCR . The presence of A25G , C50T , or T53C mutations all significantly increased the concentration of hilD message , by 4- to 8-fold compared to that of the wild type ( Fig 4A ) . As transcription is expected to be invariant in these strains , the results suggest that the increases in message levels are due to the improved stability of the hilD transcript , consistent with a predicted reduced function of CsrA in these mutants . Message levels were , conversely , decreased in the A25G , T53C double mutant , suggestive of enhanced efficacy of CsrA and consistent with its defect in invasion . To test directly the interaction of SL1 with CsrA , we measured the ability of CsrA to bind to wild type and mutated RNA fragments of this region . Biotinylated RNA probes consisting of the SL1 region alone were incubated with purified CsrA protein , transferred to nitrocellulose membranes , fixed with UV light , and quantitated using HRP-conjugated streptavidin . Small RNA molecules are unable to bind efficiently to nitrocellulose and thus will do so only if associated with protein . We found that the A25G mutation reduced CsrA binding of SL1 to 53% ( P < 0 . 0001 ) of the wild type level , indistinguishable from the binding of CsrA to a scrambled RNA ( randomly generated with the same length and G-C content as SL1 , but without discernable secondary structure ) ( Fig 4B ) . The effect of the T53C mutation was not as strong , but significant , with CsrA binding reduced to 82% ( P < 0 . 001 ) of the wild type level . Conversely , the A25G , T53C double mutation increased binding to 119% ( P < 0 . 0001 ) . These results thus show that changes that reduce SL1 stability , A25G or T53C , also reduces CsrA binding , and one that enhances that stability , A25G , T53C , has the opposite effect . To examine the consequences of these interactions with CsrA , we next tested the effects of a csrA mutation on invasion gene expression in the wild type and secondary-structure mutants . Strains with deletions of csrA grow very poorly , and so we instead used a truncated mutant lacking the 11 amino acids of the carboxyl terminus , reducing its size to 50 amino acids ( csrAΔ50 ) . An equivalent mutant has long been successfully employed in E . coli , with its identical CsrA protein , and demonstrates defects in multiple CsrA-controlled pathways that indicate impaired regulatory function by this truncated protein [24] . As anticipated , the csrAΔ50 mutant increased sopB expression in an otherwise wild type strain , demonstrating that it is unable to effectively repress invasion ( Fig 5 ) . We first tested the activity of CsrA in an SL2 mutant . If CsrA augments the stability of SL1 , thus favoring this secondary structure over the competing SL2 , one would predict the loss of CsrA to restore invasion gene expression in an SL2 mutant . Indeed , we found this to be the case: the A57T , G58C , T59C triple mutant of SL2 alone reduced sopB expression in comparison to the wild type ( Fig 5A ) . The presence of csrAΔ50 in this mutant of SL2 , however , restored sopB expression to a level greater than that of the wild type . We next tested the functional interaction of CsrA with SL1 . The effect of the csrAΔ50 mutant was , in fact , nearly identical to that of A25G mutant , with each increased more than 2-fold above the wild-type expression ( Fig 5B ) . The combination of the two , however , was additive: the strain harboring both A25G and csrAΔ50 induced 4-fold more than the wild type . This result suggests both that , although CsrA binds to SL1 and requires the native sequence to do so efficiently , it continues to bind even to the weakened secondary structure , and that SL1 continues to repress invasion gene expression even in the absence of functional CsrA . We conversely tested the effects of increased CsrA activity using mutants of the BarA/SirA/Csr regulatory cascade . As expected , the loss of both csrB and csrC greatly reduced the expression of sopB , due to the likely abundance of free CsrA within the mutant strain ( S5 Fig ) . The addition of the hilD A25G mutation to the ΔcsrBC mutant , however , greatly increased invasion gene expression , suggesting an inability of CsrA to efficiently interact with the altered hilD transcript . Similarly , the addition of the A25G mutation also enhanced sopB expression in a sirA mutant , but to a lesser degree , as would be expected as SirA is known to additionally induce invasion gene expression by means independent of CsrB and CsrC [25] . Our results demonstrate that hilD message secondary together with CsrA elicit mean changes in invasion gene expression . Yet , in vitro and in vivo , Salmonella exists as two populations , one with invasion induced and the other not [3 , 26] . HilD acts to transcriptionally induce its own gene , allowing small changes in gene expression to produce disproportionately robust down-stream effects [27] . We thus tested whether the control of hilD by its RNA secondary structure creates a threshold effect , beyond which levels of HilD exert a self-perpetuating induction of invasion . The population dynamics of invasion were thus measured using GFP fused to the promoter of sicA , an invasion gene within SPI-1 . We first ensured that HilD was required for this biphasic phenotype , as only 0 . 1% of the population produced measurable GFP in a ΔhilD null mutant , compared to 4 . 8% in the wild type strain ( Fig 6 and S6 Fig ) . We further found that both SL1 and CsrA were required to regulate the biphasic expression of invasion genes , as mutations of either altered the ratio of the two populations . Indeed , both the A25G and T53C hilD mutants , with their reduced binding of CsrA to SL1 , and the csrAΔ50 truncation mutant significantly increased the proportion of the invasion-competent population to 43% , 28% and 35% , respectively . These data thus demonstrate that CsrA and the stem-loop structure of hilD , while acting independently of the autoinduction that achieves biphasic expression , function to tune the proportion of invasion-competent bacteria .
Here we have described a sensitive mechanism for the control of Salmonella invasion , an essential virulence function . The data presented suggest a simple but elegant model ( Fig 7 ) : The message of the invasion activator HilD is capable of assuming two alternative and mutually exclusive secondary structures . Formation of the first , SL1 , sequesters the ribosome-binding site and start codon , reducing message stability and presumably preventing translation . Formation of the second ( SL2 ) , however , liberates these sites from the secondary structure and instead promotes the expression of hilD . SL2 is energetically favored , and thus in the absence of additional regulatory components , it should predominate . SL1 , however , binds to CsrA , further stabilizing it and shifting the balance of control towards the repression of hilD when in the presence of this global regulatory protein . This balance can thus be altered by the activity of the BarA/SirA two-component regulator , which induces the expression of the regulatory RNAs CsrB and CsrC . These RNAs bind CsrA , titrating it from its target within SL1 , allowing SL2 to form and increasing invasion gene expression through enhanced translation of HilD . The proposed system is thus comprised of elements familiar in the control of bacterial gene expression through mRNA regulation . Employing alternative secondary structures has long been recognized in bacteria as a means to alter translational efficiency [28] . In addition , CsrA is known to bind specific sequences within the context of message structure to reduce translation and repress gene expression [23] . The combination of these two therefore provides a sensitive means to switch between activation and repression . Of note is the fact that either a partial disruption of SL1 by mutation or the loss of functional CsrA increases invasion gene expression , but the effect of the two in combination remains additive ( Fig 5B ) . Similarly , the combined mutation of CsrA and SL2 creates an intermediate phenotype ( Fig 5A ) , as would be expected if the two were in competition . These findings thus demonstrate a fine balance in control , originating in the message but augmented by the regulatory protein . Enteric pathogens such as Salmonella experience great environmental changes as they move into and through the intestinal tract of animals . The environmental cues that affect CsrA , and thus may tip the balance toward invasion , have been only partially elucidated . The activity of CsrA is reduced through its sequestration by two small RNAs , CsrB and CsrC , that are themselves induced by the BarA/SirA two-component regulator [15–17] . The signal for the BarA sensor-kinase has not been fully characterized , but CsrB and CsrC can be induced by acetate , independently of BarA , through the SirA response-regulator [29] . As acetate is a metabolic by-product of the intestinal microbiota and exists in millimolar concentrations in the gut , it provides at least one plausible signal for invasion induction through the system we describe . In addition to their sensitivity , changes in gene expression produced by mRNA switches are predicted to be rapid . The half-lives of bacterial messages are typically measured in minutes , such that changes in stability quickly alter the message pool available for translation . Salmonella controls invasion through multiple mechanisms , including transcription and protein activity . mRNA stability , however , is likely to provide a means to do so efficiently in response to the changing environmental conditions encountered by the pathogen . As a pathogen , Salmonella must balance the costs of protein production with the expression of virulence factors needed to colonize an animal host . It has evolved to manage the costly invasion process by producing a biphasic population , with only a fraction burdened by the expression of invasion genes [3 , 26] , but functioning to create an environment conducive to the proliferation of non-invasive siblings [19–22] . This implies the existence of an invasion switch with a specific set point , and one that can be altered by genetic and environmental cues . The system described here , in conjunction with the auto-induction of HilD , constitutes an important component of such a switch . Invasion requires a complex interplay of transcriptional regulators , with HilD and others comprising a feed-forward induction loop . The fine control of the hilD message level may thus provide a threshold for induction: The small proportion of the population with sufficient hilD message to produce HilD and induce subsequent autoinduction are invasive , while those that do not reach that threshold are not . In this way , the switch described here may play an important role to produce the two distinct populations needed for virulence .
Studies involving vertebrate animals were approved by the Cornell University Institutional Animal Care and Use Committee ( Protocol 2012–0074 ) . Euthanasia was conducted using carbon dioxide inhalation in accordance with the American Veterinary Medical Association Guidelines for Euthanasia of Animals . The Cornell University Animal Care and Use program and associated animal facilities are operated in accordance with the U . S . Department of Agriculture Animal Welfare Act ( 1966 ) , Regulation ( C . F . R . , 2009 ) and policies , the Health Research Extension Act ( 1985 ) , the Public Health Service Policy on Humane Care and Use of Laboratory Animals ( PHS , 2002 ) , the Guide for the Care and Use of Laboratory Animals ( NRC , 2011 ) , the Guide for the Care and Use of Agricultural Animals in Research and Teaching ( 2010 ) , the New York State Health Law ( Article 5 , Section 504 ) , and other applicable federal , state , and local laws , regulations , policies , and guidelines . Bacterial strains and plasmids are listed in S1 Table . Chromosomal point mutants were made using CRISPR/Cas9-directed mutagenesis as described below . Strains were grown in LB broth ( 10 g tryptone , 5 g yeast extract , 5 g NaCl/liter ) with 100 mM MOPS pH 6 . 7 at 37°C unless otherwise stated . The hilD ORF and a portion of its 5’UTR extending to the transcriptional start site were amplified using error-prone PCR to create point mutations , and PCR products were cloned into a derivative of pWSK29 to create pWSK29-tetRA-hilD-3XFLAG , placing hilD under the control of the inducible tetA promoter ( S1 Fig ) . The plasmid library was transformed into a ΔhilD , sipC::gfp strain , selected on LB agar with 100 mM MOPS pH 6 . 7 , 100 μg/ml ampicillin , and 1 mM sodium nonanoate . Tetracycline was not included in the medium as basal expression of the tetA promoter was adequate to express hilD from the multi-copy plasmid in its absence . Bacteria expressing GFP under these repressive conditions were identified by green fluorescence using an Olympus OV-100 imaging system . Resulting plasmids were sequenced for mutations in hilD and the adjacent untranslated region . For β-galactosidase assays , strains carrying lacZY fusions were grown as 5 ml cultures in 18 mm glass tubes without aeration for 16–18 hours . Assays were performed as previously described [30] . Luminescence assays using luxCDABE fusions were conducted as described [31] . Briefly , strains were grown overnight in the presence of tetracycline ( 25 μg/ml ) for plasmid maintenance and then diluted 100-fold in the same medium . Samples of 150 μl were inoculated into 96-well plates , and luminescence and OD600 were read every 20 minutes for 24 hours using a Synergy 2 luminescence microplate reader ( BioTek ) . Samples were tested in replicates of four or more . Strains containing a plasmid-borne hilD-3xFLAG under tetA promoter control , with additional point mutations as indicated and chromosomal hilD deleted , were grown overnight standing in LB with 100 mM MOPS pH 6 . 7 . After 16 . 5 hr , 1 ml of each culture was collected and washed once in PBS . RNA was extracted with phenol:chloroform , and Turbo DNase ( Ambion ) was used to reduce contaminating DNA . SuperScript II RTase ( Invitrogen ) was used to synthesize cDNA , which served as template for RT-qPCR reactions using iQ Sybr Green Reagent . The ΔΔCt method was used to calculate the amount of hilD transcript relative to the housekeeping gene dnaN . Mutants were constructed using the previously reported plasmid-based CRISPR/Cas system [32] . Plasmids required for mutant construction are listed in S1 Table and oligonucleotide sequences in S2 Table . In brief , the pCRISPR::hilD plasmids carrying the sequence targeting a specific PAM site within hilD were created by phosphorylating a pair of synthesized oligonucleotides , annealing , and cloning into the BsaI sites of pCRISPR ( Addgene ) . The resulting plasmid was co-transformed along with a synthesized single-stranded oligonucleotide carrying the desired point mutation of hilD into a Salmonella strain carrying the plasmid pKD46 [33] , producing the Red λ recombinase , and pCas9 ( Addgene ) expressing tracrRNA and Cas9 . The transformants were initially selected on LB agar with chloramphenicol ( 25 μg/ml ) and kanamycin ( 50 μg/ml ) at 37°C . Colonies were purified once onto the LB agar , and further screened for loss of pCRISPR::hilD and pCas9 plasmids by susceptibility to chloramphenicol and kanamycin . HEp-2 cells were cultured in DMEM containing 10% fetal bovine serum . For invasion assays , 2 x 105 cells were seeded in 24-well plates . Bacteria were grown overnight as static cultures in LB with 100 mM HEPES , pH 8 . 0 , at 37°C . To maintain an MOI of 10 , ~2 x 106 bacteria were added to cells . Plates were then centrifuged for 10 min at 100 x g and incubated for 1 h at 37°C in 95% air/5% CO2 . Media was removed , the cells were washed four times with HBSS , followed by incubation with media supplemented with 20 μg/ml gentamicin for 1 h . Post incubation , cells were washed three times with sterile PBS and lysed with 1% Triton X-100 for 5 min . Intracellular bacteria were quantified by dilution of lysates onto LB agar . Invasion was determined by dividing recovered bacteria by its inoculum . Each strain was tested in quadruplicate wells in each of at least two independent experiments . Strains were grown overnight , washed twice and resuspended in PBS . Differently marked strain pairs were mixed in approximately equal proportions: a malXY::kan strain ( kanamycin-resistant ) with an A25G , T53C , malXY::cam strain ( chloramphenicol-resistant ) , or a malXY::cam strain with an A25G , T53C malXY::kan strain , to compensate for any effects of the extraneous resistance marker . Female BALB/c mice , 6- to 7-weeks of age , were given ~1x107 total bacteria ( 5x106 of each strain ) by mouth . After five days , mice were euthanized and the spleens and livers were homogenized in PBS , with dilutions plated onto LB agar with 25 μg/ml chloramphenicol or 100 μg/ml kanamycin . Data were pooled to determine a competitive index , the ratio of the wild type to the A25G , T53C mutant . CsrA protein was expressed as a carboxyl-terminal his-tagged construct using the pQE70 expression vector ( Qiagen ) and purified using affinity chromatography . Protein was mixed at a concentration of 100 nM with 50 nM of 5’-biotinylated RNA probes ( Sigma ) in binding buffer ( 10 mM Tris pH 7 . 5 , 10mM MgCl2 , 100 mM KCl , 7 . 5% glycerol , 20 mM DTT and 0 . 01% sodium deoxycholate ) for 35 minutes at 37°C . Reactions were spotted in replicates of five onto 0 . 2 μm nitrocellulose membrane , air dried and UV crosslinked at 120 μJ/cm2 . Membranes were blocked for one hour in TBS SuperBlock ( Thermo Scientific ) , incubated with 1:50 , 000 diluted streptavidin-conjugated horseradish peroxidase and developed with Western Lightning ECL Pro chemiluminescence substrate ( Perkins Elmer ) . ImageJ was used to quantify intensity . Strains expressing a constitutive BFP ( for gating on the Salmonella population ) , a GFP reporter downstream of the sicA promoter ( for assessing invasion gene expression ) [34] , and additional chromosomal mutations as indicated , were grown overnight . After 16 . 5 hr , 100 μl to 1 ml of each culture was fixed in 4% paraformaldehyde in PBS , turning at 4°C for 30 min . Samples were centrifuged , paraformaldehyde was aspirated , and fixed bacteria were resuspended in PBS . Samples were analyzed on a FACS Aria III Custom , gating on the blue population and interrogating for GFP-expressing bacteria . Comparisons of means were performed with Student’s t-test using JMP 11 Pro . | Pathogenic bacteria tightly regulate the expression of their virulence functions to balance survival and proliferation within an animal host against the fitness costs that these functions engender . Salmonella has evolved an energetically favorable means to invade the intestinal epithelium , required for its virulence , with only a small proportion of its population expressing the needed genes , while the remainder reaps the benefits . This work shows that the threshold of invasion induction is finely controlled through the message secondary structure of the activator hilD in conjunction with the invasion repressor CsrA . This sensitive system may thus allow Salmonella rapidly to adjust the dynamics of its invading population in response to signals within the animal . | [
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... | 2019 | Salmonella invasion is controlled through the secondary structure of the hilD transcript |
Vascular cell adhesion molecule-1 ( VCAM-1 ) interacts with its major ligand very late antigen-4 ( VLA-4 ) to mediate cell adhesion and transendothelial migration of leukocytes . We report an important role for VCAM-1/VLA-4 interactions in the generation of immune responses during experimental visceral leishmaniasis caused by Leishmania donovani . Our studies demonstrate that these molecules play no direct role in the recruitment of leukocytes to the infected liver , but instead contribute to IL-12p40-production by splenic CD8+ dendritic cells ( DC ) . Blockade of VCAM-1/VLA-4 interactions using whole antibody or anti-VCAM-1 Fab′ fragments reduced IL-12p40 mRNA accumulation by splenic DC 5 hours after L . donovani infection . This was associated with reduced anti-parasitic CD4+ T cell activation in the spleen and lowered hepatic IFNγ , TNF and nitric oxide production by 14 days post infection . Importantly , these effects were associated with enhanced parasite growth in the liver in studies with either anti-VCAM-1 or anti-VLA-4 antibodies . These data indicate a role for VCAM-1 and VLA-4 in DC activation during infectious disease .
Very late antigen-4 ( VLA-4; α4 integrin; CD49d ) is expressed on most leukocytes and plays an important role in leukocyte trafficking by interacting with vascular cell adhesion molecule-1 ( VCAM-1; CD106 ) on endothelial cells to mediate tethering , rolling , firm adhesion and transendothelial migration [1] , [2] . This interaction has also been implicated in the compartmentalisation of B cells into peripheral lymphoid tissue [3] , the association of neutrophils with bone marrow ( BM ) stromal cells [4] , the promotion of interactions between follicular dendritic cells ( FDC ) and B cells [5] , and more recently , in the formation of a docking structure that surrounds the B cell receptor ( BCR ) [6] and TCR [7] in the immunological synapse ( IS ) that forms between antigen presenting cells and antigen-specific B and T cells . Visceral leishmaniasis ( VL ) caused by Leishmania donovani infection in genetically susceptible C57BL/6 or BALB/c mice results in parasite replication in the liver , spleen and BM [8] . The liver is the site of an acute , resolving infection , associated with leukocyte recruitment to infected resident Kupffer cells ( KC ) and the subsequent generation of a localised inflammatory response ( granuloma formation ) , that includes the production of IFNγ , TNF and reactive oxygen and nitrogen species , necessary for killing intracellular parasites [9]–[11] . In contrast , a chronic infection is established in the BM and spleen , associated with major changes to tissue architecture in the latter organ , including the loss of marginal zone ( MZ ) macrophages and stromal cells in the periarteriolar lymphoid sheath ( PALS ) [12] , [13] . Interestingly , despite long-term parasite persistence in the spleen , this tissue is an important site for early dendritic cell ( DC ) IL-12p40 production that plays a key role in the generation of anti-parasitic immunity required for the control of hepatic L . donovani replication [14]–[16] . We recently showed that VCAM-1 was expressed on hepatic sinusoids during L . donovani infection in C57BL/6 mice . Furthermore , we demonstrated that VCAM-1 expression during infection required lymphotoxin alpha ( LTα ) and that in LTα-deficient C57BL/6 mice , the absence of VCAM-1 was associated with a failure of leukocytes to migrate from periportal areas to infected KC during granuloma formation , coincident with increased parasite growth [17] . In this study , we investigated whether the lack of VCAM-1 expression observed in L . donovani-infected mice deficient in LTα could explain their failure to recruit leukocytes into the liver and efficiently control parasite growth . Blockade of VCAM-1 or VLA-4 suppressed anti-parasitic immune responses and was associated with significantly higher hepatic parasite burdens . However , rather than directly mediating cellular recruitment to the liver during VL , our data indicate that VCAM-1 and VLA-4 play a role in rapid CD8+ DC IL-12p40 production in the spleen following L . donovani infection , an event critical for the generation of effective anti-parasitic immunity .
To investigate the role of VCAM-1 in VL , C57BL/6 mice were administered anti-VCAM-1 mAb 5 hours prior to L . donovani infection and then every third day thereafter until 14 days p . i . Hepatic parasite burdens were significantly increased ( p<0 . 01 ) in these animals compared with rat IgG-treated controls at day 14 p . i . ( Figure 1A ) . In addition , hepatic granuloma formation , an important process for efficient control of L . donovani [9] , was significantly impaired in these mice with an increase in the frequency of infected KC and a decrease in the frequency of immature ( IG ) and mature granulomas ( MG ) ( p<0 . 01; Figure 1B ) . As expected , this was associated with a dramatic decrease ( p<0 . 01 ) in leukocyte recruitment to the liver following VCAM-1 blockade ( Figure 1C ) , with the recruitment of all leukocytes studied ( CD4+ T cells , CD8+ T cells , NKT cells , NK cells , B cells , macrophages/monocytes , neutrophils and DC ) being significantly and similarly reduced ( data not shown ) . IFNγ , TNF and reactive nitrogen intermediates ( RNI; measured using nitric oxide synthase ( NOS-2 ) as a surrogate marker ) are all critical for the control of L . donovani infection [9]–[11] , [17] , and mRNA encoding all of these molecules was significantly reduced ( p<0 . 05 ) in the livers of mice receiving anti-VCAM-1 mAb ( Figure 1D–F ) at day 14 p . i . To confirm that VLA-4 was the main integrin interacting with VCAM-1 during VL , we also blocked this molecule using antibody over the first 14 days of infection , and obtained very similar results ( Figure 2 ) . Together these data indicate that VCAM-1/VLA-4 interactions play an important role in the generation of hepatic immune responses following L . donovani infection . To determine whether VCAM-1/VLA-4 interactions play a direct role in cellular recruitment to the liver following L . donovani infection , we delayed VCAM-1 or VLA-4 blockade until 3 days after infection . We chose this time point to begin blockade because no significant cellular recruitment to the liver had occurred before this time ( 3 . 85×106±4 . 48×105 versus 4 . 22×106±7 . 86×105 hepatic leukocytes in naïve versus day 3 p . i . mice , respectively ) . In contrast to blockade commenced prior to infection , this delayed blockade failed to have any effect on parasite burden ( Figure 3A ) , the formation of granulomas ( Figure 3B and C ) , or numbers of hepatic mononuclear cells ( MNC; Figure 3D ) , indicating that VCAM-1/VLA-4 interactions played no role in leukocyte recruitment to the liver . Therefore , the main role for VCAM-1/VLA-4 interactions in VL appeared to be in the generation of immune responses within the first 3 days of L . donovani infection . We have previously demonstrated that DC IL-12p40 production in the spleen within 24 hours of L . donovani infection is critical for the efficient generation of immunity in the liver [15] , [16] . However , the kinetics of DC IL-12p40 production , the relevance of other infected tissue sites for the generation of this cytokine and the specific DC sub-population responsible for IL-12p40 production remain unknown . Therefore , we first measured IL-12p40 mRNA levels in the spleen , liver and BM , the main sites for L . donovani infection , over the first 7 days of infection . We found that IL-12p40 production occurred predominantly in the spleen and peaked at 5 hours p . i . ( Figure 4A ) . This pattern of production was also confirmed by analysis of IL-12p40 protein expression in spleen tissue sections ( data not shown ) , as previously described [15] . The IL-12p40-producing DC were located in the PALS , as well in close proximity to the MZ ( data not shown ) , as previously reported [15] , [18] . To determine the specific DC subset producing IL-12p40 , MACS-enriched DC from naive mice and L . donovani-infected mice 5 hours p . i . , were labelled for CD11c , CD4 and CD8α , and intracellular IL-12p40 protein levels were measured by FACS . No IL-12p40 protein was detected in CD11c-negative cells from either naïve or infected animals ( data not shown ) , thereby identifying DC as the main source of IL-12p40 . Small numbers of IL-12p40-producing CD11c+ DC were observed in naïve mice ( Figure 4B ) . Five hours after L . donovani infection , there was a 2–3 fold increase in the number of IL-12p40-producing CD11c+ DC , and virtually all of the increased IL-12p40 production could be attributed to the CD8+ DC subset ( Figure 4B ) . Together , these data demonstrate that parasite-induced IL-12p40 is produced in the spleen by CD8+ DC within 5 hours of L . donovani infection . We next investigated where VCAM-1 was expressed in the spleen to identify cell populations that might influence the generation of immune responses to L . donovani infection , and in particular CD8+ DC IL-12p40 production . Studies in the mouse spleen have reported VCAM-1 expression associated with individual cells in the red pulp [19] , within a broad zone in the MZ [19] and within the B and T cell zones , including on follicular dendritic cells [20] . We confirmed that the majority of VCAM-1 expression was in the red pulp region , and demonstrated that expression could also be detected on discreet cell populations in the MZ and white pulp ( Figure 5A ) . In the red pulp , all F4/80+ macrophages expressed VCAM-1 ( Figure 5B ) . However , blood flowing into the spleen is released into the marginal sinus before flowing across the MZ into the red pulp and returning to the circulation via a venous route [21] , and the site where DC are first likely to encounter parasites and/or parasite antigens is the MZ [15] , [18] . In the MZ , there was little overlap observed between staining for VCAM-1 and marginal metallophilic macrophages ( MOMA-1+ ) ( Figure 5B ) . In contrast , there was clear co-localisation of VCAM-1 expression with MZ macrophages ( ERTR9+/SIGNR1+ ) ( Figure 5C ) , and with sinusoidal endothelial cells ( Meca-32+ ) located in the MZ ( Figure 5D ) . Both VCAM-1+ MZ macrophages and endothelial cells were located in close proximity to discrete areas of DC ( CD11c+ ) ( Figures 5C and D ) . However , no clear VCAM-1 expression was observed on these DC ( Figures 5C and D ) , a finding also confirmed by FACS analysis of VCAM-1 expression by splenic DC ( data not shown ) . There was some co-localisation of VCAM-1 with reticular fibroblasts ( ER TR7+ ) throughout the spleen ( data not shown ) . Isotype control mAbs matched in concentration to each mAb did not show any staining ( not shown ) . These results indicate that the major VCAM-1+ cells found in the MZ where DC expressing VLA-4 are first likely to contact parasites and/or parasite antigens are the sinusoidal endothelial cells and MZ macrophages . We next investigated whether VCAM-1 blockade modulated DC and/or lymphocyte trafficking in the spleen following L . donovani infection . Previous work showed that lymphocyte entry into the spleen was not prevented by integrin blocking antibodies [3] , and we also confirmed this result by measuring migration of labelled lymphocytes into the PALS following VCAM-1 blockade in naïve animals and after 5 hours of L . donovani infection ( data not shown ) . However , the role of cell adhesion molecules in DC entry into the spleen has not been previously investigated . Therefore , we monitored migration of labelled DC into the spleen following VCAM-1 blockade in naïve mice and after 5 hours of L . donovani infection , but found no role for VCAM-1 in this process , regardless whether mice were infected or not ( Figure 6A–C ) . Therefore , VCAM-1 blockade did not affect lymphocyte or DC migration into the spleen , although our data do not exclude a role for VCAM-1 in cell movement between distinct regions within the spleen . To test whether VCAM-1/VLA-4 interactions play a direct role in DC IL-12p40 production , we blocked these molecules 12 hours prior to infection and measured IL-12p40 mRNA levels in DC isolated from the spleen at 5 hours p . i . ( Figure 7A ) . In control-treated mice , DC IL-12p40 mRNA levels increased 2–3 fold 5 hours after L . donovani infection . VCAM-1 blockade inhibited 50–100% of DC IL-12p40 mRNA accumulation , while VLA-4 blockade reduced DC IL-12p40 mRNA levels by 50–90% ( n = 4 experiments ) . However , given the co-localisation of DC with VCAM-1+ cells in the spleen ( Figure 5 ) , it is possible that the ligation of Fcγ receptors ( FcγR ) on DC by anti-VCAM-1 mAbs could suppress IL-12p40 production , as previously reported for human and mouse DC [22] , [23] . Therefore , we next generated anti-VCAM-1 Fab′ fragments that comprised the antigen binding region of the mAb , but have all FcγR binding domains removed , and hence , are unable to signal via FcγR . Splenic DC isolated from mice treated with control rat IgG Fab′ fragments 5 hours after L . donovani infection had a significant accumulation of IL-12p40 mRNA , relative to DC from naïve mice ( Figure 7B ) . Importantly , IL-12p40 accumulation in splenic DC from mice treated with anti-VCAM-1 Fab′ fragments was significantly reduced ( p<0 . 01 ) , compared with DC from control-treated mice ( Figure 7B ) . The reduction of IL-12p40 mRNA accumulation with anti-VCAM-1 Fab′ fragments was not as effective as with anti-VCAM-1 mAb , and this most likely reflects the very short half-life of Fab′ fragments in plasma ( around 1 hour ) compared with mAbs ( hours-days ) , and their rapid excretion by the kidney [24] , [25] . Nevertheless , these data support the conclusion that specific blockade of VCAM-1 reduces DC IL-12p40 mRNA accumulation following L . donovani infection , and that this effect of anti-VCAM-1 mAb treatment was not caused by FcγR ligation on DC . To confirm that parasite-induced IL-12p40 mRNA accumulation in CD8+ DC was the main target of VCAM-1 blockade , we next sorted these cells following MACS enrichment , based on expression of CD11c , MHC-II and CD8α ( Figure 7C ) , from naïve animals or at 5 hours p . i . from mice infected with L . donovani that had received control rat IgG or anti-VCAM-1 mAb prior to infection . As expected , the majority of IL-12p40 mRNA accumulation occurred in CD8+ DC , and levels increased approximately 2-fold at 5 hours p . i . Importantly , this increase did not occur in CD8+ DC from mice in which VCAM-1 had been blocked ( Figure 7D ) . There was some disparity between IL-12p40 mRNA accumulation ( Figure 7D ) and IL-12p40 protein levels ( Figure 4B ) in different DC subsets from naïve mice , whereby virtually all IL-12p40 mRNA accumulated in CD8+ DC , but IL-12p40 protein expression was similar between DC subsets . This may reflect different rates of IL-12p40 mRNA turnover or protein retention in different splenic DC subsets . We also observed that a small proportion of CD8+ DC were infected with L . donovani amastigotes at 5 hours p . i . , and that infection of these cells was not reduced by anti-VCAM-1 mAb ( 0 . 50±0 . 10% versus 0 . 37±0 . 11% for mice receiving rat IgG or anti-VCAM-1 mAb , respectively , determined from cytospins ) . Our data indicate that VCAM-1/VLA-4 interactions are important for splenic CD8+ DC IL-12p40 production 5 hours after L . donovani infection , and that this is important for the efficient generation of anti-parasitic T cell responses required for control of hepatic infection . To directly examine whether L . donovani-specific CD4+ T cell activation was affected by blockade of VCAM-1/VLA-4 interactions , we next isolated splenic CD4+ T cells at day 14 p . i . , and re-stimulated them in vitro in the presence of naïve , irradiated APC and fixed L . donovani amastigotes . There was little proliferation of splenic CD4+ T cells from naïve animals in response to parasite antigen ( Figure 7E ) . However , significant parasite-specific proliferation was observed in CD4+ T cells from control-treated mice , but this was significantly reduced in CD4+ T cell from animals that had received either anti-VCAM-1 or anti-VLA-4 mAbs ( Figure 7E ) . There was no difference in CD4+ T cell proliferation in response to concanavalin A in any of the groups tested , indicating that blockade of VCAM-1/VLA-4 did not generally suppress or inactivate CD4+ T cells . Together , these data indicate that CD8+ DC require VCAM-1/VLA-4 interactions for IL-12p40 production , associated with the generation of effective anti-parasitic CD4+ T cell responses required for the control of L . donovani growth in the liver .
The interaction between VCAM-1 and VLA-4 appears to be important for the outcome of hepatic L . donovani infection . However , these molecules play no role in the recruitment of leukocytes to the infected liver during VL , and there appears to be no clear role for LTα-dependent VCAM-1 expression on hepatic sinusoids . Instead , VCAM-1/VLA-4 interactions modulate IL-12p40 production by CD8+ DC in the spleen within hours of parasite challenge . Blockade of VCAM-1 , along with its physiological ligand VLA-4 , resulted in reduced IL-12p40 production by CD8+ DC . Studies with anti-VCAM-1 Fab′ fragments also indicate a role for VCAM-1/VLA-4 interactions in DC IL-12p40 production . Blockade of VCAM-1 with antibodies was also associated with reduced CD4+ T cell proliferation in the spleen and impaired resistance to L . donovani in the liver . The spleen harbours a relatively low parasite burden at the early time points assessed in this study , and this was not affected by VCAM-1 or VLA-4 blockade ( data not shown ) . DC IL-12p40 production shortly after L . donovani infection plays a key role in the generation of anti-parasitic immune mechanisms and is critical for the effective control of VL [14] , [16] . Recently , both IL-12p70 and IL-23 , each utilising the IL-12p40 subunit , were found to be functionally important cytokines for the control of L . donovani infection [26] . We have demonstrated that splenic CD8+ DC are the major source of IL-12p40 following L . donovani infection , and that parasite-induced IL-12p40 production occurs transiently , peaking 5 hours after infection ( Figure 4 ) . This early IL-12p40 production is physiologically important because when it is blocked during the first 24 hours of infection there is a failure to effectively control parasite growth in the liver and spleen [16] . The location of DC in the spleen is important during this early phase of infection and DC are required to migrate from the MZ into the PALS within the first 5 hours of infection in order for efficient T cell priming and maximal IL-12p40 production to occur [18] . This pattern of cell movement supports a model whereby parasites are rapidly taken up by macrophages in the MZ [15] , [18] , parasite antigen is then either transferred from these cells to DC or the DC directly acquire antigen in the MZ , and subsequently migrate into the PALS for T cell activation . Our data suggest that VLA-4/VCAM-1 interactions play a role in these events . Blockade of VCAM-1 did not affect splenic parasite burden in the first 24 hours of L . donovani infection ( data not shown ) , suggesting that VLA-4/VCAM-1 interactions play no role in parasite uptake by macrophages . In addition , the acquisition of parasites by CD8+ DC was not prevented by VCAM-1 blockade . Histological examination of spleen tissue indicated that red pulp macrophages were the main VCAM-1+ cell population in the spleen ( Figures 5A and B ) . However , these cells are spatially segregated from the IL-12p40-producing DC found in the MZ and T cell zones . In the MZ , sinus lining endothelial cells and MZ macrophages express VCAM-1 , and importantly , are found in close proximity to DC ( Figures 5C and D ) . VCAM-1 on endothelium can mediate cell adhesion and transendothelial migration [1] , [2] , and early in L . donovani infection endothelial VCAM-1 may be involved in either the adhesion of DC in the MZ or the movement of these cells into the PALS after antigen acquisition . However , previous work has shown that lymphocyte entry into the spleen is not prevented by blocking any single integrin , including VLA-4 [3] , and we also observed no effect of VCAM-1 blockade on lymphocyte trafficking ( data not shown ) or on the retention of labelled naïve DC in the spleen in naïve animals and 5 hours after L . donovani infection ( Figure 6 ) . Therefore , a role for VCAM-1-mediated naïve DC retention in the MZ is unlikely . The other main VCAM-1+ cell population in the MZ were the MZ macrophages ( Figure 5C ) . These cells are highly phagocytic and rapidly take up L . donovani after infection [15] , [18] . Furthermore , these cells are lost from the spleen after a chronic infection becomes established ( day 21–28 p . i . ) via a TNF-dependent mechanism , disrupting cellular movement in this organ [12] . Therefore , VCAM-1 on MZ macrophages could mediate DC movement from the MZ into the PALS . Alternatively , it could mediate interactions between DC and MZ macrophages , allowing uptake of parasite antigen by DC and/or activation of DC . Attempts to adoptively transfer splenic DC ( 5×106 ) isolated from mice 5 hours post-L . donovani infection ( time of peak IL-12p40 production ) to mice receiving VCAM-1 blockade , thereby bypassing these early cellular interactions , failed to improve control of parasite growth , relative to control animals ( data not shown ) . Although this result suggests that later cellular interactions might be VCAM-1 dependent , only a small proportion of transferred DC ( less than 10% ) were found in the spleen 24 hours after transfer . Therefore , we cannot exclude the possibility that the failure to overcome VCAM-1 blockade resulted from altered DC trafficking caused by infection or the fact that DC from infected mice are unable to traffic effectively back to the T cell zones within the spleen . VLA-4 has also been localised at the centre of the peripheral supramolecular activation complex ( pSMAC ) that surrounds the TCR-peptide-MHC complexes localised at the centre of the SMAC in the IS [7] . We failed to detect VCAM-1 expression by DC in the spleen either prior to or during infection , thus questioning the potential for VLA-4/VCAM-1 interactions in the SMAC of any IS that formed between DC presenting parasite antigen and L . donovani-specific T cells in the spleen after infection . However , we cannot rule out the possibility that physiologically relevant VCAM-1 expression on splenic DC is present , but beyond the detection limits of the histological and FACS methods we have employed . All conventional DC subsets in the spleen are capable of T cell activation , and the CD8+ DC isolated from L . donovani-infected mice 5 hours p . i . promote IL-12/23p40-dependent skewing towards IFNγ production by responding CD4+ T cells [27] . In addition , the CD8+ DC are capable of acquiring L . donovani amastigotes independent of VCAM-1 early after infection , and these infected cells are able to produce IL-12p40 ( Maroof , unpublished ) . Our data also indicate that VCAM-1/VLA-4 interactions play an important role in the priming of parasite-specific CD4+ T cells . Short-term ( 5 hr ) experiments with anti-VCAM-1 Fab′ fragments suggested that FcγR ligation on DC was not responsible for the observed reduced DC IL-12p40 production . However , blockade of VCAM-1 and the reduction of DC IL-12p40 with Fab′ fragments was not as effective as whole mAb , perhaps due to their extremely short half-life and rapid excretion by the kidney due to their low molecular weight [24] , [25] . Ideally we would have liked to confirm our results at day 14 with Fab′ fragments , as well as whole mAb . However , it would not be possible to interpret the results of such long-term experiments accurately due to the limitations discussed above . In conclusion , we have shown that VCAM-1/VLA-4 interactions modulate CD8+ DC IL-12p40 production and may play a role in the activation of parasite-specific CD4+ T cells during disease . Furthermore , our data indicate that VCAM-1 and VLA-4 are not directly involved in cellular recruitment to the liver during VL . These findings advance our understanding of the induction of cell-mediated immune responses following pathogen challenge , and identify a potential target for modulation to either enhance or suppress inflammation during disease .
Inbred female C57BL/6 and BALB/c mice were purchased from the Australian Resource Centre ( Canning Vale , Western Australia ) , and maintained under conventional conditions . All mice used were age-matched ( 6 to 10 weeks ) , and were housed under specific-pathogen free conditions . All animal procedures were approved and monitored by the Queensland Institute of Medical Research Animal Ethics Committee . L . donovani ( LV9 ) was maintained by passage in BALB/c or B6 . RAG-1−/− mice , and amastigotes were isolated from the spleens of chronically infected mice . Mice were infected by injecting 2×107 amastigotes intravenously via the lateral tail vein , killed at the times indicated in the text by CO2 asphyxiation and bled via cardiac puncture . In experiments examining DC IL-12p40 protein production , mice were infected with 1×108 amastigotes intravenously , as previously reported [15] , [18] , [27] . Spleens and perfused livers were removed and parasite burdens were determined from Diff-Quick-stained impression smears ( Lab Aids , Narrabeen , Australia ) , and expressed in Leishman-Donovan units ( the number of amastigotes per host nuclei multiplied by the organ weight ) [28] . Liver and spleen tissue were also preserved in either RNAlater ( Sigma-Aldrich , Castle Hill , Australia ) or Tissue-Tek O . C . T . Compound ( Sakura , Torrance , USA ) . Hepatic mononuclear cells ( MNC ) were isolated immediately following death as previously described [17] . Anti-VCAM-1 ( MK2/7; CRL-1909 , rat IgG1 ) [29] and anti-VLA-4 ( P/S2; CRL-1911 , rat IgG2b ) [30] hybridomas were purchased from the American Type Culture Collection ( Manassas , VA ) . Purified antibody was prepared from culture supernatants by protein G column purification ( Amersham , Uppsala , Sweden ) followed by endotoxin removal ( Mustang membranes , Pall , East Hills , NY ) . For VCAM-1 and VLA-4 blockade , C57BL/6 mice were injected i . p . with 1 mg of appropriate mAb or purified control rat IgG ( Sigma-Aldrich ) on the day of infection and every three days thereafter , or as detailed in the text . This dosing regime was based on one previously used in a collagen-induced arthritis model [31] , except that 0 . 5 mg doses were used in this study . In our hands , 0 . 5 mg doses only achieved partial blockade of DC IL-12p40mRNA accumulation and hepatic anti-parasitic immunity , compared with 1 mg doses , hence our use of the increased amounts of mAb . We also found that the anti-VLA-4 mAb could be detected on the surface of splenic lymphocytes for at least 72 hours following injection of 1 mg into naïve mice ( data not shown ) . Anti-VCAM-1 mAb and control rat IgG Fab fragments were generated using a commercial kit according to the manufacturer's instructions ( Thermo Scientific , Rockford , IL ) . Acetone-fixed liver sections ( 6 µm ) were labelled with hamster antisera to L . donovani amastigotes at a dilution of 1 in 1000 . Labelling was detected with a biotinylated goat anti-hamster antibody ( Vector Laboratories , Burlingame , CA ) . Sections were developed with Vector-Elite ABC kit , followed by 3 , 3′-diaminobenzidine substrate kit ( Vector Laboratories ) . Granuloma density was determined from 25 fields of view per mouse liver ( ×40 magnification ) , and the maturation of granulomas was scored around infected Kupffer cells , as described elsewhere [32] . Spleens were digested in collagenase type IV ( 1 mg/ml; Worthington , Lakewood , NJ ) and deoxyribonuclease I ( 0 . 5 mg/ml; Worthington ) at room temperature for 45 minutes . Splenocytes were isolated by passing digested spleens through a 100 µm cell strainer , followed by red blood cell lysis ( Sigma-Aldrich ) . CD11c+ DC were positively selected from splenocyte preparations using magnetic-activated cell sorting ( MACS ) with metallo-conjugated anti-mouse CD11c antibodies ( N418 ) and positive selection columns , according to the manufacturer's instructions ( Miltenyi Biotec , Bergisch Gladbach , Germany ) . In some experiments , following MACS enrichment , DC were sorted into CD8α-positive and CD8α-negative populations by labelling with antibodies to CD11c , MHC-II and CD8α , and sorting on a MoFlo Cell Sorter ( Dako , Botany , NSW , Australia ) , as shown in Figure 7 . Liver MNC or splenocytes were harvested and pre-incubated with CD16/32 mAb ( 2 . 4G2; grown in-house ) to avoid non-specific binding of antibodies to FcγR . For the staining of cell surface antigens , cells were incubated with fluorochrome-conjugated or biotinylated mAbs on ice for 30 minutes followed by streptavidin incubation for an additional 30 minutes when required . T cells , NKT cells and NK cells were enumerated with allophycocyanin ( APC ) -conjugated anti-TCRβ chain ( H57-597 ) , fluorescein isothiocyanate ( FITC ) -conjugated anti-CD4 ( GK1 . 5 ) , phycoerythrin ( PE ) -conjugated anti-CD8α ( H1 . 2F3 ) , and biotinylated anti-NK1 . 1 ( PK136 ) . B cells were enumerated using FITC-conjugated anti-CD19 ( 6D5 ) and APC-conjugated anti-B220 ( RA3-6B2 ) . DC were enumerated with APC-conjugated anti-CD11c ( N418 ) and FITC- or PE-conjugated anti-I-A/I-E ( MHC-II; M5/114 . 15 . 2 ) . All mAbs were purchased from Biolegend ( San Diego , CA ) or BD Biosciences ( Franklin Lakes , NJ ) . Rat anti-mouse CR3 ( 5C6 ) and rat anti-mouse GR-1 ( RB6 8C5 ) were grown and biotinylated in house , and used to enumerate monocytes and granulocytes , respectively . Biotinylated antibodies were detected using Alexa Fluor 488-conjugated streptavidin ( Invitrogen Life Technologies , Mount Waverley , Australia ) . Flow cytometric analysis was performed on a FACScalibur flow cytometer and analysed using Cell Quest Pro Software ( BD Biosciences ) . For intracellular IL-12p40 staining , splenocytes were incubated for 4 hours at 37°C in 10% ( v/v ) foetal calf serum , RPMI containing 10 µg/ml brefeldin A ( Sigma-Aldrich ) prior to cell surface labelling with antibodies to CD11c , CD8α and CD4 ( all from BD Biosciences ) . Cells were then washed and fixed in 1% ( w/v ) paraformaldehyde , before being washed in FACS buffer containing 0 . 1% ( w/v ) saponin ( BDH , Lutterworth , UK ) and stained with PE-conjugated anti-IL-12p40 ( C15 . 6 ) or an isotype control mAb ( both from BD Biosciences ) . Sorted CD8+ and CD8− DC ( 1×105 ) in 100 µl FACS buffer were collected onto a glass slide using a Cytospin 3 centrifuge , according to the manufacturer's instructions ( Shandon Scientific Ltd , Cheshire , UK ) , prior to staining with Diff-Quick ( Lab Aids ) to visualise host cell and parasite nuclei microscopically . Total RNA was extracted from bone marrow , spleen or liver tissue using TRIzol reagent ( Invitrogen Life Technologies ) , and an RNeasy Mini Kit with on-column DNase digestion ( Qiagen , Valencia , CA ) . Total RNA was extracted from purified DC using an RNeasy Mini Kit with on-column DNase digestion ( Qiagen ) , according to the manufacturer's instructions . RNA samples were reverse transcribed into cDNA using the cDNA Archive Kit ( Applied Biosystems , Foster City , CA ) according to the manufacturer's instructions . The number of IFNγ , TNF and NOS-2 cDNA molecules in each sample were calculated using Taqman Gene Expression Assays ( Applied Biosystems ) , and the number of IL-12p40 ( 5′ CTTGCAGATGAAGCCTTTGAAGA ( forward ) and 5′ GGAACGCACCTTTCTGGTTACA ( reverse ) ) , and HPRT ( 5′ GTTGGATACAGGCCAGACTTTGTTG ( forward ) and 5′GATTCAACCTTGCGCTCATCTTAGGC ( reverse ) ) ( house-keeping gene ) cDNA molecules in each sample were calculated by real-time reverse transcriptase-polymerase chain reaction ( rtPCR ) using Platinum Sybr Green Master Mix ( Invitrogen Life Technologies ) . All real-time reverse transcriptase-polymerase chain reactions ( rtPCR ) were performed on a Corbett Research RG-3000 Rotor Gene ( Corbett Life Sciences , Sydney , Australia ) . Standard curves were generated with known amounts of cDNA for each gene , and the number of cytokine molecules per 1000 HPRT molecules in each sample was calculated . Mice were pre-injected with 100 µg FITC-dextran i . v . ( 200 , 000 MW , anionic , Invitrogen Life Technologies ) to label marginal zone macrophages , followed by 1 mg of anti-VCAM-1 or rat IgG control antibody i . p . 24 hours later . Lymphocytes were isolated from naïve splenocytes using Histopaque 1083 ( Sigma ) , according to the manufacturer's instructions . Splenic lymphocytes or CD11c+ DC were labelled with Hoechst 33342 , as described previously [12] . Mice were administered with 1×107 Hoechst 33342-labelled lymphocytes or 1×106 Hoechst 33342-labelled DC via the lateral tail vein 1 hour post mAb injection . Mice were sacrificed 3 hours following lymphocyte transfer and 24 hr following DC transfer , and spleens were removed and embedded in Tissue-Tek O . C . T . compound ( Sakura ) . The distribution of Hoechst 33342-labelled cells was analysed in 20 µm sections mounted in Pro-long Gold anti-fade ( Invitrogen Life Technologies ) using a Carl Zeiss inverted fluorescent microscope under UV illumination . Tissue-Tek O . C . T . compound-preserved sections ( 6 µm ) of spleen tissue were acetone fixed and labelled with anti-VCAM-1 ( 429; MVCAM . A , BD Bioscience ) detected by direct conjugation to Alexa Fluor 647 using a monoclonal antibody labelling kit , or with a fluorochrome conjugated goat anti-rat antibody ( both from Invitrogen Life Technologies ) . To identify cell populations the sections were then labelled with different combinations of rat antibodies to murine metallophilic macrophages ( MOMA-1 , Acris Antibodies , Hiddenhausen , Germany ) , marginal zone ( MZ ) macrophages ( ERTR9 , specific ICAM-3-grabbing nonintegrin-related 1 ( SIGNR1 ) , Bachem Ltd . Merseyside , UK ) , reticular fibroblasts ( ERTR7 , BMA Biomedicals , Augst , Switzerland ) , endothelial cells ( Meca-32 , BD Biosciences ) , FITC -conjugated CD11c ( BD Biosciences ) and Alexa Fluor 647-conjugated F4/80 ( BD Biosciences ) . Fluorochrome conjugated goat anti-rat antibodies were used for detection of purified antibodies . Sections were mounted in Pro-long Gold anti-fade ( Invitrogen Life Technologies ) and visualized using a Carl Zeiss inverted LSM META 510 confocal microscope . Splenic CD4+ T cells were positively selected by MACS from splenocytes using metallo-conjugated anti-CD4 antibodies and positive selection columns , according to the manufacturer's instructions ( Miltenyi Biotec ) . CD4+ T cells ( 5×104 cells per well ) were stimulated with 2×106 paraformaldehyde-fixed L . donovani amastigotes , and 1×106 irradiated , naïve C57BL/6 spleen cells at 37°C , 5% ( v/v ) CO2 . After 72 hours of culture , cells were pulsed with 1 µCi [3H] thymidine for 18 hours , prior to measuring thymidine incorporation using a Betaplate reader , ( Wallac , Turku , Finland ) . The statistical significance of differences between groups was determined using a Mann Whitney test or an unpaired Student's t test using GraphPad Prism version 4 . 03 for Windows ( GraphPad Software , San Diego , CA ) and p<0 . 05 was considered statistically significant . The distribution of hepatic histological responses were compared using X2 analysis with Microsoft Excel software . All data are presented as the mean values±standard errors unless otherwise stated . | VCAM-1 and its major ligand VLA-4 are adhesion molecules required for the recruitment and movement of leukocytes within tissue . In this study , we have investigated the role of these molecules during an experimental infection with Leishmania donovani , a protozoan parasite that causes a chronic disease called visceral leishmaniasis . Surprisingly , we showed that VCAM-1 and VLA-4 were not required for leukocyte migration into the liver , a site of acute L . donovani infection . Instead , there was a requirement for these molecules to initiate cell-mediated immune responses in the spleen within the first 5 hours of infection . When VCAM-1 was blocked during infection , early dendritic cell production of IL-12p40 , a potent pro-inflammatory cytokine required for control of L . donovani , was suppressed , associated with a reduced parasite-specific T cell response in the spleen , and impaired immunity and parasite clearance in the liver . These results are important because they identify a novel role for VCAM-1 and VLA-4 in the regulation of dendritic cell activation during infectious disease . | [
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] | [
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] | 2008 | VCAM-1 and VLA-4 Modulate Dendritic Cell IL-12p40 Production in Experimental Visceral Leishmaniasis |
Diabetic kidney disease , or diabetic nephropathy ( DN ) , is a major complication of diabetes and the leading cause of end-stage renal disease ( ESRD ) that requires dialysis treatment or kidney transplantation . In addition to the decrease in the quality of life , DN accounts for a large proportion of the excess mortality associated with type 1 diabetes ( T1D ) . Whereas the degree of glycemia plays a pivotal role in DN , a subset of individuals with poorly controlled T1D do not develop DN . Furthermore , strong familial aggregation supports genetic susceptibility to DN . However , the genes and the molecular mechanisms behind the disease remain poorly understood , and current therapeutic strategies rarely result in reversal of DN . In the GEnetics of Nephropathy: an International Effort ( GENIE ) consortium , we have undertaken a meta-analysis of genome-wide association studies ( GWAS ) of T1D DN comprising ∼2 . 4 million single nucleotide polymorphisms ( SNPs ) imputed in 6 , 691 individuals . After additional genotyping of 41 top ranked SNPs representing 24 independent signals in 5 , 873 individuals , combined meta-analysis revealed association of two SNPs with ESRD: rs7583877 in the AFF3 gene ( P = 1 . 2×10−8 ) and an intergenic SNP on chromosome 15q26 between the genes RGMA and MCTP2 , rs12437854 ( P = 2 . 0×10−9 ) . Functional data suggest that AFF3 influences renal tubule fibrosis via the transforming growth factor-beta ( TGF-β1 ) pathway . The strongest association with DN as a primary phenotype was seen for an intronic SNP in the ERBB4 gene ( rs7588550 , P = 2 . 1×10−7 ) , a gene with type 2 diabetes DN differential expression and in the same intron as a variant with cis-eQTL expression of ERBB4 . All these detected associations represent new signals in the pathogenesis of DN .
Diabetic kidney disease , or diabetic nephropathy ( DN ) , is the leading cause of end-stage renal disease ( ESRD ) worldwide [1] . It affects approximately 30% of patients with long-standing type 1 and type 2 diabetes [2] , [3] , and confers added risks of cardiovascular disease and mortality . DN is a progressive disorder that is characterized by proteinuria ( abnormal loss of protein from the blood compartment into the urine ) and gradual loss of kidney function . Early in its course , the kidneys are hypertrophic , and glomerular filtration is increased . However , with progression over several years , proteinuria and decline in kidney function set in , and may result in fibrosis and terminal kidney failure , necessitating costly renal replacement therapies , such as dialysis and renal transplantation . While current treatments that decrease proteinuria will moderately abate DN progression , recent studies show that even with delivery of optimal care , high risks of cardiovascular disease , ESRD and mortality persist [4] , [5] . Therefore , discovery of genetic factors that influence development and susceptibility to DN is a critical step towards the identification of novel pathophysiologic mechanisms that may be targeted for interventions to improve the adverse clinical outcomes in diabetic patients . Whereas the degree of glycemia plays a pivotal role in DN , a subset of individuals with poorly controlled type 1 diabetes ( T1D ) do not develop DN . Furthermore , strong familial aggregation supports genetic susceptibility to DN . The sibling risk of DN has been estimated to be 2 . 3-fold [6] . While prior studies of individuals with T1D have reported on the possible existence of genetic associations for DN , results have been inconclusive . In GENIE , we leveraged three existing collections for T1D nephropathy ( All Ireland Warren 3 Genetics of Kidneys in Diabetes UK Collection [UK-ROI] , Finnish Diabetic Nephropathy Study [FinnDiane] , and Genetics of Kidneys in Diabetes US Study [GoKinD US] ) comprising 6 , 691 individuals to perform the most comprehensive and well powered DN susceptibility genome-wide association study ( GWAS ) and meta-analysis to date , with the aim to identify genetic markers associated with DN by meta-analyzing independent GWAS , imputed to HapMap CEU II ( Table 1 , Figure 1 ) . As a result , we here present two new loci associated with ESRD and a locus suggestively associated with DN .
The primary phenotype of interest was DN , defined by the presence of persistent macroalbuminuria or ESRD in individuals aged over 18 who had T1D for at least 10-year duration . Controls were defined as individuals with T1D for at least 15 years but without any clinical evidence of kidney disease ( see Methods for more detailed definitions ) . Meta-analysis of the DN results from each cohort resulted in five independent signals with P<10−5 ( Table S1 , Figure S1A ) . In a parallel analysis of ESRD versus non-ESRD ( n cases = 1 , 399 , n controls = 5 , 253; referred to as “ESRD” analysis throughout the manuscript , unless otherwise stated ) , SNP rs7583877 on chromosome 2q11 . 2-q12 achieved genome-wide significance ( P = 4 . 8×10−9 ) , primarily driven by FinnDiane and the UK-ROI samples , along with six other independent signals reaching P<10−5 ( Figure 2A , Table S1 , Figure S1C ) . We invited investigators responsible for available collections with similar phenotypes to participate in the secondary genotyping phase of the top ranked SNPs ( n = 41 including proxies , representing 24 independent signals ) from the initial meta-analysis . Nine independent cohorts contributed 5 , 873 individuals with comparable phenotypic inclusion criteria ( Table S2 ) . After the combined meta-analysis of the first and second phase cohorts , the association of the intronic SNP rs7583877 in AFF3 with ESRD retained genome-wide significance ( odds ratio [OR] = 1 . 29 , 95% confidence interval [CI]: 1 . 18–1 . 40 , P = 1 . 2×10−8; Figure 3A ) , with the bulk of the association evidence still provided by the FinnDiane and UK-ROI cohorts . The population attributable risk [PAR] for the causal variant underlying the observed association at rs7583877 was estimated to be 3 . 5%–10 . 5% . AFF3 belongs to the AFF ( AF4/FMR2 ) family and encodes a transcriptional activator , with DNA-binding activity , initially found to be fused with MLL in some acute lymphoblastic leukemia patients [7] , [8] . Recent evidence points to a role for AFF3 as an RNA-binding protein , with overexpression affecting organization of nuclear speckles and splice machinery integrity [9] . Variants near AFF3 have been associated with acute lymphoblastic leukemia [10] , rheumatoid arthritis [11] , [12] and recently T1D [13] , [14] . Another locus between the RGMA ( RGM domain family , member A ) and MCTP2 ( multiple C2 domains , transmembrane 2 ) genes on chromosome 15q26 also reached genome-wide significance for association with ESRD ( rs12437854 , OR 1 . 80 , 95% CI: 1 . 48–2 . 17 , P = 2 . 0×10−9; Table 2 , Figure 3B ) . PAR estimates for this locus varied from 0 . 5% to 4 . 1% . For the primary DN phenotype , an intronic SNP in the ERBB4 gene demonstrated consistent protective effects in the replication samples and was the top associated SNP identified from the combined discovery and second stage analysis; however , this did not reach genome-wide statistical significance ( rs7588550 , OR 0 . 66 , 95% CI: 0 . 56–0 . 77 , P = 2 . 1×10−7 , PAR 28 . 3%–32 . 5% for removal of the major risk allele; Table 2 , Figure 3C ) . ERBB4 encodes an epidermal growth factor receptor subfamily member , and has been implicated in cardiac , mammary gland and neural development [15] , [16] . Mutations in ERBB4 have previously been reported in cancer [17] . Several studies using Madin-Darby canine kidney ( MDCK ) cells and conditional ERBB4 overexpression/knock-out mice , suggest a crucial role for ERBB4 in renal development and tubulogenesis [18] , [19] . It is possible that our observed signal is in linkage disequilibrium with an untyped SNP , or exerts functional effects over an extended genomic region . To explore a putative biological signature we identified , for the top three SNPs , all genes within a 2 Mb window ( 1 Mb upstream and downstream ) . Gene ontology analysis revealed no significant enrichment of biological terms or pathways within this subset of flanking genes ( Table S3 ) . We determined whether any of these genes were differentially expressed in microarray data derived from tubulointerstitial ( n = 49 ) or glomerular ( n = 70 ) human early DN renal biopsy material versus pre-transplant renal biopsies from living kidney donors ( n = 32 ) [20] . Around rs7583877 ( AFF3 ) , we noted upregulation of LIPT1 and TXNDC9 , while TSGA10 was downregulated in both tubulointerstitial and glomerular enriched kidney biopsies ( Figure 2 and Table S4 ) . NPAS2 , which flanks rs7583877 ( AFF3 ) , and FAM174B and CHD2 , which flank rs12437854 ( 15q26 ) , were downregulated in glomerular enriched biopsies of DN patients versus control , but remained unchanged in tubulointerstitial biopsies ( Figure 2 and Table S4 ) . NPAS2 ( neuronal PAS domain protein 2 ) , has been implicated in circadian rhythms in the distal nephron segments , acting as a regulator of kidney function [21] . Interestingly , mutations in chromodomain helicase DNA binding protein 2 ( CHD2 ) , encoding a chromatin-remodeling enzyme , result in impaired glomerular function in mice [22] . Furthermore , at the rs7588550 ( ERBB4 ) locus expression of ERBB4 was down , and SPAG16 upregulated in tubulointerstitial enriched kidney biopsy tissue of DN versus control subjects ( Figure 2 and Table S4 ) . We also examined whether any of the top three SNPs modulated expression of neighboring genes in cis in a dataset of glomerular and tubulointerstitial kidney biopsies of Pima Indians with type 2 diabetes and DN who had been genotyped on the Affymetrix 6 . 0 array [23] . In Pima Indians , no adequate proxies ( haplotype-based D′≥0 . 8 ) for the Affymetrix 6 . 0 SNPs that were strongly correlated with GWAS findings ( r2≥0 . 8 ) could be found for rs12437854 , and expression of AFF3 was below detectable thresholds in this dataset; however , two SNPs in the same intron of ERBB4 as rs7588550 ( rs17418640 and rs17418814 ) were associated with genotype-specific expression of ERBB4 in tubulointerstitial but not in glomerular tissue in the Pima cohort ( P<0 . 05; Figure S2 ) . Follow-up work is required to investigate the DN associated and eQTL signals in this ERBB4 intron . To explore the potential functional role of these ERBB4 SNPs , we looked for other genes whose expression is correlated with that of ERBB4 . A total of 388 ERBB4-correlated genes were found in the Pima population ( Benjamini-Hochberg Q-value<0 . 1 ) . Pathway analysis of these genes indicates coexpression of ERBB4 with collagen-related genes , which have been implicated in renal fibrosis [24] , [25] ( Genomatix Pathway System; Table S5 ) . Because the low expression level of AFF3 limited exploration of this gene using expression data , we pursued additional functional experiments in an in vitro model of renal fibrosis , namely human tubular epithelia exposed to transforming growth factor-β1 ( TGF-β1 ) . Low-level basal expression of the AFF3 mouse homologue ( LAF4 ) has been reported in kidney tubules during embryonic development [26] suggesting proximal renal tubule epithelial cells may be suitable for detection and functional interrogation of AFF3 . TGF-β1 is implicated in the development of diabetic glomerulosclerosis , and there is recent appreciation of its role as a key driver of tubulointerstitial fibrosis . TGF-β1 induces epithelial cell de-differentiation into a more mesenchymal-like phenotype , characterized by a switch in predominant cadherins from E-cadherin ( epithelial ) to N-cadherin ( mesenchymal ) , and increased vimentin , α-smooth muscle actin , connective tissue growth factor ( CTGF ) and Jagged 1 [27] , [28] . TGF-β1-mediated loss of E-cadherin in renal epithelia , is believed to be mediated through loss of miR-192 expression [29] . We and others have previously shown that Jagged 1 , a ligand for multiple Notch receptors , is up-regulated in human diabetic kidney disease [30] , [31] , with the Notch signaling pathway implicated in driving renal fibrosis [32] , [33] . CTGF is a member of the CCN protein family , with biological roles in differentiation and tissue repair . CTGF is induced by TGF-β1 and enhances expression of multiple extracellular matrix proteins observed in DN , including collagens and fibronectin , and CTGF expression is elevated in the glomeruli of STZ ( streptozotocin ) - treated rats , an in vivo model of T1D [34] . Basal AFF3 expression was detectable in HK-2 cells , and expression levels were upregulated upon stimulation with TGF-β1 ( 5 ng/ml; 48 h ) , as measured at protein and RNA level ( Figure 4A–4B ) . Inhibition of AFF3 by siRNA attenuated the expression of TGF-β1-driven markers of fibrosis - CTGF and N-cadherin ( Figure 4C–4E ) . Taken together , these data suggest that AFF3 may play a role in TGF-β1-induced fibrotic responses of renal epithelial cells . Traditionally , DN has been viewed as a continuous trait with onset at microalbuminuria , progression to macroalbuminuria , loss of GFR , and culmination in ESRD . Recent studies have called this paradigm into question , suggesting that the syndrome may perhaps be composed of varying phenotypes [35] , [36] . Association of rs7583877 ( AFF3 ) and rs12437854 ( RGMA – MCTP2 ) with the different stages of DN was tested on a time-to-event analysis of relevant endpoints using longitudinal data for participants in the FinnDiane discovery collection . Consistent with our case-control GWAS analyses , the strongest association for rs7583877 was observed for the time from T1D diagnosis to development of ESRD ( hazard ratio [HR] 1 . 33 , 95% CI: 1 . 18–1 . 49 , P = 1 . 9×10−6 ) , but also the time from T1D diagnosis to development of macroalbuminuria ( HR 1 . 15 , 95% CI: 1 . 04–1 . 27 , P = 0 . 006 ) and the time from macroalbuminuria to ESRD ( HR 1 . 16 , 95% CI: 1 . 01–1 . 36 , P = 0 . 04 ) reached nominal significance . Similarly , rs12437854 was associated with time from T1D diagnosis to development of macroalbuminuria ( HR 1 . 31 , 95% CI: 1 . 03–1 . 67 , P = 0 . 03 ) and ESRD ( HR 1 . 35 , 95% CI: 1 . 02–1 . 77 , P = 0 . 03 ) ( Text S1 , Table S6 , Figure S3 ) . When we studied these SNPs and their association with various DN-related phenotypes in the case-control setting of the discovery cohorts , similar observations were made supporting the role of these SNPs in the development of ESRD: Whereas we found evidence of association between rs7583877 ( AFF3 ) and all the examined phenotypes with ESRD as the case definition , only moderate association was observed for the DN phenotype ( OR = 1 . 14 , P = 0 . 002 ) and no association when patients with macroalbuminuria were compared to controls with normoalbuminuria ( OR = 1 . 00 , P = 0 . 95 ) . rs12437854 ( RGMA – MCTP2 ) had the strongest association with the original ESRD phenotype ( controls defined as all non-ESRD subjects ) and with the ESRD vs . normoalbuminuria phenotype , and moderate association with the DN phenotype and comparison of ESRD vs . macroalbuminuric patients ( Table S7 ) . An alternative explanation for our ESRD findings may be that the associated variants in AFF3 gene and on chromosome 15q26 might be markers of survival . Mortality rates are extremely high in patients with kidney disease and macroalbuminuria , with at least 25% of macroalbuminuric patients dying before they reach ESRD [37] . Thus , the selection of patients with ESRD may be biased towards selection of severe kidney disease survival . To address this question , we used the time until death as the final end point in the longitudinal analysis . Neither of the loci associated with ESRD was also associated with mortality ( Text S1 , Table S6 , Figure S3 ) , suggesting that these loci are associated with ESRD per se . To explore whether these SNPs contribute to DN via related intermediate phenotypes , such as adiposity , fasting lipid levels , or blood pressure we performed in silico searching of publicly available GWAS datasets for our top SNPs [38]–[41] . We found nominal , directionally consistent associations of rs12437854 with fasting glucose ( P = 0 . 03 ) [42] and of rs7583877 with waist-hip ratio ( P = 0 . 04 ) [43] ( Table S8 ) . We also considered if previously published T1D and CKD SNP associations were associated with DN or ESRD in our GWAS meta analyses . Eight of 80 SNPs at T1D-associated loci showed nominal significance with DN or ESRD ( including three at AFF3 that are in weak LD [r2 0 . 030–0 . 046 in CEU] with the SNPs described here ) , while no CKD SNPs were nominally significant ( Table S9 ) [44]–[47] . The lack of association with DN for CKD-associated SNPs suggests that the genetic risk factors for DN may differ from the genetic risk factors for CKD in a nondiabetic population . Finally , to generate further biological hypotheses based on our GWAS results , we employed MAGENTA [48] gene set enrichment analysis software integrating Gene Ontology ( GO ) terms , KEGG and Ingenuity pathways and PANTHER database entries ( Table S10 ) . In the analysis of DN as a case phenotype , enriched gene sets included “sugar binding” ( P = 0 . 0006 ) , “double stranded DNA binding” ( P = 0 . 001 ) and “nucleic acid binding” ( P = 0 . 004 ) . In the analysis of ESRD significantly enriched gene sets ( P<0 . 01 ) included an enrichment of terms associated with DNA binding , including “sequence-specific DNA binding” ( P = 0 . 003 ) , “positive regulation of transcription” ( P = 0 . 003 ) , and “homeobox transcription factor” ( P = 0 . 004 ) . Taken together , the principal biological signal found within GWAS data suggests an enrichment of transcriptional regulators . In this largest meta-analysis to date of DN from individuals with T1D , we found two genome-wide significant associations with ESRD . Variants in AFF3 have been shown to be associated with juvenile idiopathic rheumatoid arthritis , Graves' disease , celiac disease and T1D , indicating this may be a pan-autoimmune disease gene . It is possible that the AFF3 signal represents an association with T1D and/or is a false positive finding , as it was not seen in the follow-up cohorts . However , we note the following: 1 ) both FinnDiane and UK-ROI yielded very similar association results , 2 ) the number of ESRD cases in the replication cohorts is small ( n = 363 ) , indicating that statistical power to replicate the original association is limiting , 3 ) the association result in the second stage , while non-significant , trends in a consistent direction ( OR 1 . 11 ) , 4 ) after evaluating >12 , 000 individuals the AFF3 signal remained genome-wide significant ( P = 1 . 2×10−8 ) , and 5 ) we have provided supportive functional evidence that suggests AFF3 may be a relevant contributor to renal disease . Although survival bias is a possibility in the analyses of ESRD , longitudinal analysis revealed the association of the AFF3 and chromosome 15q26 loci with renal end-points and not with death . Experimental models provide independent evidence of AFF3 involvement in renal fibrosis and support an association of this locus with a renal phenotype . Importantly , despite our large sample size , we did not achieve genome-wide statistical significance for DN using a combined proteinuria/ESRD phenotype , suggesting that this phenotype may have been too heterogeneous to detect significant associations with a sample of this size . For example , lifelong glycemic control , a known risk factor for DN , is not well captured in most existing cohorts . Nevertheless , this study is the largest , well powered GWAS on DN to date . We demonstrated a suggestive signal of association at ERBB4 that is supported by experimental data showing haplotype specific mRNA expression in DN biopsies . Our findings reinforce the need for additional studies of patients with T1D and a homogeneous renal phenotype , in whom additional GWAS , fine-mapping and sequencing to uncover rare variants could be performed . Integration of our findings with ongoing GWAS in both type 1 and type 2 diabetes DN may also lead to discovery of additional genetic determinants of DN . The traditional phenotypic definition of DN for individuals with type 2 diabetes may be even more challenging for genetic studies given the heterogeneity of vascular complications and differential renal diagnoses . Several larger-scale GWAS have now been conducted for renal phenotypes [49]–[56] , however in most cases the true disease-causing variant and functional impact for specific phenotypes remains to be established . Encouraging reports include the association of uromodulin with CKD [57] , MYH9/APOL1 with non-diabetic ESRD [58] , [59] , and PLA2R1 with membranous nephropathy , where anti-PLA2R antibodies appear to predict activity of the disease as well as response to therapy [60] . Our findings point to two transcriptional networks centered around AFF3 and ERBB4 that may be operational in the pathogenesis of kidney disease in diabetes .
All human research was approved by the relevant institutional review boards , and conducted according to the Declaration of Helsinki . We implemented a two stage analysis , in which a GWAS was performed using a set of three discovery cohorts in the GENIE consortium , and top signals for the DN and ESRD analyses were analyzed further in the second phase in a set of nine independent cohorts ( described below ) with 5 , 873 patients in total . The patient numbers in the individual studies are given in Table S11 . Additional details are provided in the online material Text S1 . Inclusion criteria included white individuals with T1D , diagnosed before 31 years of age , whose parents and grandparents were born in the UK and Ireland . The case group comprised 903 individuals with persistent proteinuria ( >500 mg/24 h ) developing more than 10 years after the diagnosis of diabetes , hypertension ( >135/85 mmHg and/or treatment with antihypertensive medication ) , and retinopathy; ESRD ( 27 . 2% ) was defined as individuals requiring renal replacement therapy or having received a kidney transplant . Absence of DN was defined as persistent normal urine albumin excretion rate ( AER; 2 out of 3 urine albumin to creatinine ratio [ACR] measurements <20 µg of albumin/mg of creatinine ) despite duration of T1D for at least 15 years , while not taking an antihypertensive medication , and having no history of treatment with ACE inhibitors; 1 , 001 individuals formed the control group . After exclusion of patients with low quality DNA samples , 914 DN/ESRD cases and 956 controls remained for the GWAS . The FinnDiane study is a Finnish cohort of more than 4 , 800 adult ethnic Finns with T1D , recruited from across Finland , diagnosed prior to age 35 and insulin treatment begun within 1 year . This study comprises 1 , 721 patients with normal AER , 516 with microalbuminuria , 733 with macroalbuminuria and 682 with ESRD . The disease status was defined by urine AER or urine ACR in at least two out of three consecutive urine collections at local centers: Microalbuminuria was defined as AER≥20<200 µg min−1 or ≥30<300 mg/24 h or an ACR of 2 . 5–25 mg mmol−1 for men and 3 . 5–35 mg mmol−1 for women in overnight , 24-hour or spot urine collections , respectively . Similarly , the limit for macroalbuminuria was AER≥200 µg min−1 or ≥300 mg/24 h or ACR≥25 mg mmol−1 for men and ≥35 mg mmol−1 for women . ESRD was defined as ongoing dialysis treatment or transplanted kidney . Control patients with normal AER were required to have T1D duration of at least 15 years . 558 of these patients were included from an independent Finnish cohort collected by the National Institute of Health and Welfare . These patients met the FinnDiane diagnosis and selection criteria , and were analyzed together with the FinnDiane cohort . The GoKinD US study consists of a DN case-control cohort of individuals diagnosed with T1D prior to 31 years of age who began insulin treatment within 1 year of T1D diagnosis . Controls were 18–59 years of age , with T1D for at least 15 years but without DN , n = 889 . DN definition includes individuals with ESRD , dialysis or kidney transplant and persistent macroalbuminuria ( at least 2 out of 3 tests positive for albuminuria by dipstick ≥1+ , or ACR>300 µg albumin/mg of urine creatinine ) . Cases were defined as people 18–54 years of age , with T1D for at least 10 years and DN , n = 903 . Individuals recruited to the control group employed the same inclusion criteria as UK-ROI . Individuals were recruited at two study centers , George Washington University ( GWU ) and the Joslin Diabetes Centre ( JDC ) using differing methods of ascertainment and recruitment [64] . Analysis of the GoKinD US cohort was limited to individuals whose primary ethnicity was Caucasian . DNA was sought from worldwide case-control collections of individuals with T1D and known renal status . A total of 5 , 873 individuals from nine independent collections were genotyped or imputed for the top-ranked SNPs ( n = 41 including 17 proxies ) , with the exception of the DCCT/EDIC cohort where GWAS data was imputed . All the patients included in the phase two analysis were adults of European descent and had T1D diagnosed before 35 years of age . Controls with normal AER had duration of T1D at least 15 years , and cases with DN had minimum T1D duration of 10 years . If a collection included patients with microalbuminuria , they were excluded from the primary analysis of DN , but included as controls in the analysis of ESRD versus non-ESRD . The main clinical characteristics of all the replication cohorts are shown in the Table S2 and the cohorts are described in Text S1 . The primary phenotype of interest was DN , defined as individuals aged over 18 , with T1D for at least 10 years and diabetic kidney disease . DN includes ESRD or persistent macroalbuminuria as defined in the cohort descriptions above . Controls were defined as individuals with T1D for at least 15 years but without any clinical evidence of kidney disease . Individuals with microalbuminuria were excluded from the primary DN analysis in all cohorts . Disease status definitions were consistent across all the study cohorts . Details of clinical characteristics for each cohort are defined in Table 1 and Table S2 . We evaluated a second phenotype to gain further insights into the genetic basis of the most severe form of DN ( leading to ESRD ) , and compared ESRD cases to all those without ESRD . This phenotype is referred to as the “ESRD” or “ESRD vs . non-ESRD” phenotype throughout the manuscript . We also considered individuals with ESRD compared to T1D controls with no clinical evidence of DN . Results for this comparison are given in the online supporting material ( Tables S1 , S6 , S7 , S9 , S10 ) , where this contrast is called “ESRD vs . normoalbuminuria” or “ESRD vs . normo” . DNA from individuals in the UK-ROI collection were genotyped using the Omni1-Quad array ( Illumina , San Diego , CA , USA ) while FinnDiane samples employed Illumina's BeadArray 610-Quad array . Samples in UK-ROI and FinnDiane were excluded if they had insufficient DNA quality , quantity or poor genotype concordance with previous genotypes during the fingerprint evaluation stage . Existing genotype data for the GoKinD US genotype data was downloaded from dbGAP ( phs000018 . v2 . p1 , retrieved June 2010 ) , containing updated genotype data from Affymetrix 500 K set ( Affymetrix , Santa Clara , CA , USA ) . Samples for UK-ROI and FinnDiane were excluded for insufficient DNA quality , quantity or poor genotype concordance with previous genotypes during a fingerprint evaluation stage . In the UK-ROI sample , 1 , 830 unique case ( n = 872 ) and control ( n = 958 ) individuals were submitted for genotyping on the Omni1-Quad . For FinnDiane , 3 , 651 individuals ( cases , n = 1 , 934; controls n = 1 , 721 ) were submitted for genotyping on the 610-Quad . For all three discovery datasets ( UK-ROI , FinnDiane , GoKinD US ) , uniform and extensive genotype quality control procedures were applied: SNPs were filtered for those with call rates greater than 90% , minor allele frequency ( MAF ) exceeding 1% , and concordance with Hardy Weinberg Equilibrium ( HWE , P<10−7 ) . Sample filters included individual call rates greater than 95% , no extreme heterozygosity and cryptic relatedness as determined using identity by descent ( first degree relatives , estimated identity by descent >0 . 4 ) , and admixture assessment using principal components ( plotted with HapMap reference panel , Figure S4 ) . Additional quality control measures included test of missing by haplotype ( P<10−8 ) , missing by phenotype ( P>10−8 ) and plate effects ( P<10−7 ) . These quality control steps were performed using PLINK [65] with custom Perl and R analysis scripts . Known copy number variation and mitochondrial SNPs were excluded from analyses . Detailed results of each QC step are reported in Table S12 for each study population . A HapMap control sample was included on all genotyping plates for UK-ROI; average call rate was 99 . 9% with HapMap concordance equaling 99 . 7% . The average sample call rate was 99 . 5% in UK-ROI with sample heterozygosity 22 . 1% . Concordance with internal control for FinnDiane was 99 . 996% with an average sample call rate of 99 . 8% . Principal Component Analysis ( PCA ) was performed separately for each of the three studies with the EIGENSTRAT program [66] in order to detect genetic outliers and to adjust the analyses for population structure . Genetic outliers were defined as more than six standard deviations away from the center of distribution along any of the ten first principal components and the procedure was repeated until no outliers were detected . After filtering , PCA were calculated for each study cohort combined with unrelated individuals from three original HapMap populations ( www . hapmap . org ) , and plotted to identify additional admixed individuals . The first ten principal components were employed to adjust the association analysis for any residual population structure from the cleaned datasets . In total , directly genotyped results for 823 cases and 903 controls in 791 , 687 SNPs passed QC procedure in UK-ROI . Similarly , 549 , 530 SNPs with average genotyping rate of 99 . 9% passed the QC filters in 1 , 319 cases , 1 , 591 controls and 460 individuals with microalbuminuria for FinnDiane . 360 , 899 SNPs in 774 cases and 821 controls for GoKinD US passed quality control and were included in the analysis . Imputation was performed after the quality control employing MACH 1 . 0 software ( http://www . sph . umich . edu/csg/abecasis/MACH ) with HapMap phase II CEU population as a reference , resulting in ∼2 . 4 million SNPs for each cohort . The cross-over and error rates were estimated with 50 iteration rounds in roughly 300 randomly selected samples . The imputation was run with the greedy algorithm and the maximum likelihood method in order to obtain expected allele dosages rather than integer allele counts . SNPs with low imputation quality ( r2<0 . 6 ) are not reported . PLINK v1 . 07 [67] was employed to conduct association tests for the allele dosage data with logistic regression adjusted for sex , age , the duration of diabetes and the ten first components of the study specific principal component analysis . UK-ROI and GoKinD US were adjusted for study center , but in the primary DN phenotype the two GoKinD US centers; GWU and JDC , were analyzed separately . Results from individual studies were adjusted for study specific genomic inflation factor and then combined by fixed effect meta-analysis model using METAL [68] , to estimate the combined effect sizes and significances from beta values and standard error . Regional association plots were generated using hg18 in LocusZoom [69] . Quantile-Quantile plots were generated to evaluate the number and magnitude of observed associations compared with those expected under the null hypothesis ( Figure S1 ) . All SNPs observed with P<10−5 were selected for further analysis . These SNPs were reviewed and a top SNP ( with a proxy ) was selected for each independent signal ( SNPs more than 500 kb distant or LD r2<0 . 3 in HapMap II CEU ) using the LD-based clumping procedure implemented in PLINK . De novo genotyping was performed for all phase two cohorts except for DCCT/EDIC using identical designs of Sequenom IPLEX assays ( Sequenom Inc , San Diego , US ) . The DCCT/EDIC samples were imputed from their GWAS results that had undergone their respective quality control procedure . The statistical analysis was similar to the discovery cohorts with the difference that the models were not adjusted for principal components . All results were then combined by meta-analysis using METAL software as previously described . Time to event analyses were performed on longitudinal data from the FinnDiane discovery cohort using Kaplan-Meier and Cox proportional hazards regression with the aim to evaluate the genetic association of rs7583877 and rs12437854 with time from the diagnosis of T1D to the onset of the following end points: microalbuminuria , macroalbuminuria or ESRD . Additionally , we analyzed time from onset of macroalbuminuria to development of ESRD . The most recent kidney status data were utilized for each patient . We also examined if the two main association loci , rs7583877 and rs12437854 , were associated with mortality using data from the Finnish Death Registry ( as per 30 . 9 . 2010 ) . As DN ( defined as macroalbuminuria or ESRD ) is strongly associated with mortality , the time to death was separately analyzed for patients without DN ( time from T1D onset to death; patients who developed DN were censored at the time of the onset of DN ) and for those with DN ( time from onset of DN to death and time from onset of ESRD to death ) . Analyses were performed using the ‘survival’ package in R software ( version 2 . 36-10 , http://cran . r-project . org/web/packages/survival ) . ( See Text S1 . ) SNPs were annotated with associated genes and function using dbSNP build 132 , human build 37 . 1 . Cytogenetic locations for genes were sourced from Entrez gene; locations for SNPs that were not associated with genes were recorded from NCBI MapView . In silico analyses included gene set enrichment using MAGENTA [48] . To explore functional implications of AFF3 , human kidney epithelial cells ( HK-2 ) were cultured and evaluated ( Figure 4 ) . Gene expression was measured in renal tissue compartments micro-dissected from renal biopsies from Pima Indians with type 2 diabetes and early stage DN ( n = 77 ) , as well as from Caucasian living kidney transplant donors ( n = 20 ) . Pima Indian subjects are 25–68 in age , with measured ACR in the range 5 . 23–7162 , and GFR in the range 40 . 45–274 . 80 . Renal biopsies were micro-dissected into glomeruli and either tubulointerstitial or cortical compartments , and gene expression measured using the Affymetrix HGU-133A and HGU-133 Plus 2 platforms [70] . Background adjustment , quantile normalization and probe-set summarization were performed with in a GenePattern ( www . genepattern . org ) pipeline using Robust Multichip Analysis [71] with batch correction using Combat [72] . The differential expression data sets were processed with the Entrez Gene Custom CDF v . 10 , and the eQTL data sets were processed with the RefSeq Custom CDF v . 12 [73] for probe-sets common to both expression platforms . The Affymetrix 6 . 0 genotyping platform was used to genotype Pima Indians with glomerular expression ( n = 65 ) , a subset of which ( n = 54 ) also had tubulointerstitial/cortical expression . The cis region of each gene was defined as 150 kb upstream of the transcript start site and 50 kb downstream of the transcription end site . | The global prevalence of diabetes has reached epidemic proportions , constituting a major health care problem worldwide . Diabetic kidney disease , or diabetic nephropathy ( DN ) —the major long term microvascular complication of diabetes—is associated with excess mortality among patients with type 1 diabetes . Even though DN has been shown to cluster in families , the underlying genetic and molecular pathways remain poorly defined . We have undertaken the largest genome-wide association study and meta-analysis to date on DN and on its most severe form of kidney disease , end-stage renal disease ( ESRD ) . We identified new loci significantly associated with diabetic ESRD: AFF3 and an intergenic locus on chromosome 15q26 residing between RGMA and MCTP2 . Our functional analyses suggest that AFF3 influences renal tubule fibrosis , a pathological hallmark of severe DN . Another locus in ERBB4 was suggestively associated with DN and resides in the same intronic region as a variant affecting the expression of ERBB4 . Subsequent pathway analysis of the genes co-expressed with ERBB4 indicated involvement of fibrosis . | [
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... | 2012 | New Susceptibility Loci Associated with Kidney Disease in Type 1 Diabetes |
FUS-proteinopathies , a group of heterogeneous disorders including ALS-FUS and FTLD-FUS , are characterized by the formation of inclusion bodies containing the nuclear protein FUS in the affected patients . However , the underlying molecular and cellular defects remain unclear . Here we provide evidence for mitochondrial localization of FUS and its induction of mitochondrial damage . Remarkably , FTLD-FUS brain samples show increased FUS expression and mitochondrial defects . Biochemical and genetic data demonstrate that FUS interacts with a mitochondrial chaperonin , HSP60 , and that FUS translocation to mitochondria is , at least in part , mediated by HSP60 . Down-regulating HSP60 reduces mitochondrially localized FUS and partially rescues mitochondrial defects and neurodegenerative phenotypes caused by FUS expression in transgenic flies . This is the first report of direct mitochondrial targeting by a nuclear protein associated with neurodegeneration , suggesting that mitochondrial impairment may represent a critical event in different forms of FUS-proteinopathies and a common pathological feature for both ALS-FUS and FTLD-FUS . Our study offers a potential explanation for the highly heterogeneous nature and complex genetic presentation of different forms of FUS-proteinopathies . Our data also suggest that mitochondrial damage may be a target in future development of diagnostic and therapeutic tools for FUS-proteinopathies , a group of devastating neurodegenerative diseases .
Amyotrophic lateral sclerosis ( ALS ) is a fatal neurodegenerative disease primarily affecting motor neurons . The Cu/Zn superoxide dismutase 1 ( SOD1 ) gene was the first ALS-associated gene whose mutations were identified in familial ALS ( fALS ) patients [1 , 2] . Subsequently , genetic studies have uncovered more than ten ALS-associated genes [3 , 4 , 5] . Among these are genes encoding RNA/DNA binding proteins , including TAR-DNA binding protein of 43 kDa ( TDP-43 ) and fused in sarcoma/translocated in liposarcoma ( FUS/TLS or FUS ) [6 , 7 , 8 , 9] . Pathologically , FUS immunoreactive inclusion bodies are detected in a range of neurological diseases classified as FUS-proteinopathies . These disorders are genetically and clinically heterogeneous . Depending on the regions affected , FUS-proteinopathies can manifest as motor neuron disease such as ALS-FUS , or as various forms of dementia including frontotemporal lobar degeneration with FUS pathology ( FTLD-FUS ) , atypical FTLD with ubiquitin pathology ( aFTLD-U ) , neuronal intermediate filament inclusion disease ( NIFID ) , and Basophilic Inclusion Body Disease ( BIBD ) [4 , 10 , 11 , 12 , 13 , 14] . Interestingly , although >30 mutations in the FUS gene have been found in patients with ALS , no FUS mutations have been detected in the vast majority of sporadic or familial , pathologically proven cases of FTLD-FUS [15] . A recent study has identified several mutations in the 3’ untranslated region of the FUS gene that are associated with increased FUS expression among ALS patients [16] , suggesting that increased FUS expression could be a mechanism contributing to the pathogenesis of ALS . Several systems have been used to model FUS-proteinopathies , ranging from yeast to vertebrate animals [17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25] . A few groups , including Lanson and colleagues as well as our team , have established transgenic flies expressing wild-type ( Wt ) or ALS-mutant forms of human FUS protein [17 , 22] . In our transgenic fly model , targeted expression of either Wt- or ALS-mutant FUS protein in specific neuronal subpopulations leads to age-dependent neurodegeneration with functional deficits , recapitulating the critical features of FUS proteinopathies [17] . In transgenic mice , simply overexpressing the wild-type FUS led to progressive neurodegeneration [26] . Because no FUS mutation has been detected in most FTLD-FUS patients and because the patient samples that we examined showed elevated FUS protein levels ( see below ) , the work in this study examining the effects of increased Wt-FUS expression is pertinent to understanding FTLD-FUS , whereas the data with ALS-mutant FUS , such as P525L , is relevant to ALS-FUS . To understand the biological function of mammalian FUS in the nervous system , we searched for interaction partners of FUS . Using a FUS-specific monoclonal antibody ( S1 Fig; also see [27] ) , we developed an immunopurification-coupled mass spectrometry approach to identify proteins that interact with FUS in the bovine brain tissue . Among candidate FUS interaction partners , several mitochondrial proteins were identified , including HSP60 ( see below; detailed data to be reported in a separate study ) . Consistent with this finding , the endogenous FUS protein was detected by mass-spectrometry in biochemically-purified mitochondria ( without using the FUS antibody or overexpressing FUS ) , supporting the idea that FUS interacts with mitochondria . These observations prompted us to carefully examine mitochondria in our models for FUS proteinopathies . HSP60 proteins are a family of evolutionarily conserved ATP-dependent chaperones that play important roles in stress response , protein folding and cell signaling [28 , 29 , 30] . They are expressed constitutively as well as in response to stress signals [31 , 32] . HSP60 proteins are detected in the cytosol and inside the mitochondrial matrix . It has been reported that HSP60 , together with other heat shock proteins such as HSP10 and HSP70 , facilitates proper protein folding and assembly of protein complexes imported into mitochondria [30 , 32 , 33 , 34 , 35 , 36] . Mutations in the HSPD1 gene ( encoding the human HSP60 protein ) have been found in patients with spastic paraplegia type 13 ( SPG13 ) , a late-onset autosomal-dominant neurodegenerative disease characterized by progressive weakness and spasticity of lower limbs [37 , 38] . Mitochondrial impairment has been extensively investigated in ALS , in particular , in SOD1 animal models [39 , 40 , 41 , 42 , 43] . Aggregated mitochondria have been reported in transgenic mice overexpressing TDP-43 [44 , 45] . An EM study of spinal cord samples of two cases of ALS-FUS , including one containing P525L mutation , revealed disorganized mitochondria and endoplasmic reticulum [46] . Cytoplasmic expression of two other ALS-associated FUS mutants , R521G or R521H , was associated with shortened mitochondria in cultured motor neurons [47] . These studies suggest mitochondrial damage may be a common feature in ALS-FUS . However , no evidence has been reported for the mitochondrial localization of FUS or for mitochondrial damage in FTLD-FUS patients . Although it remains to be determined if mitochondrial impairment is a direct consequence of FUS expression , our data presented here show that mitochondrial fragmentation was detected not only in vitro in cultured neurons but also in vivo in motor neurons in the FUS-transgenic flies . Both Wt- and ALS-mutant P525L FUS interacted with HSP60 . Furthermore , mitochondrial damage was detected in brain samples of FTLD-FUS patients , with FUS levels increased in all 3 FTLD-FUS patient brain samples examined . Remarkably , elevated HSP60 expression was detected in two of these 3 cases of FTLD-FUS patient brain samples . Knocking-down HSP60 led to reduced level of mitochondrial FUS in cultured cells . Interestingly , RNAi-mediated down-regulation of the HSP60 homolog partially rescued the neurodegenerative phenotypes in FUS transgenic flies . Thus , increased FUS expression and mitochondrial impairment appear as a prominent pathological feature in FTLD-FUS . Our data also uncover the previously unknown regulation of FUS subcellular distribution by chaperon proteins such as HSP60 , suggesting a new direction for treating FUS proteinopathies by modulating mitochondrial localization of FUS and protecting against FUS-induced mitochondrial damage .
To examine if FUS expression affects mitochondria in mammalian cells , we expressed FUS in neuron-like cells , HT-22 , an immortalized mouse cell line with neuronal features [48] . To monitor changes in the mitochondrial morphology , a plasmid expressing mitochondrion-targeted red fluorescent protein ( RFP ) ( mito-Red ) was co-transfected with either a GFP vector control or a plasmid expressing GFP-tagged FUS protein into HT-22 cells ( Fig 1A ) . The majority of cells expressing GFP vector control showed mitochondria with tubular morphology ( Fig 1A ) . However , the percentage of the cells with fragmented mitochondria was significantly increased when either Wt-FUS or the ALS-mutant P525L was expressed . This mitochondrial change was particularly pronounced in cells expressing the P525L-mutant FUS ( Fig 1B ) . Next , mito-Red and FUS-GFP were co-expressed in cultured mouse cortical neurons . Fluorescent confocal microscopy revealed a significant increase in the percentage of neurons with fragmented mitochondria when expressing either Wt- or P525L-mutant FUS . The percentage of neurons expressing the P525L-mutant FUS showed fragmented mitochondria is higher than those expressing Wt-FUS ( Fig 2A–2C ) . Some of these neurons expressing FUS also showed condensed or fragmented nuclei , a sign of cell death . It is important to note that in a significant fraction of FUS-expressing HT22 cells or cortical neurons that showed mitochondrial fragmentation , there was no detectable sign of nuclear morphological changes characteristic of cell death , suggesting that mitochondrial changes may be an early event , preceding cell death . These data indicate that increased expression of FUS , especially the P525L-mutant , promotes mitochondrial fragmentation and cell death in mammalian neurons . To test in vivo effects of FUS expression , we examined motor neurons ( MNs ) expressing human FUS in transgenic flies [17] . The D42-Gal4 driver [49 , 50] was used to express either Wt- or P525L-mutant FUS specifically in Drosophila MNs . Mitochondria in MN axons were examined with confocal microscopy by the expression of mito-GFP in wandering 3rd instar larvae ( Fig 2D ) . It should be noted that the flies expressing the P525L-mutant FUS used in this study were from a line with a less severe phenotype [17] , because other P525L-mutant FUS lines exhibiting more severe phenotypes did not survive to the late larval stage . As compared with the control flies , axonal mitochondria were significantly smaller in MNs expressing the Wt- or P525L-mutant FUS protein . MNs expressing the human FUS protein showed an increase in the percentage of smaller mitochondria ( length<0 . 5μm ) and a decrease in the percentage of larger mitochondria ( length>1 . 5μm , p<0 . 05 ) , with P525L-mutant exhibiting more severe defects than the Wt-FUS ( p<0 . 01; Fig 2E ) . Consistently , the average mitochondrial size in the FUS-expressing fly MNs were significantly smaller than that in the control group ( p<0 . 01 ) , with the P525L-mutant expressing flies showing further shortened average mitochondrial length than the Wt- group ( p<0 . 05; Fig 2F ) . These data indicate that the expression of either Wt- or P525L-mutant FUS induces mitochondrial defects in vivo , with the ALS-mutant eliciting a more severe phenotype . This is consistent with our previous observation that neurodegeneration phenotypes in flies expressing the P525L-mutant were more severe than those expressing Wt-FUS [17] . Interestingly , cells transfected with either the Wt- or P525L-mutant FUS consistently showed a moderate increase in the levels of Fis1 and Drp1 proteins without affecting mitofusin 2 . A decrease in the level of Drp1 phosphorylated at its amino acid residue 637 was also detected ( S2A–S2E Fig ) . This prompted us to test whether inhibiting Drp1 function by a dominant-negative mutant K38A-Drp1 could reverse FUS-induced mitochondrial fragmentation in cultured neurons . As shown in S3A–S3C Fig , we co-transfected mitoRed and GFP control , Wt-FUS-GFP or P525L-FUS-GFP together with either the vector control , wild type Drp1-Flag ( WtDrp1 ) or K38A mutant Drp1-Flag ( K38A-Drp1 ) . Expression of Wt-Drp1 moderately increased the percentage of neurons with fragmented mitochondria as compared with the control group , whereas expression of K38A-Drp1 mutant decreased the percentage of cells containing fragmented mitochondria , especially in the neurons expressing the P525L-mutant FUS ( S3A–S3C Fig ) . Moreover , the percentage of neurons showing condensed nuclei was also reduced by the expression of K38A-Drp1 in those expressing the P525L-mutant FUS . It prompted us to test if inhibiting Drp1 in FUS transgenic flies would rescue the neurodegenerative phenotypes . However , expression of Drp1-specific RNAi or a dominant-negative K38A-Drp1mutantdid not lead to a significant rescue of neurodegeneration phenotypes in the FUS transgenic flies expressing Wt- or P525L-FUS protein ( S3D and S3E Fig ) . It has been reported that a dominant- negative mutation in Drp1 is associated with a severe neurodevelopmental syndrome in a patient and down-regulation of Drp1 leads to developmental defects in flies [51 , 52] . It is possible that simply suppressing Drp1 activity is not sufficient to block the FUS-induced neurotoxic signal ( s ) . To examine the relationship between FUS and mitochondria , we purified mitochondria from the HEK293 cells expressing the vector control or the ALS-associatedP525L-mutant FUS following published protocols [53 , 54] ( Fig 3 ) . Western blotting experiments demonstrate that our mitochondrial preparations were highly enriched in mitochondrial proteins ( such as CoxIV ) but devoid of either cytoplasmic proteins ( such as GAPDH ) or nuclear proteins ( e . g . , Histone H3 ) ( Fig 3A ) . The endogenous FUS protein was consistently detected in these highly purified mitochondrial preparations ( lane3 in Fig 3A ) . Although the level of P525L-mutant FUS protein detected was higher than the endogenous Wt-FUS protein in both the purified mitochondria and cytosol ( see Fig 3A , the upper band was the P525L-mutant FUS; the lower band , the endogenous FUS ) , the endogenous FUS protein was clearly detected in the purified mitochondria ( Fig 3A , lane 3 and lane 6 ) . These data demonstrate that both the endogenous wild-type and transfected ALS-mutant FUS are translocated to mitochondria . To confirm that the FUS protein is indeed closely associated with mitochondria , we performed immuno-electron microscopy ( IEM ) using the specific anti-FUS antibody . In the control HEK293 cells , a fraction of FUS-IEM staining signals were associated with mitochondria , whereas in cells overexpressing the Wt- or P525L-mutant FUS , many mitochondria were decorated with immuno-gold particles conjugated to the specific anti-FUS antibody ( arrows in Fig 3B and 3E ) . It should be noted that the anti-FUS antibody was specific , because pre-absorption of the antibody using the purified FUS antigen essentially eliminated the immunoreactive signals in Western blotting ( WB ) and IEM ( see S1A–S1D Fig ) . Consistently , down-regulation of FUS by specific siRNA ( siFUS ) or genetic deletion of the FUS gene reduced or eliminated the WB signals ( see S1C and S1D Fig , respectively ) . Quantification of IEM images shows that mitochondria in cells expressing either Wt- or P525L-mutant FUS were smaller with severely damaged cristae , as compared with those in the control group ( Fig 3B–3D ) . “Onion-like” mitochondria with multi-layered structure and damaged cristae were frequently detected in cells expressingP525L-mutant FUS ( marked by arrowheads in Fig 3B and 3C ) , but not in the control cells . Consistent with the data described previously , mitochondrial size was significantly decreased in cells expressing either Wt- or P525L-mutant FUS , with the P525L-mutant showing more pronounced mitochondrial defects ( Fig 3D ) . These data support the notion that increased FUS expression leads to mitochondrial damage . Together , our results demonstrate that increased FUS expression , especially that of the cytoplasm-localized ALS-associated P525L-mutant FUS protein , promotes association of FUS with mitochondria and induces mitochondrial damage . A common sign of mitochondrial damage is the change in mitochondrial membrane potential , leading to mitochondrial depolarization . We tested if FUS expression affects mitochondrial membrane potential . Tetramethylrhodamine methyl ester ( TMRM ) , a fluorescent dye labeling mitochondria in a membrane potential-dependent manner [55] was used to stain HEK293 cells expressing either the GFP vector control or Wt- or P525L-mutantFUS tagged with GFP . In cells expressing the control , the TMRM signals were detected at a similar level as in the neighboring non-transfected cells ( Fig 4A ) . However , the TMRM staining intensity in cells expressing Wt- or P525L-mutant FUS ( marked by the white arrows ) was reduced as compared with their neighboring non-transfected cells ( marked by arrowheads ) . The control and Wt- or P525L-mutant FUS expressing cells were then analyzed by fluorescence activated cell sorting ( FACS ) to quantify TMRM florescence intensity in GFP-positive cells . Consistently , TMRM intensity was significantly lower in cells expressing either Wt- or P525L-mutant FUS as compared with the GFP vector-control group , with the P525L-mutant group showing more pronounced effect ( Fig 4B and 4C ) . This indicates that FUS expression , in particular that of P525L-mutant , reduces mitochondrial membrane potential and causes mitochondrial damage . Next , we measured the mitochondrial production of the reactive oxygen species ( ROS ) in these cells . Using a specific mitochondrial superoxide indicator , mitoSOX , we compared cells expressing either Wt- or P525L-mutant FUS . Both confocal images and FACS analyses showed that ROS production was increased in the P525L-mutant FUS expressing cells ( Fig 4D–4F ) . It has been reported that ROS could activate Drp1 and induce mitochondrial fragmentation [56] , consistent with our findings of FUS-induced Drp1 activation and mitochondrial damage . To characterize molecular and cellular damages in patients with FUS-proteinopathies , we collected de-identified post-mortem tissue samples from the Cognitive Neurology & Alzheimer's Disease Center at Northwestern University . After initial testing to exclude cases with non-specific protein degradation , three FUS-proteinopathies brains together with six control samples were identified suitable for biochemical studies and electron microscopy . In all three FUS-proteinopathies cases examined , no FUS mutations were identified ( see S1 Table ) and the pathological diagnosis was FTLD-FUS with prominent FUS-positive inclusion bodies detected in the brain tissues . Using the specific anti-FUS antibody , we examined FUS protein levels in these FTLD-FUS brain samples together with six control samples . Remarkably , all three FTLD-FUS samples showed increased FUS protein levels as compared to the controls ( Fig 5A and 5B ) . This is consistent with a previous report that the brain samples from patients affected by atypical FTLD with FUS pathology ( aFTLD-FUS ) showed increased total FUS levels , as compared with the controls or FTLD-TDP-43 samples [10] . It should be noted that 2–3 bands were detected in the human brain samples , with approximate molecular weight of 53-70kD , which is consistent with previously published Western blotting data on FUS proteinopathies patient tissue samples [57 , 58] . It is possible that FUS may undergo proteolytic cleavage , and the molecular nature of such cleavage remains to be determined by future studies . Because our mass-spectrometry analyses showed mitochondrial HSP60 as a FUS-interacting protein , we also examined HSP60 protein in these brain samples and found that two FTLD-FUS cases ( A and C ) exhibited elevated HSP60 protein level ( Fig 5A and 5B ) . It is not surprising to see that not all FTLD-FUS samples exhibited increased HSP60 expression . Considering the multi-level regulation of FUS gene expression and the diverse genetic background in different individuals , it is conceivable that different mechanisms contribute to the development of FUS-proteinopathies and that HSP60 is not the only modulator of FUS induced neurotoxicity among different FTLD-FUS patients . We performed IEM of these samples using the FUS-specific antibody and the secondary antibody conjugated to gold particles . In the control samples , most mitochondria appeared healthy with well-organized cristae as packed-stacks of membrane sheets ( see panels C1-C6 in Fig 5C ) and with only a few FUS-immunostaining signals detected in the vicinity of the mitochondria . However , in all three FTLD-FUS brain samples , FUS-immuno-positive signals were frequently detected in close association with mitochondria ( arrows in panels C9-C13 in Fig 5C and 5D ) . Consistently , mitochondria in these FTLD-FUS cases showed a marked loss or disruption of cristae with frequent detection of “onion-like” deformed shape ( arrowheads in panels C7-C13 in Fig 5C ) . Quantification of the EM data indicates that all three FTLD-FUS cases showed increased FUS-immunostaining signals in mitochondria and increased mitochondrial damage ( Fig 5D and 5E ) . These observations indicate that mitochondrial impairment , accompanied by increased FUS expression , represents a prominent neuropathological feature in FTLD-FUS patients . HSP60 , an ATP-dependent mitochondrial chaperone protein associated with neurodegeneration [30] was one of the FUS-interacting mitochondrial proteins identified in our immunoaffinity-coupled mass-spectrometry experiments . We confirmed the FUS-HSP60 interaction in a co-immunoprecipitation ( co-IP ) assay using HEK293 cells transfected with the Wt- or P525L-mutant FUS tagged with GFP or the GFP vector control . Immunoprecipitation to pull-down FUS followed by Western blotting using the HSP60-specific antibody revealed that both Wt- and P525L-mutant FUS interacted with HSP60 ( Fig 6A ) . To examine whether FUS interacts with HSP60 in the cytosol or in mitochondria , we prepared mitochondrial and cytosolic fractions from HEK293 cells expressing the control vector , Wt- or P525L-mutant FUS tagged with 6xMyc tag to perform co-IP assay . Western blotting analyses of the immunoprecipitated protein revealed that Wt- or P525L-mutant FUS interacted with HSP60 in the mitochondria as well as in the cytosol ( Fig 6B ) . To test whether FUS directly interacted with HSP60 , we performed a cross-linking immunoprecipitation assay ( Fig 6D , S4B Fig ) . Following the treatment with formaldehyde ( a cross-linking reagent ) of the live cells stably expressing 6xMyc-His tagged FUS , FUS protein was purified by pulling-down with Ni-NTA resin . A 130 kDa species was detected by anti-HSP60 antibody , representing FUS-HSP60 cross-linked product , because Myc-His tagged FUS and HSP60 were detected as 70 and 60kDa proteins , respectively ( marked by the arrow , lane 2 and lane 8 in Fig 6D ) . Because live cells were treated with the cross-linking reagent immediately before immunoprecipitation in this assay , our data support the notion that FUS directly interacts with HSP60 in cells . In addition , protein purification was performed under the denaturing condition in the presence of guanidine-HCl using Ni-NTA resin , thus it is unlikely that the 130kDa cross-linked product was a result indirect interaction of HSP60 with FUS . To further demonstrate that FUS directly interacts with HSP60 and to map the region in FUS responsible for FUS-HSP60 interaction , we performed glutathione S-transferase ( GST ) pull-down experiments using purified GST-tagged proteins of either Wt- or P525L-mutant or fragments of the Wt-FUS protein and His-tagged HSP60 protein ( see Fig 6C ) . Although the interaction was not detectable when the N-terminal fragments containing 285 or 370 amino acid residues ( aa ) , the full-length Wt- or P525L-mutant or the carboxyl terminal fragment containing aa371-526 clearly interacted with the purified HSP60 protein ( Fig 6C ) . To test if the FUS-HSP60 interaction is RNA-dependent , we performed GST pull-down experiments in the presence of RNaseA . RNase A treatment did not affect the interaction between FUS and HSP60 , although there was partial degradation of FUS and HSP60 proteins following RNaseA treatment ( S4A Fig ) . Together , these data indicate that Wt- or P525L-FUS directly interacts with HSP60 in cells and in vitro . To test whether HSP60 mediates FUS mitochondrial localization , we down-regulated HSP60 expression using specific siRNA in HEK293 cells and then examined the level of mitochondrion-localized FUS ( the endogenous wild-type FUS ) using purified mitochondria followed by Western blotting . Although the total FUS levels were not altered , the mitochondrial FUS level was significantly decreased in the HSP60-siRNA transfected cells , as compared with the control-siRNA group ( Fig 6E–6G ) . Furthermore , analyses of the nuclear and cytosolic fractions revealed that the nuclear FUS level was increased in the HSP60-siRNA transfected cells , as compared with the control-siRNA group ( S4C–S4E Fig ) . We also performed siRNA knock-down experiments in the stable cells expressing the P525L-mutant FUS . Similarly , reducing HSP60 expression in these cells also decreased mitochondrial localization of both the P525L-mutant and the endogenous Wt-FUS proteins ( S4F–S4H Fig ) . These results suggest that HSP60 may play an important role in mediating FUS translocation to mitochondria . To examine HSP60-FUS interaction in vivo , we tested whether FUS genetically interacted with HSP60 in flies by knocking-down Drosophila homologs of human HSP60 , including HSP60A , B , C or D in FUS transgenic flies using RNA interference ( RNAi ) ( supplementary S5 and S6 Figs ) . Fly eyes expressing Wt- or P525L-mutant FUS exhibited rough surface and reduced pigmentation . Scanning electron microcopy ( SEM ) revealed ommatidial loss , ommatidial fusion , ectopic bristle formation and disrupted ommatidial organization , as reported previously [17] . Down-regulating expression of HSP60A , HSP60B orHSP60C in photoreceptors suppressed the FUS-induced retinal degeneration phenotype to various extents , with siHSP60B showing the most robust effect , although knocking-down HSP60 did not affect the expression level of FUS ( see S5A and S5B Fig ) . Therefore , we chose siHSP60B for subsequent experiments . Knocking-down HSP60B in FUS-expressing flies partially restored the eye morphology , rescuing FUS-induced photoreceptor degeneration , although siHSP60B by itself in the control flies did not show any effects ( Fig 7A; S7 Fig ) . Scanning EM revealed that down-regulating HSP60B in fly eyes expressing Wt- or P525L-mutant FUS partially reversed FUS-induced ommatidial loss or fusion , restoring ommatidial pattern and bristle organization , especially in the peripheral region of the eye ( Fig 7B and 7C ) . The effect of siHSP60 in rescuing FUS-induced neurodegeneration was specific because a number of fly lines tested that expressed siRNAs against other genes did not show any effect ( see S3D Fig ) . When mitochondria were examined in the motor neuron axons , reducing HSP60B expression by RNAi alone in the control flies did not affect mitochondrial size ( Fig 7D and 7E ) . In contrast , down-regulating HSP60B significantly increased the mitochondrial size in flies expressing P525L-mutant FUS in motor neurons ( Fig 7D and 7E ) , thus rescuing the effect of P525L-mutant FUS induced mitochondrial fragmentation . Importantly , HSP60B down-regulation in fly MNs significantly rescued the locomotive defects in the P525L-mutant FUS expressing fly larvae , with mitigated tail-paralysis phenotype in these siHSP60B/P525L-FUS expressing animals ( Fig 7F and 7G ) . These results indicate that FUS genetically interacts with HSP60 , suggesting a role of HSP60 in FUS-induced neurotoxicity . Then , we used transmission EM ( TEM ) to examine the fly retinal structure . Retinas displayed normal differentiation of seven rhabdomeres at day 3 , whereas the flies expressing Wt- or P525L-mutant FUS exhibited defective rhabdomeres ( marked as “Rh” in Fig 8 ) . Although knocking-down HSP60B in files expressing Wt- or P525L-mutant FUS did not completely restore the rhabdomeres to its normal morphology , it improved rhabdomere formation in the siHSP60B-expressingflies ( as marked by arrows in Fig 8 ) ( S8D Fig ) . As compared with the healthy mitochondria in the control flies , the mitochondria in the flies expressing Wt- or P525L-mutant FUS were smaller ( S8E Fig ) . Down-regulating HSP60B expression in these FUS transgenic flies significantly increased the mitochondrial size ( S8E Fig ) . It was noticed that at day 3 the nuclei of photoreceptor cells in the fly eyes expressing either Wt- or P525L-mutant FUS appeared swollen with nearby mitochondria severely damaged , although the cells remained intact . This phenomenon was not detected in the control flies ( Fig 8 , S8B and S8C Fig ) . By day 15 , a significant photoreceptor loss was observed in flies expressing Wt- or P525L-mutant FUS as compared with the control flies , or with the FUS transgenic flies at day 3 ( Fig 8 , S8A Fig ) . These results support the hypothesis that mitochondrial damage may be an early event in FUS proteinopathies and that the mitochondrial damage induced by FUS expression may precede photoreceptor neuronal death .
Originally identified as a gene involved in chromosomal translocation in liposarcoma [59] , the human FUS gene encodes a RNA/DNA binding protein involved in multiple cellular processes [60 , 61] . Although it has been reported that FUS shuttles between the nucleus and cytoplasm [62] and that FUS has many nuclear activities , the cytoplasmic function of FUS is far less clear . For the first time , our study reveals that the FUS can be localized to mitochondria and increased FUS mitochondrial localization is toxic to neurons , contributing to neurodegeneration . While the physiological role of FUS in regulating mitochondrial biology remains to be elucidated by future studies , our data provide new information about the cytoplasmic platform on which FUS is likely to play an active role . The vast majority of mitochondrial proteins are encoded by the nuclear genome and synthesized in the cytoplasm , then transported into mitochondria via different mechanisms [63] . Under physiological conditions , FUS possibly plays a functional role in mitochondria . A recent report showed that knocking down of FUS by RNAi affected the expression of a subset of mitochondrion-associated genes [64] . Although the human FUS protein shows a very low probability ( <0 . 0001 ) in computational prediction of mitochondrial import using a published algorithm [65] , our data , ranging from biochemical purification of mitochondria , immunoEM detection of the endogenous FUS to in vivo assays in transgenic flies provide clear evidence that FUS is associated with mitochondria . Previous studies have shown that some mitochondrial proteins are chaperoned by heat shock proteins [66] . The mitochondrial chaperonin HSP60 , together with HSP70 and HSP10 , are important for protein import into mitochondria [30 , 32 , 33 , 34 , 35 , 36] . Although it has been reported that HSP60 may promote mitochondrial localization of amyloid-beta peptide and mitochondrial impairment in Alzheimer disease [67] , the role of HSP60 in other neurodegenerative diseases remains unclear . Mutations in HSP60 were reported to be associated with spastic paraplegia type 13 ( SPG13 ) , a late-onset autosomal-dominant neurodegenerative disease [37 , 38] . Recently , it was reported that a heterozygous knock-out HSP60 mouse model could recapitulate the features of human disease ( SPG13 ) and increase mitochondrial ROS [68] . Here , we demonstrate that FUS interacts with HSP60 by mass-spectrometry analyses of FUS interacting proteins , co-immunoprecipitation , GST pull-down and cross-linking experiments . Importantly , down-regulation of HSP60 expression decreased mitochondrial FUS levels and partially rescued FUS-induced phenotypes , including mitochondrial fragmentation , neurodegeneration and locomotive deficits . Drosophila has four HSP60 homologs . In our experiments , knocking down expression of each HSP60 homolog by their RNAi led to a partial rescue of the neurodegenerative phenotypes in FUS transgenic flies . It is likely that different HSP60 homologs or HSP60-like genes may compensate for each other in vivo . Our data support the hypothesis that HSP60 promotes or mediates FUS translocation from the nucleus to mitochondria . HSP60 has been reported to play a dual role in cell death , as either an anti- or a pro- apoptosis factor [69] . On one hand , HSP60 together with HSP70 may play a protective role in mitochondrial unfolded protein response [33] . HSP60 may form a pro-survival complex with Bcl-2 , an anti-apoptotic mediator; and disruption of this complex formation by nutrient deprivation results in cell death [70] . On the other hand , HSP60 has been reported to facilitate pre-caspase-3 maturation [71] or induce nitric oxide production by microglia , leading to neurotoxicity [72] . Our results indicate that HSP60 interacts with FUS to promote mitochondrial damage and cell death . Future studies are necessary to determine whether modulating the HSP60-FUS interaction may alter the activity of HSP60 in regulating neuronal survival . For example , it is possible that the interaction of FUS with HSP60 might affect the ability of HSP60 to form complex with Bcl-2 , thereby inhibiting Bcl-2 pro-survival function and resulting in cell death . Our findings provide a new direction for further investigating the multi-facet role of HSP60 in neurodegenerative disorders . There are four HSP60 homolog genes in drosophila , named as HSP60A , HSP60B , HSP60C , HSP60D [73 , 74] . The predicted phylogenetic relationship between these four genes and human HSP60 is shown in S6 Fig . HSP60A is a constitutively expressed chaperonin in drosophila cells and essential for embryogenesis . Mutations in HSP60A lead to embryonic lethal phenotype in Drosophila [75] . HSP60B shares extensive homology with other drosophila HSP60 proteins and is required for spermatid individualization process . Mutations in HSP60B result in male sterility [76] . HSP60C is required for tracheal development and for early-stage spermatogenesis [77] . Therefore , HSP60A , HSP60B and HSP60C have distinct functional activities in development , whereas HSP60D is essential for caspase-mediated apoptosis in Drosophila [78] . Our data show that down-regulating the expression of HSP60A , HSP60B or HSP60C led to a partial rescue of the neurodegenerative phenotypes in FUS transgenic flies to various extent , suggesting that these three genes may share common features in FUS proteinopathies . Interestingly , HSP60D down-regulation in FUS transgenic flies did not show any rescue effects , suggesting that caspase-mediated apoptosis might not be critical for neurodegeneration in FUS proteinopathies . A number of missense mutations have been identified in the human FUS gene among ALS-FUS patients [7 , 8 , 14 , 15 , 79 , 80] . In addition , at least four mutations have been detected in the 3’ UTR of the FUS gene among sporadic or familial ALS patients but not in the control samples [16] . Remarkably , fibroblasts from the ALS patients with three different mutations in the 3’UTR showed increased FUS protein expression and accumulation of cytoplasmic FUS [16] . Animal models of FUS proteinopathies established by over-expressing Wt- or ALS-mutant FUS recapitulate major clinical and pathological features of FUS proteinopathies , providing useful systems to study pathogenic mechanisms underlying these devastating diseases [17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25] . These results , together with our observation of increased FUS protein in FTLD-FUS patient samples , support the notion that increased FUS expression and cytoplasmic accumulation of FUS likely contribute to the pathogenesis of FUS proteinopathies . Characteristic cytoplasmic inclusions containing FUS protein have been detected in the affected neural tissues of sporadic ALS ( sALS ) and FLTD-FUS patients . However , the pathogenic role of FUS and the underlying molecular mechanisms in ALS and FTLD remain to be elucidated . One common finding in ALS and other neurodegenerative diseases is mitochondrial damage [81 , 82 , 83 , 84] . Mitochondrial impairment has been reported in ALS patients as well as animal models for ALS [39 , 40 , 43] . Disorganized mitochondria and endoplasmic reticulum have been reported in spinal cord tissue samples from two cases of FUS-positive juvenile ALS patients , including one carrying P525L mutation [46] . In another study , expression of two other ALS-associated FUS mutants , R521G or R521H , was associated with shortened mitochondria in cultured motor neurons in vitro [47] . These studies suggested mitochondrial impairment in FUS-positive ALS , although it was not clear whether such mitochondrial defects were a direct consequence of FUS expression or secondary effects following neurodegeneration in ALS . Neither was it clear if mitochondrial defects could be shared pathological features for both ALS-FUS and FTLD-FUS . Our work provides the first EM evidence of mitochondrial localization of FUS not only in cultured cells but also in the FTLD-FUS patient brain samples . Our study provides direct evidence for mitochondrial damage in motor neurons in our animal model for FUS proteinopathies . This is also the first report of mitochondrial damage in FTLD-FUS at the ultra-structural level . Our data obtained from different cellular models and from motor neurons in transgenic flies clearly demonstrate that increased expression of FUS , either Wt- or ALS-mutant , leads to mitochondrial fragmentation , decrease in mitochondrial membrane potential , increased ROS production and eventually neurodegeneration . Our findings suggest that mitochondrial impairments may be an early or initiating event in FUS proteinopathies . Previous data have shown that certain ALS-associated FUS mutations , such as P525L , increase the cytoplasmic distribution of FUS protein because of the disruption of its nuclear localization signal [17 , 85] . Interestingly , the P525L-mutation in ALS patients is associated with earlier age-of-onset and faster disease progression ( see S1 Table ) [7 , 46 , 86 , 87] . Overexpressing either Wt- or ALS-mutant FUS results in a marked increase in mitochondrial fragmentation . In the FUS transgenic flies , FUS expression induced mitochondrial damage and neurodegeneration , both of which were partially rescued by down-regulation of the mitochondrial HSP60 expression . Consistently , brain tissues from at least some FTLD-FUS patients showed increased expression of both FUS and HSP60 proteins . The data presented in this report led us to propose a working model that can be further tested in our future studies ( see Fig 9 ) : increased cytoplasmic FUS protein levels triggered by cellular stresses or pathogenic FUS mutations result in an increased interaction of FUS with mitochondrial chaperone protein HSP60 , which promotes FUS localization to mitochondria . Elevated mitochondrial localization of FUS may damage mitochondria , leading to mitochondrial fragmentation . Such mitochondrial impairment may trigger the onset of neuronal damage and neuronal death , eventually resulting in neurodegenerative manifestations of FUS proteinopathies . Recent studies have revealed possible convergence between ALS and FTLD [14] . Our data suggest that one point of such convergence and a critical event in FUS-proteinopathies may be FUS-induced impairment of mitochondria . Although our data do not rule out the possibility that loss of function caused by sequestration of the wild-type FUS protein into inclusion bodies may also contribute to pathogenesis of FUS-proteinopathies , as previously proposed [88 , 89] , the present study supports the gain-of-function toxicity mechanism . Our work provides strong evidence that mitochondrial damage contributes to FUS-proteinopathies and represents a common molecular pathology shared by ALS-FUS and FTLD-FUS . Moreover , HSP60 signaling pathway may be critical for FUS-induced neurotoxicity , and reducing HSP60 expression or suppressing HSP60 activity may provide therapeutic benefit for FUS-proteinopathies patients with increased HSP60 expression .
De-identified postmortem brain samples from patients affected by FTLD-FUS and control subjects were obtained from the Cognitive Neurology &Alzheimer's Disease Center ( CNADC ) at Northwestern University following institutional and NIH guidelines . All experiments involving animal tissue samples were carried out following institutional and NIH guidelines . Fly strains are as follows: D42-Gal4 UAS-mitoGFP/TM6B was from Dr . Y . Zhang [50] . UAS-Drp1 , UAS-Drp1-RNAi and UAS-Drp1-K38A were from Dr . B . Lu . UAS-RFP , UAS-Wt-FUS-RFP , UAS-P525L-FUS-RFPwere described previously [17] . The HSP60A-RNAi , HSP60B-RNAi , HSP60C-RNAi and HSP60D-RNAi stocks were obtained from Vienna Drosophila RNAi Center ( VDRC ) . OK371-Gal4 , GMR-Gal4 and Actin5C-Gal4 were obtained from Bloomington Drosophila Stock Center . Flies were raised according to standard procedures at 25°C . Antibodies used in this study were as follows: rabbit-anti-GFP ( Millipore ) , rabbit-anti-HSP60 ( BD ) and monoclonal murine anti-myc ( Covance ) , monoclonal murine anti-FUS ( ProteinTech Group Inc ) and following rabbit polyclonal antibodies against corresponding proteins from ProteinTech Group Inc: CoxIV , Histone H3 , TOMM20 , MFN2 , GAPDH , Actin and PCNA . Anti-RhoA is from Santa Cruz and anti-p637Drp1 is from cell signaling technology ( CST ) . FUS transgenic and control flies were prepared as described [17] . The third instar larvae were dissected and fixed with 4% paraformaldehyde ( PFA , Electron Microscopy Science ) for 20 min at room temperature ( RT ) . Following rinse with PBS , larvae were mounted onto coverslips using mounting gel . Confocal images were taken under an Olympus FV1000 confocal microscope . The assay was done as described [17 , 90] . Briefly , the larval movement index was measured as the number of peristaltic waves during the period of 2 min in the late third instar larvae expressing control RFP , siHSP60B , P525L-FUS or siHSP60B+P525L-FUS under the OK371-Gal4 driver in a controlled environment ( 25°C , humidity 50%± 5% , illumination 2800 ±100 lux ) . For immuno-EM assay in HEK293 cells , cells stably expressing GFP , Wt- or P525L-mutantas GFP-tagged protein were harvested and fixed with 4% PFA and 0 . 2% glutaraldehyde ( pH7 . 2 ) in PBS for 3 hrs at RT . Following rinses and post-fixation processing , gelatin-embedded blocks were prepared in 2 . 3 M sucrose at 4°C . Ultrathin sections ( 70-nm ) were cut at -120°Cusing dry diamond knives . Following blocking , the sections were immunostained with monoclonal anti-FUS antibody ( 1:100 ) and anti-mouse IgG antibody ( 1:25 ) conjugated to 10 nm-colloidal gold particles . For human brain tissue immuno-EM , postmortem frontal cortex samples from control cases and patients affected by FUS-proteinopathies were fixed with 2% PFA and 0 . 2% glutaraldehyde for 3 hrs at RT . Samples were embedded in 6% gelatin . Immuno-staining was performed as described above . For EM study of fly eyes , adult fly heads were dissected and fixed in a solution with 4% paraformaldehyde , 2 . 5% glutaraldehyde in PBS , pH 7 . 4 , for 12 h at 4°C and in a solution with 1% osmium tetroxide in PBS , pH 7 . 4 , for 2 h at room temperature . The tissues were then dehydrated in a series of ethanol solutions ( 30-min washes in 10 , 25 , 40 , 55 , 70 , 85 and 100% ethanol ) and embedded in spurr resin . Thin sections ( 70-nm ) were prepared and were examined by transmission EM . All EM images were obtained using a Tecnai Spirit ( 120kV ) or FEI Tecnai 20 electron microscope . Scanning electron microscopy ( SEM ) was carried out as described before [17] . HEK293T or HT22 cells were cultured ( 37°C 5% CO2 ) in DMEM ( GIBCO ) supplemented with 10% FBS ( HyClone or Atlanta Biological ) . Plasmids were transfected using VigoFect ( Vigorous Biotechnology ) according to manufacturer’s instructions or calcium phosphate method as described before [91 , 92] . Cortical neurons were cultured from E15 embryonic mouse brains following the published protocols [93 , 94] . HEK293-based T-Rex™293 cells ( Invitrogen ) were transfected with pcDNA4 TO/myc-His plasmids ( Invitrogen ) expressing either Wt- or P525L-mutant FUS; and stable expressing cells were selected as individual clones in zeocin ( 400 μg/ml ) . To induce FUS expression , 0 . 5 or 1mg/ml tetracycline was added to the culture medium , and cells were incubated for different period of time at 37°C . The control siRNA and FUS siRNA or HSP60 siRNA were transfected using lipofectamine ( Invitrogen ) according to manufacturer’s instructions . SiRNA targeting human FUS: 5'-GGACAGCAGCAAAGCTATG-3' [95] . SiRNA targeting human HSP60: 5'-GTGACAAGGCTCAAATTGA-3' . HEK293T cells were used for the transfections and analyses of protein-protein interactions . The experiments were performed at 48 hours post-transfection . The harvested cells were washed with phosphate-buffered saline ( PBS ) and lysed for 30 minutes on ice in the lysis buffer [91] . The soluble fraction of cell lysates was collected and used for immunoprecipitation with specific antibodies and protein A-agarose ( Roche ) at 4°C . The immunoprecipitates were examined using Western blotting ( WB ) with proper antibodies . Mitochondria were purified by percoll gradient ultracentrifugation following published protocols with minor modifications [53 , 54] . Briefly , stable FUS-expressing HEK cells were lysed in mitochondrial isolation buffer ( 10mM Tris-MOPS , pH7 . 4 , 1mMEGTA , 250mM sucrose ) , homogenized with a Glass/Teflon Potter Elvehjem homogenizer ( Bellco Glass Inc ) and fractionated by sequential centrifugation . After expression of myc-His tagged P525L FUS mutant was induced with 0 . 5ug/ml tetracycline for 16 hours , cells were briefly washed with PBS containing 1mM MgCl2 ( PBS , MgCl2 ) , and treated with 1% PFA in PBS MgCl2 for 10 minutes . Cells were washed with 50mM Tris-HCl buffer ( pH 7 . 4 ) containing 120mM NaCl and 1mM MgCl2 twice and treated with the same buffer for 10 min . Cells were washed with PBS- MgCl2 once again and lysed with phosphate buffer ( pH 8 . 0 ) containing 1% NP-40 , 200mM NaCl and 1mM PMSF . Soluble fractions were obtained by centrifugation at 2500g for 30 min and twice volume of 6M guanidine HCl in 50mM phosphate buffer ( pH 8 . 0 ) was added . FUS proteins were collected with Ni-NTA agarose , washed with 6M guanidine HCl in 50mM phosphate buffer ( pH 8 . 0 ) twice , and then eluted with 100mM PIPES ( pH 6 . 6 ) containing 0 . 1% SDS , 5mM EDTA . The obtained solutions were diluted ten times with 20mM HEPES ( pH 7 . 4 ) containing 0 . 5% Triton X-100 , 0 . 05% deoxycholate , 100mM NaCl , 5% glycerol and 0 . 5mM PMSF to carry out immunoprecipitation with anti-myc antibody and protein A/G beads . Immunoprecipitated beads were resuspended with 6M guanidine HCl in 50mM phosphate buffer ( pH 8 . 0 ) , and FUS-myc-His protein was pull-down with Ni-NTA beads ( by repurification with Ni-NTA , antibody can be removed so that background signals can be eliminated in WB analyses ) . Pull-down fractions were analyzed by Western blotting . GST pull-down assay was performed as described by [96] . GST tagged Wt or P525L or fragments FUS protein were expressed in bacteria and purified using glutathione 4B Sepharose beads . His tagged HSP60 were similarly expressed in bacteria and purified using nickel beads . Purified GST , or Wt , P525L , fragments FUS-GST was incubated with purified HSP60-His in TNE buffer ( 10 mM Tris , pH 8 . 0 , 150 mM NaCl , 1 mM EDTA , 1% NP-40 ) on ice for 1 hour . Then , supernatants were incubated with glutathione 4B Sepharose beads at 4°C for 3 hours . The glutathione beads were then washed extensively with ice-cold TNE buffer , and bound proteins were subjected to SDS-PAGE followed by immunoblotting analysis . The RNaseA treatment assay was performed as [97] . Briefly , the purified protein were treated with 50ug/ml RNaseA for 30 min at 37°C . Then GST pull-down assay was performed as previous described . Mitochondrial membrane potential was measured by staining cells with tetramethylrhodamine methyl ester ( TMRM ) ( Invitrogen ) . HEK293 cells were transfected with the GFP vector control , Wt- or P525L-mutant FUS as GFP-tagged proteins . Cells were incubated with 20 nM TMRM for 20 min at 37°C , 19 hrs post-transfection . Cells were washed three times with PBS and cultured in opti-MEM ( without phenol red ) supplemented with 10% FBS and 5 nM TMRM . Confocal images were taken using an Olympus FV1000 microscope . The TMRM intensity in different groups was measured by FACS ( BD FACS AriaII ) and analyzed using FlowJo software . Data were obtained from four independent experiments , and 2000 cells were examined per group in each experiment . Mitochondrial ROS production was measured using mitoSOX-Red ( Invitrogen ) . HEK293 cells were transfected with plasmids expressing either the GFP vector control , Wt or P525L FUS-GFP . 24 hours post-transfection , cells were stained with 5μM mitoSOX-Red for 20 min at 37°C . After washes , cells were fixed with 4% PFA for 20 min at room temperature followed by FACS analysis with mitoSOX-Red fluorescence intensity determined using FlowJo software . Confocal images were taken using an Olympus FV1000 microscope . To measure HSP60B expression levels in the control and siRNA flies , total RNA was prepared from flies using Trizol ( Invitrogen ) , reverse transcribed , and subjected to PCR analysis . For HSP60B , PCR ( 25 cycles ) was carried out using the following primers: 5’- CTGAGGATGCCTTGCCAGACC-3’ and 5’- GCAGCACCTTTGTGGGATCAATA-3’ . For Actin , PCR analysis ( 21 cycles ) using the specific primers: 5’-GAGCGCGGTTACTCTTTCAC-3’ and 5’-ATCCCGATCCTGATCCTCTT-3’ . Mitochondria from HT22 cells or cortical neurons were quantified as [98] . Briefly , the percentage of cells showed tubular , fragmented or intermediate pattern was counted and statistic analysis . Mitochondria from fly motor neuron were measured their length by using Image J . The mitochondrial length from Wt- or P525L-mutant FUS groups was normalized to the control ( Ctr ) group and presented as the percentage of the mitochondrial size in the control group . Mitochondrial size in EM analyses were quantified by their cross sectional areas by using Image J software . The mitochondrial size from Wt- or P525L-mutant group was normalized to the control group and presented as percentage of the mitochondrial size in the control group . Normal or abnormal cristae morphology of mitochondria was defined as previously published [99 , 100] . Briefly , normal mitochondria with numerous well-organized cristae or abnormal mitochondria showing ring structures or loss of cristae were scored and quantified as the percentage of total mitochondria examined . Statistical analyses were performed using either one-way ANOVA , followed by student t-test , Chi-square test or Bonferroni multiple comparison for comparing individual groups , as indicated in corresponding figure legends . The bar graphs with error bars represent mean ± standard error of the mean ( SEM ) . Significance is indicated by asterisks: * , P < 0 . 05; ** , P< 0 . 01; *** , P< 0 . 001 . | Amyotrophic lateral sclerosis ( ALS ) and frontotemporal lobar degeneration ( FTLD ) are two groups of common and devastating neurodegenerative diseases , characterized by losses of selected groups of neurons . Mutations in the FUS gene have been associated with ALS , whereas inclusion bodies containing the FUS protein have been discovered in both ALS and FTLD patients . However , the underlying pathogenic mechanisms of FUS in these diseases remain unclear . Here , we demonstrate that wild-type or ALS-associated mutant FUS can interact with mitochondrial chaperonin HSP60 and that HSP60 mediates FUS localization to mitochondria , leading to mitochondrial damage . Mitochondrial impairment may be an early event in FUS proteinopathies and represent a potential therapeutic target for treating these fatal diseases . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | FUS Interacts with HSP60 to Promote Mitochondrial Damage |
Aedes aegypti is the primary vector of dengue fever , a viral disease which has an estimated incidence of 390 million infections annually . Conventional vector control methods have been unable to curb the transmission of the disease . We have previously reported a novel method of vector control using a tetracycline repressible self-limiting strain of Ae . aegypti OX513A which has achieved >90% suppression of wild populations . We investigated the impact of tetracycline and its analogues on the phenotype of OX513A from the perspective of possible routes and levels of environmental exposure . We determined the minimum concentration of tetracycline and its analogues that will allow an increased survivorship and found these to be greater than the maximum concentration of tetracyclines found in known Ae . aegypti breeding sites and their surrounding areas . Furthermore , we determined that OX513A parents fed tetracycline are unable to pre-load their progeny with sufficient antidote to increase their survivorship . Finally , we studied the changes in concentration of tetracycline in the mass production rearing water of OX513A and the developing insect . Together , these studies demonstrate that potential routes of exposure of OX513A individuals to tetracycline and its analogues in the environment are not expected to increase the survivorship of OX513A .
Dengue fever is a viral infection , primarily transmitted by the mosquito Aedes aegypti , with an estimated incidence of 390 million infections annually [1] . There are currently no licensed vaccines [2 , 3] so control of the disease is focused on suppression of the vector . Commonly employed vector control techniques such as larval site removal , use of larvicides and insecticide fogging have been unable to provide satisfactory control of Ae . aegypti and dengue transmission . Previously we have reported a novel method of vector control based on the sterile insect technique ( SIT ) [4–6] . In this approach , males homozygous for a tetracycline repressible transgene , which confers the self-limiting trait to progeny are released into the environment where they search for and mate with wild females . Offspring of these males inherit a single copy of the transgene and , lacking adequate exposure to the tetracycline repressor , >95% die before becoming adults and potential vectors of blood borne diseases . Using the OX513A strain of Ae . aegypti , this method resulted in the suppression of the wild Ae . aegypti population in East End , Grand Cayman [5] and in Itaberaba , Brazil . To enable the breeding of OX513A individuals in the laboratory , the self-limiting phenotype of the transgene is repressed by the provision of tetracycline in the larval rearing water . In the absence of tetracycline , the transcriptional transactivator , tTAV ( a synthetic sequence comprising a tetracycline binding domain and transactivator ) , binds to its DNA binding site , tetO ( tetracycline operator ) , to drive high levels of expression of tTAV [4] ( Fig 1A ) . The production of tTAV results in over expression of this protein via a positive feedback loop; intracellular accumulation of tTAV becomes cytotoxic , eventually leading to the organism´s death [4 , 7] . However , if OX513A larvae are reared in water containing tetracycline at sufficient concentrations , the tetracycline binds to tTAV causing a conformational change , preventing its binding to tetO and stopping establishment of the self-limiting positive feedback loop ( Fig 1B ) [8] . For this system to work as intended , the self-limiting phenotype must be sufficiently repressed by tetracycline provided during rearing in contained conditions . Conversely , for field effectiveness , the self-limiting phenotype must not be substantially repressed by tetracycline at doses found in the larval habitats of this mosquito . Here we look in detail at the interaction of OX513A with tetracycline ( s ) and the impact on phenotype . Tetracyclines are a family of broad-spectrum antibiotics . They are well known in human therapy and , owing to their low cost , are used in animal production as therapeutics , prophylactics and growth promoters , although many countries have banned this use [9 , 10] . Agricultural use in particular can result in significant quantities being excreted , ending up in sewage systems or in manure [10] . However , although tetracyclines can be adsorbed to certain soils [11] , they are highly susceptible to photolysis and hydrolysis giving some tetracyclines half-lives of only a few hours , limiting their environmental load [12] . Fortunately , there is considerable interest in antibiotic load in the environment and numerous surveys have been conducted: for tetracyclines these focus primarily on tetracycline , oxytetracycline , chlortetracycline and doxycycline . A survey of the literature found maximum reported concentrations from field sites around the world as follows; tetracycline 0 . 096 ng mL-1 to 1 . 3 ng mL-1 ( e . g . [13–17] ) chlortetracycline 0 . 04 ng mL-1 to 0 . 97 ng mL-1 [18 , 19] , oxytetracycline 0 . 7 ng mL-1 to 1 . 34 ng mL-1 [20 , 21] and doxycycline 0 . 07 ng mL-1 to 0 . 4 ng mL-1 [14 , 20] . These data have largely been generated from examination of tetracycline compound levels in input and output flows from wastewater treatment plants , where they are expected to reach particularly high concentrations as a result of excretion during therapeutic use , along with the testing of the efficiency of removal of tetracyclines during the treatment of the wastewater . However these environments are not typical Ae . aegypti larval habitats which include artificial containers such as used car tyres , flower vases , water storage vessels and discarded materials in the domestic and peri-domestic environment . Reports have also been made of Ae . aegypti breeding in septic tanks [22] and brackish waters [23] , especially where covers are broken or cracked but these are regarded as unusual . Given that tetracyclines are present in the environment , albeit at low levels , it is important to understand the possible routes of exposure of OX513A individuals to these tetracyclines and the impact that such exposure could have on its phenotype , especially on the expression of the self-limiting trait . We have therefore identified potential routes of exposure of OX513A to environmental sources of tetracyclines; these include larval breeding sites and human and veterinary patients , where potential environmental concentrations of tetracyclines have been explored and have also investigated the impact of such exposure at relevant concentrations on the phenotype of OX513A .
OX513A is a tetracycline repressible bi-sex lethal strain of Aedes aegypti [24] . The wild type ( WT ) strain used is a non-transgenic version of the genetic background of OX513A . All insects were reared at 27°C [+/- 1°C] , 70% [+/- 10%] relative humidity , 12h: 12h light: dark cycle . Unless stated otherwise , five cohorts of 200 larvae for each treatment were reared at 1 larva mL-1 in 16 oz pots ( Robertson Packaging , UK SICC65 ) and fed a standard regimen of finely ground Tetramin fish flakes ( Tetra GmbH , Germany ) . Live pupae were counted and placed into cages ( 15 cm x 15 cm x 15 cm , Bugdorm-Megaview , Taiwan ) . Dead larvae and dead pupae were counted and discarded . Adults were provided with 10% sucrose solution ad libitum . Adult cages were assessed three days after the last pupa was added . Assessment included counting the total number of dead pupae , non-viable adults ( dead adults on the water , dead adults on the floor of the cage and non-flying adults ) and functional adults ( flying adults ) . OX513A homozygous larvae were reared in the presence of 30 μg mL-1 chlortetracycline ( the standard tetracycline supplement used in rearing ) ; WT larvae were reared at a similar density in the absence of tetracycline . Larvae were fed ad libitum . Unless otherwise stated , OX513A males were outcrossed to WT females . Adults were blood fed using defibrinated horse blood ( TCS Biosciences Ltd . , UK ) . Heterozygous eggs were collected and used for the following experiments . An initial tetracycline dose response curve was produced using chlortetracycline hydrochloride ( Sigma-Aldrich , UK ) . Following this , tetracycline , oxytetracycline and doxycycline were tested ( Sigma-Aldrich , UK ) . In all of these experiments heterozygous OX513A larvae were reared in the presence of different concentrations of chlortetracycline , tetracycline , oxytetracycline or doxycycline . As a control , OX513A were also reared in parallel in the absence of tetracycline or its analogues . Preliminary experiments were used to establish the appropriate dose range for each compound . Raw data are presented in S1 Table . Samples were collected at life stages between eggs and pupae ( Fig 2 ) . For each sampling point , 5 repeats of 0 . 5 g of the relevant mosquito life-stage were collected along with 5 repeats of 1 . 5 mL of the associated rearing water . To sample L1/L2 larvae ( 1 day post hatching ) OX513A eggs were hatched and the larvae reared for 24 hours in the absence of tetracycline after which the larvae and the rearing water samples were collected . For samples of L3 , L3/L4 and pupae , OX513A eggs were hatched and the larvae reared at the mass rearing density of 3 larvae mL-1 in 1 L of water supplemented on day one post hatching with chlortetracycline ( Sigma-Aldrich , UK ) to a final concentration of 30 μg mL-1 . The samples were stored at -20°C before being shipped on dry ice to CEM Analytical Services ( Bracknell , UK ) for analysis . Samples were analysed in accordance with the Association of Analytical Communities ( AOAC ) methodology for analysis of tetracycline in edible animal tissues ( AOAC method 995 . 09 ) with the modification of LC-MS/MS ( liquid chromatography with positive-ion electro-spray tandem mass spectrometry ) for the final quantitation rather than UV , along with a recovery sample . Residues of chlortetracycline were extracted from the specimens by homogenizing in EDTA solution ( pH 4 ) . Two further extractions were combined with the first extract , filtered and the sample further purified using a C18 , solid phase extraction cartridge . After conditioning the cartridge , the total volume of extract was percolated through the cartridge . After washing , the chlortetracycline was eluted using a methanolic oxalic acid solution and the final extract diluted with water . The samples were analysed by LC-MS/MS , using the transition 479 . 2 > 444 . 1 amu with a 0 . 1% formic acid in water/methanol mobile phase and a 250 x 4 . 6 mm C8 , 5 μm column . Raw data are presented in S2 Table . OX513A homozygous larvae were reared at <1 larvae mL-1 in the presence of 30 μg mL-1 chlortetracycline and WT larvae were reared in the absence of tetracycline . Adults were placed into cages as follows; OX513A♂ crossed to WT♀ and OX513A♀ crossed to WT♂ . Adult cages were assigned to one of three chlortetracycline treatments; 50 μg mL-1 chlortetracycline sugar water and 50 μg mL-1 chlortetracycline blood meal , 100 μg mL-1 chlortetracycline sugar water and 100 μg mL-1 chlortetracycline blood meal , or chlortetracycline free sugar water and chlortetracycline free blood meal ( not performed for OX513A♀ crossed to WT♂ ) ( Table 1 ) . Each chlortetracycline treatment had three repeats for each adult cross combination . Eggs were collected . Chlortetracycline concentrations were chosen to reflect levels well above the maximum recorded plasma levels following a therapeutic treatment with tetracyclines [26 , 27] . The heterozygous progeny from each treatment were reared according to the insect rearing method described above with six cohorts set for each group to assess the effect of parental chlortetracycline ingestion . Raw data are presented in S5 Table . Data were analysed using the RStudio software package version 0 . 97 . 318 ( RStudio , USA ) . Normality of data was tested using the Shapiro-Wilk method . Bioaccumulation of chlortetracycline in the biological materials was shown to be not normally distributed and therefore analysed using Wilcoxon Rank Sum . Dose response data was analysed for significant differences between concentrations using either a Student’s t-test if normally distributed or Wilcoxon Rank Sum if not normally distributed . The data was modelled using the Weibull model for chlortetracycline and a Log-linear model for the remaining antibiotics . These models were used to calculate the EC50 ( Effective Concentration50 ) with 95% confidence intervals . Chlortetracycline ingestion data were analysed using ANOVA for normally distributed data or a Kruskal-Wallis test for non-normally distributed data . Post-hoc analysis was carried out using the Nemenyi test , using the Coin and Multcomp R packages . For the on-tetracycline reared controls , differences in functional adults were tested using the Wilcoxon Rank Sum test .
OX513A larvae reared at chlortetracycline concentrations at or below 1 ng mL-1 did not give rise to a significantly greater percentage of functional adults than larvae reared in the absence of tetracycline ( 0 μg mL-1 ) ( t ( 18 ) = -1 . 36 , p = 0 . 19 ) . Functional adult numbers begin to deviate from the baseline when reared at 3 ng mL-1 ( t ( 13 ) = -3 . 49 p = 0 . 004 ) , therefore concentrations in excess of 1 ng mL-1 give rise to an increasing fraction of functional adults . Fig 2A shows the percentage of functional adults increasing with greater chlortetracycline concentrations , with the model predicting the maximum rescue plateau beginning to appear at 1 μg mL-1 . This indicates that concentrations at or slightly above 1 μg mL-1 will give rise to the maximum percentage of functional adults . For the other tetracycline compounds tested , concentrations at or below 3 ng mL-1 tetracycline , 10 ng mL-1 oxytetracycline and 0 . 1 ng mL-1 doxycycline , did not give rise to a significantly greater portion of functional adults than if reared in the absence of tetracycline ( Z = 6 . 5 , p = 0 . 48; Z = 3 , p = 0 . 64; Z = 1 . 5 , p = 0 . 27 , respectively ) ( Fig 2B ) . For all four tetracyclines , increasing tetracycline concentrations resulted in incremental shifts towards increased survivorship of OX513A individuals ( Fig 2A and 2B ) . With reference to Fig 2A , as the chlortetracycline concentration increases to result in more functional adults than 0 μg mL-1 tetracycline , the sample population shifts towards a higher survivorship with an increasing fraction of non-viable adults and decreasing fraction of dead pupae . With further increases in the chlortetracycline concentration , the non-viable adult fraction drops and the population shifts to eclosing as functional adults . This demonstrates that increasing tetracycline concentrations have incremental rescue effects on OX513A resulting in an average increase in survivorship represented by an increased proportion of flying functional adults . The EC50 values ( half maximal effective concentration; the concentration which induces a response halfway between the baseline and maximum ) shown in Table 2 , demonstrate that doxycycline is the most effective analogue at rescuing the OX513A phenotype , being able to rescue individuals at a lower concentration than the other tetracyclines . Quantification of the changes in chlortetracycline within OX513A over the duration of rearing demonstrates that chlortetracycline is bio-accumulated to a concentration of 260 μg g-1eight times greater than the concentration initially present in the rearing water ( 30 μg mL-1 ) ( Fig 3 ) . The peak in bioaccumulation appeared at four days post hatching , when the larvae had reached the third instar ( L3 ) . After this point , the concentration of chlortetracycline in the biological samples decreased so that by the pupal stage there was no significant difference between the concentration in the pupae compared to the eggs or L1 larvae , which is before exposure to tetracycline ( Z = 29 . 5 p = 0 . 62 ) . Samples of male and female pupae were analysed separately for differences in bioaccumulation but no significant difference was found ( t ( 8 ) = 0 . 16 , p = 0 . 88 ) ; combined data are therefore presented in Fig 3A . The analysis of the associated larval rearing water indicates that the concentration of chlortetracycline decreased rapidly over the first four days of rearing from the starting concentration of 30 μg mL-1 to 8 . 4 μg mL-1 ( Fig 3B ) . This decrease correlates with the bioaccumulation of the chlortetracycline seen in the larvae , as well as some of the chlortetracycline likely being degraded by hydrolysis and photolysis . The concentration in the rearing water continued to decrease over the remaining rearing days until only 1 . 4 μg mL-1 remained by the initiation of pupation . This is 21 times lower than the starting chlortetracycline concentration . Water samples were collected from Ae . aegypti breeding sites to determine the environmental concentrations of tetracyclines that OX513A larvae are likely to encounter in the field . Water sampling sites were selected based on their potential to contain high concentrations of tetracyclines ( close to sewage plants or intensive livestock operations ) or for being an Ae . aegypti larval habitat , as determined by the presence of larvae . The concentration of each tetracycline tested; tetracycline , oxytetracycline and chlortetracycline , was below the limit of quantification for each of the field samples . The limit of detection was 1 . 0 pg mL-1 for tetracycline and chlortetracycline and 2 . 5 pg mL-1 for oxytetracycline . We investigated whether parental exposure to chlortetracycline could influence the survival of heterozygous OX513A offspring . This might occur if tetracycline-fed females loaded tetracycline in their eggs , for example . To provide maximal exposure to chlortetracycline , in all experimental groups both blood and sugar water were supplemented with chlortetracycline to 50 μg mL-1 or 100 μg mL-1 ( Table 1 ) . No significant difference was observed between the progeny of these groups ( groups A to E ) and those of the controls not provided with tetracycline ( group A ) in terms of the fraction of dead pupae ( F ( 4 , 25 ) = 2 . 44 , p = 0 . 07 ) , non-viable adults ( F ( 4 , 25 ) = 1 . 00 p = 0 . 43 ) or functional adults ( H ( 4 ) = 9 . 93 , p = 0 . 04 , followed by Nemenyi post-hoc analysis shows non-tetracycline-loaded control ( A ) vs experimental groups ( B , C , D and E ) p = ≥0 . 05 . ) . Where larvae were provided with chlortetracycline , provision of chlortetracycline to the parents did not further increase survival ( Z = 29 , p = 0 . 09 ) ( Fig 4 , group F compared to group G ) .
Owing to their use as human and veterinary therapeutics and prophylactics , tetracyclines are known to be present in the environment , albeit at low levels as they are subject to photolysis , hydrolysis and adsorption [11 , 12] . In the absence of tetracycline , the tTAV gene in the strain OX513A leads to a self-limiting phenotype , which can be rescued to near-normal viability by sufficient levels of a suitable tetracycline [4] . For OX513A to be used as an effective vector control tool , it is important to understand the concentrations of tetracyclines which will allow an increase in survival of OX513A relative to tetracycline-free conditions , and the possibility , if any , of exposure of Ae . aegypti to such concentrations in the field . Given that tetracyclines could be present in the environments where OX513A is used to supress wild Ae . aegypti populations , it is important to know the lowest concentrations of tetracyclines that would allow a greater than nominal fraction of functional adults to survive , as well as understanding the consequences if larvae were to encounter tetracyclines in the environment in terms of the numbers of potential functional females expected to emerge . This will help to inform on the efficiency and speed of suppression of the Ae . aegypti population . Models indicate that to have a significant impact on programme effectiveness , average OX513A heterozygote fitness would have to increase to about 10% of WT , which would be equivalent to 10% survival of L1s to functional adults , assuming such adults were fully fit [4] . We tested the effects of four tetracyclines reported to be found in the environment from their use in human therapy or agriculture on larval survival . We found that concentrations at or below 3 ng mL-1 tetracycline , 1 ng mL-1 chlortetracycline , 10 ng mL-1 oxytetracycline and 0 . 1 ng mL-1 doxycycline gave no significant increase in the survivorship of OX513A larvae , i . e . did not increase the proportion of functional adults . Full rescue of the OX513A individuals ( the maximum number surviving to functional adults ) was also shown in this data to require tetracycline compound concentrations that were 746 to 2500 times greater than the highest concentrations we found reported in the literature for environmental tetracyclines . In surveying the literature we found a few instances of reported environmental concentrations of doxycycline above the concentration which would allow a greater than the nominal fraction of OX513A larvae to develop to functional adults . However , such studies are often interested in the impact of treatment and removal of antibiotics in waste-water treatment plants ( e . g . [14 , 20] ) and their associated rivers , and find the maximal tetracycline compound concentrations to be in the sewage input to treatment plants . These environments are not the typical larval habitats of Ae . aegypti . This species is a ‘container breeding mosquito’; its preferred breeding sites are refuse , flower vases , water storage containers and other peri-domestic sites . Immature stages are found in clean , still water , not flowing river systems and are rarely found in collections of water in the ground such as earth drains [28–30] . Some reports have suggested that Ae . aegypti can breed in septic tanks , usually where they are cracked or broken [22 , 31] , but this tends to be in the clear water at the top of the tank whereas tetracyclines tend to bind to the sediments located at the bottom of the tanks [16 , 32] and are expected to represent a minority of breeding sites . As a consequence of the lack of relevant field data , we undertook field sampling of actual Ae . aegypti larval habitats and analysed the concentration of three tetracycline compounds . We found that for the tetracyclines assessed , the concentration was below the level of detection , which was in turn below the concentration necessary to allow any increase in survivorship of OX513A . Taken together , these data indicate that levels of environmental tetracyclines are unlikely to lead to a significant increase in the survival of the progeny of released OX513A males . For a suppression programme , OX513A males will be reared in a factory in the presence of a tetracycline compound either in , or close to , the area of male release . It is therefore important to understand how the mass production of OX513A males , which requires the use of tetracyclines , could affect the environmental concentrations of tetracyclines . Currently larvae are mass reared in the presence of excess chlortetracycline ( 30 μg mL-1 ) , but it was previously unknown how much of the chlortetracycline the larvae accumulated over the course of development and how much remained at the end of a rearing cycle . Here we have found that the OX513A larvae bioaccumulate the chlortetracycline to over 8 times the initial starting concentration . As a consequence of this process , and likely owing to some degree of photolysis and hydrolysis , the concentration of chlortetracycline in the rearing water at the end of the rearing cycle is 21 times lower than at the start . This is an important consideration in respect of potential effluent from mass rearing production . We estimate from these data that the spent water from field rearing for an operational suppression programme , for example one aimed to treat a human population of 10 million , would contain approximately 136 to 291 kg chlortetracycline per annum . Putting this figure in context , it is estimated that the USA in 2011 used over 5 million kg of tetracyclines in food-producing animals [33] and a further 113 , 832 kg in human therapy [34] . Consequently the estimated waste chlortetracycline from the production of OX513A males for a suppression programme constitutes a minute fraction of the total tetracyclines used in just one country . Finally we investigated the hypothesis that the very small percentage of co-released OX513A adult females [5] exposed to chlortetracycline in their diet could pass sufficient quantities through their eggs to allow their progeny an increased level of survival . Tetracyclines are administered in human therapy and as Ae . aegypti are highly anthropophilic , it could be envisaged that females , after a blood meal from a human ( or rarely an animal ) taking a therapeutic course of tetracyclines , could ‘pre-load’ their progeny with enough tetracycline compound to allow an increase in survival . In mammals the concentration of tetracycline compounds in the blood usually reaches a peak 2 to 6 hours following an oral or injected dose , and then gradually declines due to the body’s metabolic activity [26] . In both humans and livestock , the peak concentration of tetracyclines in blood ( plasma ) following a standard therapeutic dose normally remains below 10 μg ml-1 [26 , 27] . We found that feeding adult females 50 μg mL-1 or 100 μg mL-1 chlortetracycline in both blood and sugar water did not lead to increased survival of their progeny . The highest dose of chlortetracycline used in this study was 10-fold higher than the normal concentration found in the blood of humans or animals receiving usual therapeutic doses of tetracyclines , and 5-fold higher than the highest dose reported from any animal blood [27] . These data therefore disprove the hypothesis that a female can deliver enough tetracycline to her progeny to allow increased survival rates through biting a human or animal on a normal therapeutic dose of tetracycline . The results presented here analyse the interaction of tetracycline and its analogues with the OX513A phenotype and explore different scenarios by which OX513A individuals may be exposed to tetracyclines in the environment . Collectively , these data demonstrate that the low levels of tetracycline or its analogues likely to be encountered in the environment will not impact the efficacy or safety of using OX513A to control Ae . aegypti . | Dengue fever is spread by the mosquito Aedes aegypti and the most effective method to limit the spread of dengue is to reduce the mosquito population . We have previously reported a transgenic strain of Ae . aegypti which results in >90% population suppression: males , which do not transmit disease , are released into the field carrying a self-limiting gene to mate with wild females , passing on the self-limiting gene which causes >95% progeny to die before becoming vectors of disease . To be able to breed this mosquito in the laboratory an antidote , tetracycline , is used to suppress the effects of the transgene . Given that tetracyclines are commonly used in human and veterinary medicine , it is essential to consider whether sufficient tetracycline could be in the environment to prevent the effective use of this control method by allowing the female’s progeny ( from a mating between a released OX513A male and a wild female ) to survive . Here we have shown that the concentrations of tetracycline to which the mosquitoes will be exposed in the environment , both in breeding sites and in a blood-meal host are not high enough to influence the effectiveness of this control method . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Assessment of the Impact of Potential Tetracycline Exposure on the Phenotype of Aedes aegypti OX513A: Implications for Field Use |
Modes of sexual reproduction in eukaryotic organisms are extremely diverse . The human fungal pathogen Candida albicans undergoes a phenotypic switch from the white to the opaque phase in order to become mating-competent . In this study , we report that functionally- and morphologically-differentiated white and opaque cells show a coordinated behavior during mating . Although white cells are mating-incompetent , they can produce sexual pheromones when treated with pheromones of the opposite mating type or by physically interacting with opaque cells of the opposite mating type . In a co-culture system , pheromones released by white cells induce opaque cells to form mating projections , and facilitate both opposite- and same-sex mating of opaque cells . Deletion of genes encoding the pheromone precursor proteins and inactivation of the pheromone response signaling pathway ( Ste2-MAPK-Cph1 ) impair the promoting role of white cells ( MTLa ) in the sexual mating of opaque cells . White and opaque cells communicate via a paracrine pheromone signaling system , creating an environment conducive to sexual mating . This coordination between the two different cell types may be a trade-off strategy between sexual and asexual lifestyles in C . albicans .
Sexual reproduction is pervasive in eukaryotic organisms due to its propensity to permit genetic exchange , eliminate harmful mutations , and produce adaptive progeny to changing environments [1] , [2] . It has been demonstrated to be critical for environmental adaptation , morphological transitioning , and virulence of human fungal pathogens [3] , [4] . However , the evolutionary advantages of sexual over asexual reproduction in single-celled organisms are extremely complex when it comes to deconvoluting the interactions between host and pathogen [5]–[7] . For example , the three most frequently isolated human fungal pathogens – Cryptococccus neoformans , Aspergillus fumigatus and Candida albicans – have all maintained their mating machinery and are capable of undergoing sexual and/or parasexual reproduction , and yet their population structures appear to be largely clonal with little or no observable recombination [5]–[7] . It has been proposed that a balance between asexual and sexual reproduction may allow pathogenic fungi to generate clonal populations to thrive in their well-adapted environmental niches and to reproduce sexually and produce genetically diverse offspring in response to novel environmental pressures [6] . C . albicans has recently been shown to undergo opposite- and same-sex mating [8]–[10] . In this study , we demonstrate that morphological transitions play an important role in the control of sexual mating , and function to balance sexual and asexual lifestyles in C . albicans . This unique mode of sexual reproduction not only confers the fungus the ability to quickly adapt to the environment as a short-term strategy , but also provides a means to generate genetic diversity in response to unforeseen challenges during evolution over time . There are three configurations of the mating type locus ( MTLa/α , a/a and α/α ) in C . albicans . The majority of natural isolates are a/α at the mating type locus [11] . C . albicans can frequently undergo a transition between two distinct cell types: white and opaque [12] . To mate , C . albicans must first undergo a homozygosis at the mating type locus to become a/a and α/α , and then switch from the white to the opaque cell type [13]; only opaque cells can mate efficiently . Aside from mating-competency , white and opaque cells also differ in a number of other aspects , including global gene expression patterns , metabolic profiles , cellular appearances , and virulence properties in the host [12] , [14]– . The white cell type is thought to be the default state since white cells are more stable than opaque cells at the host physiological temperature ( 37°C ) and are also less vulnerable to stresses , antifungals and host immune system attacks [16]–[18] . Given that the white cell type is the default state and that the minority population of the opaque cell type is the only mating-competent form , one would hypothesize that mating in natural conditions would be rare . If this is the case , the many advantages of sexual reproduction over asexual reproduction in C . albicans would be very limited . This also raises an interesting question , that is , why does C . albicans undergo white-opaque switching , while still retaining such a costly sexual reproduction system ? The discovery by Daniels et al . ( 2006 ) of the ability of opaque cells to signal white cells to form biofilms provides a clue to answer this question [19] . White and opaque cells may coordinate to regulate pathogenesis and resistance to environmental stresses through the development of biofilms . Recently , Park et al . ( 2013 ) reported that biofilms formed by white cells facilitate opaque cell chemotropism and thus increase mating efficiency in C . albicans [20] . In addition , pheromone has been shown to up-regulate the expression of a number of mating-associated genes in mating-incompetent white cells [19] , [21] . In opaque cells ( MTLa/a ) , α-pheromone induces the expression of both MFA1 and MFα1 genes , which encode a- and α-pheromone precursors , respectively [21] , [22] . Alby et al . ( 2009 ) further demonstrated that the addition of extracellular pheromone released by α opaque cells can induce same-sex mating in opaque a cells of C . albicans [10] . They found that the Bar1 protease plays a critical role in unisexual reproduction by controlling the autocrine pheromone signaling pathway [10] . Interestingly , species of the basidiomycete fungus Cryptococcus can also undergo opposite- and same-sex mating [23] , [24] . Given that the population structures of these fungi are primarily clonal , unisexual reproduction may provide a long-term survival advantage , potentially increasing their ability to adapt to environmental changes . Here we demonstrate that the interaction of white and opaque cells activates a paracrine pheromone signaling pathway in C . albicans . We further show that white cells facilitate both opposite- and same-sex mating of opaque cells . Given that the white phenotype is the default state and that opaque cells are less stable and more vulnerable in the host , our study provides additional clues to understanding how sexual mating in this organism is regulated . We suggest that the two cell types of C . albicans coordinate in order to balance the organism's commensal , pathogenic and sexual lifestyles .
In several negative controls ( e . g . the “WT , wh a×WT , op α” cross ) performed in a mating assay , we observed that a very high proportion of opaque α cells formed mating projections , while the mating efficiency of the cross was extremely low ( Table S1 ) . One possible explanation for this low mating efficiency is that a small proportion of white a cells spontaneously switched to the opaque state to induce the formation of mating projections . To test this possibility , a mating assay of the cross of “wor1Δ/Δ , wh a×WT , op α” was performed and mating response was examined . The wor1Δ/Δ mutant is locked in the white phase because the white-opaque master regulator Wor1 is essential for opaque cell formation [25]–[27] . As shown in Table S1 , similar to the cross of “WT , wh a×WT , op α” , the mating efficiency of the cross of “wor1Δ/Δ , wh a×WT , op α” was also very low , while the proportion of opaque α cells with mating projections was over 75% . These results indicate that white a cells induce opaque α cells to form mating projections in C . albicans , but do not increase the mating efficiency of the cross between white cells and opaque cells . To further confirm this phenomenon , we tested the effect of white a cells of four strains with different genetic backgrounds on the induction of mating projection formation of opaque α cells . The assay was performed on nutrient solid agar ( Lee's glucose medium ) . As shown in Figure 1 , over 75% of opaque α cells formed mating projections in all of the mixed cultures containing white a cells of the wild type strains . Consistently , cells of the wor1Δ/Δ and wor2Δ/Δ mutants , which are “locked” in the white phase under this culture condition , also induced mating projection formation in opaque α cells ( Figure 1B ) . Opaque a cells served as a positive control , and white a/α cells and white α cells served as negative controls . As expected , opaque a cells induced mating projection formation in opaque α cells , while white a/α cells and white α cells did not . The images of single strain cultures and the ratios of opaque cells with mating projections are shown in Figure 1A and 1B , respectively . Consistently , white cells of another clinically independent WT a strain ( SZ306a , a/Δ ) and the wor1Δ/Δ mutant ( GH1248 , a/a ) also induced mating projection formation in opaque α cells when cultured in liquid medium ( Figure S1 ) . These results indicate that the induction of mating projections of opaque cells by white cells is a general feature of clinical isolates of C . albicans . We next examined the effect of the ratio of opaque α cells to white a cells in the mixed cultures on the formation of projections in opaque α cells . White a cells of the WT ( SZ306a ) , wor1Δ/Δ and wor2Δ/Δ mutants were tested . As shown in Figure S2 , the percentages of projections in opaque α cells were inversely related to the ratio of initial cell numbers of opaque α cells to white a cells added to the mixture . To ensure that the observed projections of the opaque cells were indeed mating projections , 4′-6-diamidino-2-phenylindole ( DAPI ) -DNA staining assays were performed . A single nucleus was observed by fluorescence microscopy in cells with newly formed projections ( Figure S3 ) . These results provide additional evidence that white a cells can induce mating projection formation in opaque α cells . MFα1 is constitutively expressed in opaque α cells examined over a 48-hour growth period ( Figure S4 ) [28]–[30] , while MFA1 is poorly expressed in opaque a cells [22] . When treated with α-pheromone , MFA1 is induced in opaque a cells [22] . We hypothesized that MFA1 may also be induced in white a cells upon addition of α-pheromone to the medium or through production by opaque α cells . If true , in a mixed culture of white a cells and opaque α cells , the α cells should form mating projections as a result of exposure to a-pheromone produced by white a cells . To test this hypothesis , two reporter strains ( SZ306MFA1p-GFP and wor1Δ/ΔMFA1p-GFP ) , in which a GFP coding sequence was integrated at the MFA1 locus and controlled by the MFA1 promoter , were constructed . As shown in Figure 2A , α-pheromone clearly induced MFA1 expression in a proportion of white cells of the two reporter strains as indicated by the GFP fluorescence . Opaque cells of the SZ306a-MFA1p-GFP strain treated with α-pheromone served as a positive control . In the mixed cultures of white a cells and opaque α cells ( Figure 2B ) , the expression of GFP in the two reporter strains was also clear . However , the GFP fluorescence was not observed in the single strain cultures . These results indicate that the presence of α-pheromone , either from its addition to the medium or produced by opaque α cells , is able to induce the expression of MFA1 in white a cells . MFα1 is constitutively expressed in opaque α cells , but not in white α cells ( Figure S4 and S5 ) . We next tested whether the expression of MFα1 could be induced in white α cells by a-pheromone . A MFαp-GFP reporter strain was constructed as described in the Materials and Methods section . Considering that the expression level of MFA1 is extremely low in opaque a cells in the absence of α-pheromone , opaque α cells were added to one of the mixed cultures to provide α-pheromone to the mixture . As shown in Figure S5 , the expression of MFα1 in white α cells was induced as indicated by the GFP fluorescence in the mixed cultures of both “white α cells + opaque a cells” and “white α cells + opaque a cells + opaque α cells” . We were surprised that MFα1 was induced in the former culture since the expression level of MFA1 in opaque a cells is extremely low in the absence of opaque α cells . There are two possible explanations for this finding . First , low levels of a-pheromone secreted by opaque a cells may have induced MFα1 expression in white α cells . Alternatively , a small proportion of white α cells may have spontaneously converted to the opaque form and induced the expression of MFA1 in opaque a cells , which in turn induced MFα1 expression in white α cells . Overall , these results suggest that white α cells can produce pheromone and may play a similar role as white a cells in promoting an environment conducive to mating . We hypothesized that α-pheromone produced by opaque α cells could induce MFA1 expression in white a cells . We , therefore , deleted the MFα1 gene , encoding the precursor protein of α-pheromone , in a WT α strain ( α/α , GH1617 ) . We found that opaque cells of the mfα1Δ/Δ mutant could not induce MFA1 expression in white a cells ( Figure S6 ) . Consistently , white a cells were unable to induce mating projection formation in opaque cells of the mfα1Δ/Δ mutant ( Figure S6 ) . To test whether a-pheromone is essential for the communication between white and opaque cells , we next deleted MFA1 in a WT a strain ( a/a , GH1609 ) . As shown in Figure 3 , white cells of the mfa1Δ/Δ mutant failed to induce mating projection formation in opaque α cells . These results indicate that pheromones act as signaling molecules in the interaction between white and opaque cells . The pheromone receptors ( Ste2 and Ste3 ) and the downstream MAPK pathway are highly conserved in the regulation of sexual mating in fungi [5] , [31] , [32] . In Saccharomyces cerevisiae , Cryptococcus neoformans , and C . albicans , the MAPK pathway governs both mating and filamentation [33]–[36] . The downstream transcription factor Cph1 in C . albicans , a homolog of Ste12 in Saccharomyces cerevisiae , is also essential for mating [37] , [38] . The Ste2/3-MAPK-Cph1 pathway is involved in pheromone response in both white and opaque cells of C . albicans [39] , [40] . Given this information , we examined the role of this pathway in the interactions between white and opaque cells . As shown in Figure 3 , deletion of genes ( STE2 , STE11 , HST7 and CPH1 ) of the α-pheromone response pathway in white a cells blocked the induction of mating projection formation by opaque α cells . The morphological images of single strain cultures and the ratios of projected opaque α cells are presented in Figure 3A and 3B , respectively . These results suggest that both the α-pheromone response pathway and a-pheromone are required for white a cells to induce mating projection formation in opaque α cells . The Ste3-MAPK-Cph1 pathway is required for mating of opaque cells [22] , [38] , [41] and is essential for pheromone-induced biofilm formation [39] . We next tested the ability of the ste3Δ/Δ , cph1Δ/Δ and cek1Δ/Δ cek2Δ/Δ opaque α cell mutants to form mating projections when co-cultured with white a cells . As shown in Figure 4 , opaque cells of these three mutants failed to form mating projections , while over 80% of opaque cells of the WT control formed mating projections . Sexual pheromones are essential for mating in C . albicans . We next tested whether white a cells could facilitate mating of opaque cells by producing a-pheromone , causing an increase in pheromone concentration to the level required to activate the mating signaling process . We designed an opposite-sex mating system , which contained 1×104 opaque a cells , 3 . 2×106 opaque α cells and 4 . 8×106 “helper” white cells . The system was so designed for the following reason . Mating between opaque a and α cells in the presence of high cell densities may increase mating efficiency . This system should amplify the promoting function of white cells by using less opaque a cells in order to reduce the high mating efficiency between opaque cells . White cells of the a/α strain ( BWP17 ) , wor1Δ/Δ ( a/a ) , mfa1Δ/Δ ( a/a ) , and wor1Δ/Δ mfa1Δ/Δ ( a/a ) mutants served as the “helper” white cells in the mating system . The wor1Δ/Δ mutant was used for this experiment because cells of this strain are “locked” in the white phase under all conditions tested [25]–[27] . As shown in Table 1 , the mating efficiency of the cross when the wor1Δ/Δ ( a/a ) mutant served as the “helper” was about six-fold higher than that of the other three crosses with the a/α strain , mfa1Δ/Δ , and wor1Δ/Δ mfa1Δ/Δ mutants as the “helpers” . To further verify these results , we tested the roles of white cells of the wor1Δ/Δ mutants in facilitating opposite-sex mating of opaque cells in two additional genetic backgrounds ( derivatives of SZ306 and SN152 ) . Consistently , compared with the a/α strains ( the WT and wor1Δ/Δ mutant ) , white a cells of the wor1Δ/Δ mutant ( a/Δ ) increased mating efficiencies by six- to nine-fold ( Table 1 ) . Consistently , deletion of MFA1 in white cells blocked this facilitation role in opposite-sex mating of opaque cells ( Table 1 , mfa1Δ/Δ and wor1Δ/Δ mfa1Δ/Δ mutants ) . To further demonstrate that white a cells are able to facilitate opposite-sex mating of opaque cells by producing a-pheromone , we designed another mating system . We deleted the MFA1 gene in opaque cells of the “MTLa” mating partner ( mfa1Δ/Δ , a/a ) , resulting in the failure to produce a-pheromone . White cells of the a/α strains ( the WT and wor1Δ/Δ mutant ) and the a strain ( wor1Δ/Δ , a/Δ ) served as the “helpers” in the mating cross of the “a-op ( mfa1Δ/Δ , a/a ) ×α-op ( WT , GH1349 , α/α ) ” . Strains of two different genetic backgrounds ( SZ306 and SN152 ) were also used as “helpers” . As shown in Table 2 , mating of the “a-op ( mfa1Δ/Δ , a/a ) ×α-op ( WT , GH1349 , α/α ) ” cross only occurred in the presence of white a cells . These results indicate that white a cells facilitate opposite-sex mating of opaque cells by producing a-pheromone . Opaque cells of C . albicans can undergo same-sex mating in the presence of the opposite mating pheromone [10] . We , therefore , predicted that white a cells could facilitate same-sex mating of opaque α cells by producing a-pheromone in a co-cultured mating system . As shown in Table 2 , opaque α cells of SZ306α and GH1349 ( α/α ) were unable to mate when white cells of the WTa/α ( mating efficiency <5×10−9 ) or wor1Δ/Δ ( a/α ) mutant ( mating efficiency <4×10−9 ) served as the “helper” strains . However , the mating efficiency was increased to ( 2 . 7±0 . 5 ) ×10−7 ( over 54 fold ) when white a cells of the wor1Δ/Δ mutant ( a/Δ , a derivative of SZ306 ) served as the “helper” strain . To further validate these results in another genetic background , we performed the same mating assays using white cells of the SN152 background strain as the “helpers” . Consistently , compared with the controls ( when a/α cells severed as “helpers” ) , the mating efficiency was dramatically increased when white a cells of SN152a ( over 450-fold increase ) or the wor1Δ/Δ mutant ( a/Δ , a derivative of SN152 , over 220-fold increase ) served as the “helpers” ( Table 2 ) . Consistently , in the absence of white α cells , same-sex mating was unable to occur in opaque a cells ( Figure S5 ) , suggesting that white α cells are also capable of promoting same-sex mating of opaque a cells . To evaluate the in vivo relevance of our findings , we next tested whether white cells could facilitate opaque cell mating in a mammalian host . As shown in Figure 5 , white a cells of the WT strains or the wor1Δ/Δ mutant strain induced the development of mating projections in opaque α cells . However , in the absence of white a cells or in the presence of white a/α cells , opaque α cells were unable to develop mating projections on the mouse skin . Quantitative mating assays also demonstrated that white a cells promoted opaque cell mating in this mouse skin infection model ( Table S2 ) . These results demonstrate that white cells are capable of facilitating opaque cell mating in a natural environmental niche . Although white cells can be induced to produce pheromone , they are unable to mate ( Table S1 ) , suggesting that the pheromone response pathway of white cells is different from that of opaque cells . To explore how white cells respond to pheromone and how white cells create an environment conducive for opaque cell mating , we performed RNA-Seq analysis to investigate the global gene expression profile in white a cells in response to α-pheromone . Although gene expression profiling of white cells in response to pheromone have been previously published [21] , [28] , [42] , these studies used white cells of wild type strains , which are opaque-competent ( able to spontaneously switch from the white to the opaque phase ) . Because these strains were switching competent , a small proportion of opaque cells in the population could confound the results . In order to mitigate the effects of switching on the gene expression profile in response to pheromone treatments , we used the wor1Δ/Δ mutant ( GH1602 ) , which is “locked” in the white phase [25]–[27] , for our RNA-seq experiment We believe that this dataset improves and strengthens the already published datasets , and more accurately reflects the effects of pheromone treatment on white cells exclusively . As shown in Table 3 , 75 genes involved in a number of biological aspects were up-regulated and 124 genes were down-regulated in the presence of α-pheromone ( using a two-fold cutoff ) . Our key findings are summarized below: ( i ) We observed differential expression in a subset of the mating-related or pheromone receptor-MAPK signaling pathway genes , including MFA1 , HST6 , FAV1 , and STE2 . Consistent with our quantitative real-time PCR ( Q-RT-PCR ) and MFA1p-GFP reporter results ( Figure 2 and S7 ) , the expression of the MFA1 gene was up-regulated hundreds of fold when treated with α-pheromone . Q-RT-PCR assays were performed to verify the expression levels of MFA1 , STE2 , and STE3 , in white cells of the WT a/α , WT a , and wor1Δ/Δ mutant strains , as well as opaque a cells ( Figure S7 and S8 ) . ( ii ) A number of mating-related genes up-regulated by pheromone in opaque cells were not up-regulated in white cells [21] , [28] , [42] . These genes include FIG1 , FUS1 , CEK1 , CEK2 , FAR1 , CPH1 , and HST6 . This result suggests that the mating and cell fusion pathways are not fully activated by pheromone in white cells as is the case in opaque cells , and provides an explanation as to why white cells are unable to mate . ( iii ) We observed a reduction in metabolism-related genes in the presence of α-pheromone , especially for nucleotide , lipid and fatty acid metabolism as well as for genes encoding ribosomal proteins and transporters . ( iv ) We also found that genes encoding cell surface proteins , which are involved in cohesion , adhesion , and biofilm formation , were differentially regulated by α-pheromone . We validated the expression levels of eight genes using quantitative RT-PCR assays ( Figure S8C ) . Some pheromone-regulated genes observed in our study were also identified in previous studies performed in different strain backgrounds [19] , [21] , [42] . A detailed functional categorization and description of the differentially expressed genes in response to pheromone are presented in Figure S8 and Table S3 .
White and opaque cells of C . albicans are two distinct cell types differing in a number of biological aspects [14] , [16] , [18] . Given that only opaque cells are mating-competent [13] and that white cells are the majority population in nature [18] , the relationship between white-opaque transitions and sexual mating in C . albicans is extremely complex . These facts also raise several intriguing questions . Why is the white-opaque switch required for mating in C . albicans ? What roles do white cells play in the process of sexual reproduction ? How do white and opaque cells communicate ? The discovery of pheromone-induced biofilm formation in white cells of C . albicans [19] , provides some intriguing clues to address these questions . It was suggested that biofilm formation by MTL-homozygous white cells in turn facilitate opaque cell mating [19] , [20] . The white cell biofilm ( or “sexual biofilm” ) formed by MTL-homozygous white cells is distinct from that formed by MTL-heterozygous ( a/α ) cells . For example , the former was reported to be more permeable than the latter and to form gradients of pheromone for chemotropism [40] . In this study , we provide additional evidence for the evolution of coordination between white and opaque cells during sexual mating in C . albicans . We demonstrate that opaque cells can induce mating-incompetent white cells to secrete pheromone ( Figure 2 and S5 ) . Consistent with our data , Lin et al . recently reported that the expression level of MFA1 in white a cells was increased ∼475 fold upon treatment with α-pheromone [42] . We note that the studies by Yi et al . [39] and Sanhi et al . [43] demonstrate that the expression of MFA1 in white a cells remains unchanged in response to α-pheromone . As discussed in a recent review article [44] , this discrepancy may be due to differences in laboratory growth conditions . In a system where white and opaque cells co-exist , pheromone signaling leads to the formation of a positive feedback loop , promoting the occurrence of opposite- and same-sex mating . Example scenarios of white and opaque cells co-existing , and the functional consequences of these interactions are summarized in Figure 6A and 6B , respectively . As shown in Figure 6B , opaque α cells constitutively secrete α-pheromone , which activates the pheromone response signaling pathway ( Ste2-MAPK-Cph1 ) of white a cells . “Activated” white a cells are then induced to produce a-pheromone , which in turn activates the pheromone response signaling pathway ( Ste3-MAPK-Cph1 ) and induces mating projection formation of opaque α cells . Of note , the expression of MFA1 is extremely low , even in opaque a cells , although it can be enhanced by treatment of the opaque a cells with α-pheromone ( Figure 2 and [22] ) . This positive feedback loop for pheromone response is widely conserved in other yeasts . It is known that α cells can induce a-pheromone secretion of a cells in Saccharomyces cerevisiae [45] . Nielsen and coworkers reported that mating pheromone also triggers a positive feedback response in the fission yeast Schizosaccharomyces pombe [46] . This positive feedback loop for pheromone response is , therefore , a general feature in yeast species . Since sexual mating in C . albicans is directed by the pheromone-mediated signaling pathway , it is perhaps not surprising that pheromone released by white cells is able to facilitate opaque cell mating by increasing the levels of extracellular pheromone . This is the case for both opposite- and same-sex mating of opaque cells ( Tables 1 and 2 ) . Given that the white phase is the default state , opaque cells are likely to be the minority in a natural population . In such a situation , mating between opaque cells would be rare because the concentration of pheromone produced by opaque cells would not reach the threshold required for activating the mating signaling process . Moreover , low pheromone levels do not arrest opaque cells in the G1 phase of the cell cycle [21] , [47] , which is a prerequisite for mating in C . albicans . In the presence of pheromone-secreting white a or α cells , the general pheromone level of the population may be increased and thus opaque cell mating could become possible . In the absence of opposite MTL type cells , same-sex mating is unable to occur due to the absence of the opposite mating type pheromone . In Figure 7 , we propose a model depicting how white cells could facilitate same-sex mating of opaque cells under natural conditions . In response to α-pheromone released by opaque α cells , white a cells secrete a-pheromone and thus promote same-sex mating of opaque α cells ( Figure 7A ) . In the absence of white a cells , same-sex mating of opaque cells could not occur ( Figure 7B and 7C ) . Our experiments were performed on colonies on plates and on planktonic liquid cultures . We believe that both of these culture conditions are relevant for commensal and pathogenic lifestyles in C . albicans . Colonies represent an architecturally structured community , where cells exist in close proximity to one another , while the planktonic state is the state that cells exist in during a disseminated bloodstream infection . It was suggested that C . albicans can use different strategies to increase mating efficiency [20] . Alby and Bennett ( 2011 ) recently reported that interspecies pheromone signaling can promote same-sex mating in C . albicans [48] . This is interesting because C . albicans is often present with other microbiome members within the host , including other fungal , bacterial , and archaeal species . Therefore , to mate efficiently , opaque cells likely take advantage of a number of different strategies and may utilize a multitude of environmental signals to communicate in natural environments . Sexual reproduction has many adaptive benefits over asexual reproduction in eukaryotic organisms . However , sexual reproduction is also an extremely costly process in terms of energy expenditure . How does C . albicans balance these reproductive strategies to better adapt to the changing host micro-environments , and increase its fitness during evolution over time in the host ? The discovery of phenotypic switching may provide some clues to address this question . Differentiated white and opaque cells of C . albicans play specialized roles in these processes . Mating machinery can be simply shut down or activated through phenotypic transitions . Inducing expression of pheromone in mating-incompetent white cells and opaque a cells , may not only serve to save energy , but could also serve to promote sexual mating when there is a need for it . We believe that the existence of this cell type heterogeneity , creating , in a sense , a “labor division , ” amongst the population , in addition to the multicellular coordination between the white and opaque cell populations , may be the primary reasons as to why this fungus is so successful at surviving and thriving in the human host as both a commensal and pathogen .
The strains used in this study are listed in Table S4 . All strains used are derivatives of the following independent clinical isolates: SC5314 , WO-1 , SZ306 , and P37005 . All strains used in this study are diploid . In Figures and Tables , MTLa ( or “a” ) and MTLα ( or “α” ) indicate the mating type locus is a/a ( or a/Δ ) and α/α ( or “Δ/α” ) , respectively . Modified Lee's glucose medium [49] was used for routine culture of C . albicans cells and for mating projection formation and mating assays . Construction of strains: A 14-mer α-pheromone peptide ( GFRLTNFGYFEPGK ) of C . albicans was chemically synthesized . Cells of C . albicans were first grown in liquid media at 25°C for 36 hours to stationary phase and then inoculated into fresh Lee's glucose medium ( 1×107 cells/ml ) for pheromone treatment assays . α-Pheromone peptide was added to the cultures every two hours after inoculation over an eight-hour period of growth . The final concentration of α-pheromone in the cultures was 8×10−6 M . 4×106 opaque cells were mixed with equal number of white cells of different background . The mixtures were spotted onto Lee's glucose medium plates and cultured at 25°C in air . After a 24-hour incubation period , cells were examined under a microscope and the ratio of “projected” opaque cells in each mixture was calculated . Opposite-sex mating assays were performed according to our previous publication with modifications [50] . Briefly , 4 . 8×106 of “helper” white cells of different background were added to the mating mixture ( 1×104 opaque a cells plus 3 . 2×106 opaque α cells ) . The mating mixtures were spotted onto Lee's glucose medium plates and cultured at 25°C for two to five days as indicated in the table legends . The mating mixtures were replated onto SD-histidine-uridine , SD-arginine-uridine and SD-uridine-arginine-histidine media for prototrophic selection growth . All the “helper” strains are ura3Δ/Δ mutants and could not grow on media without uridine . The opposite-sex mating assay of “a-op ( mfa1Δ/Δ ) ×α-op” cross: 9 . 6×107 white cells of “helper” strains ( 60% ) were mixed with 3 . 2×107 opaque a cells of GH1013 ( a/a , ura3Δ/Δ , mfa1Δ/Δ , 20% ) and 3 . 2×107 opaque cells of GH1349 ( α/α , arg4Δ/Δ , 20% ) . The mating mixtures were spotted onto Lee's glucose medium plates and cultured at 25°C for five days . Then , mixed cells were replated onto SD-uridine and SD-arginine-uridine media for prototrophic selection growth . The same-sex mating assay for the “α-op×α-op” cross: 9 . 6×107 white cells of “helper” strains ( 60% ) were mixed with 3 . 2×107 opaque cells of SZ306α ( Δ/α , ura3Δ/Δ , 20% ) and 3 . 2×107 opaque cells of GH1349 ( α/α , arg4Δ/Δ , 20% ) . The mating mixtures were spotted onto Lee's glucose medium plates and cultured at 25°C for five days . Then , mixed cells were replated onto SD-uridine and SD-arginine-uridine media for prototrophic selection growth . PCR of the MTLa1 and α2 was used to confirm the tetraploid colonies of “α-op×α-op” fusion and to exclude possible tetraploid α/a colonies due to the low-frequency of the “α-op×a-wh” fusion . The same-sex mating assay for the “a-op×a-op” cross ( Figure S5C ) . Opaque cells of GH1013h ( a/a , his1Δ/Δ ) were first grown in liquid Lee's glucose medium at 25°C for 24 h . Cells were then harvested and resuspended in fresh Lee's glucose medium ( 2×108 cells/ml ) containing 10−4 M of α-pheromone peptide and incubated at 25°C for an eight-hour period of growth . 9 . 6×107 white cells of “helper” strains ( 60% ) were mixed with 3 . 2×107 α-pheromone-treated opaque cells of GH1013h ( a/a , his1Δ/Δ , 20% ) and 3 . 2×107 opaque cells of SZ306u-a ( a/Δ , ura3Δ/Δ , 20% ) . The mixture of opaque “a” cells ( GH1013h ) and opaque “a” cells ( SZ306u-a ) served as a negative control . The mating mixtures were spotted onto Lee's glucose medium plates and cultured at 25°C for four days . Then , mixed cells were replated onto SD-uridine and SD-histidine-uridine media for prototrophic selection growth . PCR of the MTLa1 and α2 was used to verify the tetraploid colonies of “a-op×a-op” fusion and to exclude possible tetraploid a/α colonies due to the low-frequency of “a-op×α-wh” mating . Cells were first grown in Lee's glucose liquid medium at 25°C for 24 hours and inoculated into fresh Lee's glucose medium ( 1×107 cells/ml ) . α-Pheromone peptide was added to the cultures every 8 hours over a 24 hours period . The final concentration of α-pheromone in the culture was 1 . 6×10−5 M . Cells were then collected and total RNA was extracted for RNA-Seq analysis and quantitative PCR assays . RNA-Seq analysis was performed by the company BGI-Shenzhen according to the company's protocol [55] . Approximately 10 million ( M ) reads were obtained by sequencing each library . The library products were sequenced using the Illumina HiSeq 2000 . Illumina OLB_1 . 9 . 4 software was used for base-calling . The raw reads were filtered by removing the adapter and low quality reads ( the percentage of low quality bases with a quality value ≤5 , >50% in a read ) . Clean reads were mapped to the genome of C . albicans SC5314 using SOAP aligner/soap2 software ( version 2 . 21 ) [56] . The more complete and detailed RNA-seq dataset has been deposited into the NCBI Gene Expression Omnibus ( GEO ) portal ( Accession number: GSE56039 ) . Q-RT-PCR assays were performed to verify the relative gene expression levels of pheromone-treated and untreated samples . Quantitative PCR was performed according to our previous publication with modifications [57] . Briefly , 0 . 6 µg of total RNA per sample were used to synthesize cDNA with RevertAid H Minus Reverse Transcriptase ( Thermo Scientific ) . Quantification of transcripts was performed in Bio-Rad CFX96 real-time PCR detection system using SYBR green . The signal from each experimental sample was normalized to expression of the ACT1 gene . Skin infection assays were performed as described previously [57] . Newborn ICR mice ( 2 to 4 days old ) were used . In vivo skin mating assay of the “a-op×α-op” cross: 1 . 2×108 white cells of “helper” strains ( ∼60% ) were mixed with 2 . 5×105 opaque cells of GH1013h ( a/a , his1Δ/Δ ) and 8×107 opaque cells of GH1349 ( α/α , arg4Δ/Δ , ∼40% ) . The mixture of opaque “a” cells ( GH1013h ) and opaque “α” cells ( GH1349 ) served as a control . The mating mixtures were spotted onto the skin on the back of a newborn mouse . After water evaporated , a small sterile filter paper with First Aid tape was used to cover the area of the fungal spot . After two days of infection , C . albicans cells colonized on mouse skin were washed with PBS and plated onto SD-arginine-histidine-uridine and SD-histidine-uridine media for prototrophic selection growth . SEM assays . 1×107 white “helper” cells were mixed with 1×107 opaque “α” cells . The mixtures were used for the skin infection . The infection method was similar to that of the quantitative mating assay . After 24 h of infection , the infected skin areas with C . albicans cells were excised for SEM assays according to our previous protocols [57] . All animal experiments were performed according to the guidelines approved by the Animal Care and Use Committee of the Institute of Microbiology , Chinese Academy of Sciences . The present study was approved by the Committee . | In eukaryotic organisms , cells often undergo differentiation into distinct cell types in order to fulfill specialized roles . To achieve a certain function , different cell types may behave coordinately to complete a task that they may otherwise be incapable of completing independently . The human fungal pathogen Candida albicans can exist as two functionally and morphologically distinct cell types: white and opaque . The white cell type is thought to be the default state and may be the majority cell population in nature . However , only the minority opaque cells are mating-competent . In this study , we report that white and opaque cells show a coordinated behavior in the process of mating . When in the presence of opaque cells with an opposite mating type , white cells release sexual pheromones , and thus create an environment conducive for both opposite- and same-sex mating of opaque cells . The two cell types communicate via a paracrine pheromone signaling system . We propose that this communal coordination between white and opaque cells may not only support the fungus to be a successful commensal and pathogen in the host , but may also increase the fitness of the fungus during evolution over time . | [
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] | 2014 | White Cells Facilitate Opposite- and Same-Sex Mating of Opaque Cells in Candida albicans |
Antisense transcription is a pervasive phenomenon , but its source and functional significance is largely unknown . We took an expression-based approach to explore microRNA ( miRNA ) -related antisense transcription by computational analyses of published whole-genome tiling microarray transcriptome and deep sequencing small RNA ( smRNA ) data . Statistical support for greater abundance of antisense transcription signatures and smRNAs was observed for miRNA targets than for paralogous genes with no miRNA cleavage site . Antisense smRNAs were also found associated with MIRNA genes . This suggests that miRNA-associated “transitivity” ( production of small interfering RNAs through antisense transcription ) is more common than previously reported . High-resolution ( 3 nt ) custom tiling microarray transcriptome analysis was performed with probes 400 bp 5′ upstream and 3′ downstream of the miRNA cleavage sites ( direction relative to the mRNA ) for 22 select miRNA target genes . We hybridized RNAs labeled from the smRNA pathway mutants , including hen1-1 , dcl1-7 , hyl1-2 , rdr6-15 , and sgs3-14 . Results showed that antisense transcripts associated with miRNA targets were mainly elevated in hen1-1 and sgs3-14 to a lesser extent , and somewhat reduced in dcl11-7 , hyl11-2 , or rdr6-15 mutants . This was corroborated by semi-quantitative reverse transcription PCR; however , a direct correlation of antisense transcript abundance in MIR164 gene knockouts was not observed . Our overall analysis reveals a more widespread role for miRNA-associated transitivity with implications for functions of antisense transcription in gene regulation . HEN1 and SGS3 may be links for miRNA target entry into different RNA processing pathways .
Non-coding genes , such as those producing miRNAs and small interfering RNAs ( siRNAs ) , are key components of gene expression in eukaryotes , forming a regulatory network superimposed on the central dogma of molecular biology [1] , [2] , [3] . miRNAs are expressed through nucleolytic maturation of hairpin precursors transcribed by RNA Polymerase II or III [4] , [5] . siRNAs are derived either from endogenous transcripts that form perfect double-stranded RNA ( dsRNA ) structures , or from transcripts of transgenes , viral genomes and protein-coding genes including miRNA targets that act as substrates for the RNA-induced silencing complex ( RISC ) . Both classes of smRNAs are involved in post-transcriptional gene regulation in plants , fungi and animals [1] , [3] . miRNAs bind to target RNA transcripts and guide their cleavage ( mostly for plants ) or act to prevent translation [6] , [7] , [8] . siRNAs act via a similar mechanism of cleavage of their target genes , but they also can direct genomic DNA methylation and chromatin remodeling [9] . It is estimated that at least 20–30% of all human genes may be post-transcriptionally regulated by miRNAs [10] . Transcriptome profiling experiments have demonstrated the extensive presence of endogenous antisense transcripts both in plants and animals [11] , [12] , [13] , but the mechanisms and significance of such transcriptional activities are still not clear . One hypothesis is that miRNAs trigger the production of the antisense transcripts from their cognate sense transcripts , which in turn generate smRNAs for gene silencing , in a phenomenon known as transitivity [14] , [15] , [16] . This hypothesis is derived from several indirect and direct lines of evidence . Parizotto et al . [17] observed that stringent mutations within miRNA target sequences can prevent cleavage , but may not entirely prevent transitivity through siRNAs . This suggests that miRNAs may have additional activities or determinants in post-transcriptional regulation that are independent of cleavage . Furthermore , miRNAs are known to generate trans-acting siRNAs ( ta-siRNAs ) , a subclass of smRNAs , through antisense transcription associated with RNA DEPENDENT RNA POLYMERASE 6 ( RDR6 ) [14] , [15] , [18] , [19] . ta-siRNAs differ from classical siRNAs by silencing mRNAs unrelated to their primary transcript . For example , ta-siRNAs target pentatricopeptide repeat-containing genes ( PPR ) of unknown function and transcription factors involved in vegetative development and organ polarity [18] , [19] . A more direct line of evidence for miRNA target-associated transitivity comes from several studies that characterized antisense transcripts or smRNAs for miRNA targets , including SPL3 , SPL10 , TIR1 , HAP2C and a clade of PPR genes [15] , [16] , [20] , [21] . Those antisense transcripts appear to function in transitive silencing involving RDRs and miRNA/siRNA processing [16] , [21] . Axtell et al . [15] described a mechanism for transitivity of some miRNA target genes , including PPR and TAS3 . These transcripts have a second , cryptic miRNA binding site that can trigger siRNA production without cleavage . It has also been speculated that methylation of miRNAs at the 3′-terminal hydroxyl group by HEN1 may serve to counteract the antisense transcription activity primed possibly by unmethylated miRNAs [22] . However , the known cases of transitivity associated with miRNA target genes to date are few and limited to RDR6-dependent production of siRNAs downstream ( direction relative to the coding strand ) of the miRNA binding site in the plant Arabidopsis thaliana [15] , [16] , [23] . In work presented here , we show that antisense transcription of miRNA targets and MIRNA genes in the model plant Arabidopsis is more prevalent than previously observed . Our findings were guided by statistical analyses of extant whole-genome and smRNA transcriptome databases . Antisense transcripts were characterized by RNA transcript profiling of smRNA pathway-defective mutants with a custom high-resolution ( 3 n . t . ) microarray , and their existence was corroborated by semi-quantitative reverse transcription PCR ( qRT-PCR ) . Most antisense transcripts near the miRNA target sites were elevated in hen1-1 and a few were also upregulated in the sgs3-14 mutant , which affects post-transcriptional gene silencing and leaf development [14] , [24] . Our findings suggest that HEN1 and SGS3 may work in the same process/step to suppress synthesis or stability of miRNA target-associated antisense transcripts , which might serve as a link between miRNA and RNA silencing pathways .
The digital and normalized nature of Massively Parallel Signature Sequencing ( MPSS ) data enables one to mathematically analyze the expression relationship of all transcriptional signatures ( e . g . sense and antisense ) both within and between samples . We analyzed the abundances of sense and antisense signatures for miRNA targets from the MPSS Plus Database ( http://mpss . udel . edu/at ) [25] , [26] . A scalar value was calculated representing the abundance of antisense signatures divided by that of total signatures for a given gene . Thirteen out of the total seventeen MPSS libraries showed a higher percentage of normalized antisense signatures associated with the experimentally validated miRNA targets ( n = 94 , Tables 1 , S1 and S2 ) than for paralogous non-targets ( n = 140 ) . The paralog genes included fourteen experimentally verified non-miRNA-targets [19] , [27] and were chosen as biological controls based on the presence of a remnant pseudo-miRNA binding site that presumably does not associate with a miRNA because of sequence divergence ( see Materials and Methods ) . For the six inflorescence libraries ( the INF , INS , AP1 , SAP , AP3 and AGM samples in Table 1 ) , five had a greater abundance of normalized antisense signatures for validated targets than did paralogs , and the higher expression in the INS library was significant ( P<0 . 05 , one-sided Student's t-test , equal variance model ) . Other tissues , including callus , leaf , root , silique and seedling ( the CAS , LES , ROS , SIS , GSE libraries in Table 1 ) showed the correlation of higher antisense expression for validated targets as well , arguing against a tissue-specific bias for these antisense transcripts despite high levels of miRNAs in flowers [20] . It is noteworthy that all twelve “signature method” MPSS libraries ( labeled by † in Table 1 ) gave higher normalized antisense signatures for validated miRNA targets , whereas four out of five of the “classic method” libraries did not ( labeled by * in Table 1 ) , raising questions about possible technical bias in the classic MPSS datasets as noted ( http://mpss . udel . edu/at/ ) . Discounting the “classic method” signature data , a combined statistical analysis of the “signature method” libraries showed that validated miRNA targets have significantly higher normalized antisense transcript expression than their paralog genes ( P<0 . 05 , one-sided Student's t-test , equal variance model , Tables 1 and S2 ) . The TAS1–TAS4 genes are targets of miR173 , miR390 or miR828 and they require antisense transcription to generate ta-siRNAs [19] , [28] . When these target genes were removed from the analysis , the average normalized antisense signature abundance for the validated miRNA targets in all 17 libraries increased ( data not shown ) , demonstrating that antisense transcription of non-TAS miRNA target genes is substantial . Our observations suggest that mechanisms similar to those operating in the production of ta-siRNAs may also act on many bona fide miRNA targets previously concluded to be intransitive [20] . The high percentage of MPSS normalized antisense signatures for the validated miRNA targets prompted us to perform a systematic survey of antisense transcription for miRNA targets and MIRNA genes . We collectively plotted the sense and antisense transcript abundance as a function of miRNA cleavage sites for validated targets ( n = 78 ) , predicted targets ( n = 188 ) , non-target paralogs ( n = 120 ) , and the miRNA* sites of MIRNA genes ( as potential cleavage sites by miRNAs [29] , n = 159 ) ( See Text S1 and Table S3 ) . This analysis excluded PPR genes , ARGONAUTE1 ( AGO1 ) , DICER-LIKE1 ( DCL1 ) ( which harbors MIR838 within intron 14 ) , and the ARF2/3/4 targets of ta-siRNAs derived from miR390 cleavage of TAS3 ( AT3G17185 ) , because these are reported evidence for miRNA target-associated transitivity [16] , [20] , [23] , [28] . Figure 1 presents the sense and antisense strand expression as a function of the miRNA target sites . We identified a pair of expression peaks associated with validated miRNA targets flanking the miRNA cleavage site on the sense and antisense strands , which was not seen in paralogs relative to their cryptic pseudo miRNA-binding sites ( Figure 1A and D ) . For the validated targets , an expression peak was observed immediately downstream of the miRNA cleavage site on the sense strand ( Figure 1A open arrow , referred to as “downstream sense signal” hereafter ) . This could be a manifestation of higher stability of the 3′ RISC cleavage fragment for miRNA target mRNAs . This interpretation is consistent with previous reports describing the accumulation of 3′ endonucleolytic cleavage products of miRNA targets by Northern blot [6] , reverse genetic analysis [30] , and deep sequencing of non-capped polyA+ “degradome” libraries [31] , [32] , [33] . Associated with this downstream sense signal was an additional peak of transcription signal located in a 200 n . t . region upstream of the miRNA target sites on the antisense strand ( Figure 1A black arrow , referred to as “upstream antisense signal” hereafter ) . Figure S1 provides additional examples of this phenomenon for high downstream-sense coupled to corresponding upstream-antisense transcript signals around the miRNA binding site for twelve different miRNAs , in which target genes also produce smRNAs . For the predicted miRNA targets , an expression pattern similar to that of validated targets was observed spanning the predicted cleavage sites ( Figure 1B , open arrow for downstream sense signal and black arrow for upstream antisense signal ) . Statistical analysis indicated that the downstream sense and upstream antisense signals were significantly higher than the average signal elsewhere on either sense or antisense strand for validated miRNA targets and predicted targets ( P<0 . 01 , one-sided Student's t-test , equal variance model; Table S3 ) . The pairs of downstream sense and upstream antisense signals for the validated targets were significantly higher compared to the same region for paralogs ( Table S4 , 95% confidence interval calculated ) . In line with the recent report of miR172-mediated cleavage of the pri-miR172b transcripts [29] , we observed some sense expression signals immediately downstream of the miRNA* sites of MIRNA genes along with some antisense expression signals immediately upstream of the miRNA* sites ( Figure 1C ) . This implies that MIRNA genes may share the same process of antisense transcription with the validated miRNA targets , possibly by miRNA interaction with miRNA primary transcripts . These observations suggested a causal relationship between miRNA target site regulation and antisense transcripts of miRNA targets and MIRNA genes that warranted further study . With the confirmation by two pilot custom tiling microarray experiments that the upstream antisense expression for the validated miRNA targets was technically and biologically reproducible ( see Text S1 ) , we designed two custom 3 n . t . high resolution tiling microarrays ( 25mer and 36mer probe lengths; Agilent Technologies , Santa Clara , CA ) to test the role of HEN1 , DCL1 , HYPONASTIC LEAVES1 ( HYL1 ) , RDR6 and SGS3 in production of antisense transcripts associated with validated miRNA targets . The 22 target genes on the arrays were chosen based on the presence or absence of associated smRNAs that mapped to the loci , on various amplitudes of the antisense transcription signals in published whole genome tiling microarray experiments [11] , [13] ( Table S3 ) , and in order to provide a representative cross section of miRNA families . The sensitivity and precision of the custom high resolution tiling microarray to detect bona fide transcripts was evidenced by three sense strand analyses: ( 1 ) by excellent concordance of the sense strand signals of Col-0 inflorescence samples relative to the two independent whole genome tiling array transcriptome datasets ( Figure S2 ) , ( 2 ) by an absence of signals from probes corresponding to annotated introns ( see Figure S2A , E , F ) , and ( 3 ) by the observation of reasonably good concordance for the changes in miRNA target gene sense strand expression in hen1-1 mutant versus Ler-0 wild type between the custom tiling microarray and published data [19] using ATH1 microarrays ( Figure S3 ) . Having validated the custom tiling microarray sense strand signals , the antisense signals for the miRNA targets were characterized for smRNA pathway mutants . Sixteen out of 22 genes on the microarray showed clear antisense transcription signals usually falling within 200 n . t . range upstream and/or downstream of the miRNA cleavage sites ( Table S6 and Figure S4 ) . We employed “normalized delta plots” for antisense transcripts ( to facilitate gene-by-gene analyses ) representing the differences between the means of signal intensities for biological and technical replicates of smRNA pathway mutants versus corresponding wild-type controls divided by the signals from wild-type . Fourteen of these sixteen genes displayed different amplitude antisense signals in at least one of the five smRNA pathway mutants hen1-1 , dcl1-7 , hyl1-2 , rdr6-15 , and sgs3-14 . Most strikingly , the antisense signals of thirteen genes were increased in hen1-1 mutants ( Table S7 ) . Figure 2 shows normalized delta plots for APS1/AT3G22890 , MYB12/AT2G47460 , AP2/AT4G36920 , and GRF8/AT4G24150 antisense transcript signals which demonstrate 20–40% increases in hen1-1 relative to Ler-0 wild type ( Figures 2A , B , E , F; Figures S5 , S6 , S7 and S8 , black arrows ) . For SCL6 ( IV ) /AT4G00150 and TOE2/AT5G60120 , there were 1 to 2 . 5- fold increases relative to wild type ( Figures 2C and D; Figures S9 and S10 ) . In the dcl1-7 mutant , the relative expression levels of antisense transcripts for five genes were decreased by 20–40% , including APS1 , MYB12 , SCL6 ( IV ) , DCL1/AT1G01040 , and SPL10/AT1G27370 ( Figures 3 , S5 , S6 , S9 , S11 , S12 ) . The hyl1-2 mutant had decreased antisense transcript expression by 20–50% for APS1 , MYB12 , SCL6 ( IV ) and TOE2 ( Figures 4 , S5 , S6 , S9 , S10 ) . Conversely , ARF17/AT1G77850 and MET2/AT4G14140 antisense transcript expression levels were up-regulated in hyl1-2 ( Table S7 , Figure S13 ) , and there was a more complex pattern of expression for TCP4/AT3G15030 antisense transcripts that were elevated in the upstream region while decreased in the downstream region in hyl1-2 ( Figure S14 ) . Another striking observation was seen in the sgs3-14 mutant: APS1 , MYB12 , TOE2 , DCL1 , SPL10 , and TCP4 had increased expression of antisense transcripts ( Figures 5A , B , D; S5 , S6 , S11 S12 , S14 ) . For MYB12 , SCL6 ( IV ) and TCP4 , there were some antisense transcripts with complex changes corresponding to increases as well as decreases ( Figures 5B , C; S6 , S9 , S14 ) . In the rdr6-15 mutant , MYB12 , SCL6 ( IV ) , TOE2 , and TCP4 antisense transcript expression was down-regulated , while there was an increase of UBC24/AT2G33770 antisense transcripts ( Figures 6B–D; S6 , S9 , S10 , S14 , S15 ) . Taken together , around 80% of the sixteen validated miRNA targets were elevated in the hen1-1 mutants for the antisense transcript expression , whereas about a quarter to one third of these 16 targets were affected in one of the other four smRNA pathway mutants , including dcl1-7 , hyl1-2 , rdr6-15 or sgs3-14 . MYB12 and SCL6 ( IV ) were affected by all five mutants in that there was elevated antisense transcript expression in hen1-1 , complex up and down signal levels in sgs3-14 , and decreased expression in dcl1-7 , hyl1-2 and rdr6-15 . Because the antisense transcript topologies were replicated precisely ( i . e . in the same probe sets ) in completely different sets of experiments with different control genotypes Landsberg erecta and Columbia ( Ler-0 , Col-0 ) , we conclude that despite their low abundance relative to sense transcripts , the antisense transcripts mapping near to the miRNA binding sites of target genes are highly reproducible . Some general features characterize the identified antisense transcripts: ( 1 ) the expression peaks appeared to be concordant with sense transcripts . For example , comparison between the wild type sense and antisense strand raw signals for AP2 and SPL10 showed that these genes with introns in the probe set had no antisense transcripts in the sense intronic region ( Figures S2A , S4E , S2F , and Table S6 ) . This suggested the antisense transcripts associated with miRNA targets were generated from the mature mRNA transcripts . Supporting evidence comes from APS1 , AP2 and SPL10 which also had concordant changes in antisense signals to sense signals in smRNA pathway mutants ( Figures S5 , S7 , S12 ) . ( 2 ) The effect on antisense transcript abundance by smRNA pathway mutants did not strictly correlate with that of sense transcripts expression except for a few cases in hen1-1 and sgs3-14 . For instance , elevated expression of DCL1 antisense transcripts in hen1-1 and sgs3-14 mutants was not correlated to that of sense transcripts which were unchanged in these two mutants ( Figure S11 ) . A similar situation was seen for MET2 , where the antisense transcripts of MET2 were increased in the hyl1-2 mutant . Nevertheless , its sense transcript abundances were unchanged in the corresponding mutant ( Figure S13 ) . In some other cases , the antisense transcripts had reciprocal expression patterns compared to their cognate sense transcripts , for example , MYB12 in hyl1-2 and sgs3-14 , SCL6 ( IV ) in dcl1-7 , sgs3-14 and rdr6-15 , and TOE2 in hyl1-2 and rdr6-15 ( Figures S6 , S9 , S10 ) . This suggested a possible regulatory function of antisense transcripts on their coordinate sense transcripts . For hen1-1 mutants , most antisense transcripts of validated miRNA targets were elevated along with their sense transcripts . We interpret the increased antisense transcripts as an indirect consequence of the increased stability of their sense transcripts due to the loss of function of HEN1 in the mutant , because for some targets , such as DCL1 and MET2 , the antisense transcripts were up-regulated whereas the levels of their sense transcripts did not change ( Figures S11 , S13 ) . For CC-NBS-LRR/AT5G43740 , the observed increases in antisense transcript abundance were accompanied by a concordant decrease of its cognate sense transcript expression in hen1-1 ( Figure S16 ) . In general , these observations support the notion that the increased antisense transcripts associated with miRNA targets are due to the loss of HEN1 function , presumably due to the loss of the 2′-methylated hydroxyl group on the 3′ end of smRNAs in the hen1-1 mutant [22] . ( 3 ) In sgs3-14 , adjacent probes for MYB12 and TCP4 reported signals of widely differing amplitudes , where a few probes showed high signals ( black arrows in Figures 5B , S6 and S14 ) and nearby probes recorded decreased signals relative to wild type ( open arrows in Figures 5B , S6 and S14 ) . The variable effects of sgs3-14 on transcript topology suggested a dynamic process affecting antisense transcript stability , which may also explain the complex expression pattern for the antisense transcripts with SCL6 ( IV ) and TCP4 in dcl1-7 or hyl1-2 ( Figures 3 and S14 ) . We propose this phenomenon seen with the high resolution microarray is evidence of transitive mechanisms in action , e . g . rapid smRNA production by the cleavage of antisense and/or sense transcripts detected as fluctuating microarray signals . qRT-PCR was employed for select miRNA targets on the microarray as well as for other miRNA target genes . qRT-PCR primers were designed from ∼200 n . t . range 5′ upstream and 3′ downstream of the miRNA cleavage sites ( Figure 7A ) for AP2 , APS1 , CATION/H+ EXCHANGER 18 ( ATCHX18/AT5G41610 ) ( miR856 cleavage site ) , CUC2/AT5G53950 , NAC1/AT1G56010 and a negative control gene VARIANT IN METHYLATION 1 ( VIM1 ) /AT1G57820 previously shown not cleaved by miR164 [27] . The results of qRT-PCR for sense strands were generally consistent with previous [19] and our custom tiling microarray results ( Figure S2 ) . AP2 sense transcript expression was unchanged in hen1-1 , hyl1-2 and sgs3-14 , whereas it was decreased in dcl1-7 and rdr6-15 ( Figure 7B right panel “Downstream sense expression” ) . Also in agreement with the microarray data was the finding that AP2 antisense transcripts were increased by ∼30% in hen1-1 mutants , and decreased in dcl1-7 . We also examined the effect of a RNA silencing suppressor protein P1/HC-Pro from Turnip mosaic virus which binds to the miRNA/miRNA* duplex and probably inhibits the 3′-terminal methylation of smRNAs [34] . We found that AP2 antisense transcripts were up-regulated in a P1/HC-Pro over-expressing line . A slightly higher expression was observed by qRT-PCR for antisense transcripts in the rdr6-15 mutant than by microarray analysis ( compare Figure 6E with Figure 7B ) . APS1 sense transcripts were increased in all mutants , supporting the microarray results for hen1-1 and sgs3-14 , but in contrast to those for dcl1-7 , hyl1-2 , and rdr6-15 ( Figure S5 ) . The differences observed might be due to sensitivity limitations ( note the low signal to noise ratios for Figures 2–6 in some cases ) or amplification differences inherent to the two methods . APS1 antisense transcripts were upregulated in hen1-1 , down-regulated in dcl1-7 and hyl1-2 , which was congruent with tiling array results . ATCHX18 is a member of putative Na+/H+ antiporter family targeted by miR856 and miR780 . The expression level of the downstream sense region for the miR856 target site was increased in hen1-1 , P1/HC-Pro lines , and rdr6-15 , whereas corresponding upstream antisense transcripts were elevated in all mutants ( Figure 7B ) . Interestingly , a natural antisense transcript ( AT5G41612; TAIR Release 8 ) overlaps with ATCHX18 and might be queried in the qRT-PCR assay , despite the primers being over 1 kb distal to the annotated natural antisense transcript . CUP-SHAPED COTYLEDON 2 ( CUC2 ) and NAC1 are members of NAC domain-containing transcription factors and are validated targets of miR164 . qRT-PCR data showed that CUC2 sense transcripts were up-regulated in all mutants , whereas the levels of its antisense transcripts were unchanged in most mutants except for a decrease in hen1-1 . NAC1 had more sense transcript expression in hen1-1 and hyl1-2 and less expression in rdr6-15 . For NAC1 antisense transcripts , expression was elevated in hen1-1 and dcl1-7 , but decreased in rdr6-15 ( Figure 7B ) . VIM1 encodes a SRA ( SET- and RING-associated ) domain methylcytosine-binding protein , and it has been shown to have a cryptic miR164 binding site that fails to generate a cleavage product as probed by 5′-RACE [27] . Thus , it was selected as a reference control for the qRT-PCR assays . VIM1 locus clearly showed some altered sense transcripts in the smRNA pathway mutants , however , as hypothesized , no antisense transcripts were detected under experimental conditions ( Figure 7B ) . In order to test the functional significance of MIR164 expression on transcripts of CUC2 and NAC1 , their sense and antisense transcript levels were assayed in mir164a-4 , mir164b-1 , mir164c-2 single mutants and mir164a-4 b-1 c-1 triple knockout mutants [35] . As expected , CUC2 sense transcripts accumulated in the mir164a-4 and mir164c-2 mutants ( Figure 8 right panel ) , but the antisense transcripts of CUC2 were unchanged in these knockout mutants except for a slight decrease in the mir164c-2 mutant ( Figure 8 left panel ) . NAC1 sense transcript levels were elevated in all the knockout mutants and its antisense transcripts also increased in mir164a-4 , mir164c-2 and mir164a-4 b-1 c-1 mutants ( Figure 8 ) . These results suggest that miR164 is probably not a primer for the observed antisense transcription , as previously speculated based on the function of HEN1 as a methyltransferase [36] . Northern blot for miR164 expression from inflorescence samples of these mutants showed that even in mir164a-4 b-1 c-1 triple mutants , miR164 expression was not completely abolished with ∼20% detectable expression level comparing to that of wild type [35] . The expression of a distinct miR164 species of 24-n . t . in length was generally unchanged in all these mir164 single and triple mutants [35] . These results imply that there should be more direct determinants regulating the abundance of miRNA target-associated antisense transcripts other than miRNAs themselves . The availability of deep sequencing datasets for smRNAs [20] , [28] , [37] , [38] affords the means to correlate antisense transcript abundances with their presumptive DCL products and gain insight into the causal relationships of antisense transcripts and smRNAs . We mined the unique smRNAs having only one locus in the A . thaliana genome that matched perfectly to the sense or antisense strand of test sets of miRNA-associated genes ( Table S8 ) . Figure 9 shows the average number smRNAs of different size classes normalized for gene length in validated or predicted miRNA targets , paralogous non-targets , and MIRNA genes . In the categories of 20–22 n . t . smRNAs , validated miRNA targets had significantly more smRNAs matching to the sense strand compared to paralogs ( Figure 9A , P<0 . 05 , one-sided Student's t-test , equal variance model ) , especially in the size class of 21 n . t . Predicted miRNA targets also generated abundant smRNAs , in which 20 , 22 , 23 , and ≥24 n . t . groups gave higher numbers of smRNAs from the sense strand when compared with validated miRNA target genes . The 21 n . t . predicted target-originated sense smRNAs were significantly more abundant than those from paralogs ( Figure 9A ) . For reference , the number of sense strand smRNAs generated from 187 miRNA hairpins ( miRBase , microrna . sanger . ac . uk ) was also calculated . MiRNA hairpins produced predominantly 20–22 n . t . smRNAs , which is well known as due to the processing of miRNA hairpin precursors to generate mature miRNAs and miRNA* by DCL1 and/or DCL4 [28] . MiRNA hairpins also produced 23–24 n . t . and longer smRNAs , consistent with a report on functional 23 to 25 n . t . -long miRNAs generated by DCL3 [39] , indicating the overlapping functions of different DCLs on the processing of miRNA hairpin precursors . The antisense strand of miRNA targets produced smRNAs to a similar extent as those from the sense strand compared to paralogs ( Figure 9B ) . Validated miRNA targets had significantly more 20–22 n . t . smRNAs than paralogs ( P<0 . 05 , one-sided Student's t-test , equal variance model ) . The 21 n . t . sense and antisense smRNAs were the main class of smRNAs generated from validated and predicted miRNA targets , suggesting they are mechanistically linked to the RNA silencing pathway through DCL1 . Remarkably , MIRNA hairpins generated antisense smRNAs as well , in which 21 n . t . antisense smRNA were also the major class ( Figure 9B ) . Table 2 summarizes the known cases of miRNA targets and their MIRNA genes that generated antisense smRNAs , ranked according to abundances of antisense smRNAs and grouped into MIRNA gene families . It is interesting that several of the transitive MIRNA genes correlate with top-ranking miRNA targets , for example ATCHX18 and MIR780 , AGO1 and MIR168a , SCL family and MIR171c , SAMT and MIR163 , AP2 and TOE2 with MIR172 , and the SPL family with MIR156 ( Table 2 ) . Careful analysis of the location for these sense and antisense smRNAs on the miRNA hairpins showed that about 30% of unique sense smRNAs overlap with mature miRNA sites , whereas another 28% overlap with the miRNA* sites by at least 16 n . t . ( Figure S17 ) . For the unique antisense smRNAs on the miRNA hairpins , about 14% overlap with the locus of the mature miRNA on the sense strand , whereas 27% of them overlap with the miRNA* sites Interestingly , several antisense 24 n . t . smRNAs were found to be in phase with the middle of the mature miR783 or miR854b* site on their individual hairpins ( Figure S18 ) . We propose this is evidence for the miRNA hairpin processing via the RNA silencing pathway in which the miRNA* or miRNA may be programmed into a RISC that triggers cleavage [29] and/or antisense transcription and subsequent dicing on their primary transcripts , in these cases presumably by DCL3 . We further investigated the topology of antisense transcription manifested in smRNAs by plotting the abundance of unique smRNAs ( extracted from the MPSS Plus database ) as a function of the distance between the smRNA loci and the miRNA target sites for validated miRNA targets , predicted targets and paralogous non-targets ( Figure 10 ) . Validated targets had sense and antisense smRNAs clustered around 1000 n . t . upstream and downstream of the miRNA cleavage sites , with a few cases of hits >2000 n . t . upstream and 3000 n . t . downstream of the cleavage sites ( Figure 10A ) . The numbers of sense: antisense smRNA signatures associated with validated targets were about the same ( 70: 62; Table S9 ) . However , the topology of these smRNA signatures showed that the numbers of sense and antisense smRNA signatures downstream of miRNA cleavage sites were greater than those upstream ( 22 up: 48 down and 7 up: 55 down for sense and antisense smRNA signatures , respectively; Table S9 and Figure 10A inset ) . Antisense smRNAs were significantly more abundant than the sense smRNA signatures even when the two most abundant antisense smRNA signatures were removed ( transcripts per quarter million = 416 and 192 corresponding to NF-YA8/AT1G17590 [miR169 target]; ATHB15/AT1G52150 [miR166 target] , respectively; P<0 . 05 , one-sided Student's t-test , equal variance model ) . This same phenomenon was observed in predicted miRNA targets as well , with significantly higher abundances for antisense smRNA signatures than sense smRNA ones ( P<0 . 05 , one-sided Student's t-test , equal variance model; Table S9 , Figure 10B ) . There were also more antisense smRNA signatures located downstream of the predicted miRNA cleavage sites than upstream antisense ones ( 50 up: 113 down , respectively ) . Paralog genes showed no significant correlation ( Table S9 , Figure 10C ) . These results indicate that generally more smRNA signatures were generated towards the 3′ end of miRNA target transcripts , presumably from the downstream region of the miRNA cleavage sites on the antisense strand . These data fit with the observation that uncapped transcripts are more susceptible to RNA silencing pathways , which lead to the production of sense and antisense smRNAs [33] .
To date , two models have been proposed for post-transcriptional gene silencing which can be applied to the question of miRNA-associated transitivity in terms of generation of antisense transcripts and secondary smRNAs: 1 ) RDRs may use rare primary siRNAs to “prime” ( in the formal sense ) dsRNA using the target mRNA as template , i . e . extend the dsRNA into the 5′ ( upstream ) end of the sense transcript [42] , [43] , [44] . 2 ) Copy RNA synthesis may occur by un-primed initiation , supported by the evidence that siRNAs spread both 5′ and 3′ along the target relative to the trigger in plants and Neurospora [45] , [46] . There is biochemical evidence for both pathways [43] , [45] and they probably overlap at some key point ( s ) in the pathways . The situation is confounded by the issue of causality: the generation of secondary smRNAs could be the consequence of , or the source of , antisense transcripts . There are several unanswered questions that impact the origin of miRNA-associated antisense transcripts and secondary smRNAs: 1 ) Is miRNA or smRNA required as primer ? 2 ) What are the sources of template that serve as triggers for these antisense transcripts ? 3 ) Is there any specificity determinant involved in the process ? Concerning the requirement of miRNA as primer in the miRNA target-associated antisense transcription , Ronemus et al . [16] have suggested that transcription activity in the complementary region to 5′ upstream targeted sequences on miRNA targets might correlate with those miRNAs which have 3′ ends that match perfectly to their targets . However , we observed strong transcription signals and upstream smRNAs in many targets regulated by miRNAs that have substantial 3′ mismatches ( e . g . Figure S1; data not shown ) . HEN1 is a methyltransferase involved in the methylation of 2′-OH on the 3′ end of miRNAs and siRNAs [22] , [36] . The methylated 2′-OH is postulated to protect the 3′ end of smRNAs from uridylation and presumably from antisense transcription of template strands that share high homology with miRNAs or siRNAs [22] . Loss of HEN1 function alters miRNA abundances and exposes the free 3′ end of smRNAs , which might serve as triggers via priming per se or otherwise in the generation of antisense transcripts . In the hen1-1 mutant , the expression of antisense transcripts for 80% of examined miRNA targets on our custom tiling microarray increased substantially relative to wild type ( Figure 2; Table S7 ) . This is consistent with an indirect ( non-priming ) trigger mechanism when taken in light of the abundance of secondary smRNAs mapping downstream of cleavage sites ( Figure 10A ) and assuming that antisense transcripts are causal to smRNA production . We hypothesize there should be homeostasis between an antisense transcription pathway and the degradation of smRNAs by a family of exoribonucleases encoded by the SMALL RNA DEGRADING NUCLEASE ( SDN ) genes [47] , raising the issue of the steady state levels of “functional” miRNAs and siRNAs in hen1-1 that could impact the hypothesized trigger for antisense transcription . Another indirect evidence for dispensability of miRNAs as primers is that RDR6 possesses primer-independent RNA polymerase activity on single-stranded RNAs no matter the substrate has a cap or poly ( A ) tail [48] . This fact indicates that at least in RDR6-dependent antisense transcription , priming activity by miRNAs is not needed and indeed most of our data do not support a requirement for RDR6 in antisense transcription of miRNA targets ( Figures 6 , 7; Table S7 ) . Regarding the source of templates in miRNA target and MIRNA gene-associated antisense transcription , the 5′ and 3′ cleavage fragments of miRNA targets and pri-miRNAs targeted by RISCs could serve as a supply . It is reported that transcripts without a cap or a poly ( A ) tail are preferentially directed to the RNA silencing pathway and secondary siRNAs could be generated from these “aberrant” RNA transcripts [33] , [49] , [50] , [51] by antisense transcription . Similar to the catabolism of smRNAs , there are known degradation pathways ( containing 3′ to 5′ or 5′ to 3′ exoribonucleases [52] , [53] ) for the mRNA cleavage fragments that compete with RNA silencing pathways in Arabidopsis [54] . In human cells , the addition of a 3′ terminal oligo U-tract on mRNAs or mRNA fragments can promote decapping and stabilization of the 3′ end of the RNA by binding the Lsm1-7 complex that ensures 5′-directional degradation [55] . This implies the 3′ end of the 5′ fragment of miRNA target transcripts in Arabidopsis could be stabilized by a similar mechanism and would have a longer half life than its 5′ end , thus increasing the probability for it to serve as a template for RNA silencing . For the 3′ endonucleolytic fragment of miRNA targets , the lack of a 5′ cap could facilitate its entry into RNA silencing pathways in competition with the surveillance of the EXORIBONUCLEASE 4/ETHYLENE INSENSITIVE 5 ( XRN4/EIN5 ) and/or ABA-HYPERSENSITIVE-1/CAP BINDING PROTEIN80 ( ABH1/CBP80 ) [33] . Our observation of SGS3-dependent accumulation of sense and antisense transcripts for several miRNA targets that produce siRNAs ( Figures 5 and 7; Figures S5 , S10 , S11 , S14 ) supports the notion that SGS3 could be a determinant in the production of miRNA target-associated antisense transcription . SGS3 is predicted to encode a coiled-coil RNA binding protein with a novel XS domain [56] , [57] . SGS3 functions as a key component of the unprimed post-transcriptional transgene- and virus-induced gene silencing pathway [24] , [58] . It is also required for vegetative phase change mediated by targets of miR156 that produce antisense transcripts [21] . Many of the same genes are up-regulated in sgs3 , asymmetric leaves1 ( as1 ) , and ago7/zippy mutants [14] , [59] and we postulate that these altered genes may produce antisense transcripts that are important for gene regulation . Yoshikawa et al . [60] reported that SGS3 , RDR6 and DCL4 work sequentially to generate the 21 n . t . species of smRNAs from the 3′ cleavage fragment of TAS1/2 , while the 24 n . t . smRNAs are dependent on DCL3 . SGS3 stabilizes the 3′ cleavage fragments of TAS1a and TAS2 transcripts [60] , but it is unknown why the 5′ cleavage fragments of TAS1a and TAS2 can accumulate in sgs3-11 and generate 24 n . t . smRNAs . We speculate that SGS3 involvement in the production of miRNA target-associated antisense transcripts might be uncoupled from RDR6 or require other RDRs , for example RDR1 or RDR2 . SGS3 might be a transporter/stabilizer of cleaved products of miRNA targets , analogous to the LSm1-7 complex in humans . It could bind the single-stranded cleavage fragments of miRNA targets and promote their 5′ to 3′ degradation . Loss of function for SGS3 would channel these cleavage products into the RNA silencing pathway mediated by RDR ( s ) as shown for RDR6-dependent TAS1/2/3 processing . This pathway for metabolism of unstable transcripts would be in competition with the mRNA degradation pathways , including the 3′ to 5′ exosome or the 5′ to 3′ exoribonucleases [52] , [53] . The production of antisense transcripts and antisense smRNAs from the miRNA targets probably induces a series of subsequent reactions in vivo . Antisense transcripts are prerequisites for formation of long dsRNA duplexes which may function in post-transcriptional gene silencing as hypothesized for natural antisense transcripts [61] . This could result in the generation of secondary smRNAs and probable down-regulation of transcripts with little homology to the primary smRNAs . This action would likely be restricted to some specific cell types or some extreme physiological conditions such that it would not affect the normal biological functions of the cognate genes in vivo . Our finding that not every miRNA target gene generates antisense transcripts or smRNAs is in line with this notion . Another aspect is that the antisense smRNAs and antisense transcripts can function in transcriptional gene silencing by DNA or chromatin modifications . Recent results show that human genes are regulated transcriptionally by promoter-associated and terminator-associated antisense RNAs that are targets of the exosome [62] , [63] , [64] , [65] . Other examples are the p21 and E-cadherin genes that have antisense transcripts which produce smRNAs that drive transcriptional gene silencing of the cognate genes [66] . Our findings suggest the existence of a novel antisense pathway generating RNA transcripts complimentary to the sense strand of miRNA target mRNAs . However , we believe such transitivity is under stringent control for the majority of non-TAS miRNA targets , as evidenced by the elucidation of a downstream antisense transcription pathway for some miRNA targets that mimics ta-siRNA pathways ( Figure S1 ) [15] . Because miRNAs are under strong selection pressure for their target mRNAs and act dominantly , their cell-specific expression must be tightly regulated . Therefore , transitivity may be under negative selective pressure because extensive amplification would compromise miRNA function . siRNAs can move through plasmodesmata and act non-cell-autonomously in nearby cells , and RDR6 functions in transitive gene silencing in these neighbor cells [17] , [42] . The few neighboring cells adjacent to cell-specific miRNA gene expression might be the source of antisense signals we observe , which could also explain the low abundance signals . As previously suggested [16] , [42] , coupled miRNA/siRNA mechanisms might function in tissues where the miRNA is not expressed to generate gradients of developmental effectors , e . g . in meristems and primordia , or to allow miRNA activity to be amplified where a limiting amount of miRNA may be present , e . g . in response to stress [67] . Vaucheret et al . [68] have shown that minor perturbations of MIR168 and/or its target AGO1 expression leads to fine-tuned posttranscriptional adjustment of miR168 and AGO1 levels , thereby maintaining a proper balance of other miRNAs . This suggests that modulating the efficiency of assembling miRNA-programmed RISCs may be important in other contexts or require other determinants . This homeostatic mechanism may help explain our unexpected results on some miRNA target gene antisense transcripts and genotypes ( Figures 2–6; also compare Figures 7 and 8 ) . Another possible explanation for the lack of strong effects on antisense and sense miRNA target transcript abundance in hen1-1 and sgs3-14 mutants is genetic redundancy , a hallmark of polyploid plant genomes . This hypothesis is congruent with phenotypes of ago1 , ago7 , dcl1 , hyl1 and rdr6 mutants that have only modestly altered miRNA and target gene abundances [14] , [16] , [19] , [21] , [69] , and the existence of parallel genetic pathways for miRNA activity defined by SERRATE , AS1 , AS2 , and ABH1 [33] , [70] , [71] , [72] .
Arabidopsis thaliana seeds were sown to the soil directly , stratified for 72 h at 4°C , and then placed at 21°C under long day condition of 16 h of light . RNA was extracted with TriZol reagent ( Invitrogen , Carlsbad CA ) or using RNAqueous-Micro isolation kit ( Ambion , Austin TX ) , including the DNAse treatment step , from plants harvested 4 weeks after stratification . The protocols for the pilot array experiment are identical to those of Ref . [13] . For 15k arrays with 22 selected miRNA targets , a dye swap loop experiment design was utilized with 12 blocks for 7 genotypes on two chip arrays . The details of the experimental design are in Table S5 . Total RNA was isolated from aerial parts of wild type Ler-0 and hen1-1 , or from inflorescences of wild type Col-0 , dcl1-7 , hyl1-2 , sgs3-14 and rdr6-15 . For the hen1-1 versus Ler-0 experiment , a dye swap with two versus three biological replicates and four array blocks was performed . After washing , arrays were scanned using a GenePix Autoloader 4200AL with laser excitation at 532 and 635 nm , and saved as 16-bit grayscale TIFF images . Intensity values were extracted using GenePix Pro , and the data for each sample were normalized using standard procedures [73] . Original MIAME-compliant data is stored at the Gene Expression Omnibus ( http://www . ncbi . nlm . nih . gov/geo/ ) with the following locator: GSE15199 . Analyses were done according to standard protocols and manufacturers' instructions except as noted below . Total RNA was treated by RQ1 RNase-free DNase ( Promega , Madison WI ) and purified with a standard phenol∶chloroform extraction followed by ethanol precipitation . qRT-PCR was performed using M-MLV reverse transcriptase ( Promega , Madison WI ) with 5 µg total RNA as input for each reaction followed by 32 cycles of PCR and incorporation of α-32P-dCTP . ACTIN8 primers were added to the qRT-PCR system as a quantitative internal control for the efficiency of amplification . Products were separated on 12% non-denaturing polyacrylamide gels and results were documented by imaging with a Storm 860 phosphorimager instrument ( GE Healthcare , Piscataway NJ ) . The intensity of signal for the bands on the gels was quantified and normalized by ImageQuant TL software ( GE Healthcare ) . The PCR products that were of the predicted size were the major bands in all experiments , which range from 60 b . p . –220 b . p . To confirm the authenticity for the antisense transcripts of select genes , different controls have been applied in PCR reactions such as control PCR with no primers , with only forward primer or reverse primer , or with no template . The AP2 PCR products were cloned and sequenced to confirm their identities ( data not shown ) . Primer sequences are shown in Table S10 . Paralogous non-targets for validated miRNA targets were first chosen based on PBLAST scores using the cognate miRNA target gene amino acid sequences for all miRNA families with the highest complementarity and thermodynamic duplex stability scores [74] , [75] . The best paralog candidates out of the PBLAST screening were aligned with the corresponding miRNA targets using nucleotide sequence in Vector NTI 9 . 0 ( Invitrogen , Carlsbad CA ) . The pseudo miRNA binding sites on the paralogs were manually chosen based on the alignment results . In the statistical analysis of MPSS data , if a gene had no sense expression , a transcripts-per-million value of 1 was given to avoid division by zero in calculating the percentage of antisense expression as a function of total expression . When comparing the signal intensities for validated targets , predicted targets , and paralogous non-targets from previously published whole genome microarray data [11] , [13] , 95% confidence intervals for the mean values of the signals of 200 n . t . upstream and downstream miRNA binding sites were calculated . The confidence intervals of two different mean values which did not overlap were identified as statistically significantly different . We did not include the confidence intervals for brevity but we assigned different letters to denote statistically different values ( See Table S4 ) . smRNA sequences were obtained from published data [20] , [28] , [37] , [38] and were searched against the cDNA sequences ( TAIR release 7 , ftp://ftp . Arabidopsis . org/home/tair/Sequences/blast_datasets/TAIR7_blastsets/ ) or miRNA hairpins [76] by the program BLAST . The output sequences were further queried by BLAST against the Arabidopsis genome to find the smRNAs with single loci . All smRNAs matching with known miRNA , miRNA* , or genes previously reported to generate abundant smRNAs including PPR , AGO1 , ATCHX18 , ARF2/3/4 , etc . [15] , [23] were eliminated from this analysis . | Antisense transcription is a pervasive but poorly understood phenomenon in a wide variety of organisms . We have found evidence for a novel source of antisense transcription in Arabidopsis thaliana associated with miRNA targets via computational analyses of published whole-genome tiling microarray data , deep sequencing smRNA datasets , and from custom high-resolution ( 3 nt ) tiling microarray analysis . Our data show increased antisense transcription for select miRNA targets in the hua enhancer1-1 ( hen1-1 ) , a smRNA methyltransferase mutant , and the suppressor of gene silencing3-14 ( sgs3-14 ) mutant that affects post-transcriptional gene silencing and leaf development . Additional results suggest that miRNA targets and MIRNA genes are subject to the activities of both the miRNA and RNA silencing pathways in which HEN1 and SGS3 may represent associated nodes . The analysis of sense–antisense transcripts using high-resolution tiling microarrays and genetic mutants provides a precise and sensitive means to study epigenetic activities . Our method of mining expression data of plant miRNAs targets and smRNAs is potentially applicable to the identification of epigenetic targets in metazoans , where computational methods for prediction of miRNAs and their targets lack power because of sequence degeneracy , and to identify loci producing antisense transcripts by triggers other than miRNA-directed cleavage . | [
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] | 2009 | Evidence for Antisense Transcription Associated with MicroRNA Target mRNAs in Arabidopsis |
Tungiasis is a neglected tropical disease caused by female sand fleas ( Tunga penetrans ) embedded in the skin . The disease is associated with important morbidity . Tungiasis is endemic along the Coast of Kenya with a prevalence ranging from 11% to 50% in school-age children . Hitherto , studies on epidemiological characteristics of tungiasis in Africa are scanty . In a cross-sectional study 1 , 086 individuals from 233 households in eight villages located in Kakuyuni and Malanga Sub-locations , Kilifi County , on the Kenyan Coast , were investigated . Study participants were examined systematically and the presence and severity of tungiasis were determined using standard methods . Demographic , socio-economic , environmental and behavioral risk factors of tungiasis were assessed using a structured questionnaire . Data were analyzed using bivariate and multivariate regression analysis . The overall prevalence of tungiasis was 25 . 0% ( 95% CI 22 . 4–27 . 5% ) . Age-specific prevalence followed an S-shaped curve , peaking in the under-15 year old group . In 42 . 5% of the households at least one individual had tungiasis . 15 . 1% of patients were severely infected ( ≥ 30 lesions ) . In the bivariate analysis no specific animal species was identified as a risk factor for tungiasis . Multivariate analysis showed that the occurrence of tungiasis was related to living in a house with poor construction characteristics , such as mud walls ( OR 3 . 35; 95% CI 1 . 71–6 . 58 ) , sleeping directly on the floor ( OR 1 . 68; 95% CI 1 . 03–2 . 74 ) , the number of people per sleeping room ( OR = 1 . 77; 95% CI 1 . 07–2 . 93 ) and washing the body without soap ( OR = 7 . 36; 95% CI 3 . 08–17 . 62 ) . The odds of having severe tungiasis were high in males ( OR 2 . 29; 95% CI 1 . 18–44 . 6 ) and were very high when only mud puddles were available as a water source and lack of water permitted washing only once a day ( OR 25 . 48 ( 95% CI 3 . 50–185 . 67 ) and OR 2 . 23 ( 95% CI 1 . 11–4 . 51 ) , respectively ) . The results of this study show that in rural Kenya characteristics of poverty determine the occurrence and the severity of tungiasis . Intra-domiciliary transmission seems to occur regularly .
Tungiasis ( sand flea disease ) is a parasitic skin disease caused by female sand fleas ( Tunga penetrans ) penetrated into the skin of human or animal hosts . Tungiasis belongs to the family of neglected tropical diseases ( NTDs ) [1 , 2] . It is prevalent in resource-poor rural communities in sub-Saharan Africa , the Caribbean and South America [3–7] . Children between 5 and 14 years and the elderly bear the highest disease burden with prevalences up to 85% [7] . While the great majority of patients harbours less than 10 embedded sand fleas , single individuals may have hundreds of parasites [8 , 9] . Once embedded in the skin , typically of the toes , the soles and the heels [10] , the flea matures . Within the period of up to five weeks it grows until it reaches the size of a pea , produces and releases eggs and finally dies [11] . Morbidity is related to an intense inflammatory response triggered by the development of sand fleas embedded in the epidermis [10 , 12 , 13] . Bacterial superinfection is common and intensifies the inflammation . Inflammation and mutilation of the feet eventually lead to impairment of mobility [12] . Main risk factors found in previous studies in Brazil and Nigeria are poor housing and the presence of animals on the compound [14 , 15] . Awareness of the public health importance of tungiasis has been growing in Kenya in recent years , but valid data on epidemiological characteristics do not exist . In order to develop a sustainable control program for tungiasis in resource-poor communities along the Kenyan Coast , two population-based studies were performed: one in households and the other in schools . Here , we report the results of the household-based study .
The study was approved by the Ethics Review Committee at Pwani University , Kilifi County , Kenya; approval number ERC/PhD/010/2014 . The custodians and their protégés were informed about the objectives and procedures of the study in their mother language ( Giriama or Swahili ) by a Community Health Worker ( CHW ) . The right to deny participation and withdraw consent at any given time was clearly explained . The informed consent form was read out loud word by word in Giriama or Swahili and explained further when required , before any interviews were conducted . Questions of the custodian and the children were discussed and answered by a CHW . Consent was obtained via fingerprint or signature from the legal guardian . The examination was performed in a protected surrounding to guarantee the privacy of the patient . Children and adolescents were only examined in the presence of their caregiver . Any individuals found to have tungiasis were referred to the local CHWs for treatment and follow up according to their standard protocols which have been approved for use by the Ministry of Health at national and county level . For other illnesses requiring treatment a referral form was prepared by a CHW , and patients were referred to the nearest Health Facility . Washing and treatment were also made available for compound members with tungiasis who did not participate in the study . The information provided to the households verbally is included as supplementary electronic information along with the consent form which was to be signed ( S1 Appendix ) . The study was performed in eight villages located in Kakuyuni and Malanga Sub-locations of Malindi Sub-county , Kilifi County , eastern Kenya , in the dry season from August to October 2014 . In the area tungiasis is endemic with prevalences ranging from 30 to 85% in school age children ( S2 Appendix ) . In Malindi Sub-county rural communities are small and consist of clusters of two to five houses separated by bush or farm land . The area is divided into two ecological zones: Kakuyuni Sub-location , a very densely populated area in the coastal strip with homesteads located side by side . It has a tropical climate with an average annual rainfall of 1 , 200 mm , temperatures ranging from 28–34°C and high humidity most of the year . Malanga Sub-location is located inland and is much drier with average annual rainfall of 400 mm . Homesteads are located about 100 m from each other in this area . There are two rainy seasons: one between March and May and the other between October and November , interspersed with dry seasons . Malindi Sub-county has a population of 272 , 000 with 42 . 3% being under 15 years of age . The population included in the survey are entirely of the Giriama tribe . While 55% of households have access to piped water and 60% to improved sanitation , only 17% have access to electricity ( Malindi Public Health Office 2015 ) . Many of the people live in mud-walled houses with a thatch roof and sandy floor ( First Kilifi County Integrated Development Plan 2013–2017 ) . For Kilifi County as whole the poverty rate ( i . e . < 1 US$ per day ) is 71 . 4% ( http://www . crakenya . org/county/kilifi/ ) . The majority of the population in the study area practice subsistence agriculture , charcoal burning and small scale businesses . The main foodstuffs cultivated are maize , cassava , coconuts , and mangoes . The study was a cross-sectional survey of a random sample of households in Kakuyuni and Malanga Sub-locations , Kilifi County , Kenya . These sublocations were selected because no intervention against tungiasis had been performed so far . For this study a household was classified as a single structure/house . Since most people live in homesteads of extended families , sharing eating , washing and sanitation facilities , we selected one structure/house per homestead in a standardized manner , always choosing the first house on the left when entering the compound . Individuals of any age and sex were eligible for participation as long as they had spent at least 4 nights per week in the selected household for the last three months . To be included , a household needed to have someone over the age of 18 present at the time of the visit to sign the consent forms and respond to the interview questions . During the preparation phase contact was made with the County and District leadership in both the Ministry of Health and the Ministry of Education , the Zonal Education Officer and the Community Health Officers to obtain their approvals and support for the study . We held meetings with all CHWs in each Sub-location , gave specific training on tungiasis and explained the aims and procedures of the study , emphasizing that participation was completely voluntary and subjects had the opportunity to withdraw from the study at any point of time . The study was carried out between August 13 and October 5 , 2014 , i . e . during a dry season . A total of 1 , 086 individuals from 233 households in eight villages were included in the study . Data were collected through a door-to-door survey of the selected households with the help of local CHWs . Eligible patients were explained the procedure and were asked for consent . In case of minors a caregiver ( usually the mother ) was asked to provide informed consent . If household members were not present during our first visit , we returned to the house on one further occasion . Individuals who could not be reached at home during the second visit were invited to come to the local health facility within the next days . Household members who could not be examined on any occasion were not included in the study . In order to identify risk factors for the occurrence of tungiasis and severe disease , we requested information about demographic , socio-economic , environmental and behavioural characteristics of the individuals and the household . Structured interviews were conducted with the head of household ( usually the mother ) using a pre-tested questionnaire in Giriama or Swahili . Environmental , socioeconomic and some behavioural risk factors were assessed at the household level , other risk factors were assessed on the individual level . Since cash flow does not correctly indicate the economic status of a household in low-income communities [16 , 17] , we used an asset score similar to the one previously established for cutaneous larva migrans , another neglected tropical skin disease associated with poverty [18] . The score is composed of the following assets: Presence in the household of a radio ( 2 points ) , television ( 5 points ) , fridge ( 5 points ) , gas/solar lamp ( 1 point ) ; possession of at least one mobile phone ( 1 point ) , bicycle ( 3 points ) and motor bike ( 10 points ) . The score can vary between 0 and 27 points . For the diagnosis of tungiasis , the feet of the patients were carefully washed with soap in a basin . Each individual was examined for tungiasis based on a standardized procedure [3] . Since a high number of lesions at the feet frequently coincides with the presence of ectopic lesions at the hands [19] , we also systematically examined the hands of the patients . Patients were also asked whether they had tungiasis lesions in other regions of the body . Lesions were staged according to the Fortaleza classification and counted [11]: Stage I to III are viable sand fleas; in stage IV the parasite is dying or already dead [11] Lesions manipulated with a sharp instrument ( by the patient or their caregiver ) with the intention to remove the embedded parasite were documented as manipulated lesions . Based on the number of lesions present , the intensity of tungiasis was classified as light ( 1–5 lesions ) , moderate ( 6–30 lesions ) or high ( >30 lesions ) [14] . The data were entered into an Excel database ( Excel Version 2013 , Microsoft , Redmont , Washington , USA ) , checked for errors which might have occurred during data entry and then transferred to SPSS ( PASW Statistics 18 . 0 , SPSS Inc . , Chicago , IL , USA ) . The data analysis was carried out using the Analysis ToolPack Add-In ( Microsoft , Redmont , Washington , USA ) . Graphs were created with the PowerPivot Add-In ( Microsoft , Redmont , Washington , USA ) . Relative frequencies were compared with the Chi-square test and Fisher’s exact test . The Spearman rank correlation coefficient was calculated to determine the significance of correlations . Odds ratios together with their 95% confidence interval ( CI ) were calculated first in a bivariate analysis . In a second step , variables which were significantly ( p < 0 . 05 ) related to the occurrence of tungiasis and/or severe disease were entered in a multivariate logistic regression model with stepwise forward inclusion of variables to identify independent exposure variables . Factors which showed up as significant in the bivariate analysis but were assessed only in individuals older than 18 were not included in the logistic regression model . For risk factors suitable for an intervention , population attributable fractions ( PAF ) were calculated . The PAF , calculated as % exposed among cases x attributable risk ( AR ) , is the fraction of cases which would not have occurred if an exposure had been avoided , assuming the exposure is causal and the other risk factors in the population remain unchanged . AR is calculated as ( OR– 1 ) /OR and is the risk of tungiasis in the exposed group due to the exposure . The sample size of this study was estimated based on field studies performed in Brazil and Nigeria and contained the following assumptions: control-case-ratio 1:3; hypothetical proportion of controls with exposure 30%; least detectable odds ratio 1 . 75; power of the test 0 . 90; confidence level 0 . 95 . This would require 205 cases and 610 controls . To account for uncertainties and drop out we attempted to include a sample of 1000 individuals .
Of the 239 homesteads visited 233 fulfilled the criterion of having an inhabited first house on the left . Of the 1 , 203 individuals living in these households , 114 ( 72 males and 42 females ) were not encountered on any of the visits , reducing the study population to 1 , 089 . Of these , three did not fulfil the inclusion criterion of having spent at least four nights per week in the selected homestead during the last three months . Thus , the number of individuals available for the assessment of risk factors was 1 , 086 , all of which agreed to being interviewed and examined ( Fig 1 ) . Three hundred and twenty four patients ( 70 households ) were recruited from Kakuyuni , 221 ( 41 ) from Goshi , and 172 ( 43 ) from Vihingoni community in Kakuyuni Sub-location; 116 ( 24 ) from Mtoroni , 27 ( 5 ) from Yembe , 133 ( 28 ) from Kadzitsoni , 76 ( 18 ) from Chembe and 17 ( 4 ) from Bahati community in Malanga Sub-Location . The study population comprised 57 . 3% females , and 58 . 6% under the age of 15 years . Of those over 18 years , 54 . 1% reported being Christians while 19 . 6% were Muslims , 31 . 4% were illiterate and a further 34% had not completed primary school education . The majority of houses ( 89% ) had dirt floors and mud walls ( 84 . 5% ) , did not have improved latrines ( 56 . 7% used the bush , 32 . 6% used traditional latrines ) and shared community taps for their source of water ( 83 . 7% ) ( Tables 1 and 2 ) . The overall prevalence of tungiasis in the study population was 25 . 0% ( 95% CI 22 . 4–27 . 5% ) , but in 42 . 5% of the households at least one individual had tungiasis . Of those with tungiasis , 52 . 8% had a light ( 1 to 5 lesions ) , 32 . 1% a moderate ( 6 to 30 lesions ) and 15 . 1% a high intensity of infection ( >30 lesions ) . Five percent of the patients had ectopic lesions , almost exclusively on the hands . Age-specific prevalences and intensity of infection are shown in Fig 2 . There was a tendency of higher occurrence of tungiasis in elderly individuals living alone , although it was not significant ( p = 0 . 2111 ) . In 14 single-person households there were two adults < 40 years without tungiasis , six 40 to 59 year olds of whom 2 had tungiasis and six > 60 year olds of whom 4 had a mild to severe tungiasis . The prevalence of infection and high intensity of infection correlated significantly ( Fig 3 ) ( rho = 0 . 90 , p = 0 . 0059 ) , with the highest prevalence being in the under 15 year olds and over 40 years . The youngest patient was four months old , 4 patients were younger than one year , while the oldest patient was 80 years old . Prevalence and severity of tungiasis varied considerably between the villages with Yembe and Bahati having a prevalence of 59 . 3% and 64 . 7% respectively , while Mtoroni and Vihingoni had prevalences of 7 . 8% and 13 . 4% ( Table 3 ) . Residence in Yembe and Bahati was a significant risk factor for tungiasis ( OR 17 . 3 and 21 . 8 respectively , p<0 . 0001 ) and in Kakuyuni for both occurrence of tungiasis ( OR 6 . 5 , p<0001 ) and severe tungiasis ( OR 9 . 2 , p<0 . 05 ) . Tables 3 to 5 show demographic , socio-economic , behavioral , environmental and geographic risk factors in the bivariate analysis . The bivariate analyses identified many risk factors for tungiasis ( Table 4 ) . These included being of male sex ( OR = 1 . 59 , p = 0 . 001 ) and age < 15 and ≥ 40 years ( OR between 4 . 04 and 12 . 45 , p<0 . 001 and p<0 . 01 , respectively ) . Living in a house with a floor of sand/earth ( OR = 4 . 31 , p < 0 . 0001 ) and mud walls ( OR = 4 . 11 , p < 0 . 0001 ) were significantly related to the occurrence of tungiasis . Other significant risk factors were: using a traditional latrine or bush as a toilet; spreading waste on the compound or disposing waste on a pile; using mud puddles as a water source ( all p < 0 . 05 ) ; a low frequency of washing ( only once a day , OR = 1 . 99 , p<0 . 0001 ) and not using soap ( OR = 3 . 81 , p<0 . 001 ) ; living in crowded houses ( 4–6 persons per household , OR = 1 . 69 , p < 0 . 05 ) ; sleeping together with many other persons in a room ( p < 0 . 001 ) or children sleeping on the floor ( OR = 1 . 89 , p < 0 . 001 ) . In individuals 18 years or older , not completing primary school or never having attended primary school at all increased the odds of being affected by tungiasis by a factor of three ( OR = 3 . 37 , p<0 . 05 , Table 5 ) . On conducting the multivariate analyses , only the demographic exposure variables male sex and age under 15 remained highly significant ( Table 6 ) . Exposure variables indicating a low economic status such as poor construction characteristics of the house , direct sleeping on the floor , many people sleeping in a single room and restricted access to water also remained as significant factors . Population Attributable Fractions were calculated for those variables which are amenable to modification ( Table 7 ) . The PAF for living in a house with mud walls was 64 . 45% , for washing without soap 16 . 61% and washing only once a day 20 . 18% .
Tungiasis is a NTD prevalent in resource-poor communities in South America , the Caribbean and sub-Saharan Africa [3–7] . Although the disease is associated with important morbidity , it is neglected by health care providers globally [2 , 20–23] . Widespread control has never been attempted , only isolated efforts to treat infected individuals , often by non-governmental organizations . In East Africa , this is largely due to the lack of data on prevalence and severity of disease and hitherto risk factors have only been investigated in restricted age groups . This study showed a prevalence of 25% in the overall study population and 33 . 8% in children under 15 years . The overall prevalence is similar to that found in a community-based study in Central Uganda ( where the median prevalence in humans was 22% , but only animal keeping households were included ) , but considerably lower than prevalences observed in rural and urban resource-poor communities in Brazil and Nigeria ( with prevalences up to 45% ) [6 , 7 , 20 , 24 , 25] . Age-specific prevalence followed an S-shape curve , peaking in the 5 to 9 year age group and the elderly , an unusual epidemiological characteristic which seems to be true for all geographic areas and independent of the overall prevalence [6 , 7 , 15 , 21] . This may be due to certain age-specific behavioural patterns associated with different degrees of exposure , e . g . young children playing on the ground , as suggested by Muehlen et al . [6] and the elderly spending large amounts of time lying on the ground . Other hypotheses are a protecting effect of the increasing corneal layer of the feet [26 , 27] , a higher level of practice and dexterity in taking out embedded sandfleas with increasing age [7] and more attention given to personal hygiene . More than half of all cases ( 52 . 8% ) had a low intensity of infection ( less than 6 lesions ) , while 15% had more than 30 lesions . The percentage of patients with severe tungiasis was lower than observed in Brazil [7 , 15 , 20 , 24 , 25] . However , this is not surprising , taking into account that prevalence and intensity of infection are positively correlated [6 , 21 , 28] . The observation that age-specific prevalence significantly correlated to high intensity of infection ( rho = 0 . 90; Fig 3 ) confirms that children and the elderly bear the highest burden of disease . Anecdotal reports show that elderly individuals without social support structures tend to be infected with tungiasis more frequently [21] . This tendency was confirmed in this study , although it was not significant . Tungiasis is a zoonosis in which sylvatic , peri-domiciliary and domestic cycles are interlinked in a complex manner [2] . The situation becomes even more intricate when transmission also occurs inside the house , without the involvement of an animal reservoir . Intra-domiciliary transmission indicates that the off-host cycle of T . penetrans is completed inside the house . Usually , this is a room in which family members spend many hours a day , such as the sleeping room . If the floor in this room consists of sand , dried mud or rugged cement with holes and cracks , eggs that have been expelled by embedded female sand fleas overnight and which have fallen on the floor are swept into crevices of the floor or into the cracks between floor and wall , when the room is cleaned with a broom in the morning . Eggs can develop into larvae and pupae in such cracks [29] . That intra-domiciliary transmission occurs in the study area is supported by the finding that direct sleeping on the floor or if walls of the sleeping room consisted of mud remained significant risk factors in the multivariate analysis . The more people slept in a room the higher were the odds of tungiasis in household members . It is known that different animal species act as reservoirs in different countries [25 , 30 , 31] . In our study population , 74% of all households had chicken , 60% had goats , 25% had dogs and 25% had cats . However , no specific animal species was identified as a risk factor for tungiasis in this study . This finding supports the assumption that perhaps in these coastal communities the Tunga penetrans cycle is almost entirely human and does not involve animal reservoirs . It should be noted that animals were not examined for infection in this study , only observed as present in the compound and reported as to where they sleep at night ( S3 Appendix ) . In Northeast Brazil , stray dogs and cats are important reservoirs in urban areas , whereas in rural areas pigs are the most import species [30 , 31] . Pigs were also identified as the major reservoir of T . penetrans in Nigeria and in Uganda [15 , 25] . However , pigs were not kept in any of the households in the study area , because a considerable part of the population is Muslim . Actually , being Muslim was identified as a significant protective factor in the bivariate analysis ( Table 5 ) , which may be explained by the fact that Muslims wash their feet several times a day before entering the mosque for prayer . Other risk factors which remained significant after multivariate regression analysis were the limited access to water ( water only available from muddy pools ) , frequency of washing as well as bathing without soap . A similar finding was made in a resource-poor community in Northeast Brazil [14] . It is tempting to speculate that these risk factors are correlated to the reproductive biology of T . penetrans . Female sand fleas are fertilized by males exploring the skin only after females are embedded in the epidermis and have started neosomy [32] . There is circumstantial evidence that males are attracted by odor emitted from the faecal material released by females in regular intervals [12 , 13] . The faecal material spreads into dermal papillae around the lesion , and since it is very sticky , it needs soap to be washed off . Hence , when soap is not used or unavailability of water prevents any washing at all , more male sand fleas should be attracted to the skin and , hence , more females will be fertilized . Over time , this will lead to a higher intensity of infection . It has previously been reported that within endemic areas , tungiasis is heterogeneously distributed [2] . This was confirmed in this study: where prevalence varied between villages from 7 . 8% to 64 . 7% in the five study villages in Malanga Sub-location , all situated within 4 km of each other and from 13 . 4% to 35 . 5% in the three study villages in Kakuyuni Sub-location , within 2 km of each other . Whether the heterogeneity is determined by differences in the predominant type of exposure within a community , such as intra-domiciliary versus peri-domestic could not be clarified in this study . We found very high Population Attributable Fractions for biologically very plausible variables . Trickling of sand and dust from mud walls creates ideal conditions for the off-host life cycle of sand fleas in cracks of the floor . Building walls of stone or cement would reduce the prevalence of tungiasis by 64 percent . Similar , promoting better hygiene , particularly washing with soap , would reduce the prevalence of tungiasis in the community by 17 and 20% , respectively . We realize that this study has several limitations . First , there is an overrepresentation of adult females in the study group . The study was conducted during the day on all days of the week , including Saturday and Sunday , in order to encounter school children on the compound . However , since the majority of adult males in our study population worked as farmers and returned only after sunset we could not examine them . Extending our working periods towards the evening was not possible due to insufficient lighting and safety concerns . The distances between the households in Malanga and our time constraints , also meant that there were fewer households included in the study from this area than from Kakuyuni . Ecologically the two areas are quite different . Taken together , many factors which—by one way or another—are linked to poverty were identified as important risk factors in the bivariate and/or multivariate regression analysis , such as poor construction characteristics of the house , absence of a ventilated pit latrine , no access to drinking water on the compound , a single sleeping room for children and adults , absence of beds and mattresses , unavailability of soap for body wash , an asset score below 5 points and a low level of education among adults . Thus , as seen elsewhere in the world , tungiasis in rural Kenya is a poverty-associated disease in which the poorest of the poor bear the highest burden of disease , but that it can be controlled with simple housing improvements , improved access to water and hygiene practices . | Tungiasis ( sand flea disease ) is an ectoparasitic skin disease and belongs to the group of NTDs ( Neglected Tropical Diseases ) . It is caused by sand fleas penetrating into the skin of the feet , causing an inflammatory reaction with pain and itching . Attempts to remove the flea with inappropriate sharp tools are painful and cause bacterial superinfection , eventually leading to restricted mobility . In resource-poor communities without access to health care , prevention is the most valuable control measure . In this study we identified important risk factors for the occurrence of tungiasis and sever disease . The most relevant risk factors were poor hygiene practices and poor housing conditions . Simple control interventions such as having solid walls and floors in the house , improved access to water and washing with soap could reduce the disease burden considerably . | [
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"mathemat... | 2017 | Prevalence, intensity and risk factors of tungiasis in Kilifi County, Kenya: I. Results from a community-based study |
The diagnosis of human African trypanosomiasis ( HAT ) caused by Trypanosoma brucei gambiense relies mainly on the Card Agglutination Test for Trypanosomiasis ( CATT ) . There is no immunodiagnostic for HAT caused by T . b . rhodesiense . Our principle aim was to develop a prototype lateral flow test that might be an improvement on CATT . Pools of infection and control sera were screened against four different soluble form variant surface glycoproteins ( sVSGs ) by ELISA and one , sVSG117 , showed particularly strong immunoreactivity to pooled infection sera . Using individual sera , sVSG117 was shown to be able to discriminate between T . b . gambiense infection and control sera by both ELISA and lateral flow test . The sVSG117 antigen was subsequently used with a previously described recombinant diagnostic antigen , rISG65 , to create a dual-antigen lateral flow test prototype . The latter was used blind in a virtual field trial of 431 randomized infection and control sera from the WHO HAT Specimen Biobank . In the virtual field trial , using two positive antigen bands as the criterion for infection , the sVSG117 and rISG65 dual-antigen lateral flow test prototype showed a sensitivity of 97 . 3% ( 95% CI: 93 . 3 to 99 . 2 ) and a specificity of 83 . 3% ( 95% CI: 76 . 4 to 88 . 9 ) for the detection of T . b . gambiense infections . The device was not as good for detecting T . b . rhodesiense infections using two positive antigen bands as the criterion for infection , with a sensitivity of 58 . 9% ( 95% CI: 44 . 9 to 71 . 9 ) and specificity of 97 . 3% ( 95% CI: 90 . 7 to 99 . 7 ) . However , using one or both positive antigen band ( s ) as the criterion for T . b . rhodesiense infection improved the sensitivity to 83 . 9% ( 95% CI: 71 . 7 to 92 . 4 ) with a specificity of 85 . 3% ( 95% CI: 75 . 3 to 92 . 4 ) . These results encourage further development of the dual-antigen device for clinical use .
Human African Trypanosomiasis ( HAT ) , or African Sleeping Sickness , is caused by two sub-species of Trypanosoma brucei . T . b . gambiense accounts for approximately 95% of HAT infections and occurs across East and Central sub-Saharan Africa . The remaining infections are caused by T . b . rhodesiense in West and Southern Africa . The disease has two stages: Stage 1 , where the parasites are limited to the bloodstream , interstitial fluids and lymph of the patient , and stage 2 , where parasites are also found in the central nervous system . In recent years , the official number of recorded HAT cases has fallen below 10 , 000 per year , although possible under-reporting suggests that this is likely a minimum figure [1]–[3] . Nevertheless , with new therapeutic regimes [4]–[6] and with a repurposed drug ( fexinidazole ) [7] and a new chemical entity ( an oxaborole ) [8] in clinical trials , the potential to eliminate HAT from many regions of sub-Saharan Africa at last exists . However , disease elimination requires excellent and convenient field diagnostics . Currently , the diagnosis of infected individuals relies principally on screening teams that visit at-risk communities and from patients seeking medical help [9] , [10] . Some patients with T . b gambiense infections remain asymptomatic for years , so early diagnosis of infected individuals benefits not only the patient but also the community where these individuals can act as parasite reservoirs [11] . The most widely used diagnostic for suspected T . b . gambiense infections is the Card Agglutination Test for Trypanosomiasis ( CATT ) . This serological test detects host antibodies to a suspension of fixed and stained T . b . gambiense trypanosomes expressing variant surface glycoprotein ( VSG ) variant LiTaT1 . 3 [12] . Over the years , the CATT screening tool has been optimised to improve stability , sensitivity ( ranging from 87% to 98% ) and specificity , ( 95% ) and thermostability [1] , [13]–[16] . A positive CATT is followed up by microscopic examination of blood buffy coat smears . Until recently , stage 1 and stage 2 treatment regimes were different , and the latter much more toxic , such that positive diagnosis of infection was then staged by microscopic examination of Cerebral Spinal Fluid ( CSF ) for the presence of trypanosomes and/or lymphocytes . However , the use of nifurtimox and eflornithine combination therapy ( NECT ) [4]–[6] in recent years has largely removed the need for staging diagnosis in T . b gambiese infections . Despite its usefulness , the CATT screening tool has several widely acknowledged limitations [17]–[20] . It requires cultivation of infectious parasites for its manufacture , trained personnel for use and the read out is subjective , causing variability in reported sensitivity and specificity [16] , [21] . Significantly , some T . b . gambiense strains do not express the LiTat1 . 3 VSG gene and , therefore , patients infected with these strains do not generate detectable antibodies [22] . For the same reason , the CATT test cannot detect T . b . rhodesiense infections [23] . There are challenges to developing improved diagnostic assays and devices for HAT . Due to the very low parasite levels in patients infected with T . b . gambiense , a test that detects host antibodies ( rather than parasite antigens ) is considered more likely to have the necessary sensitivity . The WHO recommends that point of care tests ( POCT ) should follow the ‘ASSURED’ criteria; which states that a POCT device should be affordable , sensitive , specific , user-friendly , rapid , equipment-free and deliverable to the people at need . Lateral flow tests ( LFTs ) are inexpensive and simple devices that can rapidly detect nanogram amounts of antibodies in finger-prick blood samples without the need for any ancillary equipment [24] . A first-generation LFT for T . b . gambiense infections , that uses two different purified native VSG antigen bands ( LiTat1 . 3 and LiTat1 . 5 ) to detect anti-VSG antibodies , has recently entered clinical use as CATT replacement [25] . We have also produced a promising prototype LFT using a recombinant invariant surface glycoprotein ( rISG65 ) antigen [26] . In this paper , we identify another soluble form VSG ( sVSG117 also known as sVSG MITat1 . 4 ) with excellent diagnostic properties that we have used together with rISG65 to create a prototype dual-antigen LFT that detects T . b . gambiense infections and , to some extent , T . b . rhodesiense infections .
The serum samples used in this study were from the WHO HAT Specimen Biobank , archived at the Pasteur Institute , Paris . Patients were recruited by WHO to provide serum samples as described in [27] for the development of new diagnostic tests for HAT and patient consent was collected by WHO at the time of sample collection . Further local ethical approval for this study was granted by the Tayside Ethics Review Board . Rodents were used to propagate T . brucei parasites for the purification of soluble form variant surface glycoproteins ( sVSGs ) . The animal procedures were carried out according the United Kingdom Animals ( Scientific Procedures ) Act 1986 and according to specific protocols approved by The University of Dundee Ethics Committee and as defined and approved in the UK Home Office Project License PPL 60/3836 held by Michael A . J . Ferguson . All patients were tested with the CATT test ( which was followed by parasitological analysis ) and examined for clinical symptoms of HAT [27] . Serum samples were stored in the WHO HAT Specimen Biobank at −80°C and shipped to Dundee on dry ice where they were thawed , divided into aliquots and stored at −20°C . Bloodstream form T . b . brucei Lister strain 427 clones expressing four different VSG variants ( 117 , 118 , 121 and 221 ) were cultivated in rodents as described in [28] and sVSGs were purified by a simplified version of the method of Cross [28] , as described in [29] . The sVSGs were further purified by gel-filtration using a Sephacryl-S200 column ( 4×90 cm ) equilibrated and eluted with 0 . 1 M NH4HCO3 . The gel-filtration purified sVSGs were lyophilised to remove NH4HCO3 and stored as dry powders at 4°C before use . Samples were run on an SDS-PAGE gel to check for purity and were considered >95% pure ( data not shown ) . The ELISA plate preparation details and protocols were as described in [26] . ELISAs were carried out on both pooled and individual serum samples . The pooled sera were from stage 1 T . b . gambiense patients ( n = 10 ) , stage 2 T . b . gambiense patients ( n = 40 ) and matched uninfected patient sera ( n = 50 ) . Pooled sera were diluted 1∶1000 in in phosphate buffered saline containing 0 . 1% w/v bovine serum albumin ( PBS/BSA ) and plated in triplicate in serial ( doubling ) dilutions in PBS/BSA to 1∶32000 . Individual sera were diluted to 1∶1000 in PBS/BSA and applied to ELISA plates in triplicate . For the sVSG117 single antigen lateral flow test pilot study , forty T . b . gambiense infection sera and forty matched uninfected control sera were randomised and coded by a member of the University of Dundee Tissue Bank . For the dual-antigen lateral flow test virtual field trial , 431 serum samples , representing a mixture of T . b . gambiense ( n = 150 ) and T . b . rhodesiense ( n = 56 ) infection sera and matched uninfected control sera ( n = 150 for T . b . gambiense and n = 75 for T . b . rhodesiense ) were randomised and coded by the WHO HAT specimen Biobank . We supplied BBI Solutions with 5 mg sVSG117 to make single antigen sVSG117 LFT prototype devices for preliminary studies and with a further 7 mg of sVSG117 and 7 mg of rISG65 [26] to make dual-antigen LFT prototypes . BBI Solutions is an inmmunoassay development and manufacturing company that has completed more than 250 lateral flow projects over the last 25 years , with manufacturing sites in Europe , USA and South Africa . Both serum- and blood-accepting pad devices were made . For LFTs without blood pads , aliquots of 5 µl of patient sera diluted with 15 µl of PBS were added to the LFTs followed by an 80 µl of chase-buffer ( PBS containing 0 . 05% Tween 20 ) . For LFTs with blood pads , aliquots of 5 µl of patient serum were mixed with 5 µl PBS and 10 µl of freshly reconstituted human type-O blood erythrocytes . These mixtures were added to the LFTs , followed by 80 µl of chase-buffer ( PBS containing 0 . 05% Tween 20 ) . Tests were discarded if upper control line was not clearly visible . After 30 min , scoring of the test bands was performed by visual comparison of freshly completed tests with a scoring card . For the virtual field trial , two people scored all of the LFT devices independently . If there was disagreement about the infection-status of a given serum sample , a third individual provided adjudication . Line graphs were generated by Microsoft Excel . Receiver Operator Characteristic ( ROC ) curves , antigen scatter plots and tables of sensitivity and specificity scores were generated by SigmaPlot 12 .
Our original rationale for testing HAT sera against a panel of different sVSGs was to look for the presence of anti-Cross Reacting Determinant ( CRD ) IgG antibodies . The CRD is a peptide-independent epitope common to all sVSGs that is created upon the cleavage of VSG glycosylphosphatidylinositol ( GPI ) membrane anchors by GPI-specific phospholipase C ( GPI-PLC ) during cell lysis [30] . However , the ELISA data showed that while there was anti-peptide and/or anti-CRD IgG antibody titre to all four sVSGs , the immunoreactivity of both stage 1 and stage 2 T . b . gambiense HAT patient sera to sVSG117 was far higher than to the other three ( Figure 1 ) . From this result , we decided to pursue sVSG117 as a potential diagnostic antigen in its own right . We therefore proceeded to screen randomised and coded sera from 40 T . b . gambiense infected patients and 40 matched uninfected control patients against sVSG117 coated ELISA plates ( Figure 2A ) . These data strongly suggested that immunoreactivity to sVSG117 might be used to reliably discriminate infection from control sera . Consequently , sVSG117 was developed into an un-optimised single-antigen prototype lateral flow test ( Figure 3A ) , which was used with the same set of 80 randomised and coded serum samples . The visual test scores of the decoded data are shown in ( Figure 2B ) . The bands were also assessed by quantitative laser densitometry , as described in [26] , ( Figure 2C ) which showed an excellent correlation between visual- and densitometer-based scoring , with an r2 correlation value of 0 . 957 . These data enabled us to set a cut-off threshold of ≥1 visual units for discriminating infected from uninfected sera on this LFT device . Using this threshold , the test appeared to have 100% sensitivity and 100% sensitivity , albeit based on a relatively small sample set . An un-optimised dual-antigen lateral flow test prototype , containing one band of recombinant antigen rISG65-1 , previously described in [26] , and one band of the native sVSG117 antigen , described here , was manufactured by BBI Solutions ( Figure 3B ) . The dual-antigen LFTs were manufactured using the same antigen coupling conditions as the individual rISG65 [26] and sVSG117 ( this study ) single-antigen LFTs . Thus , visual score cut-offs of ≥2 for the rISG65 band [26] and of ≥1 visual units for the sVSG117 band were expected to define positive immunoreactivity to each antigen , respectively . However , to establish visual cut off values directly for this new LFT , the same 80 randomised serum samples described above were tested blind with the dual-antigen LFT and scored . After decoding , cut-offs were confirmed as being ≥2 and ≥1 for the rISG65 and sVSG117 test lines , respectively . Using these values , and the criterion of two positive test lines to define an infection , the device gave 100% sensitivity and 97 . 5% specificity in this pilot study with a limited number of serum samples ( n = 80 ) . A virtual field study was performed to assess the diagnostic potential of the dual-antigen LFT . First , aliquots of 431 randomized and coded serum samples , provided by the WHO HAT Specimen Biobank , were mixed with an aliquot of human type-O erythrocytes , provided by the Tayside blood-bank , to produce pseudo blood samples containing red blood cells as well as serum antibodies . These samples were added to the LFTs fitted with blood pads , followed by chase buffer , and read independently by two individuals after 30 min . The LFT was deemed to be positive if the rISG65 band and sVSG117 had mean visual scores of ≥2 and ≥1 , respectively . After decoding by colleagues at the WHO HAT Specimen Biobank , we were able to plot ROC curves ( Figure 4 ) and separately assess the sensitivity and specificity of the LFT to detect T . b . gambiense and T . b . rhodesiense infections using the following criteria: ( i ) two positive antigen bands = infection , ( ii ) a positive sVSG117 band = infection , ( iii ) a positive rISG65 band = infection and ( iv ) any one positive antigen band = infection . The results , in terms of sensitivity , specificity and the respective 95% confidence intervals ( CI ) are summarised in ( Table 1 ) .
Although we selected sVSG117 as a potential diagnostic antigen from empirical data in this study , our results are also consistent with population genetics studies that show that the gene encoding this VSG variant ( the same as VSG AnTat 1 . 8 ) is ubiquitous in T . b . gambiense isolates [31] , [32] , whereas those for VSGs 121 and 221 are not [33] . In the virtual field trial , using two positive antigen bands as the criterion for infection , the sVSG117 and rISG65 dual-antigen lateral flow test prototype showed a sensitivity of 97 . 3% ( 95% CI: 93 . 3 to 99 . 2 ) and a specificity of 83 . 3% ( 95% CI: 76 . 4 to 88 . 9 ) for the detection of T . b . gambiense infections . The sensitivity is comparable to those reported ( 87–100% ) for the currently deployed CATT test and for the latex agglutination test which uses diluted blood and latex beads coated with three different VSG variants ( LiTat1 . 3 , 1 . 5 and 1 . 6 ) [17] , but is poorer with respect to specificity , which have been reported as 85–97% for CATT and 96–99% for the latex test [13]–[17] . Nevertheless , the dual-antigen LFT described here is only a prototype that needs to undergo extensive optimization with respect to antigen-gold coupling , antigen loading of the test lines and composition of the chase buffer . We therefore suggest that one or both of the sVSG117 and rISG65 antigens be seriously considered for use in the next generation of clinical LFT devices for the diagnosis of T . b . gambiense HAT . We note that for our prototype dual-antigen LFT the specificity performance of each individual antigen is relatively poor for detecting T . b . gambiense infections ( Table 1 ) . For example , the rISG65 test line shows a sensitivity of 98 . 0% ( 95% CI: 94 . 3 to 99 . 6 ) but a specificity of only 65 . 3% ( 95% CI: 57 . 1 to 72 . 9 ) . We have previously reported the sensitivity and specificity performance of a visually-read single-antigen LFT using rISG65 as 88% ( 95% CI: 73 to 96 ) and 93% ( 95% CI: 80 to 98 ) , respectively [26] . While there is good overlap between the 95% confidence intervals for these two assessments with respect to sensitivity , we note that there is a discrepancy with respect to specificity . However , the previous assessment [26] only used 80 randomized infection and control serum samples and we suggest that the figures reported in ( Table 1 ) are likely to be more accurate given the significantly greater sample size ( and wider geographic sampling ) used in the virtual field trial . Another possibility is that some of the ‘false-positive’ results , which drive down the specificity figures for the dual-antigen LFT , might be due to asymptomatic true positives that had been missed by the CATT test in the virtual field trial cohort . As previously noted , this is entirely possible as not all T . b . gambiense strains express the LiTat1 . 3 VSG upon which the CATT test is based [22] . The dual-antigen LFT did not perform as well for detecting T . b . rhodesiense infections using two positive antigen bands as the criterion for infection , with a sensitivity of only 58 . 9% ( 95% CI: 44 . 9 to 71 . 9 ) and specificity of 97 . 3% ( 95% CI: 90 . 7 to 99 . 7 ) . A potentially confounding issue for T . b . rhodesiense immunodiagnosis is the typically acute nature of these infections compared to typically chronic T . b gambiense infections , with the latter more likely to produce robust antibody responses to parasite antigens . However , using any one ( or both ) positive antigen band ( s ) as the criterion for T . b . rhodesiense infection improved the sensitivity to 83 . 9% ( 95% CI: 71 . 7 to 92 . 4 ) with a specificity of 85 . 3% ( 95% CI: 75 . 3 to 92 . 4 ) . As of yet there have been no confirmed cases of co-existing T . b . rhodesiense and T . b . gambiense infections [34] , and given the current lack of immunodiagnostics for T . b . rhodesiense infections [35] , an optimized version of the dual-band LFT using the relaxed criteria of one or two positive band ( s ) to diagnose HAT might be clinically useful in T . b rhodesiense endemic regions . Taken together , the results described in this paper encourage further development of the dual-antigen LFT device described here ( or one or both of its antigens , i . e . , recombinant rISG65-1 and native sVSG117 ) for clinical use for the detection of T b . gambiense infections and , possibly , for T . b rhodesiense infections . LFT technology offers advantages over CATT with respect to the “affordable , user-friendly , rapid , equipment-free and deliverable to the people at need” components of the WHO ‘ASSURED’ criteria of “affordable , sensitive , specific , user-friendly , rapid , equipment-free and deliverable to the people at need” . However , the “sensitive” and “specific” components of the criteria are clearly also key to success and , while data on the currently deployed first-generation LFT [25] ( that uses native sVSGs LiTat1 . 3 and LiTata1 . 5 ) are yet to be published , the FIND web site suggests that its performance is comparable to CATT . Like the CATT test , and the currently deployed LFT [25] , our dual-antigen LFT requires the cultivation of parasites to make the native sVSG117 component , although sVSG117 can at least be prepared from non-human infectious T . b . brucei . Nevertheless , the ideal second-generation LFT is likely to use two recombinant , rather than native , antigens and recombinant ISG65 [26] and/or VSG domains could be the answer . | Human African Trypanosomiasis ( HAT ) is caused by infection with Trypanosoma brucei gambiense or T . b . rhodesiense . The diagnosis of T . b . gambiense infections currently relies primarily on a Card Agglutination Test for Trypanosomiasis ( CATT ) , which has acknowledged limitations , and there is no simple test for T . b . rhodesiense infection . Our overall aim is to produce a simple lateral flow test device with a similar or better sensitivity and specificity than CATT but with better stability and ease of use at point of care . In this study , we identified a particular variant surface glycoprotein , sVSG117 , with good diagnostic potential and combined it with a previously identified recombinant diagnostic antigen , rISG65 , to produce a prototype dual-antigen lateral flow test . We performed a virtual field trial by testing the device blind with 431 randomized serum samples provided by the WHO HAT Specimen Biobank . The results show that , although the prototype lateral flow test is un-optimized , it was able to diagnose T . b . gambiense HAT with a sensitivity and specificity of 97 . 3% and 83 . 3% and T . b . rhodesiense HAT with a sensitivity and specificity of 83 . 9% and 85 . 3% . | [
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] | 2014 | Identification of sVSG117 as an Immunodiagnostic Antigen and Evaluation of a Dual-Antigen Lateral Flow Test for the Diagnosis of Human African Trypanosomiasis |
Neisseria meningitis remains a leading cause of sepsis and meningitis , and vaccines are required to prevent infections by this important human pathogen . Factor H binding protein ( fHbp ) is a key antigen that elicits protective immunity against the meningococcus and recruits the host complement regulator , fH . As the high affinity interaction between fHbp and fH could impair immune responses , we sought to identify non-functional fHbps that could act as effective immunogens . This was achieved by alanine substitution of fHbps from all three variant groups ( V1 , V2 and V3 fHbp ) of the protein; while some residues affected fH binding in each variant group , the distribution of key amino underlying the interaction with fH differed between the V1 , V2 and V3 proteins . The atomic structure of V3 fHbp in complex with fH and of the C-terminal barrel of V2 fHbp provide explanations to the differences in the precise nature of their interactions with fH , and the instability of the V2 protein . To develop transgenic models to assess the efficacy of non-functional fHbps , we determined the structural basis of the low level of interaction between fHbp and murine fH; in addition to changes in amino acids in the fHbp binding site , murine fH has a distinct conformation compared with the human protein that would sterically inhibit binding to fHbp . Non-functional V1 fHbps were further characterised by binding and structural studies , and shown in non-transgenic and transgenic mice ( expressing chimeric fH that binds fHbp and precisely regulates complement system ) to retain their immunogenicity . Our findings provide a catalogue of non-functional fHbps from all variant groups that can be included in new generation meningococcal vaccines , and establish proof-in-principle for clinical studies to compare their efficacy with wild-type fHbps .
Neisseria meningitidis is a human specific pathogen that is a leading cause of bacteraemia and sepsis in children and young adults [1] . The initial symptoms of meningococcal disease are non-specific , so the diagnosis is often missed in its early stages; infection can then progress rapidly over only a few hours in severe cases [2] . Mortality rates remain high despite optimal medical therapy , with septicaemia associated with a 10% case fatality [3] . These features mean that prophylactic vaccination remains the best approach to protect individuals from this important human pathogen [4] . Considerable progress has been made in the development of conjugate capsular polysaccharide vaccines against certain serogroups of N . meningitidis ( namely A , C , Y and W135 ) , while outer membrane vesicle ( OMV ) vaccines have been successfully employed to combat epidemic disease caused by a single clones of the bacterium [5] . However , these strategies cannot be employed to prevent endemic serogroup B infection , which is the commonest form of disease in countries across Europe and North America [1] , [6] . This is because of the structural identity of the α2–8 linked polysialic acid serogroup B capsule with a modification on human N-CAM1 , preventing its use as an immunogen because of fears of autoimmunity [7] . Furthermore the phenotypic diversity of serogroup B strains limits the potential efficacy of OMV vaccines [5] . As a consequence , there have been considerable efforts to identify sub-capsular antigens as vaccine candidates that elicit appropriate immune responses . Pioneering studies with serogroup C strains have demonstrated that the serum bactericidal antibodies ( SBA ) , that develop either naturally ( following carriage of bacteria ) or through immunisation , are sufficient to provide protection against meningococcal disease [8] , [9] . Factor H binding protein ( fHbp ) is a 27 kDa surface lipoprotein consisting of two β-barrels [10] that promotes resistance against complement mediated lysis [11] and is a key meningococcal antigen that elicits SBAs [12] , [13] . It is a component of two serogroup B vaccines undergoing Phase III clinical trials; one vaccine contains fHbp alone , while the other consists of a single fHbp in combination with other protein antigens as well as an OMV [5] . fHbp can be divided into three variant groups , V1 , V2 , and V3 [13] , or two sub-families [14] based on its predicted amino acid sequence . fHbps belonging to the same variant group share over 85% amino acid identity , and only 60–70% similarity between variant groups . Furthermore immunisation with a protein belonging to one variant family generates responses with some immunological cross-reactivity within , but not between , variant groups [12] , [13] . fHbp binds the complement regulatory molecule factor H ( fH ) at high affinity , with a dissociation constant ( KD ) in the nanomolar range [10] , tighter than for any other known fH ligand . fH is the major regulator of the alternative pathway ( AP ) of complement activation; this pathway is critical to complement homeostasis as it serves to amplify activation initiated by the recognition of foreign antigens by antibodies or lectins [15] . fH consists of 20 complement control protein domains ( CCP ) , each of approximately 60 amino acids , and joined by short linker sequences . Different CCPs possess distinct functions and interact with cognate partners [16] , precisely modulating their activity to mediate the diverse regulatory roles of fH ( as a co-factor for fI mediated cleavage of C3b and a decay accelerating factor ) [16] , [17] . Although structure:function studies have been performed to characterise V1 proteins and their interaction with fH [10] , [18] , little is known about V2 and V3 fHbps . We have shown previously that fH CCP 6 and 7 ( fH67 ) are necessary for high affinity interactions with V1 fHbp , and that these two CCPs are sufficient to inhibit binding of full length fH to fHbp [10] . As fHbp binds human ( hfH ) but not murine fH ( mfH ) , novel models are required to assess the efficacy of fHbp-based vaccines . However , it is not sufficient to simply introduce a gene encoding hfH into rodents , as it is not known how this molecule will bind and regulate murine complement factors [19] . Binding of fH to fHbp could affect its efficacy as a vaccine given the high affinity of the interaction , serum levels of fH ( the second most abundant complement component in the circulation ) , and the large surface area of fHbp occupied in the interaction ( 2 , 860±177 Å ) [10] which could mask immunogenic epitopes . Recruitment of fH by fHbp to sites where antibody responses are initiated could also reduce immunogenicity due to down regulation of complement activation [20] or lead to anti-fH responses and autoimmune phenomena [21] . Furthermore , it has been suggested that sequestration of fH by pathogens ( or indeed by vaccines ) could co-opt this regulator from endothelial surfaces and render them susceptible to complement mediated damage [21] . Therefore the overall aim of this work was to define fHbps which are significantly impaired in their ability to bind fH ( i . e . non-functional fHbps ) . Our approach was to perform detailed structure:function analyses , and to assess their immunogenicity in a relevant model . Our work identifies novel residues in fHbp from each variant family that substantially affect the interaction with fH . For those fHbps examined as vaccine candidates , we demonstrated that the lack of fH binding did not simply result from a change in their structure . Of note , the distribution of amino acids in fHbp that contribute to fH binding are distinct for proteins from each variant family , despite conservation in the overall atomic structure of the proteins and affinity of their interaction with fH . The V1 , V2 and V3 fHbps exhibited similar nanomolar dissociation constants with fH even though we found single amino acid substitutions that significantly enhance binding; this suggests there is selective pressure to maintain a specific strength of fH:fHbp interaction . We also demonstrate that impaired binding of the murine fH to fHbp is not solely due to amino acid differences at the binding site; structural analyses revealed a different orientation of CCPs 6 with 7 in the human and murine molecules that would sterically inhibit interactions with mfH . As a consequence we analysed the immune responses of non-functional fHbps in mice expressing a single chimeric fH consisting of both human ( to allow binding to fHbp ) and murine ( to allow complement regulation ) domains . We found that non-functional fHbp retained their immunogenicity and elicited protective immune responses in both transgenic and wild-type mice , supporting the need to evaluate their efficacy in clinical trials .
We have shown previously that Ala substitution of two Glu residues ( Glu283 and Glu304 ) in V1 fHbp ( V1 . p1 , variant group and peptide number , www . neisseria . org ) , resulting in fHbpDM , impairs interactions with fH [10] , with data using full length fH ( Fig . S1 ) consistent with results obtained with the binding domain , fH67 . To determine whether these modifications affect the overall structure of fHbp and thence fH binding , we determined the atomic structure of fHbpDM in complex with fH67 . The only detectable changes in the fHbpDM structure are loss of the side chains of Glu283 and Glu304 compared with V1 fHbp ( Fig . 1 A , Table S1 ) , even though the dissociation constant ( KD ) of fHbpDM with fH67 is three orders of magnitude higher than with the wild-type protein assessed by surface plasmon resonance ( SPR , Fig . 1B , KD for fHbp and fHbpDM , 2 nM±0 . 4 and 3 , 330 nM±40 , respectively ) . Although substitution of Glu283 or Glu304 individually ( fHbpE283A and fHbpE304A , respectively ) results in a loss of detectable fH binding by far Western ( Fig . 1C ) , neither residue alone accounts for the profound reduction in affinity observed with fHbpDM ( Fig . 1B and Table 1 ) , probably because both residues form independent salt bridges with fH and are therefore both critical for binding . Distinct from Glu304 in V1 fHbps , V2 and V3 proteins have Thr in position 304; however this residue cannot substitute for Glu304 in V1 fHbp as fHbpE304T also exhibits significantly increased KD with fH67 ( 297 nM±17 ) in comparison with the wild-type protein ( Table 1 ) . Furthermore both Glu283 and Thr304 make independent contributions to the binding to fH of V2 and V3 fhbps albeit to different extents ( Table 1 ) . To determine whether these modified fHbps retain their immunogenicity , wild-type mice were immunised with the recombinant proteins and immune sera assayed for the titres of anti-V1 fHbp antibodies; non-transgenic mice have been used previously to determine the immunogenicity of fHbp [12] , [13] . Specific antibody levels were not significantly different from those obtained following immunisation with modified fHbps compared with wild-type V1 fHbp ( Fig . 1D ) ; V1 fHbp and the modified proteins all elicited antibody titres in excess of a 1∶32 , 000 serum dilution . Consistent with this , modified fHbps elicited SBA responses at levels that were not significantly different from the functional , wild-type fHbp . The average SBA titres from at least two independent immunisation experiments ( each using pooled sera from at least eight mice ) against N . meningitidis strain MC58 ( which expresses V1 . p1 fHbp ) were as follows: with sera raised against V1 fHbp , 340; against fHbpE283A , 180 ( unpaired t test vs . V1 fHbp , p = 0 . 17 ) and fHbpE304A , 384 ( p = 0 . 913 ) ; fHbpE304T , 192 ( p = 0 . 302 ) ; and fHbpDM 170 ( p = 0 . 148 ) . Taken together , our results show modification of V1 fHbp at Glu283 and/or Glu304 does not affect the structure or immunogenicity of the protein , even though these residues contribute significantly to interactions with fH67 ( Fig . 1B ) . To date , only Glu283 and Glu304 ( Fig . 1 ) and Arg106 [22] in V1 fHbp have been shown to influence interactions with fH; while no data are available for V3 family proteins , a recent report describes three amino acids in V2 fHbp that contribute to the interaction [23] although binding was analysed by ELISA and the affinity of the interaction was not measured . There is relatively low sequence conservation between the fHbp variants in residues buried in the V1 fHbp:fH interface , and of the two Glu residues in V1 fHbp required for fH binding , only Glu283 ( using V1 numbering , [10] ) is conserved in V2 and V3 fHbps . We therefore constructed single and double mutants in the V2 and V3 proteins replacing the equivalents of Glu283 and Glu304 with Ala , or Glu in the case of position 304 . We then determined the effect of these mutations on binding of fH by SPR ( Table 1 ) . All modified proteins had reduced capacity to bind fH , but the effect of the mutations at each position differed between the variant families , demonstrating that it is not possible to extrapolate findings from one fHbp variant to others . Therefore to identify critical amino acids involved in fHbp:fH interactions , we undertook extensive mutagenesis of V1 ( V1 . p1 ) , V2 ( V2 . p21 ) and V3 ( V3 . p28 ) fHbps of amino acids that lie in or around the interface of fH in complex with V1 fHbp [10] . In total , 46 , 46 , and 48 amino acids were individually replaced with Ala in V1 , V2 , and V3 fHbp respectively , or with the equivalent V1 residue if they were an Ala in the V2 or V3 proteins . We targeted residues at the interface between V1 fHbp and fH67 ( or equivalent amino acids in V2 and V3 proteins ) , together with neighbouring amino acids , as well control mutations involving two amino acids , fHbpK92A and fHbpH248A , on a region of fHbp opposite to the fH binding site , as well as Leu171 , a residue buried between the fHbp barrels to probe the effect of a structural alteration . The affinity of the modified fHbps with fH67 was determined by SPR with corresponding wild-type fHbps and V1 fHbpDM as controls . The affinity of parental fHbps for fH67 demonstrates that they recognise fH with similar affinities ( KD for V1 2 . 2±0 . 4 nM , V2 1 . 9±0 . 2 nM , and V3 2 . 8±0 . 0 nM , and Table S2 ) implying some selection for a specific affinity . The similar affinities of all three variant families for fH is striking as several of our mutations to Ala actually increase the affinity with which fH is bound ( Fig . 2A , B and C , and Tables S3 , S4 and S5 ) . For instance in V2 fHbp , mutation to Ala at position 157 increases binding by approximately five-fold , whilst mutation at position 106 increases the strength of binding by ∼100-fold in V3 fHbp . The mutagenesis studies show that the mode of fH binding is conserved between the three families with the same surface of fHbp involved in each variant ( Fig . 2A , B and C ) , with all amino acids in V1 , V2 and V3 fHbps that have a substantial contribution to fH binding ( i . e . Ala substitution causing a ten-fold or greater increase in KD ) located at the interface of V1 fHbp with fH in the crystal structure [10] . In line with this , modifications at several positions ( i . e . 195 , V1Arg , V2/V3Leu; 272 , Val; 283 , Glu; 313 , V1His , V2/V3Glu ) reduce binding by at least five-fold in all three families; these residues form an extended surface on both β barrels of fHbp . However there are evident differences , with mutation at certain residues having profound effects in the context of a particular variant family , but with little or no effect in others . For instance , in V1 fHbp a key point of contact with fH is seen to be a packing of Arg106 , Arg145 , Leu156 , Glu157 , Arg195 against Tyr368 in fH6 [10] . This same patch of residues is of some importance in V3 , but mutation of only some of these residues affects V2 fHbp binding . For instance , Ala substitution of Arg106 or the corresponding residue ( i . e . Pro106 in V3 fHbp ) reduces , does not affect , or increases interactions of fH with V1 , V2 and V3 proteins respectively ( Fig . 2A , B and C and Table S4 ) ; our results with V2 fHbp are consistent with others showing no effect of this residue albeit by ELISA [23] . Overall , comparing the amino acids in V1 , V2 and V3 fHbps that reduce affinity to fH by over 90% ( Fig . 2A , B and C ) , V2 fHbp is more dependent on contacts within the C-terminal barrel and less susceptible to alteration by mutation within the N-terminal barrel than V1 and V3 , where residues critical for high affinity fH binding are spread across the surface of both barrels . This implies that , whilst the overall affinity and mode of interaction are conserved , there is significant variation in which precise fHbp amino acids are critical , indicating a degree of plasticity in the mode of fH binding . In addition to examining the effect of fHbp sequences on fH binding we also investigated whether the common Tyr402His polymorphism in fH7 [24] has any significant effect on the interaction with V2 or V3 fHbp . Our previous work showed no significant effect on binding of V1 fHbp and we confirmed that this polymorphism also has no impact on binding of fH67 to V2 or V3 proteins ( not shown ) . This suggests that susceptibility to N . meningitidis does not contribute to the maintenance of this polymorphism in human populations . To further characterise V2 and V3 fHbps , attempts were made to obtain the atomic structure of these proteins either alone or in complex with fH67 . The structure of V3 fHbp with fH was solved to a 2 . 3 Å resolution ( Fig . 3A , Table S1 ) . Despite sharing only approximately 60% amino acid identity , the structures of the fHbps are well conserved ( Root Mean Square Deviation in all atom positions ( RMSD ) 0 . 65 Å ) and so is the structure of the complex with fH67 ( RMSD 0 . 91 Å ) as predicted on the basis of the similarities in distribution of amino acids critical for binding to fH revealed by mutagenesis ( Fig . 2 ) . To further understand the contribution of Pro106 to V3 fHbp:fH interactions ( which markedly increases binding when changed to Ala ) , we also determined the structure of V3 fHbpP106A; with the exception of the change of the side chain , there was no significant alteration in the structure of V3 fHbpP106A compared with the wild-type protein ( Fig . 3B ) , suggesting that the difference is due to a kinetic effect of the conformation in the loop containing this amino acid . Direct comparison of the fHbp V1 and V3 structures in this region did reveal a difference . The presence of an Arg106 in V1 fHbp pushes the loop away from fH in order to accommodate the long amino acid side chain . The presence of a Pro in V3 fHbp brings the loop closer to fH and allows the side chain of Gln107 in this variant to hydrogen-bond with the fH , consistent with mutation of this residue in V3 reducing binding by 25-fold ( Fig . 2C ) . Attempts to grow crystals of a complex of V2 with fH67 were unsuccessful . Although crystals grown from a mixture of these proteins did diffract , these were found to contain only the C-terminal barrel of the V2 fHbp . This agreed with the observation that V2 fHbp is prone to cleavage to a smaller fragment consistent with the C-terminal barrel alone ( not shown ) . The structure of the V2 fHbp C-terminal barrel is highly conserved with respect to both V1 and V3 fHbp even though it shares under 65% sequence identity with the V1 protein ( Fig . 3C ) . To further examine the apparent instability of the N-terminal β barrel of V2 fHbp , differential scanning calorimetry ( DSC ) was performed on all three variant fHbps ( Fig . 3D ) . The DSC profiles show independent unfolding of the two barrels with the peak representing unfolding of the C-terminal barrel melting at temperatures above 80°C in all three variants . ( 86 . 8 , 84 . 9 and 84 . 5°C for V1 , 2 and 3 , respectively ) . In contrast , the N-terminal barrel exhibits highly variable melting at 69 . 5°C in V1 , 60 . 6°C in V3 and at 36 . 6°C in V2 fHbp . The much reduced melting point in V2 fHbp suggests that the tendency of this barrel to be cleaved is due to unfolding of the N-terminal barrel , giving access to protease recognition sites within the N-terminal portion . While fHbp binds human fH67 with high affinity , murine fH ( mfH ) interacts but with a KD>10 , 000 fold higher than hfH ( Fig . S2 ) , meaning there is no significant interaction at serum fH concentrations ( 150–500 µg/ml , i . e . <5 µM ) . Therefore to develop a physiologically relevant model to test non-functional fHbp vaccines , we sought to define the basis of the binding of fHbp to hfH but not mfH . Alignment of the amino acid sequences of hfH and mfH revealed multiple residues located at the site of interaction with fHbp that differ between the two species ( Fig . 4A ) . Initially , to evaluate the contribution of these residues to interactions with fHbp , we generated two hfH67 mutants , each with two amino acids replaced with the equivalent residues from mfH , resulting in hfHH337Y/R341L and hfHK351R/Y352K; both modified proteins had significantly reduced affinity for fHbp regardless of variant family ( Fig . 4B , for example KD for hfHH337Y/R341L and hfHK351R/Y352K with V1 fHbp 7±1 and 2 . 8±0 . 5 µM , respectively ) , demonstrating that amino acid modification of fH can influence binding to fHbp . Therefore , we next humanised 13 residues in mfH that span the region corresponding to the interaction site of hfH with fHbp ( Fig . 4A ) . However this extensive replacement of residues was not sufficient to enable mfH to bind fHbp at appreciable levels as demonstrated by far Western analysis ( Fig . 4C ) ; PPX , the meningococcal exo-polyphosphatase was used as a control on blots [25] . To further understand the basis of the lack of interaction , we determined the crystal structure of mfH67 ( Fig . 4D ) . The overall CCP folds are conserved despite many sequence differences between the two species throughout the two structures , including the surface where hfH interacts with fHbp . Additionally the arrangement of CCPs 6 and 7 in mfH with respect to each other is distinct from hfH ( Fig . 4D ) , distorting the entire shape of the potential interface with fHbp . This would sterically hinder engagement of mfH with fHbp , providing an explanation for the high KD of the fHbp:mfH interaction , and why replacement of multiple amino acids with the human equivalents in mfH did not confer binding . Therefore to examine the impact of the interaction with fH on the immunogenicity of fHbps , we took advantage of mice lacking endogenous mfH [26] , and expressing a transgene encoding a chimeric fH molecule [27] . The chimeric fH consists of mfH CCPs 1–5 and 9–20 ( enabling interaction with murine C3b and other complement components ) , flanking hfH CCPs 6–8 ( allowing binding to fHbp , Fig . S3A ) . The chimeric fH is under the control of the apoE promoter to facilitate expression in the liver , the site of endogenous fH synthesis [27] . The chimeric fH effectively regulates the murine complement system; mice have normal C3 levels and do not develop renal disease ( Fig . S3B and not shown ) . Therefore this transgenic model provides a physiologically relevant system to examine the pre-clinical vaccine candidacy of fHbp and its derivatives . These mice were used to evaluate the vaccine candidacy of non-functional V1 fHbps compared with V1 fHbp . Further work focussed on the other non-functional mutants , fHbpR106A and fHbpI311A; modification of Arg106 has been described previously , while Ala substitution of Ile311 has the one of the most marked effects on fH67 interactions of single amino acid substitutions as demonstrated by SPR ( fHbpI311A KD with fH67 , 1 . 3±0 . 5 µM ) . The structure of the co-complex of fHbpR106A with fH67 ( Fig . 1A ) confirmed that there was no significant change in its overall structure . Immunisation of transgenic mice with the non-functional proteins , fHbpDM , fHbpI311A , and fHbpR106A elicited similar levels of anti-V1 fHbp antibodies as determined by ELISA ( Fig . 5A ) , demonstrating that the non-functional fHbps retain their antigenicity . The non-functional proteins also elicited SBA titres ( measured using human complement ) , that were similar as raised against the wild-type V1 fHbp ( Fig . 5B ) . Therefore non-functional fHbps retain their immunogenicity and elicit protective immune responses .
fHbp is an important virulence factor and a key component of vaccines designed for the prevention of serogroup B N . meningitidis infection . Furthermore , fHbp-based vaccines could provide coverage irrespective of serogroup by either combining it with other antigens or using proteins from different variant families [4] . The antigen has been delivered as a recombinant protein in vaccines undergoing Phase II and III clinical trials , but can also be overexpressed in OMV vaccines by genetic modification of strains used for vaccine production [28] . Here we characterised members of the three variant families by identifying amino acids that are critical for fH binding and through structural analysis , to inform future vaccine design and to understand the basis of the interaction of fHbp with fH . The three variant family fHbps we examined all exhibited nM KDs with fH67 , which is lower than for any human ligand of this important complement regulator . Although fHbps have been found in clinical isolates with reduced affinity for fH [29] , none have displayed increased binding . Despite this , we were able to identify several single amino acid substitutions that led to a substantial increase in affinity with fH67 , suggesting that selection may not favour tighter binding , indicating that there could be circumstances when uncoupling of fHbp from fH is beneficial for the bacterium . It is possible that fHbp has other functions [30] which are impaired by the presence of fH . Alternatively disengagement from fH could promote colonisation of different sites in the human host , similar to the way modification of pili facilitates disaggregation of bacteria on the surface of cells [31] . Characterisation of the two Glu residues in V1 fHbp that form salt bridges with fH , and their equivalent residues in V2 and V3 proteins ( i . e . Thr304 ) indicated that these residues make independent contributions to high affinity fH binding and that different variant family proteins engage fH in distinct ways . To identify residues that are necessary for high affinity fH interactions , we performed extensive Ala substitution mutagenesis to produce a catalogue of amino acids in each variant family that contribute to binding to fH , and could be modified in vaccine design . This adds to the three residues already described for V1 fHbp and for V2 fHbp which are required for high affinity binding , although the affinity of the modified V2 proteins for fH was not reported [23] . Our findings illustrate differences in the precise mechanisms by which fH engages fHbp from different families , even though the same face of fHbp is involved . This is emphasised by the finding that Ala substitution of amino acids at the same position ( i . e . V1 and V2 Arg106 , and V3 Pro106 ) have profoundly different effects , markedly reducing , not affecting , or increasing fH affinity for V1 , V2 and V3 fHbps , respectively . This demonstrates that it is not possible to extrapolate data from one variant family protein to others . The dramatic increase in tightness of binding on mutating Pro to Ala at this position in V3 probably results from a kinetic effect , suggesting that in the unbound fHbp the loop containing this residue adopts a different conformation which must be refolded into the conformation seen in the complex . It may be that the Pro converts less readily to the structure required for binding than the loop bearing an Ala at this position Despite these distinctions , all amino acids from V1 , V2 , and V3 fHbps necessary for high affinity binding are located at the interface previously identified in the V1 fHbp:fH co-complex [10] . We determined the first structures of the entire V3 fHbp and the C-terminal β barrel of V2 fHbp . There is a striking conservation in the overall structure of the V1 and V3 proteins despite their relatively low level of sequence identity . Although amino acids that contribute to high affinity interactions are grouped in the same regions of these proteins , the precise interactions required to achieve the same affinity and overall interaction with fH differ . Such plasticity could permit the bacterium to alter the fH recognition site for immune evasion whilst retaining the same biological function . The instability of V2 fHbp and its susceptibility to proteolysis are not desirable in a vaccine antigen , and might explain why it has not been included in any vaccines in clinical trials to date [5] . Such instability is less likely to present an issue in the context of the protein on the exterior of bacteria where interactions with surrounding molecules are likely to stabilise the structure , rendering it competent for binding fH; however it might explain why more C-terminal residues appear to be critical for fH binding compared with fHbp from other variant families . Further work is on-going to define the basis of the instability of V2 fHbp , as there is no obvious explanation for this by molecular modelling using the V1 and V3 structures ( not shown ) . The use of transgenic mice to study human pathogens has been an important advance in infectious diseases research and prevention . For instance , introducing single amino acid changes into murine molecules [32] or transgenes encoding complete cellular receptors or nutritional sources [33] , [34] have allowed the study of human-specific pathogens in rodents . However care must be taken when modifying regulatory factors that govern the activity of complex pathways such as the complement system . We attempted to make minimal changes to mfH within the region that mediates high affinity interactions with fHbp , which would allow binding to the antigen without compromising the important regulatory functions of the molecule . Initial efforts to achieve this by introducing multiple amino acid changes in mfH proved unsuccessful , most likely due to the orientation of CCP 6 with 7 in mfH which would sterically inhibit interactions with fHbp . Therefore , we used a chimeric fH which was humanised through substitution of the CCPs involved in interactions with fHbp together with hfH8 in case it induced unforeseen structural changes in fH7 [35] . This model provides a physiological assay to evaluate non-functional fHbps , rather than simply introducing an intact human transgene , and was employed to examine the immunogenicity of functional and non-functional fHbps . Overall there were no substantial differences in the immune responses in transgenic and wild-type mice vaccinated with the same protein; both generated similar levels of IgG and SBA responses against the antigen and relevant strain . Previous work suggests that the immunogenicity of fHbpDM is impaired compared with V1 fHbp [36] . However we found that the structure of this protein is unchanged except for the loss of the side chains of Glu283/304 , and that it retained its immunogenicity in both transgenic and non-transgenic mice . We also examined the immunogenicity of fHbpI311A which we predict reduces the affinity due to the loss of interactions with the bulkier Ile side chain in the Ala mutant . Previous work indicated that V1 fHbpR106S exhibits a degree of enhanced immunogenicity compared with wild-type fHbp in mice possessing extra copies of hfH as well as endogenous mfH [37] . SBA activity was increased by only a single dilution in mice immunised with the non-functional fHbp compared with the wild-type protein , and the effect was only seen in mice with hfH levels above a certain threshold . However we were unable to replicate this finding either with the corresponding protein , V1 fHbpR106A , or with two other non-functional fHbps , fHbpDM and fHbpI311A , and did not observe a relationship between fH levels and SBA titres in individual mice ( Fig . S4 ) . This is unlikely to result from the hydroxyl side chain in Ser in fHbpR106S compared with fHbpR106A ( used here ) . Potential explanations for these discrepancies in immunogenicity include differences in antigen and adjuvant preparation , immunisation schedules , and the age of mice and their genetic background ( C57/Bl6 here vs . BALB/c ) . Furthermore the effects on immunogenicity of the presence of both murine and human fH in a single animal , or an antigen binding hfH ( which might not function efficiently in a heterologous environment ) are not known . Any rodent model of immunogenicity has inherent limitations . For instance , both we and others [37] immunised mice with 20 µg of fHbp on each occasion . This is relatively a much higher dose than given to infants in current formulations ( 50 µg ) [5] , so the proportion of antigen bound by fH might be significantly lower in rodent than in humans . Additionally the route of immunisation ( intraperitoneal in rodent models , subcutaneous in clinical trials ) will affect delivery to and the site of immune induction , while results from inbred rodent lines will not be directly applicable to human populations . Despite these reservations , ours and other's findings demonstrate that a series of non-functional , structurally defined fHbps elicit at least equivalent responses to V1 fHbp , and provides proof in principle that these antigens merit evaluation in clinical trials which would provide the only definitive evidence of whether they offer advantages as a vaccine compared with wild-type proteins in terms of safety and immunogenicity . The efficacy of vaccine antigens can be substantially enhanced by structure based studies to generate non-toxic derivatives of bacterial molecules or antigens with increased efficacy [38] . Here we show that even though V1 , 2 and V3 fHbps exhibit remarkably conserved atomic structures , differences in key amino acids necessary for interactions with fH are only revealed by functional studies . Our findings both provide a catalogue of proteins that could be included in the rational development of the next generation of vaccines containing non-functional fHbps , and could be informative about the basis of the diversity in fHbp sequences seen among clinical isolates , and the genetic susceptibility of individuals to meningococcal disease [28] .
N . meningitidis was grown in 5% CO2 on Brain Heart Infusion ( BHI ) agar plates with Levanthal's supplement , and Escherichia coli propagated in LB liquid medium with shaking at 200 r . p . m . or on LB agar plates ( 1 . 5% agar wt/vol ) . Whole cell lysates were prepared of N . meningitidis grown overnight on solid media then re-suspended in PBS . The concentration of bacteria was determined by measuring the optical density at 260 nm of bacterial lysates in 1% SDS/0 . 1 M NaOH [39] and adjusted to 1×109 CFU/ml , and re-suspended with an equal volume of 2× SDS-PAGE loading buffer ( 100 mM Tris-HCl pH 6 . 8 , 20 µM β-mercaptoethanol , 4% SDS , 0 . 2% bromophenol blue , 20% glycerol ) , and boiled for 10 minutes; polyacrylamide gels which were either stained with Coomassie blue or proteins were transferred to nitrocellulose membranes in a Mini Trans-Blot Cell . Membranes were incubated with primary then secondary antibodies diluted in PBS-T and 1% skimmed milk ( PBS-TM ) , which were detected using Amersham ECL Western blot detection method ( GE Healthcare ) . To detect fH binding , blots were incubated in either normal human serum ( NHS 1∶100 ) , purified ( 5 µg/ml ) or recombinant fH diluted in PBS-TM for two hours , washed then incubated with goat anti-fH pAb ( Quidel , 1 in 2 , 000 ) ; membranes were then incubated with murine anti-goat HRP-conjugated IgG ( Sigma , 1 in 10 , 000 ) . Female six to eight-week-old BALB/c mice ( Charles Rivers , Margate ) were immunised with antigens ( 20 µg ) with aluminium hydroxide adsorbed by spinning the mixture for one hour at room temperature . Immunogens were given intraperitoneally ( transgenic mice ) on days 0 , 21 and 35; sera were collected on day 49 . In immunisation studies with C57Bl/6 transgenic mice , antigens were given intraperitoneally to twelve to sixteen-weeks-old mice on days 0 , 21 and 35 , and whole blood collected by terminal anaesthesia and cardiac puncture from the mice on day 49 . All procedures were conducted in accordance with Home Office guidelines . Wells of ELISA plates ( Nunc ) were coated with V1 fHbp ( 100 ng ) overnight at 4°C , washed , blocked for one hour with 3% normal goat serum diluted in PBS-T , then sera added at a range of dilutions . Binding was detected using goat anti-mouse HRP-conjugated IgG ( Dako , 1 in 1 , 000 ) and incubated for one hour at room temperature . The substrate ( ONPG , Sigma ) was added to wells , the reaction was stopped with 3N HCl , and the A492 read with a Multiskan photometer ( Thermo Scientific ) . For serum bactericidal assays , N . meningitidis MC58 was re-suspended in SBA assay buffer ( 0 . 1% glucose in PBS ) to a final concentration of 5×104 CFU/ml and mixed with an equal volume of human complement . Control wells were also prepared containing bacteria without serum or without complement . Sera was pooled from groups of non-transgenic mice ( n>8 ) , and immunisations repeated on two or three occasions for each antigen; for transgenic mice , sera was tested from individual animals . Following incubation , 10 µl from each well was plated onto solid media , and the number of surviving bacteria was determined after overnight growth . The bactericidal activity was expressed as the reciprocal of the highest dilution of sera required to kill more than 50% of bacteria . Point mutations in fHbp were introduced by site directed PCR mutagenesis with Roche Expand High Fidelity enzyme or using the QuikChange Site-Directed Mutagenesis Kit ( Agilent Technologies ) following the manufacturer's protocols , and primers shown in Table S6 . His-tagged proteins were expressed in E . coli B834 ( DE3 ) cells and isolated using Ni-NTA Magnetic Agarose Beads ( Qiagen ) following the manufacturer's protocols and dialysed against 50 mM Sodium acetate , pH 4 . 5 . A comparison of the numbering of fHbp amino acids here and by others is shown in Table S7 . mfH67 was cloned from the full-length Mus musculus fH gene into pET-15b expression vector ( Novagen ) using the following primers . MFH67-For 5′-GGAGATATACCATGGCCTTGAAACCATGTGAATTTCC-3′ , and MFH67-Rev 5′-AGCCGGATCCTCGAGTCAGATGCATTTGGGAGGAGG-3′ . mfH67 was expressed and purified using the method described previously [40] . Crystals were grown from a 11 . 9 mg/ml solution in a 50% dilution of 0 . 2 M Ammonium chloride , 0 . 1 M MES , pH 6 . 0 , 2% PEG 6000 and then cryo-protected in 20% glycerol . To humanise recombinant mfH , point mutations were introduced in the mouse fH cDNA by site-directed mutagenesis using the QuikChange Multi Site-Directed Mutagenesis kit ( Stratagene ) according to manufacturer's instructions . Primers used can be found in Table S8 . The eukaryote expression vector pCI-Neo ( Promega ) containing the cDNA of wild-type mfH , humanised mfH or hfH , was used for transient transfection in COS7 cells by using lipofectamine ( Invitrogen ) . Cell supernatants containing the recombinant proteins were collected [41] . C3 and fH levels were measured by ELISA . In brief , C3 levels were quantified using goat anti-mouse C3 and HRP-conjugated goat anti-mouse C3 antibodies ( both from MP Biomedicals ) as capture and primary antibodies , respectively . The results were quantified by reference to a standard curve generated from acute-phase sera containing a known amount of C3 ( Calbiochem ) . fH levels were measured using goat anti-human fH antibody ( ABIN113017 , www . antibodies-online . com ) and the biotinylated version of the same antibody as capture and primary antibodies , respectively . The results are presented as O . D . values as no reference is available to use as a standard curve for the chimeric protein . E . coli BL21 ( DE3 ) cells with relevant plasmids were grown in liquid medium to an OD A600 of 0 . 4–0 . 8 then IPTG was added to a final concentration of 1 mM . After four hours , bacteria were harvested and recombinant proteins purified by affinity chromatography with a HisTrap column ( GE Healthcare ) . Proteins were purified with an AKTApurifier ( GE Healthcare ) by elution with 200 mM imidazole . Further purification was performed by size exclusion chromatography ( Superdex S-200 ) . Protein concentrations were estimated by the Bradford assay . Surface Plasmon Resonance was performed using a Biacore 3000 ( GE Healthcare ) or ProteOn XPR36 ( BioRad ) . fHbp was immobilized on a CM5 or ProteOn GLM sensor chip and increasing concentrations of fH67 were injected over the flow channels at 40 µl/min and allowed to dissociate for 300 seconds . BIAevaluation 3 . 2 or ProteOn manager software was used to calculate the KD . DSC experiments were carried out using a VP Capillary DSC ( GEHealthcare ) using a heating rate of 1°C/min from 30 to 110°C . The V2 sample was repeated from 10 to 110°C when its lower melting event was identified at around 35°C to ensure that this transition was flanked by sufficient baseline to allow analysis . Samples contained 20 uM of each variant in 25 mM Tris pH7 . 5 , 150 mM Na Cl . Samples and buffer were degassed by stirring under vacuum before running . Data analysis was done with the software supplied with the instrument by the manufacturers ( Origin version 7 . 0 ) with buffer reference subtracted from the sample data and baseline correction . The crystals were grown using the sitting drop vapour diffusion method from 400 nl drops prepared using an Oryx Nano robot ( Douglas Instruments , UK ) . V1 fHbp crystals were grown and cryo-protected as described previously ( 8 ) . V2 crystals were grown from a 1∶1 mixture of fH67 and V2 . p21 at 10 mg/ml in 30% PEG2KMME , 0 . 1 M Sodium Acetate pH 4 . 6 , 0 . 2 M Ammonium sulphate , and cryo-protected with 10% PEG 400 . The dataset was collected on beamline ID29 at ESRF . For V3 crystals , concentrations of 13 . 6 and 15 . 2 mg/ml were used for V3 fHbp and fHbpP106A . Both grew in 0 . 2 M imidazole pH 6 , 20% PEG 4000 , and were cryo-protected with 15% ethylene glycol and 85% mother liquor . Data were obtained on I04 ( for fHbpP106A ) at Diamond Light Source ( Harwell , England ) and ID29 at ESRF ( Grenoble , France , for V2 and V3 fHbp ) . Diffraction data were processed with XDS and SCALA [42] from within the xia2 data-processing suite [43] . Structures were solved by molecular replacement with CCP4 [44] and Phaser [45] , built using CCP4-Buccaneer [46] and refined and rebuilt iteratively using autoBUSTER [47] and Coot [48] . All work with animals was conducted in accordance with the United Kingdom Home Office guidelines under relevant project licences . Work was approved by the Riverside Local Ethics Committee and performed under licence number PPL 70/6960 awarded to CMT . | Vaccines are currently available against several serogroups of Neisseria meningitidis . However broadly effective serogroup B vaccines are still required as capsule-based approaches cannot be implemented with this serogroup because of the risks of auto-immunity . As a result , vaccines based on proteins in the bacterial outer membrane are being developed . Factor H binding protein ( fHbp ) is an important meningococcal immunogen which is able to bind the human complement regulator factor H ( fH ) at high affinity; this interaction could impair the efficacy of fHbp-based vaccines . Here we perform structure:function analyses to define non-functional fHbps and to explain the basis for the host specificity of the fHbp:fH interaction . The vaccine candidacy of non-functional fHbps was compared with wild-type proteins in a relevant transgenic model . These findings should allow the design and evaluation of future fHbp vaccines against this important human pathogen . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"meningococcal",
"infections",
"medicine",
"bacterial",
"diseases",
"infectious",
"diseases",
"meningococcal",
"disease",
"immunity",
"innate",
"immunity",
"immunology",
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"host-pathogen",
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"meningoccal",
"septi... | 2012 | Design and Evaluation of Meningococcal Vaccines through Structure-Based Modification of Host and Pathogen Molecules |
C-type lectins ( CTLs ) are characterized by the presence of a C-type carbohydrate recognition domain ( CTLD ) that by recognizing microbial glycans , is responsible for their roles as pattern recognition receptors in the immune response to bacterial infection . In addition to the CTLD , however , some CTLs display additional domains that can carry out effector functions , such as the collagenous domain of the mannose-binding lectin . While in vertebrates , the mechanisms involved in these effector functions have been characterized in considerable detail , in invertebrates they remain poorly understood . In this study , we identified in the kuruma shrimp ( Marsupenaeus japonicus ) a structurally novel CTL ( MjCC-CL ) that in addition to the canonical CTLD , contains a coiled-coil domain ( CCD ) responsible for the effector functions that are key to the shrimp’s antibacterial response mediated by antimicrobial peptides ( AMPs ) . By the use of in vitro and in vivo experimental approaches we elucidated the mechanism by which the recognition of bacterial glycans by the CTLD of MjCC-CL leads to activation of the JAK/STAT pathway via interaction of the CCD with the surface receptor Domeless , and upregulation of AMP expression . Thus , our study of the shrimp MjCC-CL revealed a striking functional difference with vertebrates , in which the JAK/STAT pathway is indirectly activated by cell death and stress signals through cytokines or growth factors . Instead , by cross-linking microbial pathogens with the cell surface receptor Domeless , a lectin directly activates the JAK/STAT pathway , which plays a central role in the shrimp antibacterial immune responses by upregulating expression of selected AMPs .
Like other invertebrates , the shrimp’s defense against microbial pathogens relies on innate immune responses , which generally encompass their recognition , killing and disposal through humoral and cellular mechanisms . Humoral responses are rapid and effective , and include recognition factors such as lectins , as well as effector mechanisms , including hemolymph clotting , production of antimicrobial peptides ( AMPs ) and oxygen reactive intermediates , and , melanization of the microbe [1] . The cellular immune responses include pathogen recognition , killing and clearance by phagocytosis , immobilization by hemocyte extracellular traps , or nodulation or encapsulation of larger microorganisms [2 , 3] . Initiation of the innate immune response is triggered by pathogen sensors called pattern recognition receptors ( PRRs ) such as Toll-like receptors and lectins . More than 10 different types of PRRs are found in shrimp [4] . Among these PRRs , C-type lectins ( CTLs ) are important in the recognition of carbohydrate moieties ( microbe-associated molecular patterns , MAMPs ) displayed on microbial surfaces . CTLs are a structurally diverse family of carbohydrate-binding proteins of wide taxonomic distribution in both vertebrates and invertebrates , characterized by calcium-dependent ligand recognition through a C-type carbohydrate recognition domain ( CTLD ) that displays a unique sequence motif and structural fold . By recognizing microbial glycans , the CTLD is responsible for the CTLs’ roles as pattern recognition receptors ( PRRs ) in the innate immune response to bacterial infection of both vertebrates and invertebrates . In addition to the CTLD , however , most CTLs display additional domains that can carry out effector functions . In CTLs from vertebrates some of these effector functions have been well established and the mechanisms involved have been described in detail . One of the most thoroughly characterized example , the mannose-binding lectin ( MBL ) a prototypical CTL , displays a collagenous domain that by associating with an MBL-associated serine protease ( MASP ) can activate the complement cascade [5 , 6] . In contrast , the effector functions of CTLs from invertebrates remain poorly understood . During the past few years , we have focused on the roles of CTLs in the shrimp immune response to bacterial infection [7–9] . Based on their domain organization , at present time the shrimp CTLs can be classified in three distinct groups: ( 1 ) CTLs that contain only one CTLD , ( 2 ) CTLs with two CTLDs , and ( 3 ) CTLs with one CTLD and an additional domain [7] such as a low-density lipoprotein receptor class A domain [10] , and an immunoglobulin-like domain [11] . The shrimp CTLDs can recognize viral or bacterial glycans , and thus immobilize the invading microorganisms , CTLs can also induce downstream immune responses aimed at their killing , destruction , and elimination . For some shrimp CTLs , their effector innate immune functions such as phagocytosis , prophenoloxidase activation , and promotion of respiratory burst have been described [7 , 8] , although the mechanisms involved still remain unclear . We recently identified in the kuruma shrimp Marsupenaeus japonicus a novel CTLs ( MjHeCL ) that in addition to recognizing microbial glycans via the CTLD , displays unique functional properties , as the inhibition of proliferation of the hemolymph microbiota by maintaining the homeostatic expression of antimicrobial peptides [7] . The mechanistic aspects of this regulation of AMP expression in shrimp by CTLs , however , remained to be elucidated . In this study , we identified in M . japonicus a novel CTL that we designated MjCC-CL , and that like MjHeCL , can regulate the expression of selected AMPs . MjCC-CL has a chimeric structure comprising the canonical CTLD that can recognize bacterial glycans and a coiled-coil domain ( CCD ) . Our study revealed that the mechanism by which upon bacterial challenge , MjCC-CL regulates AMP expression is based on signaling by the JAK/STAT ( Janus Kinase/Signal Transducer and Activator of Transcription ) that leads to the transcription of five AMPs . The JAK/STAT pathway , which is common to both invertebrates and vertebrates , controls multiple biological processes , including cell development , growth and survival , tissue homeostasis and immune responses [12 , 13] . In vertebrates , the JAK/STAT pathway is indirectly activated by cytokines or growth factors released upon stress signals and cell death . In striking contrast , the present study revealed that activation of the JAK/STAT pathway and AMP upregulation in shrimp takes place by direct MjCC-CL-mediated recognition of the microbial pathogen glycans by the CTLD , and binding of the CCD to the hemocyte surface Domeless , the receptor that activates JAK/STAT pathway .
From our transcriptome analysis of the kuruma shrimp ( M . japonicus ) we identified a unique CTL which we named MjCC-CL ( GenBank accession no . KU213612 ) , and comprises a signal peptide , a coiled-coil domain ( CCD ) , and a CTLD ( Fig 1A ) . A phylogenetic analysis of MjCC-CL and other CTLs from invertebrate and vertebrate species showed that in contrast with MjHeCL , a shrimp CTL that we recently characterized [7] , MjCC-CL clusters with CTLs from vertebrates ( Fig 1B ) . Further SMART analysis indicated that the N-terminal region ( including the CCD ) of MjCC-CL is similar to the interleukin ( IL ) 10 domain from mammals at the threshold level . Multiple alignment of the N-terminal region of MjCC-CL with Hs-IL10 ( Homo sapiens interleukin 10 , CAG46790 . 1 ) and Ms-IL10 ( Mus musculus interleukin 10 , EDL39722 . 1 ) showed that they share low similarity ( Fig 1C ) . A comparison of homology models of the CCD of MjCC-CL and IL10 revealed an α-helix-rich structure for the CCD ( Fig 1C1 ) , with overall similarity to IL10 ( Fig 1C2 ) . The tissue distribution of MjCC-CL transcripts and protein in the control and challenged animals were analyzed by qRT-PCR and western blotting , respectively . The results in control shrimp showed that MjCC-CL was expressed in all tissues tested , with a relatively high expression in the intestine ( Fig 1D ) . In the challenged shrimp , a time-course expression analysis showed that MjCC-CL was increased significantly in hemocytes at 3 and 6 h after V . anguillarum injection ( Fig 1E ) , and with no obvious changes in the relatively high level of expression in intestine ( Fig 1F ) . To analyze in vivo the potential immune function of MjCC-CL , we knocked down MjCC-CL expression by RNAi ( MjCC-CL-RNAi shrimp ) ( Fig 1G ) and assessed the bacterial clearance and survival rate of shrimp relative to the control animals with normal MjCC-CL expression . The results showed that in the experimentally challenged MjCC-CL-RNAi shrimp the number of bacteria increased significantly ( Fig 1H ) , while the survival rate of the shrimp decreased significantly ( Fig 1I ) as compared to the controls . These results strongly suggested that MjCC-CL participates in the antibacterial response in shrimp . To gain further insight into the role ( s ) of MjCC-CT in antibacterial immunity , we analyzed the potential binding to both Gram-positive ( GP ) and Gram-negative ( GN ) bacteria of: ( a ) the intact recombinant MjCC-CL ( S1B Fig ) ; ( b ) the separate recombinant CC and CTL domains ( S1C and S1D Fig ) ; and ( c ) the authentic MjCC-CL purified from shrimp intestine . The results showed that the intact rMjCC-CL and the rCTLD bound to the GP bacteria S . aureus and B . subtilis , and to the GN bacteria V . anguillarum and E . coli , whereas the rCCD showed no binding activity to any of the bacterial species tested ( Fig 2A ) . Like the rMjCC-CL , the native MjCC-CL purified from shrimp intestine ( Fig 2B ) also bound to four types of bacteria ( Fig 2C ) . Direct binding analysis with enzyme-linked immunosorbent assay ( ELISA ) revealed that the rMjCC-CL and CTLD bound to lipopolysaccharide ( LPS ) from E . coli and peptidoglycan ( PGN ) from S . aureus or B . subtilis , whereas no binding activity was detected for the rCCD ( Fig 2D–2F and S1 Table ) . We then examined the potential binding of MjCC-CL to the shrimp hemocyte surface by immunocytochemical analysis using an anti-GST antibody . The results showed that rMjCC-CL or rCC could bind to the hemocyte surface ( Fig 2G ) . To detect if the LPS/rMjCC-CL complexes could bind to the hemocyte surface , rMjCC-CL that had been pre-incubated with LPS was injected into shrimp , and the hemocytes were collected for immunocytochemical analysis . The control animals received LPS-incubated GST instead of LPS/rMjCC-CL complexes . The results revealed that the injected LPS/rMjCC-CL complexes localized on the hemocyte external surface , whereas in the GST control animals the label localized to the hemocyte cytoplasm ( Fig 2G and 2H ) . Taken together , the results indicate that MjCC-CL not only binds to both GN and GP bacteria by recognizing their cell surface polysaccharides , but also the “self” ligands on the shrimp hemocyte surface . As MjCC-CL binds to the bacterial surface , we next examined the possibility that this shrimp lectin could have direct bacteriostatic or bacteriocidal activity . Exposure of bacterial cultures to intact rMjCC-CL at increasing concentrations ranging from 10 μg/ml to 100 μg/ml showed no differences in bacterial growth with control cultures that received no MjCC-CL ( Fig 3A ) . In light of these results we investigated if the antibacterial activity of MjCC-CL could be indirect , such as via the upregulation of AMP expression . For this , we first evaluated the expression of six AMPs , including 4 antilipopolysaccharide factors ( ALFs ) ( GenBank accession nos . ALF-A1 KU213607 , ALF-C1 KU213608 , ALF-C2 KU160498 , and ALF-D1 KU160499 ) and 2 Crustins ( Crus ) ( CruΙ-1 KU160502 , and CruΙ-5 KU213606 ) , in shrimp intestine after V . anguillarum or LPS challenge; the results showed that the expression of all six AMPs was significantly upregulated ( Fig 3B ) . Then , we performed RNA interference ( RNAi ) of MjCC-CL and the expression of these six AMPs upon LPS challenge was analyzed . After transfection with RNAi targeting MjCC-CL in shrimp ( Fig 3C ) following challenge with LPS , no upregulation of expression of ALF-A1 , ALF-C1 , ALF-C2 , CruΙ-1 and CruΙ-5 was observed . In contrast , expression of ALF-D1 was equally upregulated in the RNAi transfected animals and in the control animals in which MjCC-CL was normally expressed ( Fig 3D ) . To confirm the above results , the RNAi-transfected shrimp were first injected with rMjCC-CL protein , and subsequently challenged with LPS as above , and the expression of six AMPs was analyzed . The results showed that ALF-A1 , ALF-C1 , ALF-C2 , CruΙ-1 and CruΙ-5 expression was increased significantly by LPS challenge in the rMjCC-CL-rescued shrimp , as compared to the control animals ( Fig 3E ) . To confirm the specificity of the rMjCC-CL-mediated rescue we also injected a group of RNAi-transfected shrimp with recombinant CTL2 , another shrimp CTL , and carried out the LPS challenge as above . The results showed that AMP expression was not induced significantly after rCTL2 injection , as compared to the control ( Fig 3F ) . All the above results indicated that MjCC-CL specifically upregulates the expression of five different AMPs . Next , we investigated the signaling pathway through which MjCC-CL regulates AMP expression . In Drosophila , AMP expression is mainly regulated by the Toll and IMD pathways [14] . Additionally , the cytokine-activated JAK/STAT pathway is key for antiviral responses in both Drosophila and mammals [13 , 15–18] . Since in mammals the JAK/STAT signaling pathway is activated by different cytokines , our observation that MjCC-CL contains an IL10-like domain led us to hypothesize that its function might be also related to this signaling pathway . Therefore , we examined the potential role ( s ) of MjCC-CL in activation of the three signaling pathways , Toll , IMD , and JAK/STAT by assessing the translocation into the hemocyte nucleus of their transcription factors Dorsal , Relish and STAT ( GenBank accession no . KU213611 ) , respectively , upon increasing circulating MjCC-CL levels , and using LPS challenge as a positive control . The results of immunocytochemical analysis of hemocytes from experimental and control shrimp showed that while LPS challenge induced Dorsal or Relish translocation ( Fig 4A and 4B ) no translocation of Dorsal ( Fig 4A , a ) or Relish ( Fig 4B , b ) was detected in rMjCC-CL-injected shrimp . In contrast , STAT did translocate into the hemocyte nucleus in both rMjCC-CL-injected and LPS-injected shrimp ( Fig 4C , c ) . Western blot analysis of proteins extracted from cytoplasm and nucleus of intestinal cells yielded results similar to those from the immunocytochemical study ( Fig 4D and 4E ) . To further confirm the results , LPS was first incubated with rMjCC-CL and upon which any remaining free LPS was washed off . The LPS-rMjCC-CL mixture was injected into shrimp and translocation of Dorsal , Relish and STAT into the hemocyte nucleus was examined . The results showed that the LPS-rMjCC-CL complex could induce STAT translocation into the hemocyte nucleus ( Fig 4F-f ) but not translocation of Dorsal ( Fig 4G ) or Relish ( Fig 4H-h ) . To confirm the role of MjCC-CL in activation of the JAK/STAT pathway , we analyzed STAT phosphorylation in MjCC-CL-RNAi shrimp . The results showed that in the MjCC-CL knockdown shrimp ( Fig 5A ) the LPS challenge reduced STAT phosphorylation ( Fig 5B ) and inhibited STAT translocation into the nucleus ( Fig 5C and c ) . When rMjCC-CL protein was injected into the MjCC-CL knockdown shrimp , the LPS challenge induced STAT phosphorylation ( Fig 5D ) and translocation into the nucleus ( Fig 5E and e ) as in the control shrimp . However , if in the rescue experiments rMjCC-CL was replaced by rCTL2 , STAT phosphorylation ( Fig 5F ) and translocation did not markedly change ( Fig 5G , g ) . Taken together , our results suggest that MjCC-CL regulates AMP expression via the JAK/STAT pathway , without involving the Toll or IMD pathways . We subsequently investigated whether the JAK/STAT pathway regulates AMP expression in shrimp . The shrimp STAT contains an N-terminal domain ( NTD ) , a coiled-coil domain ( CC ) , a DNA-binding domain ( DB ) , a linker domain ( LD ) , an SH2 domain ( SH2 ) and a transactivation domain ( TAD ) ( S2A Fig ) , and in a phylogenetic analysis , it clusters with other invertebrate STATs ( S2B Fig ) . Analysis of the spatiotemporal distribution of shrimp STAT revealed that it was expressed in all tested tissues , but had relatively low expression in the hemocytes and stomach ( Fig 6A ) . Expression of STAT was upregulated in hemocytes and the intestine at 3 h after bacterial challenge and gradually returned to normal levels from 6 to 24 h ( Fig 6B and 6C ) . To investigate whether expression of ALF-A1 , ALF-C1 , ALF-C2 , CruΙ-1 and CruΙ-5 is regulated by the JAK/STAT pathway , we knocked down STAT expression by RNAi ( Fig 6D and 6E ) , and analyzed AMP expression . The results showed that the expression of the above five AMPs was reduced significantly in the intestine of STAT-RNAi shrimp ( Fig 2F ) , a finding that is similar to the effect observed in MjCC-CL-RNAi shrimp . To confirm that the JAK/STAT pathway regulates AMP expression , we injected the shrimp with a STAT inhibitor prior to the LPS challenge , and analyzed STAT translocation and AMP expression . The results showed that in the STAT inhibitor-injected shrimp STAT did not translocate into the nucleus of the hemocytes upon LPS challenge ( Fig 6G and g ) , and STAT phosphorylation in the intestine was also inhibited ( Fig 6H ) . Further , in the STAT inhibitor-injected shrimp the LPS challenge failed to upregulate AMP expression ( Fig 2I ) . These results suggest that STAT phosphorylation and translocation are functionally related to the increased expression of ALF-A1 , ALF-C1 , ALF-C2 , CruΙ-1 and CruΙ-5 . To identify putative STAT-binding sites in the promoter sequences of the AMPs of interest we used a genome walking approach . We succeeded in identifying putative NF-κB and STAT-binding sites ( Fig 6J ) in the CruΙ-1 promoter sequence ( S3 Fig ) . Next , we conducted a chromatin immunoprecipitation ( ChIP ) assay with anti-pSTAT , purified and analyzed the DNA fragment obtained , and amplified by RT-PCR the CruI-1 sequence of interest ( Fig 6K ) . Subsequently we carried out an electrophoretic mobility shift assay ( EMSA ) with a Dig-labeled CruΙ-1 probe containing the predicted STAT binding site , and purified the recombinant GST-STAT protein and native STAT protein from LPS-injected shrimp to confirm whether STAT directly binds to the predicted STAT binding site in the promoter sequence of CruΙ-1 . These results showed that STAT could bind to the predicted STAT binding site in the CruΙ-1 promoter sequence ( Fig 6L and 6N ) and that the binding ability was increased in LPS-injected shrimp ( Fig 6N ) . All these results suggested that the JAK/STAT pathway regulates the expression of AMPs . To investigate the antibacterial function of the JAK/STAT pathway in shrimp , we first evaluated signaling activation by detecting STAT phosphorylation with an antibody specific for phosphorylated STAT ( anti-pSTAT ) after challenge with bacteria ( V . anguillarum , Escherichia coli , Staphylococcus aureus or Bacillus subtilis ) , LPS ( E . coli ) , or PGN ( S . aureus or B . subtilis ) . The results showed that they all could induce STAT phosphorylation in the intestine 3 h post challenge , whereas no pSTAT could be detected in the intestine of control shrimp challenged with PBS ( Fig 7A ) . As both GN and GP bacteria induced STAT phosphorylation , only the shrimp pathogen V . anguillarum and purified LPS were used in subsequent experiments . To expand the above results , we conducted a time-course analysis by assessing STAT phosphorylation in the intestine of shrimp at 1h and 3 h upon V . anguillarum challenge . The results revealed that the STAT phosphorylation increased in the intestine of V . anguillarum-challenged shrimp from 1 to 3 h after challenge ( Fig 7B ) . We also examined STAT phosphorylation and translocation in hemocytes by immunocytochemical analysis , and the results indicated that pSTAT translocated from the cytoplasm into the nucleus 1 to 3 h after bacterial challenge ( Fig 7C ) . We then extracted cytoplasmic and nuclear proteins from the intestine , and examined the subcellular distribution of STAT by western blotting using an anti-STAT antibody . The results showed that levels of total STAT in the intestinal tissue remained unchanged in the untreated , PBS-challenged or bacteria-challenged shrimp ( Fig 7D ) . However , when STAT levels were analyzed separately in the cytoplasm and nucleus of the intestinal cells , when compared with the STAT levels in the cytoplasm of intestinal cells from PBS-challenged and untreated control shrimp , the STAT level at 3 h post-bacterial challenge was relatively lower ( Fig 7E ) . Consistently , STAT was only detected in the nucleus of intestinal cells in the bacteria-challenged shrimp ( Fig 7E ) . Taken together , these results suggest that challenge with bacteria and bacterial polysaccharides induce STAT phosphorylation and translocation into the nucleus at 3 h post-challenge in both hemocytes and intestinal cells , and indicate that bacterial challenge can activate the JAK/STAT signaling pathway in shrimp . To confirm that the JAK/STAT pathway is involved in the antibacterial response , we knocked down STAT by RNAi ( Fig 7F ) , and comparatively analyzed bacterial clearance and the survival rate of the STAT-RNAi and GFP-RNAi control shrimp . Injection of V . anguillarum into the STAT-RNAi shrimp resulted in impaired bacterial clearance ( Fig 7G ) , and their survival rate declined significantly compared with the GFP-RNAi control shrimp ( Fig 7H ) . These results suggest that the JAK/STAT pathway plays an important role in antibacterial immunity in shrimp . To further investigate the mechanism by which MjCC-CL mediates activation of the JAK/STAT pathway , we examined in vivo the potential role of the CCD on STAT phosphorylation and translocation into the nucleus of shrimp hemocytes . For this , the full length CCD ( GST-CCD ) expressed from E . coli , and two synthetic truncated forms , sCC1 ( 3–39 aa ) and sCC2 ( 47–119 aa ) ( Fig 8A ) , were injected into shrimp and STAT phosporylation and translocation were analyzed as described above for the whole MjCC-CL . Injection of GST-CCD , resulted in significantly increased STAT phosphorylation at 2 and 3 h ( Fig 8B-b ) , whereas no change in STAT phosphorylation levels at 1 h , 2 h , and 3 h after injection of sCC1 or sCC2 were detected ( Fig 8C ) . Subsequently , cytoplasmic and nuclear proteins were extracted from hemocytes and the subcellular distributions of total STAT and pSTAT were assessed by WB using an anti-STAT and anti-pSTAT antibodies , respectively . The results showed that total STAT and pSTAT in the nucleus were increased at 3 h in hemocytes of the GST-CC-injected shrimp ( Fig 8D-d ) , whereas no changes were observed in the sCC1- and sCC2 -injected shrimp ( Fig 8E ) . We also assessed STAT translocation in hemocytes by immunocytochemical analysis , and the results showed that STAT translocated into the nucleus of the GST-CC-injected shrimp ( Fig 8F and f ) , but no changes in sCC1- and sCC2-injected shrimp were observed ( Fig 8G and g ) . The results strongly suggest that the CCD of MjCC-CL is responsible for activation of the JAK/STAT signaling pathway , and that the intact CCD structure is required for activity . It has been established in Drosophila that Domeless ( Dome ) is the type I cytokine cell surface receptor involved in activation of the JAK/STAT pathway [19] . Thus , we investigated the possibility that in shrimp the Dome orthologue could also function as a lectin cell surface receptor , and therefore be involved in the MjCC-CL-mediated activation of the JAK/STAT pathway , upon an initial interaction of MjCC-CL with microbial pathogens . For this , we first cloned the kuruma shrimp Dome , ( GenBank accession no . KX358405 ) . Dome comprises a signal peptide , an interleukin 6 receptor ( ILR ) alpha domain , five fibronectin-type 3 ( FN3 ) domains , and a transmembrane ( TM ) region ( Fig 9A ) . We analyzed the potential interaction of MjCC-CL with Dome and cross-linking of bacteria by co-immunoprecipitation ( co-IP ) , pulldowns , and bacterial binding assays . The pIEx-4-RFP plasmid with MjCC-CL or its CC and CTL domains was constructed and co-transfected into HaEpi cells [20] with the pIEx-4-RFP containing ILR domain of Dome , and a co-IP assay was performed to study the interaction between MjCC-CL and Dome . The results showed that MjCC-CL interacts with the ILR domain of Dome via its CCD ( Fig 9B and 9B1 ) and that the CTL domain does not interact with ILR ( Fig 9B2 ) . There was no interaction between RFP and the His-tagged protein ( Fig 9B3 ) . Next , MjCC-CL and its CC and CTL domains , as well as the ILR domain of Dome , were expressed in E . coli ( S1A–S1E Fig ) . A GST pulldown assay was performed to verify the interaction . The results showed that full-length MjCC-CL ( Fig 9C ) and the CCD of MjCC-CL ( Fig 9C1 ) interacted with the ILR domain of Dome , but the CTL domain ( Fig 9C2 ) and GST could not interact with ILR ( Fig 9C3 ) . The same results were obtained with His pulldown analysis ( Fig 9D and 9D1-3 ) . We then performed the Co-IP assay using antibodies specific for Dome or MjCC-CL ( Fig 9E ) , and the results showed that upon Vibrio challenge Dome and MjCC-CL interacted with each other ( Fig 9F ) . Taken together , the above results suggest that MjCC-CL functions both as a PRR of bacteria and a ligand of Dome , binding to bacteria with its CTL domain and cross-linking them to the Dome receptor with its CCD , thereby activating the of the JAK/STAT pathway . JAK , the principal component of the JAK/STAT pathway , is present in the kuruma shrimp ( GenBank accession no . KU213610 ) . The architecture of JAK consists of a Band 4 . 1 homolog ( B41 ) domain and an Src homology 2 ( SH2 ) domain ( S2A Fig ) . To investigate in vivo the potential role ( s ) of JAK and Dome in the shrimp immune response to bacterial challenge , we first examined the distribution and expression patterns of JAK and Dome . The results showed that Dome was mainly expressed in heart , gill and intestine , with lower expression in hemocytes , hepatopancrease and stomach . In contrast , JAK was similarly expressed in all tissues , with heart showing the lowest levels ( S2B and S2C Fig ) . To confirm the role of the JAK/STAT pathway and specifically the functions of the key components Dome and JAK , we knocked them down by RNAi and analyzed STAT phosphorylation and AMP expression upon LPS challenge . Upon LPS challenge of the Dome-RNAi shrimp ( Fig 10A ) , STAT phosphorylation decreased significantly in the intestine ( Fig 10A1 ) , STAT translocation into the nucleus was inhibited in the hemocytes ( Fig 10A2 and a2 ) , and the expression of ALF-A1 , ALF-C1 , ALF-C2 , CruΙ-1 and CruΙ-5 was also significantly impaired ( Fig 10A3 ) . Similar results were obtained after treatment with RNAi targeting JAK ( Fig 10B–10B3 ) . The above results suggest that JAK and the MjCC-CL cell surface receptor Dome are key components of the JAK/STAT pathway and are involved in STAT phosphorylation and nuclear translocation , and AMP expression . To investigate the possibility that MjCC-CL could activate the JAK/STAT pathway in a heterologous system , we examined the MjCC-CL mediated JAK/STAT pathway activation in mouse macrophages , using STAT3 phosphorylation as an indicator , and IL6 as a positive control for pathway activation . After treatment with rMjCC-CL , the cells were collected and examined by immunocytochemical and WB analysis . The results showed like IL6 , rMjCC-CL could induce STAT3 phosphorylation in mouse macrophages ( Fig 11A and 11B ) . The results suggested that in a mammalian system MjCC-CL functions as a cytokine to activate the JAK/STAT pathway .
In this study we identified in the kuruma shrimp a novel chimeric CTL , MjCC-CL , which by a unique “self” protein-protein interaction at the cell surface plays a central role in the shrimp antibacterial immune response . The MjCC-CL protein comprises a typical CTLD that function as a PRR for “non-self” microbial glycans , and a CCD that by interacting with the Dome receptor at the hemocyte surface , directly activates the JAK/STAT signaling pathway to upregulate the expression of five AMPs . Upon recognition of glycans on the surface of invading microbes via the canonical CTLD , soluble CTLs may participate in antibacterial responses in several ways , such as agglutinating and immobilizing the potential pathogens , functioning as opsonins to promote their phagocytosis , and by direct microbicidal activity or indirectly by activating enzymatic pathways leading to complement activation in vertebrates , or melanization by activation of the prophenoloxidase pathway in invertebrates [8] . We recently reported that the shrimp CTL MjHeCL directly regulates the AMP levels in plasma , which in turn maintain the homeostasis of the hemolymph microbiota [7] . MjHeCL only contains a CTLD , is structurally similar to CTLs from other invertebrate species , and is mainly expressed in hemocytes at relatively high levels that are not affected by experimental microbial challenge . In contrast , in addition to the CTLD , MjCC-CL displays a CCD rich in α-helix content with overall similarity to the mammalian IL10 , and that a phylogenetic analysis finds clustering with vertebrate CTLs . Further , MjCC-CL was detected in all tested tissues and its expression was significantly upregulated by microbial infection . Although both MjHeCL and MjCC-CL regulated AMP expression , given the structural differences between the two CTLs , this likely to take place through different mechanisms , and aimed at very different functional outcomes: while the former maintains homeostasis of the internal microbiota , the latter is key to immune responses for exogenous infectious challenge . Most importantly , our findings in this study revealed a novel mechanism by which a CTL activates the JAK/STAT signaling pathway and regulates AMP expression . The JAK/STAT pathway was originally identified as a cytokine signaling pathway in mammals [21] , and its relevance in the regulation of both innate and adaptive immunity has been widely recognized [13] . The JAK/STAT pathway consists of three main components: cytokine receptors at the cell surface , Janus kinases ( JAKs ) , and signal transducers and activators of transcription ( STATs ) . In mammals , many cell surface cytokine receptors , four JAKs ( JAK1 , JAK2 , JAK3 and TYK2 ) and seven STATs ( STAT1 , STAT2 , STAT3 , STAT4 , STAT5A , STAT5B , and STAT6 ) have been identified [22] . Further , over fifty cytokines and growth factors , including interferons , interleukins and colony-stimulating factors , have been identified as indirect activators of the JAK/STAT pathway via cell surface receptors and mediate various immune responses to infection [23–25] . Although the JAK/STAT signaling pathway is ubiquitous in vertebrates , it can also be found as an intact pathway in some invertebrate taxa [26] . Thus , an evolutionarily conserved function of the JAK/STAT signaling pathway in immune responses in humans and insects , including fruit flies and mosquitos , has been suggested [15–18] . A complete JAK/STAT pathway is found in Drosophila and mosquito [12 , 18 , 27] . In mollusks [28] and echinoderms [29] , only some components have been identified and it remains unclear whether they have a complete JAK/STAT pathway . Other organisms , such as Caenorhabditis elegans and Dictyostelium , do not have a completely functional JAK/STAT cassette , but homologs of some JAK/STAT pathway proteins have been found [30 , 31] . However , there are no JAKs in Dictyostelium; instead , STAT activation is mediated by G protein-coupled receptors [32] . In Drosophila , Toll signaling and IMD pathways control the humoral immune response to bacterial or fungal infections , leading to the production of several AMPs [33–35] , and the primary function of the JAK/STAT pathway is control of the cellular immune response [36] . The Drosophila JAK/STAT signaling pathway comprises two receptor-like molecules: one is Dome , which shows weak similarities to the cytokine-binding modules of the vertebrate IL6 receptor family and functions as the receptor of the pathway . Another is Latran ( or Eye transformer ) , which has similarity to Dome and is encoded by a predicted gene , CG14225 . Latran was reported to be a negative regulator of Drosophila JAK/STAT signaling [37 , 38] . Cytokine-like proteins called Upds are ligands that bind to Dome and consequently activate the JAK/STAT pathway [19] . In contrast to the well-characterized Toll and IMD pathways in Drosophila immunity , however , relatively little is known about the transcriptional responses induced by the JAK/STAT pathway in humoral immune response . In shrimp , the main components of the JAK/STAT pathway have been identified . Only one STAT , the key component of the pathway , is present in various shrimp species , including Penaeus monodon [39] , Fenneropenaeus chinensis [40] and Marsupenaeus japonicus [41] . The shrimp STAT is similar to the mammalian STAT5 . In mammals , STAT5 participates in the regulation of a wide range of physiological processes of the cell , including proliferation , differentiation , survival , apoptosis , and others [42 , 43] . A single receptor domeless ( Dome ) , which shares functional and sequence similarity with the mammalian cytokine class I receptors , has been identified in Litopenaeus vannamei [44] . A JAK has also been reported in L . vannamei [45] . Unlike the antiviral function of the JAK/STAT pathway in mammals and insects , white spot syndrome virus ( WSSV ) uses a shrimp STAT as a transcription factor to enhance viral gene expression in host cells [46] , and the pathway is helpful and beneficial for WSSV replication [44 , 47] . However , another report has shown that silencing of the shrimp JAK causes higher mortality and increased viral load in L . vannamei , and the pathway has antiviral function [45] . In contrast to the function of JAK/STAT pathway in antiviral responses , relatively little is known about the function of the pathway in antibacterial defense . In our study , the ligand of the JAK/STAT pathway receptor , MjCC-CL , was identified and found to contain a CCD with sequence similarity to IL10 and a CTLD . Through protein secondary structure prediction and SWISS-MODEL analysis , the CCD was also predicted to have a highly α-helical nature , which is highly similar in overall structure with cytokine , IL10 in mammals . The fundamental property of coiled coils is their stability . The biological functions resulting from coiled-coil stability are related with membrane fusion , transmembrane signal transduction , and interaction with different proteins [48] . The CCD binds to the Dome receptor to activate the JAK/STAT pathway . The CTL domain of the MjCC-CL binds both Gram-positive bacteria and Gram-negative bacteria by binding to different polysaccharides , including LPS and PGN , on the bacterial surface . Numerous C-type lectins have been identified in shrimp and other invertebrates , and these lectins have functional diversity [8 , 49] . Therefore , this study identifies an example of direct activation of the JAK/STAT pathway and provides novel ways to identify new ligands of the JAK/STAT pathway in invertebrates . MjCC-CL from shrimp also induces STAT3 phosphorylation in mouse macrophages , suggesting that MjCC-CL function as a cytokine in mammals . Although the principal components of the JAK/STAT pathway have been identified in other invertebrate species , the ligands capable of activating the pathway have been described only in Drosophila . Three Upds that activate the JAK/STAT pathway in flies were identified , including Upd and Upd3 [15 , 50] . Although the Upds bear no sequence similarity to cytokines , the predicted highly α-helical nature is consistent with an overall structure that could be similar to cytokines . In our study , the lectin MjCC-CL not only activates the JAK/STAT pathway in shrimp hemocytes , but can also function as a cytokine by activating JAK/STAT pathway in mammalian macrophages . In the present study , significant changes in the principal components of the shrimp JAK/STAT signaling pathway were observed after challenge with bacteria or their surface glycans , such as LPS and PGN . These challenges induced the phosphorylation and translocation of STAT in shrimp , and up regulation of expression of five AMPs via JAK/STAT signaling . Further , our study revealed a putative STAT-binding site in the promoter sequence of an AMP , CruI-1 . The JAK/STAT pathway is associated with various immune responses , including anti-viral and anti-bacterial responses [12 , 51] . The first evidence that the JAK/STAT pathway is involved in invertebrate immunity came from studies performed in Anopheles mosquitoes , which indicated that the STAT protein accumulates in the nucleus after immune challenge [52] . A subsequent study found that the JAK/STAT pathway participates in hematopoiesis and the cellular immune response in Drosophila [12] . Gene expression profile studies have identified a subset of Drosophila immune-responsive genes that are regulated by the JAK/STAT pathway , namely the genes encoding the complement-like protein Tep2 and the Turandot stress genes , but the exact function of these genes in immunity remains elusive [53] . In shrimp , STAT is upregulated in shrimp challenged with bacteria , WSSV , poly IC and PGN [54] . STAT can enhance the replication of WSSV in Penaeus monodon [46] . In conclusion , the components of the shrimp JAK/STAT pathway were identified , including a ligand , MjCC-CL; a receptor , Dome; a kinase , JAK; and a transcription factor , STAT , and the mechanistic aspects of their interactions upon immune challenge were elucidated , as schematically illustrated in Fig 12 . Upon bacterial challenge ( including Gram-positive or Gram-negative bacteria ) , MjCC-CL can recognize the pathogens’ surface glycans , via the CTLD . MjCC-CL then interacts via the CCD with the receptor Dome to activate the JAK/STAT pathway and induce the phosphorylation and translocation of STAT , which in turn regulates the expression of ALF-C1 , ALF-C2 , ALF-D1 , CruΙ-1 and CruΙ-5 . Taken together , our results suggest that MjCC-CL functions both as a PRR for recognition of infectious pathogens , and as the ligand of Dome to activate JAK/STAT signaling , and that the MjCC-CL/Dome/JAK/STAT pathway plays an important role in the anti-bacterial response by regulating AMP expression in shrimp . Thus , our study of the shrimp MjCC-CL revealed a striking functional difference with vertebrates , in which the JAK/STAT pathway is indirectly activated by cell death and stress signals through cytokines or growth factors . Instead , by cross-linking microbial pathogens with the cell surface receptor Domeless , a lectin directly activates the JAK/STAT pathway , which plays a central role in the shrimp antibacterial immune responses by upregulating expression of selected AMPs .
The full-length cDNA sequence of MjCC-CL was obtained from shrimp intestine by transcriptomic sequencing . The sequence of MjCC-CL was amplified by RT-PCR using corresponding primers ( S2 Table ) and re-sequenced for confirmation . Similarity analysis was conducted using BLASTx ( http://www . ncbi . nlm . nih . gov/ ) . The corresponding cDNA was conceptually translated , and the deduced proteins were predicted using ExPASy ( http://www . expasy . org/ ) . Domain architecture prediction of the proteins was performed using SMART ( http://smart . embl-heidelberg . de/ ) . Homology modeling of MjCC-CL was performed using SWISS-MODEL ( https://swissmodel . expasy . org/ ) . MEGA 5 was used for phylogenetic analysis . Healthy kuruma shrimp ( M . japonicus; about 10 g each ) were obtained from a fish market in Jinan , Shandong Province , China , and were acclimated in a laboratory aquarium tanks with aerated seawater at 23°C for 3 days before the infectious challenge . For this , each shrimp was injected in the abdomen with either 20 μl of bacteria ( Vibrio anguillarum , 1 × 107 CFU in PBS , 1 ml ) or PBS alone . For hemocyte collection , hemolymph was extracted from three challenged or control shrimp with a syringe containing 1 ml cold anticoagulant buffer at 4°C ( 0 . 45 M NaCl , 10 mM KCl , 10 mM EDTA and 10 mM HEPES , pH 7 . 45 ) and immediately centrifuged at 800 g for 10 min ( 4°C ) . Organs ( heart , hepatopancreas , gills , stomach and intestine , were collected , and total RNA was extracted with TRIzol reagent ( Cwbio , Beijing , China ) . The full-length cDNA sequences of Dome , Jak and Stat were obtained from shrimp intestine by transcriptomic sequencing . All sequences were amplified by RT-PCR using specific primers ( S2 Table ) and the amplicons sequenced and analyzed by BLASTx ( http://www . ncbi . nlm . nih . gov/ ) for sequence confirmation . Prediction of the domain architecture of the corresponding proteins was performed using SMART ( http://smart . embl-heidelberg . de/ ) . The mRNA tissue distribution of five genes was analyzed using semi-quantitative RT-PCR with the primers RT-F and RT-R for MjCC-CL , Dome , JAK and STAT ( S2 Table ) . RNA from the hemocytes , heart , hepatopancreas , gills , stomach , and intestine was used in this assay . β-actin was used as the control with the primers ActinF and ActinR ( S2 Table ) . qRT-PCR was used to detect the expression profiles of five genes after V . anguillarum infection following a previously described method [7] . The qRT-PCR was programmed at 95°C for 10 min , followed by 40 cycles at 95°C for 10 s and 60°C for 60 s . The plate was read at 78°C for each cycle . The expression profiles of MjCC-CL , Dome , JAK and STAT were detected in the hemocytes and intestine challenged with V . anguillarum . All experiments were repeated at least three times using individual templates . The obtained data were evaluated using the 2-ΔΔCt method , as described previously [55] , and statistically analyzed; significant differences in the unpaired sample t-test were accepted at p< 0 . 05 . The 3’-terminal sequence ( about 500 bp ) for siRNA of MjCC-CL , Dome and STAT were amplified by the primers Fi and Ri linked to the T7 promoter ( S2 Table ) and were used as templates for the synthesis of dsRNA . The cDNA fragment of GFP used for dsGFP synthesis was amplified using the primers GFP-Fi and GFP-Ri ( S2 Table ) . The dsRNA was synthesized using T7 polymerase ( Fermentas , USA ) based on the method of Wang et al [56] . The RNA interference ( RNAi ) assay was performed as described in previous reports [57] . dsRNA ( 20 μg ) for MjCC-CL , Dome or STAT was injected into the abdominal segment of each shrimp . To enhance the RNAi effect , a second injection was performed 12 h after the first injection . dsGFP was used as a control . The intestine was collected from the shrimp 24 h after the second injection , and total RNA was extracted and assessed by qRT-PCR using the primers RT-F and RT-R ( S2 Table ) to evaluate the efficacy of the RNAi . To screen potential AMPs regulated by JAK/STAT signaling , AMP expression in challenged shrimp after receiving STAT-RNAi plus challenge were analyzed . The RNA interference was followed previous mentioned method . The shrimp were divided into four groups: one control group received PBS injection and the other RNAi groups were injected with V . anguillarum , lipopolysaccharides ( LPS; E . coli , Sigma ) , peptidoglycan ( PGN , S . aureus , Sigma; PGN , B . subtilis , Sigma ) . AMPs , including ALF-A1 , ALF-C1 , ALF-C2 , ALF-D1 [58] , CruΙ-1 and CruΙ-5 , were detected by qRT-PCR with specific primers ( S2 Table ) after 6 h of bacterial challenge . The above AMPs were detected after STAT-knockdown and then challenged with LPS . dsGFP was used as the control . After the RNAi method was established , a bacterial clearance assay was performed . Shrimp were separated into three groups and injected with dsMjCC-CL , dsSTAT or dsGFP as a control . Then , the three groups were injected with V . anguillarum ( 20 μl , 1 × 109 CFU ) . Thirty minutes after bacteria injection , shrimp hemolymph was collected , diluted and then cultured on solid LB plates overnight . The numbers of bacterial colonies were counted . The assay was repeated three times . Shrimp ( 30 shrimp per group , about 10 g each ) were divided into three groups to evaluate the shrimp survival rate after STAT and MjCC-CL knockdown and V . anguillarum infection . dsGFP was used as a control . After STAT and MjCC-CL were knocked down by dsRNA injection , all shrimp were injected with V . anguillarum ( 20 μl , 1 × 1010 CFU in PBS , 1 ml ) . The number of dead shrimp was monitored every day , and the survival rates of the three groups of shrimp were calculated . The experiments were repeated three times . The data were statistically analyzed by t-test , and a difference was considered to be significant at p< 0 . 05 . MjCC-CL , the CCD and CTLD of MjCC-CL , CTL2 ( as a control C-type lectin ) , and the ILR domains of Dome and STAT were recombinantly expressed in Escherichia coli . The sequences of the above five proteins were amplified from shrimp hemocytes using the primers ExF and ExR ( S2 Table ) . The PCR procedure was as follows: one cycle at 95°C for 3 min; 35 cycles at 94°C for 30 s , 55°C for 45 s , and 72°C for 45 s; and one cycle at 72°C for 10 min . The PCR products were then cloned into the pET32a ( Novagen ) or pGEX4T-1 ( GE Healthcare ) vectors . The recombinant proteins were purified by affinity chromatography using His-Bind resin ( Ni2+-resin; Novagen , Darmstadt , Germany ) or GST-resin ( GenScript , Nanjing , China ) following the manufacturer’s instructions . MjCC-CL , STAT antiserum preparation was performed as previously described [59] . The truncated forms of CCD ( 3–39 aa ) and ( 47–119 aa ) from MjCC-CL ( sequences were shown in Fig 6A ) were synthesized by DgPeptidesCo . , Ltd ( Hangzhou , China ) , and named as synthesized truncated CC1 , sCC1 ( 3–39 aa ) and synthesized truncated CC2 , sCC2 ( 47–119 aa ) . Dorsal and Relish expression and antiserum preparation were carried out as previously described [60 , 61] . Gram-positive bacteria ( B . subtilis and S . aureus ) and Gram-negative bacteria ( E . coli and V . anguillarum ) were used to test the binding activity of recombinant MjCC-CL and the CTL and CCDs of MjCC-CL . Bacteria were cultured in 2–4 mL of Luria-Bertani ( LB ) medium ( 1% tryptone , 0 . 5% yeast extract , and 1% NaCl ) overnight and then gathered by centrifugation at 6000 g for 5 min . After washing three times with TBS , the bacteria were resuspended in TBS and adjusted to an OD600 of 1 . 0 . The bacteria ( 400 μL ) in TBS were incubated with purified rMjCC-CL , rCTLD and rCCD ( 100 μg ) for 60 min at 28°C with rotation , collected by centrifugation , and then washed four times with TBS . Finally , the bound proteins were eluted with 7% SDS for 1 min and subjected to 12 . 5% SDS-PAGE . The proteins in the gel were transferred to a nitrocellulose membrane for western blot analysis . An anti-histidine antibody ( ZSGB Bio , Beijing , China , 1:3000 dilution in TBS containing 5% nonfat milk ) was used as the primary antibody , and secondary antibody was alkaline phosphatase-conjugated horse anti-mouse IgG ( ZSGB Bio , Beijing , China , 1:10 , 000 dilution in TBS containing 5% nonfat milk ) . Native MjCC-CL protein was purified from shrimp intestine according to a previous report [7] and also incubated with four bacteria . The mixture was gently rotated at 28°C for 1 h . Bacterial pellets were collected by centrifugation at 6000 g for 5 min , washed three times with Tris buffer ( pH 8 . 0 ) and then analyzed by western blotting . An enzyme-linked immunosorbent assay ( ELISA ) was used to test the direct binding activity of rMjCC-CL , rCTLD and rCCD to different bacterial cell wall components . LPS from E . coli and PGN from S . aureus and B . subtilis separately were chosen for the assay . Each well of the microplate was coated with 2 μg of the polysaccharide and incubated at 37°C overnight . The microplate was incubated at 60°C for 30 min , blocked with BSA ( 1 mg/mL , 200 μL ) at 37°C for 2 h , and washed with TBS ( 200 μL ) . Purified rMjCC-CL , rCTLD and rCCD ( final concentration 0–20 μg/mL in TBS with 0 . 1 mg/mL BSA ) was added to each well of the coated plates and incubated at room temperature for 3 h . The plate was then washed four times with TBS , and alkaline phosphatase-conjugated horse anti-mouse IgG ( 1:3000 dilution in binding buffer containing 0 . 1 mg/mL BSA ) was added ( 100 μL per well ) and incubated at 37°C for 2 h . After the plate was washed four times with TBS , the color was developed with p-nitro-phenyl phosphate ( 1 mg/mL in 10 mM diethanolamine and 0 . 5 mM MgCl2 ) at room temperature for 30 min . The OD value was read at 405 nm . Each binding assay was performed three times . The dissociation constants ( Kd ) and maximum binding ( Bmax ) parameters were calculated by GraphPad Prism version 5 . 00 software for Windows ( San Diego , CA , USA ) . Gram-positive bacteria ( B . subtilis and S . aureus ) and Gram-negative bacteria ( E . coli and V . anguillarum ) were used to test the potential antibacterial activity of recombinant MjCC-CL . Each protein ( 10 , 30 , 50 , 100 μg ) was added to a 96-well culture plate which contained 180 μL of mid-log phase bacteria ( 2 × 105 CFU ) cultured in Poor Broth ( 1% tryptone , 0 . 5% NaCl ( w/v ) and pH 7 . 5 ) . The plate was incubated for 24 h at 28°C and the absorbance at 600 nm was measured using an ELX800 Universal Microplate Reader ( Bio-Tek Instruments , INC ) to evaluate the bacterial concentration . The assays were repeated thrice . GST protein was used as negative control . Recombinant MjCC-CL was used for the “overexpression” assay . rMj-CC-CTL ( 20 μg per shrimp ) was injected into shrimp for 1 and 2 h . GST was used as control . Hemocytes were then collected for immunocytochemical assay and intestine protein was extracted for western blotting to detect Dorsal , Relish and STAT translocation into nucleus . rMj-CC-CTL , sCC1 ( 3–39 aa ) and sCC2 ( 47–119 aa ) were also injected into shrimp to detect STAT phosphorylation and translocation into nucleus by western blotting assay and immunocytochemical assay . To confirm the function of MjCC-CL in JAK/STAT pathway activation , rMj-CC-CTL and rCTL2 incubated with LPS ( 4 μg ) were injected into shrimp for 3 h . GST was used as control . Hemocytes were then collected for immunocytochemical assay and intestine protein was extracted for western blotting to detect Dorsal , Relish and STAT translocation into nucleus . The AMPs were also assessed in intestine in rMj-CC-CTL- and rCTL2-injection shrimp at 6 h . Western blotting was used to detect STAT phosphorylation with the commercial anti-STAT5 ( phospho Tyr694 ) antibody ( Abcam , USA ) . Tissue proteins were obtained from the hemocytes and intestine of normal shrimp and bacterially challenged shrimp . Cytoplasmic proteins and nuclear proteins were extracted using a Nuclear Protein Extraction Reagent Kit ( BioTeke , China ) following the manufacturer’s instructions . The samples were separated by 12 . 5% SDS-polyacrylamide gel electrophoresis following the Laemmli method [62] . The proteins in the gel were then transferred onto nitrocellulose membranes . The membranes were blocked for 1 h with 3% non-fat milk in TBS ( 10 mM Tris-HCl , pH 7 . 5 , 150 mM NaCl ) and incubated with 1/100 diluted antiserum against STAT , phosphorylated STAT , Dorsal , Relish or β-actin in TBS with 3% non-fat milk for 2 h . Then , alkaline phosphatase-conjugated goat anti-rabbit IgG ( 1/10 , 000 diluted in TBS ) was added after washing to remove the free , nonspecifically binding antiserum , and the membranes were incubated for 2 h . The membrane was dipped in the reaction system ( 10 ml of TBS with 45 μl of NBT and 35 μl of BCIP ) in the dark for 5 min to visualize the signal . Hemolymph obtained from shrimp was fixed with 1 ml of a mixture containing anticoagulant ( pH 7 . 4 ) and 4% paraformaldehyde and then centrifuged at 600 g for 10 min at 4°C . The collected hemocytes were deposited onto a glass slide , washed with PBS ( 140 mM NaCl , 10 mM sodium phosphate , pH 7 . 4 ) and incubated in 0 . 2% Triton X-100 at 37°C for 5 min . After washing with PBS , the hemocytes on the glass slides were blocked with 3% BSA ( 30 min , 37°C ) and incubated with anti-pSTAT , anti-STAT , anti-Dorsal or Anti-Relish ( 1:400 in 3% BSA ) overnight at 4°C . The hemocytes were then washed with PBS and incubated with 3% BSA for 10 min; the Alexa Fluor 488-conjugated second antibody to rabbit ( 1:1 , 000 ratio , diluted in 3% BSA ) was then added , and the samples were incubated for 1 h at 37°C in the dark . After being washed three times , the hemocytes were incubated with 4’-6-diamidino-2-phenylindole dihydrochloride ( DAPI , AnaSpec Inc . , San Jose , CA; 1 μg/ml in PBS ) for 10 min at room temperature and washed six times . Fluorescence was observed under an Olympus BX51 fluorescence microscope ( Shinjuku-ku , Tokyo , Japan ) . WCIF ImageJ software was used to analyze the colocalization of STAT and DAPI-stained nuclei in hemocytes . As STAT in M . japonicus is clustered together with STAT5 in human ( S1 Fig ) , STAT5 inhibitor , ( 573108-10MG , Merck ) ( 2 μg ) was used to inject into each shrimp and then challenged by LPS . DMSO injection was used as a control . The intestine was collected for protein and RNA extraction at 3 and 6 h after LPS challenge . The phosphorylation level of STAT was analyzed by western blotting with anti-pSTAT antibody , and the AMP expression was detected by qRT-PCR at 6 h after LPS challenge . The promoter sequences of antimicrobial peptides regulated by the JAK/STAT pathway were cloned using Genome Walker Kits ( Takara , Japan ) following the manufacturer’s instructions . The specific primers against CruΙ-1 ( G-CruΙ-1-1 , G-CruΙ-1-2 and G-CruΙ-1-3 ) used for genome walking are listed in S2 Table . ChIP was performed following previously described methods [63 , 64] using the primers CruΙ-1 RTF/R for CruΙ-1 ( S2 Table ) . The shrimp intestine was lysed , and STAT protein was purified using anti-STAT antibody- CNBr-activated Sepharose 4B ( 60 mg; Amersham Biosciences AB , Uppsala , Sweden ) . The digoxigenin-labeled probes ( sense , 5'-GCGTAAGGTTTTCTTGGAATA-3'; antisense , 5'-TATTCCAAGAAAACCTTACGC-3' ) were synthetized and labeled by Sangon Company ( China ) . Two micrograms of purified proteins was mixed with 3 μl of 5× binding buffer ( Beyotime Institute of Biotechnology , Shanghai , China ) for 10 min and incubated with 20 fmol of digoxigenin-labeled probe for 20 min . In competition experiments , unlabeled probe was pre-incubated with the relevant proteins for 10 min before the Dig-labeled probe was added and incubated for 20 min at room temperature . The reaction solution was run on a 6% polyacrylamide/0 . 5× TBE gel at 80 V , and the samples were transferred onto a nylon membrane ( IMMOBILON-NY+ , Millipore , Milford , MA , USA ) . The membrane was first blocked with blocking buffer for 30 min and then incubated with anti-Dig phosphatase antibody ( 1:10000 in blocking solution; Roche , Germany ) for 1h . The signal was visualized with 5-bromo-4-chloro-3-indolyl phosphate and nitroblue tetrazolium chloride . To analyze the interaction of MjCC-CL with Dome , co-immunoprecipitation was performed after these molecules were overexpressed in HaEpi cells . The appropriate cDNA sequences encoding whole MjCC-CL or the CC region and CTL domain of MjCC-CL were amplified with primers ( S2 Table ) and inserted into the pIEx-4-RFP plasmid ( with a C-terminal red fluorescent protein tag ) ; then , the ILR domain of Dome was also amplified and inserted into the pIEx-4 plasmid ( with a His tag ) . HaEpi cells [20] were incubated in a 6-well tissue culture plate containing 2 ml of Grace’s medium with 10% FBS at a density of 70% to 90% . Before transfection , the cells were pre-incubated in Grace’s medium for 1 h . Afterward , 8 μg of vector DNA and 8 μg of DNAfectin transfection reagent ( Tiangen , Beijing , China ) were mixed , suspended in 200 μl of Grace’s medium and incubated for 20 min; then , this solution was added to the medium in the culture plate . After 12 h , the cells were re-fed in Grace’s medium containing 10% FBS and cultured for an additional 48 h . The cells were then harvested and washed twice with ice-cold 1× PBS . Afterward , the cells were re-suspended in SDS-lysis buffer ( 1% SDS , 10 mM EDTA , 50 Mm Tris-HCl , pH 8 . 1 ) , and the lysates were pre-cleared with protein A resin at 4°C for 1 h and incubated with anti-GFP antibody at 4°C overnight . The mixture was then incubated with protein A resin at 4°C . After 2 h , the complex was washed three times and analyzed by western blotting with anti-His antibody . To further confirm the interaction of MjCC-CL with Dome , whole MjCC-CL , the CC and CTL domains of MjCC-CL with GST tags and the ILR domain of Dome with a His tag were expressed in E . coli , and GST-pulldown and His-pulldown were performed . Recombinant proteins ( 30 μg ) were added to 20 μl of glutathione resin ( for GST-tagged proteins ) or charged Ni-NTA beads ( for His-tagged proteins ) and incubated at room temperature for 2 h with slight rotation . The mixture ( resin and binding proteins ) was washed three times by centrifugation at 500 g for 3 min to remove the unbound proteins . The test protein with a His tag or GST tag was added into the mixture and was gently rotated at room temperature for 2 h . After the resin was washed three times , bound proteins were eluted and analyzed by SDS-PAGE . Co-immunoprecipitation ( Co-IP ) assay . Proteins from shrimp intestine were extracted with lysis buffer ( 150 mM NaCl , 1 . 0% Nonident-P40 , 0 . 1% SDS , 50 mM Tris [pH 8 . 0] ) and incubated with protein A for 10–15 min to remove non-specific binding proteins . Then , proteins were incubated with antibodies specific for Dome or MjCC-CL for 3 h at room temperature , after which the mixture was incubated with protein A for 3 h at room temperature , and the pellet washed with PBS five times . The resulting pellet ( bound protein , antibody and protein A ) was analyzed by western blot . The mouse primary peritoneal macrophages were obtained from Dr . Cheng-Jiang Gao laboratory in Medical School of Shandong University , the procedure of cell isolation following previous report [65] . The cells were cultured at 37°C under 5% CO2 in DMEM supplemented with 10% FCS ( Invitrogen Life Technologies ) , 100 U/ml penicillin , and 100 μg/ml streptomycin . IL6 ( ProSpec , Israel ) , rGST and rMjCC-CL ( 20 ng/ml ) were added in the cell culture for 30 min . Then the cells were collected and used for immunocytochemical assay and western blotting . PSTAT3 antibody ( Abcam , USA ) was used as the first antibody to detect the STAT3 phosphorylation in mouse macrophages . | The JAK/STAT pathway mediates the effects of a large number of cytokines and growth factors . It is activated following binding of a cytokine or growth factor to its respective receptor . To date , over 50 cytokines and growth factors have been shown to utilize the pathway to regulate cell growth , survival differentiation , motility , and immune responses . The JAK/STAT pathway is ubiquitous in vertebrates but can also be found as an intact pathway in some invertebrates , including shrimp . However , few cytokines and growth factors like molecules are identified in invertebrates and the function of the pathway in invertebrates is seldom studied . In this study , we identified core components of JAK/STAT pathway in shrimp and found the pathway had an important function in antibacterial immunity . Bacterial pathogens directly activate the JAK/STAT pathway through a secreted C-type lectin containing a coiled coil domain and a C-type lectin domain ( MjCC-CL ) in shrimp . Working as a cytokine like ligand , the MjCC-CL binds to polysaccharides from bacteria and the ILR domain of Domeless , induces STAT phosphorylation and translocation into the nucleus , and expression of several AMPs . The MjCC-CL , both as the pattern recognition receptor of bacteria and the ligand of Dome mediates activation JAK/STAT pathway . | [
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"cells"... | 2017 | Binding of a C-type lectin’s coiled-coil domain to the Domeless receptor directly activates the JAK/STAT pathway in the shrimp immune response to bacterial infection |
Onchocerciasis causes a considerable disease burden in Africa , mainly through skin and eye disease . Since 1995 , the African Programme for Onchocerciasis Control ( APOC ) has coordinated annual mass treatment with ivermectin in 16 countries . In this study , we estimate the health impact of APOC and the associated costs from a program perspective up to 2010 and provide expected trends up to 2015 . With data on pre-control prevalence of infection and population coverage of mass treatment , we simulated trends in infection , blindness , visual impairment , and severe itch using the micro-simulation model ONCHOSIM , and estimated disability-adjusted life years ( DALYs ) lost due to onchocerciasis . We assessed financial costs for APOC , beneficiary governments , and non-governmental development organizations , excluding cost of donated drugs . We estimated that between 1995 and 2010 , mass treatment with ivermectin averted 8 . 2 million DALYs due to onchocerciasis in APOC areas , at a nominal cost of about US$257 million . We expect that APOC will avert another 9 . 2 million DALYs between 2011 and 2015 , at a nominal cost of US$221 million . Our simulations suggest that APOC has had a remarkable impact on population health in Africa between 1995 and 2010 . This health impact is predicted to double during the subsequent five years of the program , through to 2015 . APOC is a highly cost-effective public health program . Given the anticipated elimination of onchocerciasis from some APOC areas , we expect even more health gains and a more favorable cost-effectiveness of mass treatment with ivermectin in the near future .
Onchocerciasis is caused by Onchocerca volvulus , a filarial nematode restricted to human hosts . The adult female worms reside in subcutaneous nodules where they produce millions of microfilariae during their on-average ten-year life span [1] . The microfilariae are found predominantly migrating through the skin and eyes and are transmitted by biting flies of the genus Simulium ( the vector ) , an obligatory part of the parasite's life cycle . Onchocerciasis is responsible for a considerable burden of disease , mainly because of visual impairment , blindness , disfiguring skin lesions , and severe itching , which are the results of continuous exposure to microfilariae . Most of the global burden of onchocerciasis ( >99% ) is found in sub-Saharan Africa . In the West African savanna , where onchocerciasis is of a severely blinding form ( savanna type ) , fear of blindness previously led to abandonment of fertile river basins . However , by now , onchocerciasis has been largely eliminated from West Africa by the Onchocerciasis Control Programme ( 1974–2002 ) , which relied on intense vector control and mass treatment with the drug ivermectin [2] . In the more central and eastern parts of Africa , where onchocerciasis is usually of the less blinding form ( forest type ) , there was no control or control only at a limited scale until the inception of the African Programme for Onchocerciasis Control ( APOC ) in 1995 . APOC is a morbidity control program scheduled to be active until 2015 , requiring that by that year , participating countries support and coordinate control measures independently . Since 1995 , APOC has mapped infection with O . volvulus in 20 countries [3] and has coordinated interventions in 16 countries where onchocerciasis is considered a public health problem ( Angola , Burundi , Cameroon , Central African Republic , Chad , Congo , Democratic Republic of Congo , Equatorial Guinea , Ethiopia , Liberia , Malawi , Nigeria , South Sudan , Sudan , Tanzania , and Uganda ) , covering endemic areas inhabited by about 71 . 5 million people in 1995 . APOC's main strategy is to implement annual mass treatment with ivermectin . Ivermectin kills microfilariae and permanently reduces the production of microfilariae by adult female worms , slowing down transmission and preventing morbidity [4] , [5] . Annual mass treatment with ivermectin is implemented through a community-directed treatment approach , empowering communities to take responsibility for ivermectin delivery and to decide how , when , and by whom ivermectin treatment is administered . Mass treatment with ivermectin is enabled by donation of the drug by the pharmaceutical company Merck through the Mectizan Donation Program . Furthermore , coordination of the program is funded by donor countries ( through the World Bank ) and national onchocerciasis task forces ( including beneficiary governments and non-governmental development organizations ) . To demonstrate APOC's importance , validate the efforts of endemic communities and national task forces , and maintain commitment of all stakeholders , it is essential to establish the health impact and cost of APOC . Here , we present the estimated impact of APOC on population health and the costs involved up to 2010 , with extrapolated trends up to 2015 . An impact assessment would ideally be based on observed trends of infection and morbidity , but such longitudinal data are of limited availability in APOC areas . We therefore estimated trends of infection and morbidity based on APOC data of pre-control levels of infection and history of mass treatment , and literature-derived associations between infection and morbidity and the effect of treatment on infection and morbidity . For our calculations , we used ONCHOSIM , an established micro-simulation model for transmission and control of onchocerciasis [6] , [7] .
The impact of APOC was estimated at project level ( a project being an implementation unit for mass treatment with ivermectin ) , while taking account of the prevailing type of onchocerciasis ( i . e . , savanna versus forest or mixed forest/savanna , with different patterns of morbidity ) and the project-specific history of control . Project populations were further stratified by endemicity groups , to take account of differences in the pre-control prevalence of morbidity ( which is non-linearly associated with infection ) and the potential impact of mass treatment ( e . g . , the impact is relatively lower in highly endemic areas due to more residual transmission between treatment rounds ) . We considered four endemicity levels: non-endemic ( prevalence of onchocercal nodules in adult males <1% ) , hypoendemic ( nodule prevalence ≥1% and <20% ) , mesoendemic ( nodule prevalence ≥20% and <40% ) , and hyperendemic ( nodule prevalence ≥40% ) . We estimated the size of the population at risk for infection in the 107 geographical project areas covered by APOC , for the years 1995–2010 ( see File S1 ) . These estimates were based on records kept by community-appointed drug distributors , aggregated to the project level . From the same data , we took the reported number of individuals who were treated with ivermectin during mass treatment ( File S1 ) and calculated the average therapeutic coverage of mass treatment in each project per calendar year ( i . e . , the fraction of the population at risk that was treated ) . Based on data from extensive pre-control mapping studies , we estimated the fraction of the population in the different endemicity categories and the mean pre-control infection level in each endemicity category ( File S1 ) . For the years 2011–2015 , we assumed that population size will increase according to the latest known national growth rate ( as reported by the United Nations World Population Prospects , published 11 May 2010 , accessed 24 October 2011 ) . If therapeutic coverage in 2010 was already at or above 75% , we assumed that coverage in the years 2011–2015 will remain equal to that in 2010 . For those few project in which this was not yet the case , we assumed that between 2011 and 2015 , therapeutic coverage will be scaled up by 10 percentage points per year ( conservative compared to reported coverage patterns in projects that started mass treatment between 1995 and 2010 ) , to a maximum of 75% ( conservative compared to the longest-running projects that reported stable coverage levels around 80% in 2008–2010 ) . For each unit of analysis ( project , onchocerciasis type , endemicity ) , we simulated trends in infection , morbidity , and mortality in the ONCHOSIM model [6]–[8] , considering the project-specific history of mass treatment ( File S1 ) . For each endemicity stratum , ONCHOSIM was calibrated so that it could reproduce the average pre-control level of infection ( File S1 ) . Furthermore , ONCHOSIM was calibrated to reproduce the association between the prevalence of infection and morbidity ( visual impairment , blindness , and itch ) as estimated by analysis of literature data ( File S1 ) . Based on previous studies with ONCHOSIM , we assumed that ivermectin instantly kills all microfilariae and permanently reduces the capacity of adult female worms to release microfilariae by 35% in treated individuals ( with cumulative effects for repeated treatments ) [4] , [7] . Individual participation in mass treatment was assumed to depend on age , sex ( pregnant women and children under the age of five were assumed to be excluded from treatment ) , random non-compliance ( i . e . , temporal factors ) , and systematic non-compliance ( i . e . , fixed individual factors other than age and sex e . g . inclination towards participation ) . Systematic non-compliance was assumed to play a larger role when overall treatment coverage was lower ( i . e . when there is lower inclination to participate in general ) , and vice versa [6] , [8] . No simulations were performed for hypoendemic areas , as ONCHOSIM predicts that transmission of infection is unsustainable without migration of infected flies and/or human , and information on migration was lacking . Instead , we assumed that the prevalence of infection and morbidity in hypoendemic areas was 1/3 of that in mesoendemic areas , both pre-control and during control . For non-endemic areas , we assumed that prevalence of infection and morbidity was always zero . We combined the predicted trends in prevalence of infection , morbidity , and mortality with information on the number of people at risk , yielding an estimate of the absolute number of cases of infection , morbidity , and deaths in each stratum . After aggregation of these results over all APOC projects , we calculated the burden of disease in terms of disability-adjusted life years ( DALYs ) , which in our case is the sum of years lived in disability due to troublesome itch , visual impairment , and blindness , weighted by the loss of quality of life due to each symptom: 0 . 068 , 0 . 282 , and 0 . 594 , respectively [9]; and years of life lost due to excess mortality from blindness ( File S1 ) . Every incident case of blindness was attributed 8 years of life lost , based on the average age of onset of blindness in ONCHOSIM , the associated life-expectancy ( 16 years ) of a healthy person of the same age , and an estimated 50% reduction in remaining life-expectancy due to blindness ( File S1 ) . The estimated annual burden of disease was compared to the burden in a counterfactual scenario in which the pre-control prevalence of infection and morbidity did not change ( i . e . , as if there were no mass treatment ) , yielding an estimate of the averted disease burden . All DALY estimates in the present study are undiscounted . We assessed the influence of uncertain model assumptions on the estimated health impact , by means of univariate and multivariate sensitivity analyses ( File S1 ) . In a univariate sensitivity analyses , we assumed extreme , though plausible parameter values for each of the selected parameters . In a multivariate sensitivity analysis , the analysis was repeated , based on 200 sets of random parameter values . Parameter values were randomly drawn from triangular distributions with modes equal to the values used in the main analysis , and minimum and maximum values equal to those used in the univariate sensitivity analyses . To arrive at a crude estimate of the uncertainty in the estimated health impact , the results of the multivariate sensitivity analysis were expressed as the 2 . 5 and 97 . 5 percentiles of results from 200 repeated analyses . We estimated the financial costs for coordination of ivermectin mass treatment taken on by APOC and national onchocerciasis task forces ( beneficiary governments and non-governmental development organizations ) , based on APOC financial reports for The World Bank , which acts as fiscal agent for APOC . Because governments of beneficiary countries will eventually have to finance and coordinate ivermectin mass treatment , costs were estimated from a program perspective , not accounting for community costs and costs of donated drugs . For the years 1995–2003 and 2010 , cost data for national onchocerciasis task forces were not available and were assumed to be proportional to APOC expenditures by a factor based on data available for other years . Expenditures for 2011–2015 were estimated based on the expected number of treatments in that period multiplied by the estimated cost per treatment in 2010 . All costs are reported in nominal values , by which we mean that the presented costs are the amounts that were actually spent ( i . e . uncorrected for inflation , and undiscounted ) .
In 1995 , the total population size in the APOC target area was 71 . 5 million ( Figure 1 ) , with 30% of the APOC target population living in hyperendemic communities , 31% in mesoendemic communities , 38% in hypoendemic communities surrounded by mesoendemic or hyperendemic areas , and 1% living in non-endemic communities . About 30% of the APOC population lived in savanna areas and 70% in forest or forest–savanna mosaic areas ( Table 1 ) . Before the inception of APOC in 1995 , about 32 million people ( 45% ) in APOC areas were infected with onchocerciasis , with 404 , 000 people ( 0 . 6% ) blind because of onchocerciasis , another 889 , 000 ( 1 . 2% ) suffering from visual impairment , and 10 million people ( 14% ) suffering from troublesome itch . In the same year , a total of 1 . 6 million DALYs ( 22 . 8 DALYs per 1 , 000 persons ) were lost due to onchocerciasis: 694 , 000 because of troublesome itch , 684 , 000 from blindness , and 251 , 000 due to visual impairment . Mass treatment effectively started in 1997 ( 80 , 000 treatments ) and was scaled up over the years , reaching an overall therapeutic coverage of about 73% in 2010 ( 75 . 8 million treatments; Figure 1 ) . We estimated that the therapeutic coverage will increase to 78% by 2015 ( 92 . 5 million treatments ) . By 2010 , about 65% of the population lived in areas subjected to 10–13 rounds of mass treatment , 17% in areas subjected to 6–9 rounds of mass treatment , 18% in areas subjected to 3–5 rounds of mass treatment , and less than 1% in areas subjected to only 1–2 rounds of mass treatment ( Table 1 ) . Cumulatively , about 500 million treatments with ivermectin were given between 1995 and 2010 , with another 430 million expected to follow in the period 2011–2015 . Considering the differences between projects in start year and patterns of scaling up of mass treatment , the prevalence of infection for APOC as a whole declined gradually and non-linearly over time , from 45% in 1995 to 31% in 2010 , and to 18% in 2015 ( Figure 2 ) . Similarly , the prevalence of troublesome itch was reduced from 14% to 6% to 2% , and prevalence of visual impairment was reduced from 1 . 2% to 0 . 8% to 0 . 6% . Because of excess mortality among the blind and the fact that ivermectin prevented blindness in individuals who were already visually impaired , the prevalence of blindness declined more rapidly than that of visual impairment: from 0 . 6% to 0 . 3% to 0 . 2% . In the counterfactual scenario without mass treatment , in which levels of infection and morbidity were stable , the absolute number of DALYs lost due to onchocerciasis would have increased over the years with population growth . In contrast , in the scenario that considers mass treatment with ivermectin , the absolute number of DALYs lost was predicted to decrease over the years . Due to these divergent trends , the number of DALYs averted by mass treatment with ivermectin was predicted to increase year by year ( Figure 3 ) . Overall , mass treatment with ivermectin averted 8 . 2 million DALYs between 1995 and 2010 ( 3 . 2 million due to itch , 4 . 4 million due to blindness , 0 . 6 million due to visual impairment ) . Moreover , we expect that APOC will avert another 9 . 2 million DALYs in the period 2011–2015 , adding up to an expected total of 17 . 4 million averted DALYs by 2015 ( Table 2 ) . In relative terms , the disease burden of onchocerciasis was reduced from 22 . 8 DALYs per 1 , 000 persons in 1995 to 9 . 6 DALYs per 1 , 000 persons in 2010 , and is expected to be further reduced to 5 . 0 DALYs per 1 , 000 persons by 2015 . Univariate sensitivity analyses identified the following parameters as having the most influence on the estimated health impact: the population at risk , pre-control levels of infection , and the associations between infection and itch and eye disease ( Figure 4 ) . The multivariate sensitivity analysis showed that the estimated cumulative number of DALYs averted could be up to 25% higher or lower , when we considered the separate sources of uncertainty simultaneously ( 6 . 0–9 . 8 million DALYS cumulatively averted by 2010 , and 13 . 1–21 . 3 million DALYs cumulatively averted by 2015; Figure 4 ) . Between 1995 and 2010 , coordination of mass treatment cost roughly US$257 million ( Table 2 ) , of which US$175 million was disbursed by APOC and US$82 million by national onchocerciasis task forces ( cost of donated drugs and government salaries not included ) . Assuming that costs will rise proportionally with the number of treatments , mass treatment was expected to cost another US$221 million between 2011 and 2015 , adding up to a total cost of US$478 million by 2015 .
We estimated the health impact and cost of mass treatment with ivermectin for the 20-year period that APOC is scheduled to run as a morbidity control program ( 1995–2015 ) . Our simulations suggest that mass treatment with ivermectin has markedly reduced the prevalence of infection with O . volvulus , troublesome itch , visual impairment , and blindness in APOC areas , averting an estimated 8 . 2 million DALYs due to onchocerciasis by 2010 at a nominal financial cost of about US$257 million ( excluding cost of donated drugs ) . We expect that APOC will avert another 9 . 2 million DALYs between 2011 and 2015 , at a nominal financial cost of US$221 million . Our estimate of APOC's health impact only considered eye disease and troublesome itch , and would be even higher if other clinical manifestations of onchocerciasis would have been taken into account . For instance , disfiguring skin disease also contributes to the disease burden of onchocerciasis and is known to be reduced by ivermectin [10]–[13] . Further , epilepsy may be associated with onchocerciasis , as suggested by a growing but still uncertain base of evidence [14] . However , we chose to include only the most important disease manifestations for which data were available for model calibration ( i . e . , eye disease and troublesome itch ) . Furthermore , we did not include the effect of ivermectin on diseases that are co-endemic with onchocerciasis , such as soil transmitted helminthiases , ectoparasitic infections , and lymphatic filariasis [15] . Other minor factors leading to an underestimation of the health impact are that we only considered the effect of ivermectin on the capacity of adult female worms to release microfilariae and its microfilaricidal effect , whereas ivermectin may additionally have a modest effect on adult worm viability [16] , [17] . Furthermore , we ignored between-village variation in coverage , which is perhaps most extreme in the phase of scaling up: in some projects , treatment started in a subpopulation with high coverage , while the other part of the population did not yet receive mass treatment ( which is more efficient than treating the entire project population at an equivalent average coverage ) . We may have somewhat overestimated the number of life years lost due to excess mortality from blindness during and after mass treatment , causing a small underestimation in the number of DALYs averted . This is because we appointed a fixed number of life years lost to every new case of blindness , while regular ivermectin treatment is expected to postpone the onset of blindness to a higher age , reducing the number of life years lost due to blindness . Furthermore , we did not consider a possible association between excess mortality and ( high ) microfilarial load [18] , [19] . There are several factors that may ( partly ) counterweigh the underestimation of the health impact of APOC described above . Therapeutic coverage may have been over-reported by community members responsible for the distribution of ivermectin , either because of incomplete estimates of the community population or to inflate their own performance . Yet , the estimated health impact of APOC by 2015 would decrease by only 0 . 8 million averted DALYs if we assume that coverage were to be systematically 10% lower than reported . Also , we ignored any mass treatment prior to the inception of APOC , whereas in reality , ivermectin distribution had already started in a limited number of foci ( here morbidity levels had already been reduced somewhat , but not on account of APOC ) . Taking all above sources of under- and over-estimation into account , we believe that the true health impact of APOC is still slightly higher than our calculations . The validity of our results , as in any simulation study , depends on the quality of the model and its assumptions . ONCHOSIM was first developed in the early nineties and has earned trust over the years from the large scale control programs . ONCHOSIM has been used to successfully mimic observed epidemiological data from various locations [4] , [20]–[22] , and has been used for policy making in the West-African Onchocerciasis Control Programme [7] . Efforts to validate the model continue . We have recently compared ONCHOSIM predictions to longitudinal data from Senegal and Gambia [23] and found that model-predicted trends in mf prevalence during 14 to 16 years of mass treatment were broadly consistent with the observed trends , although the mf prevalence sometimes seemed to decline slightly faster than predicted ( unpublished data ) . Furthermore , our model predictions for trends in itch were comparable to the reported average trend in APOC sentinel areas [13]; after five to six years of mass treatment at 70–80% coverage , itch prevalence was reported to decline from 16% to 7% , and we predicted a decline from 14% to 6 . 5% for areas with similar pre-control levels of infection and history of mass treatment . Likewise , our model adequately reproduced trends in onchocercal blindness during vector control in West Africa ( File S1 ) . Although the above suggests that our model predictions are realistic , our estimates remain subject to uncertainty and it would be good to have them confirmed by more field data , especially regarding trends in morbidity during mass treatment . Even though the model seems to be reliable , we should consider potentially important sources of uncertainty in our analysis . An often debated factor concerns the effect of ivermectin on adult worms . The univariate sensitivity analysis showed that the assumed treatment effects of ivermectin on the capacity of adult worms to release microfilariae influenced the estimated health impact only marginally . We did not study the effects of assuming no cumulative effects of ivermectin on worm fecundity , whereas it has been suggested that the latter may be the case [24] . However , if we had , ivermectin efficacy parameters would have been calibrated such that the model-predicted trends in mf prevalence and density were still in agreement with observed trends [4] , [22] , and therefore predicted trends in infection levels and morbidity should not have differed much from the current model's predictions . The sensitivity analysis showed that alternative assumptions for the effect of ivermectin on itch ( the only reversible symptom under consideration ) also influenced the estimated health impact only marginally . The most influential assumptions in our analysis were related to the estimated size of the population at risk , pre-control levels of infection , and the assumed associations between infection and morbidity , which were all based on data . Even though the multivariate sensitivity analysis suggested considerable overall uncertainty in our estimate of the health impact ( ±25% ) , the magnitude of the predicted impact was always large . With an estimated 8 . 2 million DALYs averted in a 15-year period and a predicted doubling in the subsequent 5 years , the predicted health impact of APOC is impressive . According to our calculations , mass treatment against onchocerciasis cost about a nominal US$31 per undiscounted DALY averted between 1995 and 2010 . According to World Health Organization guidelines [25] , this is highly cost-effective , as it is below the per capita gross domestic product of most countries covered by APOC ( 27–1 , 545 international dollar per capita; Global Health Observatory Data Repository , accessed 2 August 2012 ) . Furthermore , this cost-effectiveness is comparable to or even better than those for several other public health interventions . For example , the life-time cost-effectiveness of prophylaxis against mother-to-child transmission of HIV in a resource-limited setting has been estimated at US$52 per undiscounted DALY ( incremental cost-effectiveness ratio of World Health Organization guidelines versus minimal standard of care ) [26] . The cost-effectiveness of large-scale , long-term ( 30-year period ) public health interventions targeting other neglected tropical diseases has been estimated at US$4–US$29 per DALY ( mass drug administration against lymphatic filariasis ) , US$38 per DALY ( case detection and treatment for leprosy ) , US$260 per DALY ( vector control against Chagas disease ) , and US$48–US$303 ( vector control against lymphatic filariasis ) [27] . Mass treatment against onchocerciasis is of even better value ( US$27 per DALY ) if expected health gains and costs for the period 2011–2015 are included . In view of the anticipated elimination of infection so that mass treatment can be stopped altogether , the cost-effectiveness will be even better than our calculations suggest [23] . The objective of APOC is to establish country-led systems for onchocerciasis control by 2015 , which means that countries and their partners will have to carry full financial responsibility by that year . Our results indicate that cost per treatment with ivermectin in APOC areas is affordable ( US$0 . 51 per treatment , excluding cost of donated drugs ) and comparable to the costs of existing national mass treatment programs for the elimination of lymphatic filariasis ( US$0 . 06–US$2 . 23 per treatment ) [28] . Mass treatment with ivermectin , however , also involves costs for society not covered by the program . From published data for two Nigerian communities , we derived that these costs are about US$0 . 23 per treatment ( excluding start-up costs ) [29] . Based on this estimate , the sum of program and community costs for mass treatment with ivermectin was approximately US$370 million from 1995 to 2010 and will be another US$320 million for 2011–2015 . In addition to costs , there are significant benefits for society that countries need to take into account , such as prevented productivity losses resulting from blindness and itch . Blindness in rural Africa has previously been assumed to result in an annual productivity loss of US$150 per case [30] . Likewise , the productivity loss due to itch among coffee plantation workers in an Ethiopian site has been estimated at around US$5 . 32 per month per case [12] . Combined with our predictions of health impact , these figures suggest that by 2015 , APOC will have averted a staggering US$2 . 2 billion due to productivity losses from blindness ( US$517 million ) and itch ( US$1 . 7 billion , assuming productivity losses in 25% of people with itch ) . In other words , beneficiary countries should expect economic benefit from mass treatment that outweighs any costs . Clearly , all of the above calculations apply only under the condition that countries do not themselves pay for the drug ivermectin . The amount of ivermectin donated up to 2010 represents a value of US$2 . 1 billion , assuming 2 . 8 tablets per treatment and a commercial price per tablet of US$1 . 50 plus US$0 . 005 shipping costs ( personal communication with Dr . A . Hopkins , director of the Mectizan Donation Program ) . This amount is eight times the program costs for coordinating mass treatment . Likewise , for the period 2011–2015 , the value of donated ivermectin will be an additional US$1 . 8 billion . Therefore , mass treatment with ivermectin can be sustained only with donation of ivermectin , which Merck has pledged to continue for as long as necessary . We expect that levels of infection in the APOC target area will have fallen drastically by 2015 ( overall prevalence of adult female worms 18% ) . The implication is that by that time , transmission of infection may be almost interrupted in areas with favorable conditions for elimination , such as high coverage of mass treatment , sufficient treatment rounds , and/or low to medium pre-control levels of infection [31] . Until recently , elimination of onchocerciasis from Africa was thought to be impossible by means of mass treatment alone , considering the large size of the transmission zones , mobility of the vectors and human populations , and poor compliance with mass treatment [32] . Following reports of elimination of onchocerciasis from foci in Mali and Senegal by mass treatment alone [23] , however , interest has renewed in elimination of onchocerciasis from Africa [33] . Following this , WHO has recently been advised to extend APOC mandate by ten years to 2025 with the new aim of eliminating infection with O . volvulus , where possible . With this new motivation , we may indeed expect focal elimination of infection , resulting in even more health gains from mass treatment with ivermectin in the future and the possibility of being able to end mass treatment altogether . According to our simulations , APOC has had a remarkable impact on population health in Africa between 1995 and 2010 . This health impact is expected to double during the subsequent five years . Further , APOC is a highly cost-effective public health programs , and given the anticipated elimination of onchocerciasis from APOC areas , we expect even more health gains and a more profitable cost-effectiveness of mass treatment with ivermectin in the near future . Our study fully supports the advice to continue APOC activities for another ten years . | In 1995 , the World Health Organization launched the African Programme for Onchocerciasis Control ( APOC ) with the aim to control morbidity due to the parasitic infectious disease onchocerciasis ( river blindness ) . APOC aims to set up sustainable national control programs against onchocerciasis in 16 countries in sub-Saharan Africa , covering over 100 million people who are at risk for infection . The main control strategy is mass treatment with the drug ivermectin , which is donated by the pharmaceutical company Merck . Coordination of the mass treatment programs is made possible by financial contributions from donor and beneficiary countries . We estimated that between 1995 and 2010 , APOC has had a huge impact on population health in sub-Saharan Africa , preventing 8 . 2 million years worth of healthy life from being lost due to disease and mortality , at a cost of about US$257 million . We predicted that this health impact will double during the subsequent five years , at a cost of about US$221 million . This makes APOC one of the most cost-efficient large-scale public health programs in the world . We may expect even greater health gains in the future , given the anticipated extension of the APOC mandate with the aim to eliminate infection where possible . | [
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During development , neurons extend axons to different brain areas and produce stereotypical patterns of connections . The mechanisms underlying this process have been intensively studied in the visual system , where retinal neurons form retinotopic maps in the thalamus and superior colliculus . The mechanisms active in map formation include molecular guidance cues , trophic factor release , spontaneous neural activity , spike-timing dependent plasticity ( STDP ) , synapse creation and retraction , and axon growth , branching and retraction . To investigate how these mechanisms interact , a multi-component model of the developing retinocollicular pathway was produced based on phenomenological approximations of each of these mechanisms . Core assumptions of the model were that the probabilities of axonal branching and synaptic growth are highest where the combined influences of chemoaffinity and trophic factor cues are highest , and that activity-dependent release of trophic factors acts to stabilize synapses . Based on these behaviors , model axons produced morphologically realistic growth patterns and projected to retinotopically correct locations in the colliculus . Findings of the model include that STDP , gradient detection by axonal growth cones and lateral connectivity among collicular neurons were not necessary for refinement , and that the instructive cues for axonal growth appear to be mediated first by molecular guidance and then by neural activity . Although complex , the model appears to be insensitive to variations in how the component developmental mechanisms are implemented . Activity , molecular guidance and the growth and retraction of axons and synapses are common features of neural development , and the findings of this study may have relevance beyond organization in the retinocollicular pathway .
During neural system development , groups of neurons project to various areas of the brain and produce stereotypical patterns of innervation . These organization patterns are an emergent property of the physiological mechanisms regulating neural behavior . In the visual system these mechanisms include molecular guidance [1] , [2] , spontaneous correlated activity in the form of retinal waves [3]–[5] , neurotrophic factor release and uptake [6] , spike-timing-dependent plasticity ( STDP ) [7] , [8] as well as the growth and retraction of axons and synapses . Similar phenomena are observed in many other brain areas during development [9]–[13] . An important question is how these underlying phenomena combine to produce the emergent patterns of connections seen throughout the brain . A well studied example of such organization is the retinotopically ordered projection from the retina to the thalamus and superior colliculus . Many computational models have examined how one or more of these phenomena are able to produce retinotopic organization ( e . g . , [14]–[21] ) . So far , however , none of the models has brought together this diverse set of physiological behaviors , and only a few ( e . g . , [22] ) have addressed development from the perspective of individual axons and how they can grow , branch and retract to reach their retinotopically correct termination zones . Framing development from this perspective is important , as neural connection patterns are ultimately the result of axon growth and branching , hence constraining a model by the physical and geometrical constraints of the axon is a prerequisite to understanding how projections form . An axon extending through any neuropil consisting of cells , axons and dendrites is analogous to a rope being pulled through a corn field: once the rope is extended , lateral motion is not possible without knocking over corn stalks [23] . Similarly , an axon has very restricted lateral motion once it has extended and branched throughout the neuropil . To explain neural organization such as retinotopic development , a model needs to describe not only how these physiological behaviors contribute to development , but also how observed patterns of development can be achieved in light of the physical constraints placed on axon movement . This study presents a model of retinocollicular development that combines phenomenological approximations of the aforementioned physiological behaviors and examines how these can guide the extension , branching and retraction of individual axons in such a way that leads to a refined arborization at the retinotopically correct location in the colliculus . The stages of development follow those previously described for mouse and chick [24] . In summary , retinal axons enter the anterior side of the colliculus and extend in a largely linear manner to the posterior side . Interstitial branches then sprout and extend towards the retinotopically correct area of the colliculus for the given axon , based on chemoaffinity compatibility between each axon and the expression of molecular markers in the colliculus ( Fig . 1A ) . Activity-dependent trophic feedback mediates growth and retraction of individual synapses , with trophic factor stabilizing synapses that contribute to spiking activity in the postsynaptic neurons and synapses that receive insufficient trophic feedback retracting ( Fig . 1B ) . Correlated retinal activity , in the form of retinal waves , provides spatial information allowing synapses from retinal ganglion cells ( RGCs ) originating from near the same point in the retina to stabilize on the same collicular neurons . Trophic factors enhance axon and synapse growth in the areas of the axon where they are received and STDP modulates the excitatory strength of individual synapses . This study continues in the spirit of previous theoretical work on hybrid models ( e . g . , [25] ) , allowing the relative roles of and interactions between these different physiological behaviors to be studied , and has generated several new findings . Most significantly , retinotopic organization and refinement appears to be a stable emergent property of the core assumptions so long as the functional behaviors of retinal waves , molecular guidance cues and activity-dependent trophic factor release were represented in the model . The characteristic of retinal waves that was important was the overall correlational structure of activity and not the specific spatiotemporal properties of the waves . Alteration in the correlational structure by using simulated retinal activity similar to that observed in the mutant mouse [26] disrupted the ability of axons from neighboring RGCs to produce overlapping arbors in the colliculus . Neither STDP nor any form of plasticity occurring at the level of individual synapses was necessary for refinement , and analysis of the model suggests that Hebbian synaptic plasticity is a slow-acting process that is instead realized by the addition and subtraction of synapses . Gradient detection by axon growth cones was not required to achieve retinotopic organization or refinement once axons had reached the colliculus , as each axon was able to guide growth based on gradient differentials across its arbor .
The relative contribution of the different physiological behaviors were analyzed by selectively altering or disabling them . First analyzed was the the effect of changing the spatiotemporal characteristics of retinal waves . The baseline ( control ) spatiotemporal properties of retinal waves were based on those described for young ferrets [28] , [29] , as retinal wave properties are similar between species ( see [30] ) and the size , velocity , frequency and RGC firing properties in ferret are well described . To assess the importance of specific spatiotemporal wave properties to retinotopic development and to see how the selection of control values biased the results , the model was run using patterns of retinal wave activity where the size , velocity or frequency of waves was altered . In all cases , the retinal projection and collicular receptive fields were quantitatively and qualitatively similar ( Fig . 6A–D; compare orange to black ) . Baseline waves had a velocity of 180 , average size of 0 . 161 , and average interwave interval of 94 . 2 sec . Ranging the velocity from 112 to 466 , while holding other wave properties largely constant , had minimal effect on retinotopic refinement . Similarly , refinement appeared normal for waves having small ( 0 . 101 ) and large ( 0 . 428 ) average sizes . Increased wave frequency , as measured by decreasing the interwave interval to 45 . 1 sec , had minimal effects on refinement . Decreasing wave frequency ( interwave interval 202 sec ) slowed the rate of refinement but did not have a significant effect on the refined projection . Mice lacking the subunit of the acetylcholine receptor have been reported to have significantly altered retinal activity patterns [26] , [31] , [32] as well as altered retinocollicular projections , with the projective and receptive fields of groups of nearby neurons larger than observed in wild type [33] , [34] . In wild type mice nearby RGCs having stronger correlations in activity than RGCs farther apart , while in knockout mice ( ) retinal activity is either uncorrelated [31] or strongly correlated over long distances , with RGCs from over a large area of the retina bursting almost simultaneously [26] , [32] . In either case , the spatial information provided to refining axons is disrupted , as activity in axons from neighboring RGCs is no longer significantly more correlated than in axons from RGCs located farther apart . To explore the result of this change to retinal activity , simulated retinal activity patterns were approximated based on data reported by [26] ( Fig . 6E; green ) . Using these patterns of RGC activity , the individual RF radius increased by 65% ( control: ( n = 6 simulations ) compared to ( n = 1 simulation ) ; all subsequent comparisons are reported in this format ) and the group RF radius was similarly increased ( ; from to ) . The refined arbor of each individual RGC showed a minor increase in size compared to control ( +14%; from to ) , but the group PF radius was increased significantly ( +49%; from to ) ( Fig . 6; A–D ) . These changes are qualitatively consistent with experimental findings [33] , [34] , but experimentally observed changes are quantitatively much different than observed here ( a 2–2 . 5 fold increase in RF or PF area in the model compared to a 10-fold increase observed experimentally ) . Several factors might account for this difference . One factor is that , despite strongly enhanced activity correlations between distant neurons , simulated activity still had higher correlations for nearby neurons than for distant ones compared to experimental data [26] . Another factor is that the firing properties of retinas are not fully understood ( compare [31] , [33] and [26] ) and it seems unlikely that activity is accurately represented here . Simulations in which all RGC firing was decorrelated prevented refinement , suggesting that actual activity is unlikely to be fully decorrelated ( e . g . , [31] , [33] ) and there likely exists some distance-dependent pattern of correlation , as indicated by [26] , [32] . More generally , the results suggest that it is the correlation patterns between RGCs that drives refinement , not the particular characteristics of retinal waves , and that even small amounts of heightened correlation among nearby neurons can result in nearly normal patterns of refinement . Molecular guidance cues were implemented as providing axons a bias to preferentially grow near the retinotopically correct area of the colliculus . Disabling this form of guidance by eliminating molecular guidance cues in both colliculus and retinal axons completely disrupted retinotopic organization ( Fig . 7 ) . Individual axons did refine , and nearby RGCs often projected to similar collicular areas and had overlapping arbors , but there was no global order in the projections . Disabling chemoaffinity after coarse retinotopic organization was established ( i . e . at 60 hours ) , and allowing subsequent refinement to be driven exclusively by activity-dependent mechanisms , improved refinement ( group RF −19% from to ; group PF −22% from to ) . The reason for this improvement appeared to be that while the coarse guidance provided by molecular guidance cues was necessary to guide axons to near their retinotopically correct termination zones , once the axons had arrived , coarse guidance worked against activity-dependent refinement by broadening the area of the arbor where growth occurred . Activity-dependent mechanisms focused axon and synapse growth to the vicinity of synapses that induced spikes in the postsynaptic neuron . Molecular guidance cues worked to increase axon and synapse growth in a relatively broad region of heightened chemoaffinity compatibility , thus diffusing the focusing effect of activity-dependent refinement . These results suggest that molecular guidance cues are critical for establishing initial retinotopic order , but after this order is established , they are not necessary to refine the connection , consistent with an analysis of experimental results [35] , [36] . Moreover , it appears that the influence of molecular guidance cues might actually inhibit refinement after axons arborize in the vicinity of their termination zones . To examine the contribution of STDP to refinement , synaptic plasticity was disabled and all synapses maintained a unitary strength . Unexpectedly , this did not significantly affect model behavior ( group RF +0 . 5% from to ; group PF +0 . 5% from to ) . An analysis of the synapses in the unaltered model showed a narrow distribution of synapse weights ( ; n = 301 , 343 synapses ) , so locking synapse weights to unity had little quantitative significance . It is possible that a different implementation of STDP than used here could have more strongly contributed to development , but these results show that STDP , or any form of plasticity regulating the weight of individual synapses , is not required for retinotopic organization or refinement . Plasticity was instead realized through the growth and retraction of individual synapses . LTP and LTD are associated with increases and decreases in the number of synapses , respectively , consistent with the results observed here ( see [37] ) . Activity-dependent release of brain-derived neurotrophic factor ( BDNF ) has been linked to long-term potentiation and STDP [38]–[40] . In the model , trophic factor release was linked to STDP , such that trophic factor was released by the postsynaptic terminal proportional to STDP potentiation under a simple pair-based STDP protocol ( e . g . , [20] ) . In light of these findings about STDP , the importance of this linkage was investigated by decoupling them and varying the time window for activity-dependent trophic factor release . Specifically , the time window for trophic factor release ( in Eq . 10 ) was increased by 2 , 4 and 8 times ( from 13 . 3 ms up to 106 ms ) and the magnitude of trophic factor release was proportionally reduced to account for the longer release window . Trophic factor release was also varied by using a square-wave function , such that if a postsynaptic spike followed within 25 ms of a presynaptic spike , a fixed amount of trophic factor was released ( 0 . 532 units , a magnitude that made equal the integrals of square wave and exponential release ) . In all cases , development was quantitatively and qualitatively similar ( maximum changes of +14% PF , +13% RF were observed using the longest time window ) . Eliminating the activity-dependent mechanism underlying trophic factor release and having trophic factor released on every postsynaptic spike completely prevented refinement , with too much release resulting in very little synapse turnover , as most synapses became stabilized by the trophic factor received , and too little release preventing synapse stabilization and causing very high rates of turnover . These results indicate that activity-dependent trophic factor release , or an equivalent mechanism providing performance feedback to individual synapses , guides the removal of inappropriately targeted synapses and refines the retinotopic projection . The time window for this mechanism , here described as trophic factor release , is consistent with the STDP potentiation window , but is appears not to be restricted to that interval . Development in the model was split into two distinct stages , with axon growth first being mediated by molecular guidance and later , after axons had reached the vicinity of their retinotopically correct termination zones , activity-based feedback began to contribute to guide axon growth . As shown in Fig . 1 , this behavior allows nearby RGCs to project to the same areas of the colliculus and to form a refined retinotopic map . While this temporal segregation of roles worked well and is in line with experimental literature [35] , [36] , initial assumptions of the model were that molecular guidance and activity-based mechanisms both provided instructive guidance from the time when axons first innervated the colliculus . This coincident onset of guidance cues performed poorly , as axon arbors began to refine before they reached their retinotopically correct areas of the colliculus , resulting in numerous ectopic projections ( Fig . 8 ) . Delaying activity-dependent instructive cues until after molecular mechanisms had guided axons to the vicinity of their correct termination zones greatly reduced the incidence of ectopic projections and allowed normal organization and refinement to occur . Ectopic projections were sometimes still observed , but these were largely restricted to areas of the collicular boundary . The predominant view in the experimental literature suggests that molecular guidance is required to initially drive axon development , whereafter activity-dependent mechanisms guide refinement [35] , [36] . Our findings go beyond this and suggest that , at least during initial development , a separation of mechanisms is necessary . The effect of early activation of activity-mediated guidance is that it reduces the relative strength of chemoaffinity-mediated growth , as molecular guidance cues become forced to compete with activity-dependent ones . Experimentally , weakening the molecular guidance mechanisms by blocking production of guidance molecules also results in increased ectopic projections ( e . g . [1] , [2] ) , consistent with the behavior observed here . Malformed retinotopic projections resulting from early onset of activity-dependent mechanisms were prevented by sufficiently increasing the relative strength of molecularly-driven guidance , allowing both guidance mechanisms to act simultaneously . However , such increases also resulted in accurate axon targeting in the complete absence of activity . This behavior suggests that small animals such as zebrafish , that do not require neural activity for axons to project to their retinotopically correct targets [41] ( but see [42] ) , and whose tecta are a small percentage ( 2% ) of the length of tecta in larger animals , such as chick [24] , [43] , may not be adversely affected by early onset of activity-dependent guidance . Larger animals , whose tecta ( superior colliculi ) are much larger and presumably have much shallower molecular gradients , will be impacted more significantly . Model synapses required trophic feedback for survival . It was assumed that this mechanism was cooperative , such that trophic factor received by one synapse would also help stabilize nearby synapses on the axon . The theoretical value to such a mechanism , in addition to helping to concentrate synapses to particular areas of an axon arbor , is that trophic feedback from one type of target neuron can help stabilize axonal synapses to different types of nearby neurons ( e . g . , GABAergic interneurons in retinogeniculate projections ) , thereby providing a mechanism to spatially align the projection to two ( or more ) disparate types of target neuron . Functionally similar polysynaptic mechanisms have been hypothesized , such as resulting from rapidly diffusible molecules ( e . g . , nitric oxide [44] ) . To evaluate the importance of this assumed behavior , development was examined with the stabilizing effect of trophic factor restricted to the synapse where it was received . Trophic factor receipt continued to influence axon growth and the distribution of axon resources normally . This modification did slow retinotopic refinement , but it also improved the degree of refinement realized ( Fig . 4E ) , reducing RF size by 16% ( from to ) and reducing PF by 13% ( from to ) . While the ability of synapses to help stabilize their neighbors can affect the rate of retinotopic refinement , it is not required to achieve a refined retinotopic projection . In addition to the simple integrate and fire model used to represent collicular neurons in this study , previous versions of this model used non-linear integrate and fire neurons ( i . e . , [45] ) and two-compartment neural models , and these changes did not qualitatively affect model behavior ( data not shown ) . To investigate the possibility that neural growth had an influence on retinotopic refinement , collicular neurons were allowed to grow during development , with growth defined as an increase in the resting conductance of the neuron with time , such as occurs with increased surface area of the neuron and dendrite . The effect of such growth was that individual synapses had larger somatic excitatory post-synaptic potentials ( EPSPs ) on immature neurons than on mature ones . Collicular neuron growth was found to influence refinement in a non-linear way , with maximal refinement observed in neurons having moderate growth ( −11% RF and −11% PF compared to control ) . Further increasing maximal growth reduced refinement . Preventing neural growth , such that the somatic EPSP of neurons resulting from a single presynaptic vesicle release was identical in immature and mature neurons , reduced refinement ( +16% RF and +20% PF ) . It thus appears that neural growth , as exhibited by the decreasing somatic EPSP of individual synapses with time , has an influence on retinotopic refinement . Despite this influence , refinement still appears to be a tolerant process and was observed across a wide range of growth values , and more generally , that retinotopic development remains largely normal despite changes to the mechanisms underlying organization , so long certain core behaviors remain , which are spatiotemporally correlated retinal activity , molecular guidance cues , and activity-dependent trophic factor release .
The design of the model was based on an analysis of the physiological mechanisms active during development , and the practical biological requirements of these mechanisms . From a physiological perspective , a neuronal projection is defined by the pattern of synapses that exist between bodies of pre- and postsynaptic neurons . The location of these synapses is constrained by the presence of axons , which in turn are constrained by patterns of growth , branching and retraction , as lateral axon motion is not realistic . The growth and retraction of axons and synapses must be governed by locally available information . Neurotrophins , such as BDNF , are prime candidates for mediating this process . Neurotrophins enhance axon growth and synapse numbers [49] , are hypothesized to play a role in synapse stabilization and maintenance [50]–[52] and are released in an activity-dependent manner [39] , [51] . The effects of BDNF are local to the area released [53] , may be synapse specific [13] , [51] and postsynaptic activity within tens of milliseconds of presynaptic activity results in synaptic enlargement in a process mediated by BDNF [54] . Molecular guidance cues also influence axon growth [55] and more generally , cellular behaviors are influenced by variations in firing rates ( e . g . [56] ) . Based on these points , two core assumptions were derived to guide model behavior: 1 ) Axon growth and branching , and synapse formation , had increased probabilities in areas of an arbor with greater relative ( a ) chemoaffinity compatibility with surrounding tissue than other sections of the arbor , and ( b ) trophic feedback to the presynaptic terminal , which was provided by the postsynaptic terminal when a postsynaptic spike followed shortly after a presynaptic spike . 2 ) Synapses required trophic feedback for survival , and synapses with insufficient trophic support were eliminated . To implement these principles , additional considerations were required , such as how to regulate the size of the axon and the synapse population . This led to three further assumptions: 3 ) To limit total axon arbor size , axons required a regulated substance , referred to here as axon resources , in order to grow and to persist . Axon resources were produced in finite quantities by the soma and were delivered preferentially to regions of the arbor with higher relative chemoaffinity and to near synapses receiving relatively more trophic feedback . A reduced presence of axon resources resulted in an increased likelihood of axon retraction . 4 ) To control the number of axonal synapses , the probability of new synapse formation was decreased with increased numbers of existing synapses , and each synapse required increasing amounts of trophic factor to survive with increasing numbers of synapses on the axon . 5 ) The number of dendritic synapses was controlled through direct and indirect means . The more synapses present on a dendrite , the less likely the dendrite was to accept new synapses . When a collicular neuron's average firing rate ( integrated over tens of minutes ) was above its target level , it both became less likely to accept new innervating synapses , and existing synapses decreased the trophic feedback provided to presynaptic terminals in order to induce some innervating synapses to retract . The model was based on implementation of these mechanisms . The first two assumptions were core to the model's behavior and so it was not possible to carefully evaluate alternatives . The others assumptions were tolerant to variation so long as the behavior that these assumptions were designed to produce was realized ( as assessed through both parameter variation and unpublished versions of this model ) . Homeostatic mechanisms were found to be important in the model design . The complexity of the model made it a practical impossibility to pre-define numerical quantities for the large range of mechanisms represented , such as exact EPSP magnitude , the number of synapses , total axon length , trophic feedback quantities , etc . Even when it was possible to define specific values for a quantity , minor modifications to the model often made such selections inappropriate , forcing the parameters to be readjusted . Defining quantities loosely and in such a way that they were subject to dynamic regulation ( e . g . , assumptions 3–5 ) produced a system that was very tolerant to perturbation . The same issues encountered in producing this model are also observed by nature , as there is a high degree of variability that can arise from genetic and environmental factors , and the biological system is tolerant to perturbation and it preserves its functionality despite changes to the mechanisms underlying this functionality [57] . The finding that STDP was not required for retinotopic refinement was unexpected . On reflection , this finding is consistent with the results of several experimental studies . Synaptic plasticity saturates after 60–100 spike pairings [7] , [58] , meaning that synapses that are already maximally potentiated for a given interval between pre- and postsynaptic spikes do not further potentiate . The fact that it is possible to observe significant synaptic potentiation and depression in STDP studies therefore suggests that most synapses exist in largely non-potentiated states , for otherwise such potentiation would not be observable in them . The notion that synapses are not significantly potentiated or depressed in their normal state is reinforced by findings that artificially induced STDP is lost if cells are allowed to resume their normal firing patterns [59] and that the distribution of individual synapse strengths is unimodal [60] . Further , experimental studies have indicated that it is either the timing of bursts between pre- and postsynaptic neurons , or the coincidences of individual spikes , that underlies plasticity , not STDP [61] . Cross-correlograms ( CCGs ) between pairs of monosynaptically connected cells often show a number of uncorrelated spike pairs and a peak a few milliseconds offset from time zero ( Fig . 9; e . g . , [62] ) , indicating that the postsynaptic cell has a higher than average probability of firing immediately after the presynaptic neuron , a behavior observed in modeled neurons . This CCG pattern should result from any system where there are several innervating neurons for each target neuron and when a spike in the presynaptic neuron is followed by a spike in the postsynaptic neuron only infrequently . While the peak in the CCG between monosynaptically connected cells is typically in the optimal location for inducing maximum potentiation in the synapse , the relatively large number of non-correlated firings would be expected to have a counteracting and depressing effect on plasticity . Further , when observing saturation of plasticity constraints , where maximal plasticity appears to be approached asymptotically [7] , [58] , the more a synapse is potentiated the less potentiating force there is after every pre-post spike pair . Depressing spike pairs ( i . e . , post-pre ) are far from their saturation level and so are likely to maintain full potency , further inhibiting strong potentiation . Manipulating the strength of individual synapses is not the only way to vary the effective strength of a monosynaptic connection . As indicated in Fig . 3C , variation in the strengths of synaptic coupling between two neurons can be achieved by variable numbers of synapses between the cells . Increasing or decreasing this number alters the effective strength of the “monosynaptic” connection . Based on the interpretation that “synapse” plasticity is realized by altering the number of synapses between neurons , the model's use of trophic factor release to regulate synapse stabilization , retraction and growth is consistent with the single-spike coincidence plasticity rule of Butts et al . [61] , if plasticity is considered to represent the formation and retraction of synapses rather than the modification of the weight of individual synapses . The timing of individual spikes in pre- and postsynaptic neurons is important for plasticity , and the plasticity realized is Hebbian in nature , but it appears to not be realized by the changing of the efficacy of individual synapses . It is possible that the implementation of STDP used here is too restrictive and that a different implementation could more strongly contribute to retinotopic organization and refinement . However , as argued above , STDP may not play a significant role in development . If that is the case , why is it a seemingly ubiquitous phenomenon ? The neurotrophin BDNF has been associated with STDP and LTP [38]–[40] . Further , BDNF in the presence of glutamate mediates enlargement of synaptic spines in hippocampal slices [54] , while LTP is associated with an increase in the number and size of synaptic spines , and LTD is associated with spine shrinkage and retraction [37] . It is entirely possible that what is observed as STDP experimentally is actually the byproduct of another mechanism , such as synapse stabilization . Using the retinocollicular projection as an example , numerous synapses are created during development but the only ones that persist are those that produce the refined retinotopic projection . There must exist a mechanism to remove inappropriately targeted synapses . One mechanism to accomplish this , as demonstrated here , is the activity-dependent release of trophic factors , where synapses contributing to a spike in the postsynaptic neuron receive trophic support and stabilize , while synapses receiving insufficient trophic factor retract . The timing of trophic factor release in the model is consistent with the time window for STDP potentiation . What is observed experimentally as STDP , at least in retinotectal synapses , might be an experimental artifact of a process relating to synapse stabilization and retraction , with what is observed as potentiation reflecting a mechanism that stabilizes synapses and depression reflecting a mechanism that makes the synapse more likely to retract . It is also possible that STDP is a redundant or complementary to another mechanism , or that it plays a functional role that was not examined in this study ( e . g . , [63] ) . The extent and direction of axon growth in the model was mediated by probabilistic growth and retraction . Areas of an arbor with higher chemoaffinity compatibility with their surroundings , and/or increased trophic feedback , were more likely to extend and branch , and areas with relatively lower amounts were more likely to retract . During chemoaffinity-mediated growth , this mechanism was sufficient to produce a coarse retinotopic projection ( Fig . 1C , D ) . After activity-dependent feedback began to influence axon growth , this same principle was able to generate refined arbors in the retinotopically correct termination zone . What is notable about this finding is that both chemoaffinity and activity-based axon guidance can be mediated by the same functional mechanism , and that the gradient detection and tracking of extracellular molecules by growth cones was not required during arborization and refinement . We note that growth-cone mediated guidance is still required for an axon to reach the colliculus and extend to its posterior pole . Axon growth cones can detect gradients with remarkable sensitivity [64] , [65] , however it is not clear that the expression of guidance molecules is sufficiently smooth at the cellular and sub-cellular level to support such accurate guidance during retinotopic organization and refinement , especially considering that similar guidance molecules are expressed not only on collicular neurons but also on innervating axons [24] , that measured mRNA levels for guidance molecules may not be locally smooth [1] , [66] and that there may be non-uniformities in the density of axons and dendrites . If an axon is able to sense the relative difference in chemoaffinity compatibility in different parts of the arbor , and use this to influence the relative likelihood of local growth in the arbor , the arbor is effectively able to act as a very large gradient detecting growth cone . This behavior has been previously postulated for chick tectal development [43] . Such a mechanism could guide axon growth in the presence of shallower gradients , or in a noisier environment , than possible by gradient detection in individual growth cones . Axon growth in the model does phenomenologically approximate experimentally observed patterns of axon growth , with initially coarse arbors refining into retinotopically ordered projections in the presence of normal retinal activity patterns ( e . g . , [33] , [49] ) , and that the number of synapses and axon branches increase with exposure to trophic factor [49] . However , the implementation is very simplified compared to biology . Physiologically , there are interactions between the molecular machinery underlying chemoaffinity and trophic factor influence on axon growth ( e . g . , [67] , [68] ) and it is possible that activity-dependent influences are present throughout axon arbor development , and also that trophic factors help regulate the influence of molecular guidance cues [68] . On the other hand , molecular guidance cues could simply be sharing the same signaling pathway as trophic factors , and despite this molecular overlap between mechanisms , both could remain functionally distinct . Similarly , only the positive effects of trophic release were represented , not the opposing behaviors of trophic factors , where mature forms of the molecules promote growth , and the immature uncleaved molecules , such as proBDNF , promote synapse and axon retraction [37] . While a more mechanistically accurate model of axon growth will provide better insight into the molecular interactions involved in signaling axon and synapse growth and retraction , we found nothing to indicate that our phenomenological approximation of axon growth would be significantly different with a more mechanistic representation , nor that a more mechanistic representation would alter our findings on the overall behavior of growing axons . Retinotopic development has been the subject of many computational models [69] , and these models have been used to help identify the functional mechanisms necessary for development . In order to produce an ordered projection , the majority of these models ( but not all , e . g . , [18] ) assume lateral connectivity between target neurons , where typically activity in one neuron results in excitation of nearby neurons and inhibition of neurons farther away ( see [69] ) . This excitation/inhibition mechanism imposes architectural requirements on what is necessary for organization , and the high reversal potential for chloride early in development [70] suggests that lateral inhibition is not realistic , as synapses traditionally considered inhibitory ( e . g . , GABAergic ) would be excitatory during the period of retinotopic organization and refinement . In this study we have found that lateral synaptic connectivity was not required for producing an ordered retinotopic map , simplifying the theoretical functional requirements of the developing network . Simulated axons from neighboring RGCs were able to target the same collicular neurons based on their correlated firing properties and on the stabilizing effects of trophic factor . Synapses from RGCs stabilized on collicular neurons that were responsive to their activity by means of increased trophic factor receipt ( Fig . 2B ) . Correlated activity between nearby RGCs resulted in axons from nearby RGCs targeting the similar collicular neurons . Over the course of hours of simulated time , this mechanism results in increased axon and synapse growth in the area where more trophic feedback was received and these new synapses targeted nearby collicular neurons , focusing the axon projection . Collicular neurons sought to maintain a target firing rate , producing a normalizing force that limited the number of synapses present . Because of these factors , the resulting projection was ordered at the global level ( Fig . 1F ) though was subject to scatter at the local level ( Fig . 1G ) . The focus of this study was to examine the behavior and interaction of the mechanisms underlying neural development , and the approach here follows that used in the modeling of other complex phenomena , most notably climate [71] . Both climate and neural development are examples of complex systems , and predictive and descriptively accurate models of such complex systems may themselves be complex and not necessarily capable of being simplified to a simple or mathematically analyzable form . Climate models represent approximations of many of the causal mechanisms underlying weather , such as radiation , cloud cover , humidity , momentum , sea surface temperature and pressure gradients [71] , [72] . The model described here addresses retinotopic organization and refinement as being causally produced from phenomenological approximations of many mechanisms known to be active during development of the retinocollicular projection . Two very important mechanism are the growth , branching and retraction of individual axons , and the durability of individual synapses . Axon growth is a process underlying the formation of all neural projections , and axons have extremely restricted movement once extended through neuropil . Synapses must retract based on information available to each individual synapse . A descriptively accurate model of retinocollicular development requires consideration of the physical constraints posed on development by these and other mechanisms . The model represents many physiological phenomena active during development in as simple a form as practical while still approximating the functional behaviors of the phenomena . The lack of detailed representation of these mechanisms can be justified , we would argue , because the details of the mechanisms can vary between species though the developmental outcomes are similar . For example , similar patterns of retinal waves are observed in many species yet their statistical and molecular details vary ( see [5] , [30] , [73] ) . Likewise , chemoaffinity gradients are a common phenomenon but they are mediated by different molecules in different species [24] . Despite these differences , similar patterns of retinotopic organization persist . It stands to reason that it is the commonalities of behavior observed between species that are important for producing the common patterns of development , not what are essentially biological implementational details . The results of this study support such a conclusion , as qualitatively similar development was observed despite variations and perturbations to the model . It was only when key functional mechanisms were disabled that the model failed to produce retinotopic organization or refinement . Predictions of the model include that: Although the model is restricted to the retinotectal/retinocollicular system , the phenomena represented in it are found in neurons throughout the brain , and the findings here may apply more broadly . With minor modifications , the model is potentially applicable to the description of development in different brain areas . Explicit representation of many physiological mechanisms allows the model to be more easily compared to and constrained by physiology than most contemporary modeling approaches . It may be that the most predictive and descriptively accurate models of retinocollicular development , and of neural development in general , will resemble the approach described here , incorporating phenomenological approximations of many physiological mechanisms , in particular explicit representation of the growth and retraction of individual axons and synapses .
The structure of the model is shown in Fig . 10A . A circular retina composed of 7915 RGCs projected to an octagonal colliculus having 7934 neurons . Neurons in both retina and colliculus were distributed on a hexagonal matrix . The model retina was circular ( diameter 1 . 6 mm ) and the colliculus had 110 rows of neurons with each row having 80 neurons ( 0 . 8 mm×0 . 94 mm ) , with the corners of this rectangle truncated . In a reduced size version of the model that was used for model analysis , only the central 30% of the simulated retina and colliculus were modeled ( Fig . 10A , white rectangular areas ) . The smaller model had 3023 RGCs projecting to 2694 collicular neurons . The model was not sensitive to small changes in the ratio of retinal to collicular neurons , but this was not systematically explored . Map compression and expansion was examined in a previous version of this model [74] . The dendritic radius for each collicular neuron was . The soma of collicular neurons was considered to reside at the center of the dendritic arbor . Axons in the model were represented as a connected series of segments , each in length , a size selected to be sufficiently small to allow for realistic patterns of growth but large enough to make the model computationally tractable . Each axon segment was able to extend and branch , and retraction occurred at axon tips ( Fig . 10D ) . Axon segments were considered to have an “affinity” for their surroundings , which determined their propensity to grow , sprout synapses and retract . Axon segments required resources to grow , and the availability of these resources was managed by the soma . Segments received an amount of growth resource that was a function of the segment's affinity . Axons with higher amounts of growth resources were more likely to extend , branch and generate new synapses , while segments with lesser amounts were more likely to retract . To achieve self-limiting axon growth , each soma was assumed to have a finite amount of growth resources that was distributed throughout the arbor . Simulations began with each RGC axon extending along the A-P axis of the colliculus , corresponding to development as seen in P1 mouse [33] . Initial axon placement had each RGC axon entering the colliculus at the anterior side and extending along the length of the anterior-posterior ( A-P ) axis , in a position along the lateral-medial ( L-M ) axis that corresponded the the RGCs location along the retinal dorsal-ventral ( D-V ) axis . The exact L-M position varied by a random amount ( a Gaussian distribution with mean zero and standard deviation of 20% of the width of the colliculus ) . This design was based on descriptions of mouse and chick retinocollicular development [24] , [34] , [43] . The colliculus had flat sides both so axons could linearly project along the collicular boundary and so the model did not rely on an isotropic projection from retina to colliculus . The orientation of axon segments in the initial projection was parallel to the A-P axis except for a small random variation . Specifically , the orientation of each segment was described by 2 vectors , one of unit length and parallel to the A-P axis , and a second perpendicular vector whose magnitude was a uniform random variable selected on the interval [−0 . 2 , 0 . 2] . Subsequent branching and growth occurred as described below . Development occurred in two stages , each lasting 60 hours of simulated time . During the first 60 hours , development was mediated by chemoaffinity , and interstitial branching and subsequent growth was guided only by the differential in chemoaffinity compatibility across the arbor . During the second 60 hours , trophic feedback and chemoaffinity both contributed to growth . While synapses may be present throughout axon development in the colliculus , synapse creation in the model was inhibited until the onset of trophic feedback influence on axon behavior ( i . e . , 60 hours development time ) as synapses had no influence on axon growth before this time . This allowed the first 60 hours of axon growth to be pre-computed and used as a starting point for simulations of the second development stage , reducing the computational requirements of the model . Quantitative analysis as reported in Results was performed at 119 hours as synapse generation was turned off during final hour of the simulation to assess the stability of synaptic projections and to passively allow poorly targeted synapses to retract ( e . g . , note removal of mistargeted synapses at 120 hours in Fig . 3C ) . PF and RF sizes were reduced as a result of passive pruning , but the projections were qualitatively similar ( data not shown ) . The excitation level of model neurons were updated on every simulation clock cycle ( 1 ms ) while synapses were updated only on the occurrence of a pre- or postsynaptic spike . When a neuron fired , it cycled through all its axonal synapses , “pushing” excitation onto the target cell of each , and updating synaptic potentiation based on STDP learning rules for a presynaptic spike . The neuron then cycled through its dendritic synapses , updating synaptic potentiation based on the occurrence of a postsynaptic spike . To improve simulation performance , many cellular behaviors were updated less frequently . Equations relating to axon growth , branching and retraction , and to synapse growth were recalculated every 5 sec simulated time . Equations relating to synapse resources , synapse retraction , axon resources , homeostatic controls and intra-axon diffusion were recalculated every 0 . 5 sec . With the exception of millisecond calculations ( e . g . , EPSP summation , STDP and trophic factor release ) , the model was not dependent on the interval between updates . Different intervals were used in some simulations and no change to model behavior was observed . Previous versions of this model ( [74] and unpublished ) used mathematically different but functionally similar representations of these mechanisms and produced qualitatively similar results . | Neural development is a process that involves a wide range of behaviors . As a result of these behaviors , neurons are able to extend axons to different brain areas and produce stereotypical patterns of innervation . One of the most commonly studied of these projections is in the visual system , where retinal axons project to multiple brain regions and produce retinotopic maps . This study examines the relative roles and interactions of different neural mechanisms in guiding axon growth and generating retinotopic order . We did this by producing a computational model of retinotopic development that represented many of the neural mechanisms thought to be involved , including axon and synapse growth , molecular guidance and synapse plasticity . Our results suggest that synaptic plasticity is realized by variation in the number of synapses between neurons , not through alteration of individual synaptic weights; that lateral connectivity between collicular neurons is not required for organization; and that axon arbor development does not require the gradient tracking abilities of growth cones . The mechanisms underlying neuronal development in the visual system are also observed in many other brain areas , so the findings here should apply more generally . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"neuroscience/sensory",
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"computational",
"biology/computational",
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] | 2009 | A Multi-Component Model of the Developing Retinocollicular Pathway Incorporating Axonal and Synaptic Growth |
Pneumococcal bacteriocins ( pneumocins ) are antibacterial toxins that mediate intra-species competition within the human host . However , the triggers of pneumocin expression are poorly understood . Using RNA-sequencing , we mapped the regulon of the pneumocin cluster ( blp ) of Streptococcus pneumoniae D39 . Furthermore , by analogy with pneumococcal competence , we show that several antibiotics activate the blp-genes . Using real-time gene expression measurements we show that while the promoter driving expression of the two-component regulatory system blpR/H is constitutive , the remaining blp-promoters that control pneumocin expression , immunity and the inducer peptide BlpC , are pH-dependent and induced in the late exponential phase . Intriguingly , competence for genetic transformation , mediated by the paralogous ComD/E two-component quorum system , is induced by the same environmental cues . To test for interplay between these regulatory systems , we quantified the regulatory response to the addition of synthetic BlpC and competence-stimulating peptide ( CSP ) . Supporting the idea of such interplay , we found that immediately upon addition of CSP , the blp-promoters were activated in a comD/E-dependent manner . After a delay , blp-expression was highly induced and was strictly dependent on blpRH and blpC . This raised the question of the mechanism of BlpC export , since bioinformatic analysis showed that the genes encoding the putative exporter for BlpC , blpAB , are not intact in strain D39 and most other strains . By contrast , all sequenced pneumococcal strains contain intact comAB genes , encoding the transport system for CSP . Consistent with the idea that comAB mediate BlpC export , we finally show that high-level expression of the blp-genes requires comAB . Together , our results demonstrate that regulation of pneumocin expression is intertwined with competence , explaining why certain antibiotics induce blp-expression . Antibiotic-induced pneumocin expression might therefore have unpredictable consequences on pneumococcal colonization dynamics by activating genes that mediate intra-specific interference competition .
Streptococcus pneumoniae is a Gram-positive opportunistic pathogen that resides in the human nasopharynx . Pneumococci can cause invasive and non-invasive infections to which children , the elderly and the immunocompromised are particularly susceptible . The carriage rate of S . pneumoniae in the human population can be very high . Up to 80% of children under the age of 5 are colonized [1] , and colonization with multiple strains simultaneously is widespread . Competition between strains in the human nasopharynx during co-colonization has important implications for the epidemiology of the pneumococcus , potentially influencing strain prevalence , serotype distributions and disease progression . Among the most important potential drivers of intraspecific competition are several diverse classes of pneumococcal bacteriocins ( small antimicrobial peptides ) , including the CibAB two-peptide bacteriocin [2] , a lantibiotic [3] and the Blp bacteriocins ( pneumocins ) [4–6] . The blp locus is ubiquitous in pneumococcal genomes , and the operon is exceptionally diverse , suggesting that these toxins have evolved via diversifying selection . Puzzlingly , however , in most pneumococcal strains the genes mediating export of the blp peptides , encoding the ABC transporter BlpAB , carry frameshift mutation ( s ) that render these genes non-functional . This paradox raises two questions: 1 ) are Blp bacteriocins exported in strains lacking a functional transporter ? and 2 ) if so , by which mechanism does this occur ? One possibility is that bacteriocins are not exported , but that strains with interrupted blpAB alleles constitute so-called cheater sub-populations , which cannot export pneumocins alone but are able to express immunity in response to co-colonizing strains [7] . Such cheaters would therefore only activate blp expression in response to secreted BlpC in their surroundings . A second possibility is that Blp bacteriocins have an alternative mode of export and that strains with non-functional BlpAB transporters can activate their own blp expression . Here we provide direct evidence for this second hypothesis and show that blp bacteriocins are co-regulated with competence for transformation . Moreover , we show that environmental cues regulating competence overlap with those that induce Blp secretion . These functional data clarify the mechanisms of blp regulation and cast doubt on the validity of the blpA cheater hypothesis . Genes for the production and regulation of Blp bacteriocins are organized in gene clusters with several operons , typically flanked by the genes ecsB and a putative choline kinase located at approximately 400–600 kb from the origin of replication . Pneumocin expression is regulated by a classical quorum sensing two-component regulatory system [5 , 8] which is conserved among diverse S . pneumoniae genomes [9] . The inducer peptide BlpC , which contains an N-terminal double-glycine leader sequence , is putatively processed and exported by an ABC transporter system , BlpAB . Indeed , Kochan and Dawid showed that an engineered laboratory strain with intact BlpAB efficiently processes BlpC but not in the absence of BlpA [10] . When the external concentration of BlpC exceeds a certain threshold , it binds specifically to a membrane-located histidine protein kinase BlpH [9] , which activates the DNA binding response regulator BlpR by phosphorylation ( BlpR-P ) . BlpR-P binds to specific sequence sites in the promoter region of blp to activate their expression . In addition to genes for regulation and transport , the blp gene cluster of S . pneumoniae also contains two genes of unknown function ( blpS and blpT ) along with genes encoding ( putative ) pneumocins ( known as blpD , blpE , blpI , blpJ , blpK , blpM , blpN , blpO , blpQ , pncT and pncW ) and cognate immunity genes ( known as blpL , blpX , blpY , blpZ and pncP ) [11] . Similar to BlpC , the pneumocins contain an N-terminal double-glycine leader , which is processed upon export via the ABC-transporter system . The region encoding pneumocins and immunity proteins ( referred to as the BIR , bacteriocin immunity region ) is highly variable and the number of pneumocin genes differs greatly between strains [7 , 12] . However , despite significant variation in the BIR region , a superficial analysis indicates that the cluster is intact even in strains with a degenerated blpA . A likely candidate mechanism for Blp peptide export is the paralogous quorum-based two-component signaling system regulating competence for natural transformation ( comCDE ) . com regulation is mediated by the export of a quorum sensing peptide ( CSP ) via an ABC transporter ComAB , followed by CSP concentration-dependent activation of downstream late competence genes . Importantly , the blp genes have been shown to be weakly upregulated during pneumococcus competence [13] . In addition to the Blp bacteriocins , competent S . pneumoniae also express CbpD and CibAB , a murein hydrolase and a two-peptide bacteriocin , respectively , which both cause lysis of non-competent cells [2] . The competence regulatory system is highly sensitive to environmental cues such as pH and exposure to certain antibiotics [14–16] . For example , sub-MIC concentrations of antibiotics that perturb DNA replication elongation ( e . g . ciprofloxacin , trimethoprim and mitomycin C ) , induce competence by increasing the gene dosage of the comCDE genes , which are located close to the replication origin [16] . Moreover , aminoglycosides ( e . g . kanamycin and streptomycin ) , which target protein synthesis and lead to high numbers of misfolded proteins , induce competence by decreasing the degradation rate of CSP in the external environment . This occurs because in cells exposed to aminoglycosides , the protease HtrA , which normally degrades CSP , is occupied with targeting misfolded proteins [17] . Our aims here are twofold . First , we critically evaluate hypotheses derived from the proposal that strains carrying degenerate blpA genes are cheaters that lack Blp secretion . Secondly , failing to find support for this idea , we seek mechanisms that could offset the consequences of blpA lesions , focusing in particular on the roles of the paralogous com operon and the influence of environmental cues . Briefly , we demonstrate that blp expression is co-regulated with the competence regulatory system of S . pneumoniae and show that the ‘cheaters’-hypothesis [7] is inconsistent with both bioinformatics and experimental data .
We identified blp bacteriocin open reading frames ( ORFs ) in 4 , 096 S . pneumoniae genomes , as well as all ORFs that had reciprocal best BLAST hits to blpA , blpB , comA and comB . We identified striking diversity in the length of blpA/B across these genomes , with only 23 . 5% of genomes containing full-length blpA and blpB sequences ( Fig 1A ) . We found a large diversity of indel and stop codon mutations that lead to interrupted ORFs in both of these genes . The entire blpAB region is completely deleted along with the adjoining blpC in 21 strains , while an additional 27 strains have no blpAB but still contain blpC . Thus , the majority of pneumococcal strains retain blpAB pseudogenes in the genome . If strains lacking a functional blpAB are incapable of exporting bacteriocins , then we would predict that these strains would lose bacteriocin genes by genetic drift . However , we only found a difference of 0 . 14 bacteriocins between the genomes with a full-length blpA ( average = 4 . 46 bacteriocins ) compared to the number of bacteriocins found in genomes with interrupted blpA ORFs ( average = 4 . 32 bacteriocins; Wilcoxon rank test , 1 . 55 x 10−8; Fig 1B ) . Although non-functional bacteriocin genes may be retained in the genome , this small difference in average number of bacteriocins between genomes with and without blpA suggests that bacteriocin secretion is not exclusively dependent on a functional BlpAB . In order to examine the co-occurrence of interrupted ORFs in blpA and blpB , we mapped these gene variants onto a phylogenetic tree assembled using full genome SNPs ( Fig 2 ) . Using this phylogeny and a maximum likelihood approach , we found that genomes with an interrupted blpA are more likely to have an interrupted blpB , even after accounting for phylogeny ( log ratio test , p = 7 . 86 x 10−4 ) . The ABC transporter system involved in competence , comAB , is the closest homolog to blpAB in S . pneumoniae . To examine if comA or comB variation is linked to co-occurrence of either full-length or interrupted ORFs of blpA and blpB , we divided comA and comB amino acid variants into similarity-based clusters termed phylotypes . We found no association between the phylotypes of either comA or comB and the structure of either blpA ( log ratio test , p > 0 . 133 for comA , p > 0 . 139 for comB ) or blpB ( log ratio test , p > 0 . 0889 for comA , p > 0 . 537 for comB ) . The bioinformatics results strongly suggest that pneumococcal strains with degenerate blpAB alleles are still able to express pneumocins but it remains unknown what factors stimulate natural blp expression . Exposure to antibiotics can lead to global changes in gene expression and can induce the competent state in S . pneumoniae [14–16] . To test whether antibiotics also trigger pneumocin gene expression , we re-examined our earlier RNA-seq data [16] of S . pneumoniae D39 cells treated with sub-MIC levels of HPUra ( 6- ( p-hydroxyphenylazo ) -uracil ) which blocks DNA replication [18] and kanamycin , which induces mistranslation [17] . In the absence of these agents , the blp genes were not expressed in C+Y medium at pH 7 . 4 ( S1 Table ) . However , under the same pH conditions , both HPura and kanamycin weakly induced expression of some blp genes ( S1 Table ) . To test if other antibiotics also induce blp gene expression , we performed transcriptome sequencing on RNA isolated from cells treated with ciprofloxacin ( topoisomerase IV/DNA-gyrase inhibitor ) , hydroyxurea ( decreases the cellular pool of dNTP via inhibition of ribonucleotide reductase ) and rifampicin ( RNA polymerase inhibitor ) . Similar to the earlier antibiotic exposure experiments [16] , cells were harvested for RNA sequencing in early exponential phase . As previously observed , competence was activated by ciprofloxacin and hydroxyurea but not by rifampicin ( S1 Table ) [16] . Strikingly , ciprofloxacin and hydroxyurea also induced expression of some blp genes , although to a lesser degree ( S1 Table ) . Together , the RNA-seq data shows that the same antibiotics that trigger competence also activate expression of blp genes . The blp gene cluster of strain D39 contains four transcriptional units [5]; the regulatory operon blpSRH ( promoter PblpS ) , the transport operon blpABC ( promoter PblpA ) , the bacteriocin/immunity operon pncW-blpYZ-pncP ( promoter PpncW ) and finally a transcriptional unit expressing a single gene of unknown function , blpT ( promoter PblpT ) ( Fig 3A ) . In addition , another blp promoter ( PblpK ) is located outside the blp gene cluster in S . pneumoniae D39 ( Fig 3A ) [19] and controls the expression of BlpK , which encodes a putative bacteriocin . Each of the transcriptional units is likely under control by BlpR since the promoters contain an extended -10 element and upstream 9-bp tandem direct repeats ( Fig 3B ) [5] . Of the five promoters , PblpS is the least conserved compared to PpncW , PblpT , PblpA and PblpK . In order to investigate the conditions that induce blp expression , we developed a novel tripartite reporter cassette containing firefly luciferase ( luc ) , superfolder gfp ( gfp ) and β-galactosidase ( lacZ ) in the BglBrick-compatible vector pPEP1 [20] ( Fig 3C ) . Using these different reporters , expression can be monitored in cultures through time ( luc ) , at the single cell level ( gfp ) or in colonies on agar plates ( lacZ ) . Each of the five blp promoters identified above were fused upstream of this cassette , and the reporter constructs were chromosomally integrated at an ectopic locus ( Fig 3C and Materials and Methods ) . Using luc we could follow expression of the blp promoters in a time-resolved manner . S . pneumoniae was grown in C+Y medium and expression was monitored by luc activity . Expression was observed from all five blp promoters at pH 8 . 0 but not at pH 7 . 0 ( see below ) . For four of the promoters , PblpT , PblpA , PpncW and PblpK , expression was activated simultaneously in late exponential phase and switched off when the population reached stationary phase ( Fig 3D , S1 Fig ) . A second shallow peak of expression was observed in late stationary phase ( Fig 3D , S1 Fig ) . Interestingly , PblpS , encoding the regulatory system , was active from early exponential phase , and displayed expression dynamics similar to known constitutive promoters in S . pneumoniae , such as the synthetic promoter P3 ( S2 Fig ) [20] . The observed expression pattern allows for continuous expression of the regulatory genes , blpSRH , during all growth stages ( Fig 3E ) . It should be noted that the PblpS sequence ( Fig 3B ) is the least conserved of the promoters and that this sequence is located 100 bp upstream of the blpS start codon; thus there might be an unidentified , non-blp promoter that drives the seemingly constitutive expression of blpSRH . BlpSRH together with BlpC constitute a quorum sensing regulatory system , and we can therefore assume that the extracellular BlpC concentration required to activate BlpR is reached by the end of exponential phase . Accordingly , no expression could be observed from the regulated promoters ( PblpT , PblpA , PpncW and PblpK ) when the regulatory genes blpSRHC were deleted ( Fig 3D and 3E , S1 Fig ) . Furthermore , deletion of only blpC also abolished promoter activity , demonstrating that induction is mediated via BlpC ( S1 Fig ) , and blp expression in this strain could be activated by addition of synthetic BlpC ( S1 Fig ) . Although the expression dynamics from the four inducible promoters were the same , their expression strengths were variable; PblpK was by far the strongest , showing approximately 3-fold higher maximum luc expression compared to PblpT , which again was stronger than PpncW and PblpA ( Fig 3D , S1 Fig ) . By adjusting the pH of the growth medium , we observed that natural induction of all the regulated blp promoters in D39 only occurs above a threshold medium pH of 7 . 4 ( Fig 4A and S3 Fig ) . pH-dependent expression is also seen in cells grown as colonies on agar plates ( Fig 4A , bottom panel ) . Importantly , expression from PblpS was independent of the initial pH of the medium ( Fig 4B ) , confirming that this promoter is active across a broader range of environmental conditions . To validate the RNA-seq results , which showed antibiotic-induced blp gene expression ( S1 Table ) , we grew the reporter strains in C+Y pH 7 . 4 ( which does not support blp induction ) with and without sub-lethal concentrations of HPUra , ciprofloxacin and streptomycin . As shown in Fig 4C and S4 Fig , exposure to these competence-inducing antibiotics induced expression from all the regulated blp promoters ( PblpT , PblpA , PpncW , PblpK ) . Importantly , exposure to rifampicin , which does not induce competence , also did not induce blp expression ( S4 Fig ) . Moreover , the blpS promoter still displays activity throughout all growth stages , also when exposed to antibiotics ( Fig 4D ) . The pH dependency we observed for the blp promoters ( Fig 4A ) was previously shown for the promoters of the competence regulatory system [15 , 16 , 21] . Furthermore , as described above , blp expression was induced by exposure to the same antibiotics that induce com expression ( Fig 4C , S4 Fig , S1 Table ) . This prompted us to further investigate the putative interplay between the blp system and the com system . The com system can be induced by external addition of the competence stimulating peptide , CSP . By addition of CSP to cultures growing in C+Y pH 7 , which normally does not allow for blp expression , we observed as expected that expression of the com gene ssbB was immediately induced [13 , 15] ( Fig 5A ) . Crucially , addition of CSP to the blp reporter strains also led to high expression from all regulated blp promoters in the late exponential phase , approximately 100 min after addition of CSP ( Fig 5B and S5 Fig ) . Notably , this delayed high induction of blp via CSP still depended on the blp regulatory system , as CSP-activated induction could not be observed when blpSRHC was deleted ( S5 Fig ) . The onset of high blp expression occurred 100 min after addition of CSP , prompting us to investigate in detail the more immediate consequences of exogenous CSP on blp promoters . We observed that addition of CSP caused a weak but consistent induction of the regulated blp promoters ( Fig 5B–5D , S5 Fig ) . Importantly , the promoter controlling the inducer peptide blpC , PblpA , was also induced by CSP ( Fig 5D ) . The same effects were observed in strains with or without blpSRHC deletion strains , which rule out any effect of binding by BlpR . The weak but immediate effects of the addition of CSP on the blp promoter suggest that the competence regulatory system ( comCDE ) can activate the blp promoters . To test this more specifically , we therefore deleted comCDE , and in this case , no direct effect or any delayed induction of blp expression was observed ( Fig 5E , S5 Fig ) . To further study the dynamics of com- and blp activation , we constructed a double reporter strain where ( i ) the ssbB promoter was transcriptionally fused to mKate2 , a red fluorescent protein , and ( ii ) PblpK was fused to gfp . Using fluorescence time-lapse microscopy under conditions that allowed natural blp and competence expression ( pH 8 . 0 ) , we observed that blp expression was activated after cells became competent ( Fig 5F ) . This single cell analysis showed that all cells in the population turn on expression of com or blp synchronously once the systems are activated ( Fig 5F ) . First , to investigate whether the frameshifted blpAB locus could still be responsible for export and processing of BlpC , we replaced the blpAB pseudogenes with an antibiotic resistance marker , while keeping the promoter and blpC intact . As shown in Fig 6A , the ΔblpAB strain did not change expression of blp from any of the promoters ( Fig 6 and S6 Fig ) thereby demonstrating that , under these conditions , the remnants of blpAB in strain D39 are not involved in BlpC processing and export . Next , we tested if BlpC utilizes the ComAB complex for processing and export . ComAB is highly similar to BlpAB ( protein sequences show 61 . 9% identity between ComA/BlpA , and 29 . 0% identity between ComB/BlpB ) and is located downstream ( within 2 . 2 kb ) of the BlpR-regulated blpK gene . Our bioinformatics analysis showed that ComAB are highly conserved and intact in most pneumococcal strains ( Fig 2 ) . Indeed , when deleting comAB , or even comA or comB separately , blp expression could no longer be induced by CSP ( Fig 6B ) and , natural blp expression at pH 8 . 0 was also abolished ( Fig 6C ) . Addition of extracellular BlpC , however , still induced blp expression in the comAB deletion strain ( S7 Fig ) . When expressing comA , comB or comAB from an inducible promoter at an ectopic locus , we observed that comAB ( but not comA or comB alone ) caused onset of blp expression independent of blpAB , but again this occurred in late exponential phase ( Fig 6D and S7 Fig ) . Ectopic expression of comAB also induced blp expression when the native comAB genes were deleted , but to lower levels ( Fig 6E ) . Finally , the comAB-mediated induction was dependent on comCDE ( Fig 6F ) , showing that ComCDE-stimulated blpC expression is also essential for natural blp activation .
We and others recently discovered that exposing the human pathogen S . pneumoniae to antibiotics that either stall replication forks [16] or increase the misfolding of proteins [17] can induce competence , leading to increased transformation rates [15 , 16] . Here we show that exposing S . pneumoniae to the same types of antibiotics can also activate expression of the blp genes , encoding antimicrobial peptides as well as genes necessary for self-immunity , processing , transport and regulation . Because blp bacteriocins are involved in pneumococcal inter-strain competition [4] , antibiotic exposure therefore has the potential to indirectly modify competitive interactions among coexisting pneumococcal strains within the human nasopharynx . We show that regulation of blp expression is tightly linked with the competence regulatory system . blp and competence expression is regulated by paralogous quorum sensing based two-component systems . Over 75% of all pneumococcal strains sequenced to date ( Fig 1 ) contain frameshift mutations in the ABC-transporter genes of the blp locus ( i . e . blpA and blpB ) thus rendering them inoperative and incapable of bacteriocin transport . Nevertheless , natural blp expression in strains with fragmented blpAB is frequently observed ( Fig 3C , S1 Fig ) [5 , 13 , 16] , indicating that the inducer peptide BlpC as well as the bacteriocins , which are encoded with N-terminal double-glycine leader sequences , are processed and exported by other means . We show by deletion and complementation experiments that the competence transporter ComAB mediates processing and export of BlpC . However , expression of comAB alone is not sufficient to induce blp expression; because there is no ( or very low ) basal expression of blpC ( Fig 5D and S1 Fig ) , a weak induction of blpC expression , probably via the competence response regulator ComE-P , is also required ( Figs 5 and 6 ) . Our study strongly suggests that the response regulator ComE-P can bind and activate the blp promoters . The response regulator binding boxes of the blp and com promoters are related , and there are even examples of promoters in S . pneumoniae which are activated by both ComE and BlpR [22] , suggesting that such cross-induction can occur . The model for competence-dependent natural blp activation ( Fig 7 ) thus involves at least two contact points between the two systems , and shows why the competence system needs to be activated before blp genes can be expressed . Moreover , this can explain why blp gene expression , in contrast to competence , is only observed in late exponential phase [6] . The gene encoding the competence induction peptide , comC , is co-transcribed with the regulatory genes which have a low basal expression level; therefore processing and export of CSP by the dedicated ComAB transporter can initiate in the early growth stages . By contrast , the gene encoding the blp induction peptide , blpC , is co-transcribed with the transporter genes blpAB and requires initial activation , probably via ComE-P , for basal expression . BlpC also needs to use the non-cognate transporter ComAB for processing and export ( Fig 7 ) , and this process may be inefficient compared to ComC export . Thus the accumulation of extracellular BlpC might be slow ( or BlpH might require high levels of BlpC for autophosphorylation to occur ) , and this may explain the long delay between activation of competence and activation of blp expression ( approximately 100 min ) . Another reason for slow BlpC accumulation may be that the number of active ComAB proteins present in cells is low after escape from the competent state . Furthermore , we cannot exclude that also other factors , for example unidentified σX-dependent genes , are also involved in the late exponential phase activation of blp expression . Nevertheless , activation of blp expression is dependent on accumulation of two distinct quorum sensing peptides ( CSP and BlpC ) , and this is , to the best of our knowledge , the first example of such a dual quorum sensing system . By hard-wiring bacteriocin expression with the competence system , expression is only activated at high cell densities when nutrients are scarce . Whether this timing is important for actual predation on competing bacteria , and how this occurs in strains with intact BlpAB remains to be determined . Furthermore , the mechanism responsible for shut-down of blp-expression as cultures enter stationary phase are also unknown , and future research should show whether competence genes are also involved in this process . In a previous study , Son et al . [7] suggested that strains with interrupted blpAB are so-called cheater strains that are unable to secrete pneumocins and BlpC but still respond to peptides produced by coexisting strains . While cross-induction between strains may be possible via response to different allelic variants of secreted BlpC , our results clarify this is not required in interrupted blpAB strains; induction in these strains can be entirely due to endogenous BlpC production . We show that BlpC and possibly also pneumocins , can be exported via ComAB , suggesting that strains carrying BlpAB lesions are not cheaters . Importantly , however , autoinduction and cross-induction of blp expression within cells are not mutually exclusive and cross-induction can possibly occur under environmental conditions not allowing autoinduction . Many questions remain about the evolution of this apparently redundant BlpAB transport system . Our results suggest that once blpA frameshift mutations arise , deletions in blpB likely follow ( Fig 1A ) . However , it is puzzling that the genes themselves are retained in most pneumococcal genomes despite the prediction that they would become further degraded by genetic drift since they are apparently not required for BlpC or bacteriocin transport . Our preliminary analysis suggests that blpA mutations have occurred independently several times during pneumococcal evolution ( S8 Fig ) . Thus , an intriguing possibility is that our snapshot in time of this operon has captured the slow progression of the loss of this gene as its functions are overtaken by comAB . Alternatively , the kinetics of blp activation may be different in the minority ( 23 . 5% ) of strains with intact blpAB alleles ( Fig 1A ) , or ( parts of ) blpAB may retain functions in the export of bacteriocins under certain conditions , and this may further vary as a function of the position of the blpA frameshift . It is worth noting from Fig 1A that some parts of the blpAB genes ( for example amino acid 1–159 encoding a peptidase domain ) is retained in almost all strains . At the same time , since blpC is located on the same transcriptional unit as blpAB , it is possible that the operon is not further degraded to avoid interference with blpC transcription and translation . During the competent state , pneumococci are able to lyse non-competent siblings in order to gain access to their DNA for natural transformation [19] . In contrast to the murein hydrolase CbpD and the CibAB bacteriocins , the Blp bacteriocins have not been implicated in fratricide , partially because their induction level upon competence activation by CSP appear low [19 , 23] . In the present work we show that although the initial blp activation in early exponential phase is indeed low , a strong induction of blp expression occurs approximately 100 minutes after competence , potentially causing high production of bacteriocins . The competence regulon is considered to consist of two sets of genes; those directly regulated by binding of ComE-P ( early competence genes ) and those that are activated by the alternative sigma factor , σX , which is encoded by the ComE-P induced gene comX ( late competence genes ) . Expression of both early and late genes occurs in the early exponential growth phase . The blp genes are also part of the competence regulon [13 , 16 , 23] , but their dependence on the competence system is not via σX and also not solely via ComE-P , based on the mechanism described here ( Fig 7 ) . Therefore , our results clarify that the blp genes represents a separate subgroup within the competence regulon . Furthermore , since high level blp activation is competence-dependent and Blp bacteriocins have been shown to function in inter-strain competition [4] , it remains possible that Blp-mediated cell lysis , together with CibAB and CbpD , can also contribute to the fratricide mechanism by releasing DNA from competitor strains [19] . One requirement for this proposed mechanism would be that competence is also activated . We observe in our in vitro experiments that the peak of bacteriocin export , and therefore potentially DNA release from dead cells , does not coincide with the peak of competence . This is the case both for natural expression and for antibiotic-induced expression of com and blp . Notably , however , it was recently shown that competence is constitutive in pneumococcal biofilms and in vivo [24] , suggesting that pneumocin activity during competence is possible . However , further experimentation is required to test this hypothesis .
We complied 4 , 418 S . pneumoniae genomes from several data sets: 295 genomes from GenBank , which include 121 genomes from Georgia [25]; 3017 genomes from Myanmar refugees [26]; 616 genomes from Massachusetts [27]; 82 genomes from Complex 3 strains [28]; 240 genomes from PMEN-1 strains [29] , 142 genomes from strains isolated from The Netherlands ( European Nucleotide Archive study PRJEB10892 ) and 26 additional PMEN strains ( European Nucleotide Archive study PRJEB10893 ) . All sources collected strains without regard to strain identity , except for the Complex 3 and PMEN-1 research , which purposely focused on sampling subclades of S . pneumoniae . Therefore , excluding these two sources , we considered 4 , 096 of these genomes as randomly sampled from global populations . We aligned the genomes to GenBank R6_uid57859 using Stampy 1 . 0 . 23 [30] with a substitution rate of 0 . 01 . Based on the SNP data , we used FastTree 2 . 1 . 7 [31] with the ‘no maximum likelihood’ option , Jukes-Cantor nucleotide distances , and minimum 75% Shimodaira-Hasegawa local support to construct the full-genome phylogeny . We excluded any sites with more than 5% gaps . We included 69 Streptococcus sp . viridans as an outgroup clade for this phylogeny . We used a reciprocal best-hit BLAST criterion to find sequences more similar to blpA , blpB , comA , and comB than other annotated S . pneumoniae genes . In this search , we only examined ORFs longer than 150 bp . We considered full-length alleles to be at least 717 , 435 , 717 , and 449 residues long for each gene , respectively . comA and comB had 180 ( 34 over 0 . 5% proportion ) and 121 ( 31 over 0 . 5% proportion ) amino acid variants , respectively; we grouped the amino acid sequences by similarity as phylotypes . Amino acid sequences for each gene were aligned , and a neighbor-joining tree was created using Geneious 7 . 1 . 5 [32] . These gene trees were impartially divided into subtrees based on three restrictions: branches over 3 . 5 standard deviations in length from the mean branch length for the entire tree were cut; branches with length over 0 . 025 were cut; and clades were divided so the maximum intra-clade distance was 0 . 05 . This lead to 5 phylotypes of comA over 0 . 5% proportion ( A: 73 . 1%; B: 15 . 0%; C: 6 . 3%; D: 3 . 7%; E: 1 . 5% ) and 2 phylotypes of comB over 0 . 5% proportion ( A: 97 . 5%; B: 2 . 3% ) . To find bacteriocins , we used a reciprocal best-hit BLAST criterion with annotated blp bacteriocins; we also examined all ORFs containing M[DN][TK]K leader sequence upstream of a GG site and all ORFs containing a ‘TMLS’ leader sequence upstream of a GG site . We classified any sequences with this feature that did not have a BLAST hit in the GenBank database as a bacteriocin . Afterwards , we found any resulting sequences mapped to either the blp locus or to the comAB locus , as expected of blp bacteriocins . In order to test associations between gene types along the phylogeny , we calculated the maximum likelihood of two different models for each pair of gene lengths or phylotypes using BayesTraits 2 . 0 [33] and our phylogenetic tree . One model allowed the genes to mutate independently of each other; the other model had the mutation rate of each gene depend on the state of the other gene . We only considered gene length or phylotype pairs that co-occur in at least 0 . 5% of the 4 , 418 genomes . In all cases , strains with no allele of a gene were treated as missing data . We used a log ratio test between the two likelihood models to evaluate significance . To reconstruct the phylogeny of the blpA region , a GTR+I+G model of evolution was employed , using Geneious 7 . 1 . 5 [32] and MrBayes 3 . 2 . 2 [34] . S . pneumoniae was grown in C+Y medium [35] at 37°C . For transformation , S . pneumoniae was grown until OD600 = 0 . 1 before cells were washed and incubated 12 min at 37°C with 100 ng/ml synthetic CSP-1 . DNA to be transformed was added to the cells , followed by 20 min incubation at 30°C . Cells were then diluted 10 times in fresh medium and incubated for 1 . 5 hours at 37°C . The transformations were plated in Columbia agar supplemented with 2% ( v/v ) defibrinated sheep blood ( Johnny Rottier , Kloosterzade , The Netherlands ) . For selection , the following concentrations of antibiotics were used: 1 μg/ml tetracycline , 100 μg/ml spectinomycin , 0 . 25 μg/ml erythromycin , 2 μg/ml chloramphenicol . Escherichia coli was grown in LB at 37°C with shaking . E . coli was transformed with heat-shock of chemically competent cells according to standard protocols [36] . When appropriate , 100 μg/ml ampicillin or 100 μg/ml spectinomycin was used for selection . All strains and plasmids used in this study are listed in S2 Table . Oligonucleotides are listed in S3 Table . A reporter cassette containing three reporter genes , firefly luciferase ( luc ) , superfolder GFP , ( ( sf ) gfp ) , and β-galactosidase ( lacZ ) was amplified from plasmid pAD4 ( A . Domenech and J . -W . Veening ) using primers OG48 and OG50 . The fragments were digested with restriction enzymes AseI and BamHI and ligated into the corresponding site of plasmid pPEP1 [20] , a vector which replicates in E . coli and integrates into the cep-locus of S . pneumoniae D39 by double crossover . The resulting reporter plasmid ( Fig 3C ) , suitable for insertion of promoter fragments upstream of the triple reporter system , was called pPEP1-LGZ . For construction of pPEP1-PblpT-LGZ , the promoter fragment PblpT was amplified from genomic DNA of S . pneumoniae D39 using primers OG56 and OG26 . The fragment was digested with enzymes NheI and BglII and ligated into the corresponding restriction sites of plasmid pPEP1-LGZ . Using the same template DNA , promoter fragments PblpK was amplified with primers Pspd_0046-F+NheI+NotI and Pspd_0046-R+BglII , PblpS with primers PblpS_F_NheI_NotI and PblpS_R_BglII , PblpA with primers PblpA_F_NheI_NotI and PblpA_R_BglII and PpncW with primers PblpU_F_NotI_NheI and PblpU-R+BamHI . For all promoter fragments , we amplified a region of 250 bp , containing 200 bp upstream and 50 bp downstream of the startcodon of the first gene in each transcriptional unit . Fragments were ligated into pPEP1-PblpT-LGZ using restriction sites NheI and BglII to construct vectors pPEP1-PblpK-LGZ , pPEP1-PblpS-LGZ , pPEP1-PblpA-LGZ and pPEP1-PpncW-LGZ , respectively . The ligations were transformed into E . coli DH5a and transformants were selected with 100 μg/ml spectinomycin . Correct plasmids were verified by sequencing and transformed into S . pneumoniae D39 . Correct integration of the plasmids via double crossover in the pneumococcal genome was verified by PCR . The regulatory genes of the blp operon , blpSRHC , were deleted using allelic replacement with an erythromycin resistance cassette . The erythromycin resistance cassette was amplified from strain MK110 [16] , using primers eryR-up_F_BamHI and eryR-down+Not . The region upstream of blpS was amplified using primers Blp_SRHC_up_F and Blp_SRHC_up_R_BamHI , while the region downstream of blpC was amplified with primers Blp_SRHC_dn_F_NotI and Blp_SRHC_dn_R . The upstream fragment was digested with BamHI , the downstream fragments with NotI and the fragments containing the erythromycin cassette with both BamHI and NotI . The three fragments were ligated and transformed into S . pneumoniae D39 using 0 . 25 μg/ml erythromycin for selection . Correct deletion of blpSRHC was confirmed by PCR and sequencing . The S . pneumoniae β-galactosidase gene bgaA was deleted by transforming the plasmid pMK11 ( see below ) , containing a tetracycline resistance gene and the PZn promoter , into S . pneumoniae . Transformants were selected using 1 μg/ml tetracycline . comA , comB and comAB were deleted by allelic replacement with an erythromycin resistance cassette . Briefly , the region upstream of comA was amplified with primers comA1 and comA2+AscI and the region downstream of comA was amplified with comA3+NotI and comA4 . Genomic DNA from S . pneumoniae D39 was used as template . The erythromycin resistance cassette was amplified from genomic DNA of strain ΔhexA::ery [37] using primers trmp-F+AscI and sPG20_eryR+NotI . The comA-up fragment was digested with restriction enzyme AscI , the erythromycin resistance cassette fragment with AscI and NotI and the comA-down fragment with NotI . The three fragments were ligated and transformed into S . pneumoniae . Transformants were selected with 0 . 25 μg/ml erythromycin and correct transformants were verified by PCR and sequencing . comB was deleted in a similar fashion . The comB-up fragment was amplified using primers comB_up_R+AscI and ComA1 , the comB-down fragment with primers comB_down_F+NotI and comB_down_R , and the ery fragment was amplified using primers trmp_F+Ascl and SPG20_eryR+NotI with ΔcomA::ery strain as a template . For deletion of comAB , a fragment containing the comAB-up region and the erythromycin resistance gene was amplified with primers comA1 and SPG20_eryR+NotI using the ΔcomA::ery strain as a template . The comAB-down fragment was amplified using primers pr225comB_down_F+NotI and pr226comB_down_R . Genomic DNA was used as template in all cases . Digestion , ligation and transformation was performed in the same manner as for the ΔcomA::ery deletion , to generate strains ΔcomB::ery and ΔcomAB::ery . The competence regulatory operon comCDE was deleted with allelic replacement with an chloramphenicol resistance cassette , as described previously [16] . A Zn2+-inducible promoter was amplified from plasmid pJWV25 [38] using primers pr27 and pr28 . The fragment was digested with SphI and SpeI and ligated into the corresponding sites in plasmid pJWV100 [39] to create pMK11 . For controlled expression of comA , comB or comAB , the gene ( s ) were inserted downstream of a Zn2+ inducible promoter and integrated in the genome at the bgaA-locus . comA was amplified using primers start-comA+EcoRI and end-comA+SpeI , comB with primers start-comB+EcoRI and end-comB+SpeI and comAB with primers start-comA+EcoRI and end-comB+SpeI . The fragments were digested with restriction enzymes EcoRI and SpeI/BcuI , and ligated into the corresponding sites of plasmid pMK1 , to generate plasmids pMK11-PZn-comA , pMK11-PZn-comB and pMK11-PZn-comAB . The ligation was transformed into E . coli using ampicillin selection ( 100 μg/ml ) . Correct plasmids were verified by sequencing and then transformed in S . pneumoniae using tetracycline selection ( 1 μg/ml ) . The blpAB pseudogenes in S . pneumoniae D39 were deleted by replacement with an erythromycin resistance cassette . The promoter PblpA and blpC were kept intact . The region downstream of blpAB was amplified using primers blpB-down-F and blpB-down-R-NotI , while the region upstream of blpAB was amplified with primers blpA-up-F-BamHI and blpA-up-R . In both cases , genomic DNA from S . pneumoniae D39 was used as template . The erythromycin resistance gene was amplified from strain MK304 using primers Ery-For-BamHI and sPG20_eryR+NotI . The up- and downstream fragments were digested with BamHI and NotI , respectively , while the fragment containing the erythromycin resistance gene was digested with both BamHI and NotI . The fragments were ligated and the construct was transformed into S . pneumoniae D39 . In all steps of the transformation reaction , 500 ng/ml CSP-1 was added to induce expression from the PblpA promoter driving expression of the erythromycin resistance gene . Cells were plated on C+Y agar plates containing erythromycin for selection and 500 ng/ml CSP-1 . The construct in the resulting strain was confirmed by PCR and sequencing . D39 was transformed with plasmid pPEP1-PblpK-LGZ ( including the gfp reporter ) , which integrates in the cep-locus . Transformants were selected on plates with 100 μg/ml spectinomycin . This new strain was subsequently transformed with plasmid pLA21 [37] containing the PssbB-rfp fusion which integrates in the bgaA-locus . In the second transformation round , colonies were selected on 1 μg/ml tetracycline . For RNA-sequencing , samples of S . pneumoniae , strain DLA3 ( bgaA::PssbB-luc ) were grown to OD600 = 0 . 4 in 5 ml tubes and diluted 1:100 in fresh C+Y medium ( pH 7 . 4 ) . To study the effects of antibiotics , cells grown without antibiotics were compared to cells grown with 0 . 4 μg/ml ciprofloxacin , 608 μg/ml hydroxyurea , or 0 . 04 μg/ml rifampicin . Cells were grown in microtiter plates and growth and competence development ( luc-expression ) were followed . For all the samples , when one-third of the maximum OD600 was reached , cells were harvested by centrifugation ( 7 , 500 rcf for 5 min ) and frozen . For RNA isolation , cells were lysed by bead beating and RNA was purified using phenol-chloroform extractions and ethanol precipitations . DNA was removed from the sample with RNase-free DNase I ( Fermentas ) treatment for 45 min . Ribolock ( Fermentas ) was added to avoid RNA degradation . Library preparation and whole-genome sequencing were performed by vertis Biotechnologie AG ( Freising , Germany ) . Ribosomal RNA was removed using the Ribo-Zero rRNA Removal Kit ( Epicenter ) prior to preparation of cDNA libraries . Sequencing of the cDNA libraries was performed with an Illumina HiSeq 2000 machine with 100 nt read length paired end . Sequence reads were mapped to the S . pneumoniae D39 genome ( NC_008533 ) using Rockhopper version 2 . 03 [40 , 41] , using default parameters . Reads Per Kilobase of exon per Megabase of library size ( RPKM ) were calculated using a protocol from Chepelev et al . [42] . In short , exons from all isoforms of a gene were merged to create one meta-transcript . The number of reads falling in the exons of this meta-transcript were counted and normalized by the size of the meta-transcript and by the size of the library . This was done internally by Rockhopper version 2 . 03 after aligning reads . Upper quartile normalization ( to be able to compare expression between samples ) was used to transform RPKM ( reads per kilo base per million ) values into expression values , performed by Rockhopper . Finally , Rockhopper was also used for differential gene expression analysis , using default parameters . Sequencing data used in this paper have been deposited in the Gene Expression Omnibus repository ( http://www . ncbi . nlm . nih . gov/geo/ ) with accession numbers GSE54199 and GSE69729 . S . pneumoniae were pre-grown to OD600 0 . 4 and diluted 100-fold in C+Y medium containing 340 μg/ml luciferin prior to the assay . pH of the medium was adjusted with HCl or NaOH . Production of firefly luciferase ( encoded by luc ) cause emission of light when the medium contains luciferin [43] . Luminescence assays were performed in 96-wells plates at 37°C . Absorbance ( OD595 ) and luminescence ( as relative luminescence units , RLU ) were measured every 10 min for at least 13 hours using a Tecan Infinite 200 PRO instrument . When appropriate , 100 ng/ml synthetic peptide CSP-1 or 500 ng/ml BlpC purchased from Genscript ( Piscataway , NJ ) were added to the plates after 100 min . Concentrations were selected based on previous studies [9 , 16] . β-galactosidase activity was assayed on agar plates . X-gal ( 40 μl of 40 mg/ml stock solution ) was added on top of C+Y agar with adjusted pH . Twenty μl of S . pneumoniae at OD600 = 0 . 4 ( approx . 107 cells/μl ) was then spotted . The drops with bacterial culture were allowed to dry prior to incubation overnight in a 5% CO2 incubator at 37°C . Time-lapse fluorescence microscopy was performed using a Deltavision Elite ( GE Healthcare , USA ) as described before [44 , 45] . In short , cells were grown until OD600 = 0 . 08 before they were spotted onto C+Y ( pH 8 ) agarose slides . The slides were kept in a temperature controlled chamber at 37°C , and images ( GFP , RFP and phase contrast ) were acquired with a sCMOS camera every 10 min . | Streptococcus pneumoniae is an opportunistic pathogen with high carriage rates in children . Pneumococci express pneumocins that kill competing bacteria . Pneumocin expression is controlled by a pheromone-induced two-component system ( BlpR/H ) but the triggers for the system are poorly understood . We show that the pheromone-induced two-component system driving competence for genetic transformation , ComD/E , also controls expression of BlpC , the peptide pheromone activating BlpR/H-dependent gene expression . Importantly , we show that the competence pheromone exporter , ComAB , also exports BlpC . Since antibiotics that disrupt protein quality control or DNA replication trigger competence , it follows that the same antibiotics activate pneumocin expression . Our experiments show that this dual-quorum sensing system ensures that pneumocins are expressed at the end of exponential growth when nutrients become limiting . Pneumocin expression might thus be used to liberate nutrients by lysing competing bacteria . Antibiotic-induced pneumocin production might also aid in clearing the niche after antibiotic stress . Any free DNA can then be used for transformation to acquire antibiotic-resistance . | [
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"constructi... | 2016 | Expression of Streptococcus pneumoniae Bacteriocins Is Induced by Antibiotics via Regulatory Interplay with the Competence System |
Albendazole and mebendazole are increasingly deployed for preventive chemotherapy targeting soil-transmitted helminth ( STH ) infections . We assessed the efficacy of single oral doses of albendazole ( 400 mg ) and mebendazole ( 500 mg ) for the treatment of hookworm infection in school-aged children in Lao PDR . Since Opisthorchis viverrini is co-endemic in our study setting , the effect of the two drugs could also be determined against this liver fluke . We conducted a randomized , open-label , two-arm trial . In total , 200 children infected with hookworm ( determined by quadruplicate Kato-Katz thick smears derived from two stool samples ) were randomly assigned to albendazole ( n = 100 ) and mebendazole ( n = 100 ) . Cure rate ( CR; percentage of children who became egg-negative after treatment ) , and egg reduction rate ( ERR; reduction in the geometric mean fecal egg count at treatment follow-up compared to baseline ) at 21–23 days posttreatment were used as primary outcome measures . Adverse events were monitored 3 hours post treatment . Single-dose albendazole and mebendazole resulted in CRs of 36 . 0% and 17 . 6% ( odds ratio: 0 . 4; 95% confidence interval: 0 . 2–0 . 8; P = 0 . 01 ) , and ERRs of 86 . 7% and 76 . 3% , respectively . In children co-infected with O . viverrini , albendazole and mebendazole showed low CRs ( 33 . 3% and 24 . 2% , respectively ) and moderate ERRs ( 82 . 1% and 78 . 2% , respectively ) . Both albendazole and mebendazole showed disappointing CRs against hookworm , but albendazole cured infection and reduced intensity of infection with a higher efficacy than mebendazole . Single-dose administrations showed an effect against O . viverrini , and hence it will be interesting to monitor potential ancillary benefits of a preventive chemotherapy strategy that targets STHs in areas where opisthorchiasis is co-endemic . Current Controlled Trials ISRCTN29126001
Infections with the three common soil-transmitted helminths ( STHs ) , Ascaris lumbricoides , Trichuris trichiura , and hookworm ( Ancylostoma duodenale and Necator americanus ) , are a global public-health concern , particularly in areas where poor sanitation prevails [1] , [2] . STH infections are among the most widespread of the neglected tropical diseases ( NTDs ) [3] . Indeed , more than a billion people are currently infected with one or several STH species , even though growing efforts are underway to control these parasitic worm infections [4] . In terms of their estimated global burden , hookworm is the most important among the STHs , perhaps responsible for more than 20 million disability-adjusted life years ( DALYs ) among the estimated 600 million infected people worldwide [1] , [5] . Chronic hookworm infection cause intestinal blood loss and result in poor iron status and iron-deficiency anemia , particularly in children , and women in reproductive age [1] , [6] , [7] . As a consequence , permanent impairment , including delayed physical and cognitive development , has been described [8] . In the absence of a vaccine , the global strategy to control STHs and other NTDs is to reduce morbidity through repeated large-scale administration of anthelmintic drugs , a strategy phrased preventive chemotherapy [9] . At present , the World Health Organization ( WHO ) recommends four drugs against STH infections , of which albendazole and mebendazole are the two most widely used drugs for preventive chemotherapy [10] . In 2008 , in the Western Pacific Region , 33 . 4 million children were given anthelmintic drugs [11] . According to the Lao national scheme for school deworming , there is a treatment round at the beginning of the first semester ( September–December ) and in the second semester ( January–April ) . Mebendazole ( single 500 mg oral dose ) is annually distributed to all school-aged children since 2005 [12] . Recent efforts have been made to provide mebendazole also to preschool-aged children through the Expanded Program on Immunization ( EPI ) and alongside vitamin A distribution campaigns [4] , [13] . However , the efficacy of mebendazole and albendazole against STH infections in Lao PDR remains to be determined , and such locally derived evidence is important to guide the national treatment policy . The liver fluke Opisthorchis viverrini is co-endemic in Lao PDR , and particularly high prevalences have been observed in the southern provinces [14]–[17] . Praziquantel is the current drug of choice against O . viverrini [3] . Previous work has shown that multiple doses of albendazole also show some effect [18] , [19] . Hence , in areas where STHs and O . viverrini co-exist and preventive chemotherapy targeting STHs is under way , it will be interesting to monitor for potential ancillary benefits of this control strategy against opisthorchiasis . The purpose of this study was to assess the efficacy of single-dose albendazole ( 400 mg ) and single-dose mebendazole ( 500 mg ) against hookworm infection among school-aged children in Lao PDR . In addition , the effect on other STHs ( i . e . , A . lumbricoides and T . trichiura ) and O . viverrini in co-infected children was assessed . Our study complements a recent investigation , done in the People's Republic of China that compared single and triple dosing with albendazole and mebendazole against the three common STHs [20] .
The research protocol ( see Protocol S1 ) was approved by the Ethics Committee of Basel , Switzerland ( EKBB; reference no . 146/08 ) and the Lao National Ethics Committee for Health Research ( NECHR ) , Ministry of Health , Vientiane , Lao PDR ( reference no . 170/NECHR ) . The trial is registered with Current Controlled Trials ( identifier: ISRCTN29126001 ) . Written informed consent was obtained from parents/legal guardians of eligible children . Participation was voluntary and children could withdraw from the trial at any time without further obligation . At completion of the trial , all children of the two primary schools and participants who were still found positive for hookworm ( or other STHs ) were treated with albendazole ( 400 mg ) . O . viverrini-infected children were administered praziquantel according to national guidelines [21] . A randomized , open-label trial was carried out between February and March 2009 in two primary schools ( Oudomsouk and Nongbok Noi ) in Batieng district , Champasack province , southern Lao PDR . Children in the two schools were treated with mebendazole 5–6 months prior to the start of our study . The schools are located approximately 15 km southeast of Pakse town , on the Bolaven plateau at an altitude of approximately 1 , 000 m above sea level ( geographical coordinates: 105°56′53″N latitude , 15°14′59″E longitude ) . The rainy season lasts from May to mid-October . A census done in 2007 revealed that 43 , 651 people lived in the 95 villages of Batieng district ( Dr . Nanthasane Vannavong , Champasack Provincial Health Department; personal communication ) . More than three-quarter of the households ( 77 . 5% ) lack appropriate sanitation . Drinking water is primarily obtained from unprotected boreholes and wells . Most villagers live on subsistence rice farming and rubber plantations ( Dr . Nanthasane Vannavong , Champasak Provincial Health Department; personal communication ) . Infections with STHs and O . viverrini are common in Batieng district; a recent study revealed infection prevalences above 50% and above 20% , respectively [22] . We designed a randomized , open-label trial comparing albendazole ( single 400 mg dose ) and mebendazole ( single 500 mg dose ) for treatment of hookworm infection . The sample size was calculated based on results of a meta-analysis on the efficacy of current anthelmintic drugs against common STH infections , which reported cure rates ( CR; defined as percentage of helminth-positive individuals who became helminth-egg negative after treatment ) of 75% and 15% for albendazole ( 400 mg ) and mebendazole ( 500 mg ) , respectively against hookworm infection [10] . In order to account for the large variation ( uncertainty ) of the observed efficacy of mebendazole in the individual studies ( CRs of 8–91% were found in the six randomized controlled trials ) , we more than tripled the mean efficacy of mebendazole ( 50% instead of 15% ) . Assuming superiority of albendazole ( 1-tailed test ) and taking into account a 90% power , and an alpha error of 5% , we obtained a sample size of 85 children per treatment group . Furthermore , we assumed a drop-out rate of 15% , which resulted in a total sample size of 200 hookworm-positive school-aged children . The teachers of the two primary schools , the children , and the staff of the National Institute of Public Health , Centre of Malaria , Parasitology and Entomology , Centre for Laboratory and Epidemiology , the Provincial Department of Health , the Provincial Hospital , and the Malaria Station of Champassak , and the village authorities were informed one week in advance on the study aims and procedures . Potential risks and benefits were explained to all children and their parents/guardians . An informed consent form was distributed to all parents/guardians and signed . Children assented orally . At baseline screening the consenting children ( n = 465 ) of the two schools , aged 6–12 years , provided two fresh stool samples within a period of 3 days . Stool containers were filled by children and transferred to a laboratory in the early morning ( between 8 and 9 am ) . All collected specimens were worked up on the day of collection . From each stool sample , duplicate Kato-Katz thick smears were prepared on microscope slides , using standard 41 . 7 mg templates [23] . Kato-Katz thick smears were quantitatively examined under a light microscope for helminths with a 100× magnification . Slides were read within 30–45 min after preparation . A random sample of approximately 10% of the Kato-Katz thick smears were re-examined by two senior technicians for quality control purposes . In case of discrepancies ( i . e . , positive vs . negative results and egg counts differing by >10% ) , results were discussed with the respective technicians , and the slides re-examined until agreement was reached . In addition , a questionnaire was administered to each participating child to obtain sociodemographic data ( i . e . , sex , age , parent's education and occupation , ethnic group , and sanitation infrastructure ) , and behavioral data ( i . e . , wearing shoes , sources of drinking water , food consumption , and personal hygiene ) . Hookworm-positive children ( defined by the presence of at least one hookworm egg in one of the quadruplicate Kato-Katz thick smears examined per child ) were invited for treatment ( n = 200 ) . At enrollment , a clinical examination , which included measurement of weight ( using an electronic balance measured to the nearest 0 . 1 kg ) , height ( using a measuring tap fixed to the wall and measured to the nearest cm ) , and axcillary temperature ( using battery-powered thermometers , measured to the nearest 0 . 01°C ) , anemia assessment ( finger prick capillary blood sample ) was conducted , and a medical history taken . Children were excluded if they had fever , or showed signs of severe malnutrition . Additional exclusion criteria were the presence of any abnormal medical condition such as cardiac , vascular , pulmonary , gastrointestinal , endocrine , neurologic , hematologic ( e . g . , thalassaemia ) , rheumatologic , psychiatric , or metabolic disturbances , recent history of anthelmintic treatment ( e . g . , albendazole , mebendazole , pyrantel pamoate , levamisole , ivermectin , and praziquantel ) , attending other clinical trials during the study , or reported hypersensitivity to albendazole or mebendazole . At follow-up , 21–23 days after drug administration , two stool samples were collected from each child and transferred to a hospital laboratory within one hour after collection . Each stool specimen collected at follow-up was subjected to the same procedures as during the baseline survey . Hence , duplicate Kato-Katz thick smears were prepared from each stool sample , examined under a microscope within 30–45 min by experienced laboratory technicians , and helminth eggs were counted and recorded for each species separately . We adhered to the same quality control as during the baseline survey . Children were randomly assigned to a single dose of albendazole ( 400 mg ) or mebendazole ( 500 mg ) , using a block randomization procedure ( six blocks each containing four treatment allocations ) , generated by an independent statistician who was not otherwise involved in the trial . The sequence of blocks was determined using a random number table . In addition , schools were decoded by a researcher to assign children either to albendazole or mebendazole . Eligible children were randomly assigned and allocated to treatment by an epidemiologist . Children and drug administrators were not blinded for drug treatment . Laboratory personnel and clinicians monitoring the adverse events were blinded throughout the study . Albendazole ( 400 mg; Albendazole® , South Korea ) was obtained from the Ministry of Health , Vientiane , Lao PDR . Mebendazole ( 500 mg; Vermox® , Italy ) was donated by Johnson & Johnson Pharmaceuticals , provided through the Ministry of Health and the Ministry of Education , Vientiane , Lao PDR . At treatment day , both groups received the drugs under direct medical supervision on an empty stomach . Children were monitored for at least 3 hours after drug administration and asked to report for any drug-related adverse events using a standard questionnaire administered and graded by study physicians . Data were double-entered and cross-checked in EpiData version 3 . 1 ( EpiData Association; Odense , Denmark ) . Statistical analyses were performed with STATA , version 10 . 1 ( Stata Corp . ; College Station , TX , USA ) . Efficacy was calculated for both intention-to-treat ( ITT ) and per-protocol ( PP ) analyses . ITT analysis was based on the initial treatment intent . PP analysis included only those children who had complete data records ( i . e . , quadruplicate Kato-Katz thick smear reading before and after treatment , and full treatment compliance ) . Infections with hookworm , A . lumbricoides , T . trichiura , and O . viverrini were grouped into light , moderate , and heavy infections , according to WHO guidelines ( for STHs ) and cut-offs put forward by Maleewong and colleagues and WHO ( for O . viverrini ) [24] , [25] . Infection intensity classifications are as follows: hookworm , 1–1 , 999 eggs per gram of stool ( EPG ) ( light ) , 2 , 000–3 , 999 EPG ( moderate ) , and ≥4 , 000 EPG ( heavy ) ; A . lumbricoides , 1–4 , 999 EPG ( light ) , 5 , 000–49 , 999 EPG ( moderate ) , and ≥50 , 000 EPG ( heavy ) ; and T . trichiura and O . viverrini , 1–999 EPG ( light ) , 1 , 000–9 , 999 EPG ( moderate ) , and ≥10 , 000 EPG ( heavy ) . Primary outcome measures were CR and egg reduction rate ( ERR ) , the latter defined as the positive group's reduction of geometric mean ( GM ) fecal egg count at posttreatment , divided by the GM fecal egg count at pretreatment , multiplied by 100 . Additionally , changes in class of infection intensities were determined following treatment . Negative binomial regression was applied to compare ERRs observed between both treatment groups . A Wilcoxen test was employed for the matched pair's analysis . We determined egg reduction rate ratio ( ERRR ) and 95% confidence interval ( CI ) . Pearson's χ2-test and Fisher's exact test , as appropriate , were used to assess the baseline binary characteristics between the treatment arms . Statistical significance was estimated using a likelihood ratio test ( LRT ) . P-value below 5% was considered significant . CONSORT checklist was followed to report on the trial ( see Checklist S1 ) .
Four hundred sixty-five school-aged children were enrolled in the baseline screening . Two hundred children ( 43 . 0% ) , 130 boys and 70 girls with a parasitologically confirmed hookworm infection , were randomly assigned to one of the two treatments . Data of these 200 children were included in the ITT analysis . The remaining 265 children were excluded because they had no hookworm eggs in their stool ( n = 235 ) or provided only a single stool sample ( n = 30 ) . Overall , 171 children ( 85 . 5% ) had complete baseline data , received treatment , and completed follow-up examinations , and hence PP analysis was performed on these children . Twenty-nine children ( 14 . 5% ) were lost to follow-up , 18 in the mebendazole and 11 in the albendazole group ( Figure 1 ) . The 171 children with complete data records were included in the primary analysis . Their parents most commonly had completed primary school only ( 77 . 5% of parents for the albendazole group and 80 . 5% for the mebendazole group ) . The most common profession of patients' parents was farming with 49 . 4% and 62 . 2% for albendazole and mebendazole treatment groups , respectively . The two groups were similar in terms of household assets , source of drinking water and consumption of cooked foods as well as raw fish ( data not shown ) . More specifically , the consumption of raw fish was reported by 61 . 8% and 58 . 5% , respectively , and included dishes like “Pa Dek” ( fermented fish sauce ) , “Lap Pa” , and “Koy Pa” ( raw , fish-based dishes ) . At baseline , characteristics of the two treatment groups were similar ( Table 1 ) , including age ( albendazole recipients: mean ( standard deviation , SD ) age 8 . 4 ( 2 . 1 ) years; mebendazole recipients: 8 . 7 ( 2 . 1 ) years ) , weight ( mean ( SD ) 23 . 8 ( 5 . 8 ) kg and 25 . 0 ( 5 . 9 ) kg , respectively ) , height ( mean ( SD ) 123 . 8 ( 11 . 0 ) cm and 126 . 9 ( 11 . 0 ) cm , respectively ) , and hemoglobin ( Hb ) concentration ( mean ( SD ) 11 . 8 ( 1 . 1 ) mg/dl and 11 . 9 ( 1 . 3 ) mg/dl , respectively ) . In both treatment groups , most children were diagnosed with a light hookworm infection ( 82 . 0% ) , whereas the remaining children had moderate or heavy infection intensities . The hookworm GM fecal egg counts in the mebendazole and albendazole groups were 707 . 0 and 859 . 1 EPG , respectively ( Table 2 ) . The overall infection rates of A . lumbricoides , O . viverrini and T . trichiura were 34 . 0% , 48% and 45 . 0% , respectively . O . viverrini GM fecal egg counts were 84 . 9 EPG ( albendazole ) and 120 . 8 EPG ( mebendazole ) ( Table 3 ) . In the ITT analysis , the CRs of albendazole and mebendazole against hookworm infection were 32 . 0% and 15 . 0% , respectively . Overall , 124 children ( 73% ) remained hookworm-egg positive; 68 receiving albendazole and 85 in the mebendazole treatment group . Similar results were obtained with the PP analysis ( Table 2 ) . A statistically significant difference was observed when comparing the observed CRs using albendazole vs . mebendazole ( OR = 0 . 4; 95% CI 0 . 2–0 . 8; P = 0 . 01 ) . The hookworm GM fecal egg counts obtained at follow-up were 63 . 0 EPG in albendazole recipients and 147 . 3 EPG in mebendazole recipients ( ITT analysis 96 . 5 EPG and 210 EPG , respectively ) . The respective ERRs for albendazole and mebendazole were 86 . 7% and 76 . 3% ( ERRR 1 . 0; 95%CI 0 . 7–1 . 6; P = 0 . 90 . In children with moderate infection intensities ( 2 , 000–3 , 999 EPG ) , the effect of albendazole and mebendazole was significantly different ( P = 0 . 04 ) . Table 3 shows the effect of albendazole and mebendazole against A . lumbricoides , T . trichiura , and O . viverrini . At baseline , GM infection intensities of A . lumbricoides were 1 , 567 EPG in albendazole recipients and 1 , 584 EPG in mebendazole recipients . Both albendazole and mebendazole treatments achieved high CRs above 90% and resulted in almost complete egg elimination . The CRs of albendazole and mebendazole obtained against T . trichiura were 33 . 3% and 27 . 9% , respectively . The respective ERRs were 67 . 0% and 66 . 0% . No statistically significant difference was observed for CR and ERR between the two treatments ( OR = 0 . 8; 95% CI 0 . 3–1 . 9; P = 0 . 58 and ERRR = 0 . 7; 95% CI 0 . 3–1 . 2 , P = 0 . 22 ) . Finally , CRs against O . viverrini achieved with albendazole and mebendazole were 33 . 3% and 24 . 2% , respectively ( OR = 0 . 7; 95% CI 0 . 3–1 . 9; P = 0 . 62 ) . The respective ERRs were 82 . 1% and 78 . 2% ( ERRR = 0 . 8; 95% CI 0 . 2–3 . 9 , P = 0 . 78 ) . Monitoring of children within 3 hours after drug administration revealed no drug-related adverse events , neither in the albendazole nor in the mebendazole group . Hence , both treatments were well tolerated .
This current head-to-head comparison of single-dose albendazole vs . mebendazole against hookworm infection in Lao school-aged children – to our knowledge the first comparative trial in this Southeast Asian country – shows sobering results . Indeed , the standard single oral doses of albendazole ( 400 mg ) and mebendazole ( 500 mg ) that are recommended for preventive chemotherapy targeting STHs [8] , [9] resulted in low CRs against hookworm infection ( 36 . 0% and 17 . 6% , respectively ) . The respective ERRs were moderate , ( 86 . 7% and 76 . 3% ) . A sizeable number of children were co-infected with A . lumbricoides , T . trichiura , and O . viverrini , which allowed us to determine the effect of albendazole and mebendazole against these helminth species . With regard to A . lumbricoides , high efficacy of both drugs was confirmed against this helminth species [3] , [10] . Our study also confirms the previously reported low efficacy of both drugs against T . trichiura [3] , [10] , [26] . While the results obtained with mebendazole against hookworm and the efficacy observed with both drugs against A . lumbricoides and T . trichiura are in line with previous studies [20] , [27] , [28] and in agreement with overall CRs estimated through a meta-analysis [10] , the low CR ( 36 . 0% ) achieved with albendazole in the treatment of hookworm infection is somewhat surprising . Indeed , in the aforementioned meta-analysis , randomized controlled trials of single-dose albendazole ( 400 mg ) revealed an overall CR against hookworm of 75% [10] . The reasons for the considerably lower efficacy of albendazole observed in our study are unclear . Quality control of drug samples performed in our laboratories revealed that disintegration , dissolution , and concentration of the albendazole tablets used in our trial were comparable to Zentel® ( data not shown ) . The hookworm species ( and strains ) endemic in southern Lao PDR might be an explanation . However , there is a paucity of information on which hookworm species is predominant in Southeast Asia . Indeed , in our study setting the infection rates of the two hookworm species , A . duodenale and N . americanus , are not known . Furthermore , recent studies documented that in Southeast Asia humans are at risk of acquiring Ancylostoma ceylanicum , which is endemic in dogs and cats of the region and its importance in humans might be underestimated [29] , [30] . Hence , further analysis on the circulating parasite species is required to elucidate this issue . In addition , day-to-day variability in hookworm egg counts from individuals is a well described phenomenon [31] . Finally , the study's sample size is rather small and therefore a few incidental effects such as failure of some children to swallow the tablet correctly , might have contributed to low efficacy of albendazole for the treatment of hookworm infection . To sum up , differences in strain and species susceptibilities , host factors , and co-infections with other helminths are factors that might all play a role in explaining treatment failures [28] , [32] . Nevertheless , we cannot rule out that albendazole resistance is developing in our study setting . To date , nematode resistance in humans has not been reported . On the other hand , drug resistance is a major problem in veterinary public health [33] , [34] . The development of broad spectrum anthelmintic resistance , in particular resistance of nematodes to benzimidazoles , has been recognized in ruminants for decades [34] , [35] . Extensive studies on the underlying mechanisms of drug resistance have been carried out [36] . Further investigations on failure of the drugs to completely cure the patients are necessary in our study setting to substantiate this suspicion . It is interesting to note that the two drugs employed , even at single oral doses , showed some effect against O . viverrini . Although CRs were low ( 24 . 2–33 . 3% ) , the moderate ERRs of 78 . 2–82 . 1% are encouraging . At present , praziquantel is the drug of choice against opisthorchiasis [3] , [18] . Studies carried out in the 1980s in O . viverrini-infected hamsters and patients infected with O . viverrini documented opisthorchicidal properties of albendazole and mebendazole [19] , [37] . However , long treatment courses of up to 7 days were recommended in view of these initial laboratory and clinical findings . Experiences with long treatment courses have been reported from a hospital-based randomized trial; albendazole given at dosages of 400 mg twice daily for 3 and 7 days resulted in CRs of 40% and 63% , respectively , and corresponding ERRs of 92% [19] . Furthermore , mebendazole in dosages of 30 mg/kg daily for 3 or 4 weeks resulted in CRs of 94% against O . viverrini . Long treatment courses compromise compliance , increase costs and are not feasible for large-scale community-based control , which might explain that albendazole and mebendazole were not further promoted for O . viverrini treatment [37] . It should be noted that in our study Kato-Katz thick smears served as method for helminth diagnosis . However , this diagnostic approach does not allow differentiating the eggs of O . viverrini from minute intestinal flukes [38] , [39] . In addition , since the emphasis of our research was on hookworm , the efficacy of albendazole and mebendazole against other STHs and O . viverrini could not be compared with the appropriate sample sizes . Finally , mostly light O . viverrini infections were present in our study and the sample of O . viverrini-infected patients was not representative of the overall community as hookworm infection was the leading selection criterion . Hence , additional clinical investigations are warranted to assess the opisthorchicidal properties of albendazole and mebendazole in comparison to praziquantel . Moreover , the anthelmintic drug tribendimidine [40] showed high CR and ERR against O . viverrini in a recent , open-label exploratory trial carried out in Lao PDR [41] . It would therefore be interesting to conduct a four-arm study , comparing praziquantel ( treatment of choice ) with tribendimidine , albendazole , and mebendazole . In conclusion , we have assessed the efficacy of standard single-dose regimens of albendazole and mebendazole against hookworm infection in school-aged children from Lao PDR and provide further evidence of the effects these two drugs have against other helminth species concurrently harbored in the human host . Both drugs showed a similar profile , with low efficacy against hookworm and , additionally , low efficacy against T . trichiura , and high efficacy against A . lumbricoides . The low efficacy of single-dose of albendazole against hookworm should be followed-up closely and further investigated as this drug is widely used for preventive chemotherapy against STHs and in combination with ivermectin in the current global effort to eliminate lymphatic filariasis . The effects of the two drugs against O . viverrini warrant further investigations , in comparison with the current drug of choice praziquantel as well as tribendimidine . | Parasitic worms remain a public health problem in developing countries . Regular deworming with the drugs albendazole and mebendazole is the current global control strategy . We assessed the efficacies of a single tablet of albendazole ( 400 mg ) and mebendazole ( 500 mg ) against hookworm in children of southern Lao PDR . From each child , two stool samples were examined for the presence and number of hookworm eggs . Two hundred children were found to be infected . They were randomly assigned to albendazole ( n = 100 ) or mebendazole ( n = 100 ) treatment . Three weeks later , another two stool samples were analyzed for hookworm eggs . Thirty-two children who were given albendazole had no hookworm eggs anymore in their stool , while only 15 children who received mebendazole were found egg-negative . The total number of hookworm eggs was reduced by 85 . 3% in the albendazole and 74 . 5% in the mebendazole group . About one third of the children who were co-infected with the Asian liver fluke Opisthorchis viverrini were cleared from this infection following albendazole treatment and about one forth in the mebendazole group . Concluding , both albendazole and mebendazole showed disappointingly low cure rates against hookworm , with albendazole performing somewhat better . The effect of these two drugs against O . viverrini should be studied in greater detail . | [
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... | 2012 | Low Efficacy of Single-Dose Albendazole and Mebendazole against Hookworm and Effect on Concomitant Helminth Infection in Lao PDR |
Systemic upregulation of inflammatory cytokines is characteristic of critical severe hand , foot , and mouth disease ( HFMD ) with pulmonary edema . Thus , immunomodulatory medicines such as steroids , including methylprednisolone , have been proposed to treat patients with severe HFMD in China , because it is postulated that inflammatory cytokines play a role in the development of severe complications . This study is to further investigate the inflammatory response in the relatively mild HFMD patients , and whether steroid treatment has a beneficial effect on the suppression of inflammation in HFMD patients . We measured the levels of 50 kinds of chemokines , cytokines , growth factors and soluble receptors in serum samples from control patients without HFMD and the HFMD patients with or without prior treatment of intravenous methylprednisolone . Our present study found that even relatively mild HFMD patients without central nervous system ( CNS ) complications had elevated serum levels of inflammatory cytokines , including interleukin ( IL ) -3 , IL-6 , IL-12p40 , and tumor necrosis factor ( TNF ) -α , which suggested systemic inflammation . In contrast , these patients also have decreased levels of other serum biomarkers , including IL-1Ra , IL-8 , IL-16 , soluble ICAM-1 , CXCL-1 , and CCL27 . The dysregulation of cytokine and chemokine expression may be involved in CNS complications and unbalanced circulating leukocytes in HFMD patients . Surprisingly , patients treated with methylprednisolone had no difference in the expression levels of HFMD-associated biomarkers instead had slightly increased levels of IL-17A , which was not associated with the occurrence of HFMD . Whether steroid treatment has any beneficial effect on the prognosis of HFMD patients requires to be further investigated .
Hand , foot and mouth disease ( HFMD ) is an infectious disease caused by enteroviruses including enterovirus 71 ( EV71 ) and Coxsackievirus A16 . HFMD affects mainly young children under 5 years old . The first large EV71 outbreak occurred in Japan in 1973 [1] . Another two large outbreaks subsequently occurred in Hungary and Bulgaria [2] , [3] . Even larger outbreaks later occurred in Malaysia in 1997 and subsequently in Taiwan in 1998 [4] , [5] . Since 2008 , over 6 . 5 million cases including over 1300 fatalities have been reported in China according to the official report from China Ministry of Health . Forty-eight HFMD cases in Thailand and 411 cases in Vietnam have also been reported in 2008–2009 [6] , [7] . In last April , 50 fatalities and at least 60 cases were reported in Cambodia [8] . So far , HFMD has become a great threat to public health in developing countries in Asia . EV71 is the most neurovirulent virus among the enteroviruses associated with HFMD and causes severe CNS complications and fatal outcome [9] , [10] . The CNS manifestations include aseptic meningitis , poliomyelitis-like syndrome , encephalomyelitis , autonomic nervous system ( ANS ) dysregulation , and brain stem encephalitis ( BE ) . Patients with severe CNS complications are likely to progress to fatal pulmonary edema ( PE ) and die from cardiopulmonary collapse if timely interventions and advanced life support are not initiated . Studies of the critically severe HFMD patients with PE suggest that inflammatory cytokine storm may be involved in the pathogenesis of severe CNS complications caused by EV71 infection . For example , infiltration of inflammatory cells has been observed in the brain stem and spinal cord of patients who died of PE [11]–[13] . Consistently , it has been found that IL-6 , IL-10 , IL-13 , interferon ( IFN ) -γ , and monokine induced by IFN-γ ( MIG; CXCL9 ) express at significantly higher levels in the cerebrospinal fluid of the PE patients than in those with isolated BE [14]–[16] . In addition , systemic levels of IFN-γ-induced protein ( IP ) -10 , IL-6 , IL-10 , monocyte chemoattractant protein ( MCP ) -1 , and CXCL9 are also significantly higher in patients with PE than in those with uncomplicated BE [15] , [17] . The serum levels of IL-1 , IL-6 , and TNF-α are generally higher in CNS-complicated patients than in patients with uncomplicated HFMD or healthy controls [17] . One unexplained manifestation in PE patients is that they have significantly fewer circulating T cells and natural killer ( NK ) cells but more neutrophils [16] . Local or systemic inflammation has been implicated at least partly for the increased pulmonary vascular permeability , which may result in development of PE or pulmonary hemorrhage [16] , [18] , [19] . Thus , intravenous immunoglobulin has been administrated as an immunomodulator to severe HFMD patients at high risk of progressing to PE [18] . In addition , milrinone therapy has been shown to reduce plasma levels of IL-13 and concomitantly sympathetic hyperactivity , suggesting that it has immunomodulatory effect [18] . Since 2008 , glucocorticoids , including hydrocortisone , dexamethasone , and methylprednisolone , have been empirically used to treat severe HFMD patients with CNS complications and PE . In a consensus statement of HFMD and EV71 infection in China issued in 2010 by an expert panel , glucocorticoids were also recommended for use in patients developing severe CNS complications and PE ( http://www . jkb . com . cn/htmlpage/12/123560 . htm ? docid=123560&cat=null&sKeyWord=null ) . Despite the controversy on the rationale and benefits of glucocorticoid therapy in severe enterovirus infection or other viral infections [20]–[22] , glucocorticoids indeed have been widely used in the management of severe HFMD with CNS complications in China [23]–[26] . However , little is known so far about the immunomodulatory effects and clinical benefits of glucocorticoid treatment in HFMD patients . This study systematically analyzed serum levels of inflammatory cytokines , chemokines , growth factors , and soluble immune receptors in patients with either uncomplicated HFMD or severe HFMD complicated with CNS involvement without fatal PE . This study also evaluated the inflammatory responses of HFMD patients treated with methylprednisolone . Our results revealed that even the patients without CNS complications had a distinct expression profile of cytokines , chemokines , growth factors , and soluble immune receptors in their sera as compared with control patients . None of these HFMD-characteristic biomarkers in patients with methylprednisolone treatment differed from those in the patients without the treatment , suggesting that methyprednisolone may not affect the inflammation caused by HFMD .
This study was approved by the ethics committee of the Children's Hospital of Fudan University . Written informed consents were obtained for the use of serum samples from all patients ( or their parents/guardians ) involved in this study . The criteria of case definition were based on previous reports [16] , [27]–[30] . HFMD was characterized by the manifestation of oral ulcers/vesicular plus vesicular rash on the hands , feet , or/and buttocks . Aseptic meningitis was diagnosed on the basis of the presence in CSF of more than 10×106 leukocytes/L with normal glucose , normal or mildly elevated protein , negative results on Gram stain smear , and signs of fever , vomiting , headache , irritability , and meningeal signs in various combinations without altered levels of consciousness or focal signs . Encephalitis was defined as disturbed consciousness plus CSF pleocytosis ( >10×106 leukocytes/L ) or presence of focal neurologic signs , including abducens palsy , facial palsy , dysphagia , upward gaze , and nystagmus . We prospectively enrolled 20 HFMD cases with CNS-involvement and 20 HFMD cases without CNS- involvement in late July–September , 2010 . The inclusion criteria for the enrollment were as the following: 1 ) children were hospitalized solely for HFMD within 4 days after disease onset; 2 ) children were <5 years old; 3 ) children's parents gave informed consent to participate in this study; 4 ) children had stool taken for enterovirus test; 5 ) severe HFMD cases were confirmed based on the clinical presentation and the abnormal findings of cerebral spinal fluids . Gender difference and methylprednisolone treatment were not in our initial consideration for enrollment . Serum samples were taken from the HFMD patients admitted to the infectious disease wards and 20 non-HFMD patients who had minor operations for inguinal hernia or hydrocele at the surgical wards . EV71 infection was diagnosed based on detection of virus in stool specimens . Stool samples from all patients were tested for the presence of enterovirus infection by real-time RT-PCR assay using a commercial kit ( Da An Gene Co . , Ltd . Lot No . : EV-A71 YZB- 0356-2009 ) . The expression of cytokines , chemokines , growth factors , and soluble immune receptors was examined using Bio-Plex Pro Human Cytokine 27-plex and Bio-Plex Pro Human Cytokine 23-plex kits according to the manufacturer's instructions ( BioRad , CA , USA ) . The detection limits of these parameters were in line with the manufactory instruction . Proportional data were analyzed using X2 or Fisher's exact tests . Continuous data were tested by Student's t test to determine the statistical significance of differences . Of note , the undetectable results were replaced with zeroes before the statistical analysis . The P values were further adjusted using the Benjamini-Hochberg method to control for multiple comparison false discovery [31] . The correlations between clinical parameters and biomarkers in the serum samples were evaluated with Spearman's rank correlation test . All analyses were performed using the SPSS software ( version 11 . 5; SPSS ) . A difference with P<0 . 05 was considered to be significant .
Among the 40 HFMD patients , 20 were confirmed with meningitis and encephalitis and the remaining 20 had no CNS complications . The age of the 20 control patients were aged between 15 and 56 months ( mean age: 37 . 6 months ) with a male-to-female ratio of 1∶1 . The age of the 20 patients with uncomplicated HFMD were between 8 and 47 months ( mean age: 26 . 5 months ) with a male-to-female ratio of 19∶1 . The age of the 20 patients with CNS-complicated HFMD were aged between 11 and 56 months ( mean age: 29 . 0 months ) with a male-to-female ratio of 13∶7 . Thirty ( 72 . 5% ) of the HFMD patients , were EV71-positive in stool specimens by RT-PCR assay ( Table 1 ) . Eleven ( 55% ) of 20 uncomplicated HFMD patients and 19 ( 95% ) of 20 severe HFMD patients with CNS involvement were EV71-positive . The severe CNS-complicated patients tended to have fewer white blood cells ( WBC ) and lymphocytes . All HFMD patients presented with fever . Although the maximal fever temperature was not different between the two groups of HFMD patients , the CNS-complicated patients had significantly longer fevers in average than the uncomplicated ( 4 . 2 vs . 2 . 8 days , P<0 . 0001 ) . As compared with the uncomplicated patients , the CNS-complicated also had a significantly higher proportion of CNS involvement-related symptoms , such as vomiting , tachycardia , myoclonus , and lethargy ( Table 1 ) . To gain further knowledge about systemic inflammatory responses in HFMD patients and discover potential biomarkers for disease severity , we examined the expression levels of 50 kinds of cytokines and other immune activation markers in the sera of HFMD patients and control patients with a cytokine array . We found that the expression levels of 24 biomarkers in HFMD patients were not significantly different from those of control patients ( Table S1 ) . While 14 biomarkers were significantly elevated ( Table 2 ) . Consistent with previous reports [14]–[17] , IL-6 , IFN-γ , and TNF-α are significantly elevated in the sera of HFMD patients compared to those of controls ( Table 2 ) . In addition , IL-2 , IL-12p40 , IL-15 , IL-2Rα , IL-3 , eotaxin , IFN-α2 , HGF , MCP-3 ( CCL7 ) , SCF , and TNF-related apoptosis-inducing ligand ( TRAIL ) were also significantly increased in the sera of HFMD patients . Of note , IL-2 , IL-6 , IL-15 , TNF-α , and eotaxin were 5- to 10-fold higher in HFMD patients in comparison with the control group . More dramatically , the levels of soluble TRAIL increased approximately 20 times on average , and the levels of IL-3 and IL-12p40 increased approximately 40 times on average . Unexpectedly , 12 kinds of cytokines and immune activation markers were significantly lower in the HFMD patients than in the control group ( Table 2 ) . These markers included IL-1Ra , IL-8 , IL-16 , PDGF-β , VEGF , MIP-1β , CTACK ( CCL27 ) , GROα ( CXCL1 ) , M-CSF , SCGF-β , VCAM-1 , and MIF . Of them , IL-8 , IL-16 , IL-1Ra , M-CSF , and MIF were 2–3-fold lower in HFMD patients in comparison with the control group . Altogether , HFMD patients appeared to have systemic inflammation and exhibited a distinct serum inflammatory profile in comparison with control patients . We further analyzed the correlation of the serum biomarker levels with disease severity or prognosis in HFMD patients . We found that upregulated serum biomarkers in CNS-complicated patients did not differ from those in uncomplicated patients . However , we found that the levels of VCAM-1 , CXCL-1 , and CCL27 in the patients with CNS-complicated HFMD decreased slightly in comparison with those with uncomplicated HFMD ( Fig . 1A–C ) . In particular , VCAM-1 was found to be significantly associated with maximal fever temperature ( Fig . 1D ) . Among the patients with CNS-complicated HFMD , 13 patients ( 65% ) received at least one dose of methylprednisolone ( 2–3 mg/kg intravenously ) 12 hours prior to blood sampling . We further analyzed the effect of methylprednisolone treatment on the expression of the serum biomarkers . To our surprise , none of HFMD-characteristic inflammatory biomarkers ( list in the Table 2 ) in the treated patients significantly differed from those in the untreated patients ( data not shown ) , implying that methylprednisolone treatment is not effective in inhibiting inflammation caused by HFMD . IL-17A levels in the serum samples of HFMD patients were not different from those of the controls ( Fig . 2A ) . In addition , IL-17A levels in the serum samples of HFMD patients with CNS-complications were not significantly different from the CNS-uncomplicated patients either . Yet , 13 methylprednisolone-treated patients had significantly higher IL-17A levels than the rest HFMD patients when all 40 patients were not stratified by disease severity ( Fig . 2B ) . When patients with CNS-complicated HFMD were further divided into methylprednisolone-treated or untreated groups , the treated group still had slightly higher IL-17A levels than the untreated group ( Fig . 2C ) .
We found that systemic levels of 26 kinds of cytokines , chemokines , soluble receptors , and growth factors differed between patients with HFMD and control patients without HFMD . The increased expression levels of IL-6 , IFN-γ , and TNF-α in HFMD patients are consistent with previous studies by others [15] , [17] , [32] . In addition , this study found that the serum levels of IL-2 , IL-15 , IL-3 , IL-12p40 , eotaxin , and soluble TRAIL in HFMD patients increased 5 to 48 times . These observations suggest that HFMD patients had generally elevated inflammation . Griffiths et al . recently found that IL-1β , IL-1Ra , and granulocyte colony-stimulating factor ( G-CSF ) were significantly elevated in HFMD patients with cardio-respiratory dysfunction [33] . The IL-1Ra levels were actually decreased and G-CSF levels were not changed significantly in our HFMD patients in comparison to control patients . This discrepancy makes one wonder whether the expression levels of biomarkers are specifically associated with different nature of HFMD-related complications . Therefore , the dramatic increase of soluble TRAIL , IL-3 and IL-12p40 in this study highlights a need for further investigation into their role in the pathogenesis of HFMD and their potential roles as biomarkers for predicting disease progression . The serum expression levels of 26 serum biomarkers in HFMD patients were significantly different from those in control patients , but their expression levels were not associated with the presence of CNS complications in present study . This result is not exactly consistent with the previous findings that the significantly higher levels of inflammatory cytokines were mainly found in fatal PE patients but not in severe patients without PE [16] , [18] , [19] . Nevertheless , the change of some biomarkers , like IL-1Ra [33] , may indicate the poor prognosis of HFMD patients . A previous study found that HFMD patients with PE have significantly lower numbers of CD4+ T cells , CD8+ T cells , and NK cells but higher numbers of neutrophils in peripheral blood than HFMD patients without PE [16] . The present study also revealed that even the relatively mild HFMD patients without complications had significantly more neutrophils and fewer lymphocytes than the control group . The increased levels of chemokines MCP-3 and CXCL9 and the decreased levels of MIP-1β , CCL27 , and CXCL1 in this study imply that the altered numbers of leukocytes in peripheral blood may result from the differential chemotactic functions of these chemokines . In particular , CXCL9 is an inducible T-cell chemoattractant that is regulated by IFN-γ and mediates the recruitment of effector T cells to sites of inflammation [34] . The decreased number of T cells and NK cells may result from sequestration from the peripheral blood to infected tissue sites because of systemic increased levels of CXCL9 , which is fueled by systemic increased IFN-γ . The dysregulation of other chemokines may contribute to the higher number of circulating neutrophils . To our surprise , none of the 26 HFMD-characteristic biomarkers in the methylprednisolone-treated patients differed from those in untreated patients . Nevertheless , the treatment had a tendency to increase the expression levels of IL-17A ( one of the other 24 serum biomarkers ) in the CNS-complicated HFMD patients . IL-17A as a Th17 cytokine plays a pathogenic role in CNS-related inflammation , such as multiple sclerosis [35] . Thus , it is unlikely that HFMD patients benefit from methylprednisolone treatment through its induction of IL-17A . Steroids , such as methylprednisolone , have been also recommended to treat critical severe patients who were infected with H5N1 influenza virus , SARS coronarvirus or pandemic H1N1 influenza virus [20] , [36] , [37] . The treatment indeed significantly reduced the plasma levels of IL-8 , MCP-1 and IP-10 in patients infected with SARS coronarvirus [38] . In addition , short-period steroid treatment appeared to have beneficial clinical effect on the severe patients infected with H1N1 influenza virus or SARS coronavirus [36] , [37] . However , it was also reported that steroid treatment had no effect on H5N1-infected patients' survival [20] . Therefore , beneficial or side effects of steroid treatment of HFMD patients require to be further investigated . At least three limitations exist in the present study . First , the patients were enrolled prospectively for studying biomarkers associated with HFMD and its disease severity . Gender difference was not considered in the initial enrollment of patients . As a result , majority of mild patients were male and had a bias sex ratio in comparison to the severe patients . One of possible reasons is that the male patients were preferentially hospitalized during the enrollment . Nevertheless , there was no significant difference in the cytokine and chemokine expression profiles between male patients and female patients ( data not shown ) . Second , the effect of methylprednisolone treatment was analyzed retrospectively , thus the observations require further justification in a prospective study . Third , limited number of patients with CNS-complications made us impossible to analyze the biomarkers associated with the most critical CNS-complication with cardio-respiratory dysfunction . Further investigation using a large cohort with various CNS complications will likely overcome the limitations . | Systemic inflammation is characteristic of severe hand , foot , and mouth disease ( HFMD ) . Steroids are considered immunomodulators and have been officially recommended to treat the severe HFMD patients with CNS complications in China . So far , it is uncertain whether steroid treatment has an immunomodulatory role in inflammation in HFMD patients and has a real beneficial effect on their prognosis . This study revealed that even relatively mild HFMD patients without CNS complications had elevated inflammation . Unexpectedly , the inflammatory cytokine levels in patients treated with methylprednisolone , one kind of steroid , were not significantly different from those in patients without the treatment . Rather , the treated patients tended to have elevated levels of IL-17A , whose expression levels were actually not significantly associated with the presence of HFMD . IL-17A is known to play a role in the pathogenesis of CNS-related inflammatory diseases . Altogether , our study does not support the presumption that steroids have beneficial effect on the prognosis of HFMD patients by inhibiting systemic inflammation . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2013 | The Cytokine and Chemokine Profiles in Patients with Hand, Foot and Mouth Disease of Different Severities in Shanghai, China, 2010 |
Plasmacytoid dendritic cells ( pDCs ) are innate sensors of viral infections and important mediators of antiviral innate immunity through their ability to produce large amounts of IFN-α . Moreover , Toll-like receptor 7 ( TLR7 ) and 9 ( TLR9 ) ligands , such as HIV and CpG respectively , turn pDCs into TRAIL-expressing killer pDCs able to lyse HIV-infected CD4+ T cells . NK cells can regulate antiviral immunity by modulating pDC functions , and pDC production of IFN-α as well as cell–cell contact is required to promote NK cell functions . Impaired pDC-NK cell crosstalk was reported in the setting of HIV-1 infection , but the impact of HIV-1 on TRAIL expression and innate antiviral immunity during this crosstalk is unknown . Here , we report that low concentrations of CCR5-tropic HIV-1Ba-L promote the release of pro-inflammatory cytokines such as IFN-α , TNF-α , IFN-γ and IL-12 , and CCR5-interacting chemokines ( MIP-1α and MIP-1β ) in NK-pDCs co-cultures . At high HIV-1BaL concentrations , the addition of NK cells did not promote the release of these mediators , suggesting that once efficiently triggered by the virus , pDCs could not integrate new activating signals delivered by NK cells . However , high HIV-1BaL concentrations were required to trigger IFN-α-mediated TRAIL expression at the surface of both pDCs and NK cells during their crosstalk . Interestingly , we identified the alarmin HMGB1 , released at pDC-NK cell synapse , as an essential trigger for the secretion of IFN-α and IFN-related soluble mediators during the interplay of HIV-1 exposed pDCs with NK cells . Moreover , HMGB1 was found crucial for mTRAIL translocation to the plasma membrane of both pDCs and NK cells during their crosstalk following pDC exposure to HIV-1 . Data from serum analyses of circulating HMGB1 , HMGB1-specific antibodies , sTRAIL and IP-10 in a cohort of 67 HIV-1+ patients argue for the in vivo relevance of these observations . Altogether , these findings identify HMGB1 as a trigger for IFN-α-mediated TRAIL expression at the surface of pDCs and NK cells , and they suggest a novel mechanism of innate control of HIV-1 infection .
The innate immune response to infection serves as the first line defense against incoming pathogens and is essential for shaping the quality of the ensuing adaptive immune response [1] [2] . A unique subset of myeloid cells , dendritic cells ( DCs ) , mediate the link between innate and adaptive immunity [3] [4] . DCs include myeloid DCs ( mDCs ) that are immune sentinels involved in the recognition of pathogens , antigen-presentation and initiation of T-cell immunity in lymphoid organs , and production of proinflammatory cytokines in response to a variety of stimuli [5] , and plasmacytoid DCs ( pDCs ) that secrete high amounts of interferon-α ( IFN-α ) , and initiate the antiviral immune response [6 , 7] . Different studies have highlighted an important immunoregulatory role of the interaction of DCs with several other cells of the innate immune system , in particular natural killer ( NK ) cells [8] . Indeed , during innate responses , NK cells may interact with both pDCs and mDCs and regulate antiviral immunity [9] [10] [11] [12] [13] [14] . Crosstalk between NK cells and mDCs results in activation of both cell types , with DCs triggering NK-cell proliferation , NK-cell mediated killing of immature DCs ( iDCs ) ( editing process ) , and NK-dependent DC maturation through the release of TNF-α and IFN-γ [15] . NK cells also interact with pDCs and promote the release of IFN-α in an IL-12-dependent way , which in turn triggers the ability of NK cells to kill iDCs [14] . In the setting of HIV infection , several reports identified both numerical and functional defects in the DC and NK cell compartments [16] [17] , and the crosstalk between NK cells and DCs is disrupted . The anergic CD56- NK cells that accumulate during progressive HIV-1 infection are impaired in their ability to promote mDC maturation [18] . Little is known about the effect of HIV-1 infection on the crosstalk between NK cells and pDCs . The response of NK cells to direct IFN-α stimulation is defective in viremic HIV-1-infected patients [19] [20] [21] , and reduced amount of IFN-α and TNF-α are produced by CpG-stimulated PBMC from HIV-1-infected patients [19] . A defective NK cell response to IFN-α was also reported in other chronic viral infections , such as HCV infection , and it was proposed to be the consequence of the release by pDCs and hepatocytes of substantial amounts of IFN-α which will preferentially stimulate STAT1 rather than STAT4 phosphorylation , resulting in reduced IFN-γ secretion and upregulation of cytolytic activity . These alterations would result with the inability to eliminate the virus and may contribute to the resistant of some patients to IFN-α-based therapies [22] . IFN-α triggers the up-regulation of the TRAIL apoptotic ligand , thus turning pDCs into Interferon-producing Killer pDCs ( IKpDCs ) , capable of killing HIV-infected and uninfected CD4+ T cells through TRAIL activation of the Death receptor 5 ( DR5 ) pathway [23] [24] . TRAIL , a TNF superfamily member , is a proapoptotic ligand which is regulated by type-I IFNs on T cells [25] and dendritic cells [26] . The crucial role of TRAIL in antiviral innate response was shown in the context of EMCV murine infection , in which the control of viral replication in the heart is mediated by TRAIL-expressing NK cells . In this model , the expression of TRAIL on NK cells was dependent on IFN-α/ß signaling [27] . HIV-1 infected patients exhibit higher serum levels of TRAIL than non-infected healthy controls , and serum TRAIL levels correlate with serum HIV viral load [28] . The release of TRAIL during HIV-1 transmission occurs very early at the onset of plasma viremia ( i . e . , the eclipse phase ) [29] , and we reported the expression of TRAIL in lymphoid tissues of both HIV-1 infected individuals and SIV-infected macaques [30] . In vitro studies demonstrated that monocytes are the major source of soluble TRAIL production under exposure to HIV-1 [28] , and in vivo study demonstrated that TRAIL participates to CD4+ T cell depletion in HIV-1-infected hu-PBL-NOD-SCID mice [31] . However , it is still debating whether TRAIL-mediated apoptosis contributes to CD4+ T cell depletion , the hallmark of HIV disease progression , although HIV-1 infection upregulates in vivo DR5 expression on CD4+ T cells , which are then prone to TRAIL-mediated apoptosis [28] [32] . These observations suggest that the lack of control of viral replication is responsible for HIV-driven activation of pDCs and prolonged IFN-α production , which can lead to detrimental effects of IFN-α including the upregulation of TRAIL on pDCs thus becoming able to kill uninfected DR5-expressing T cells [24] . We recently reported the crucial role of an alarmin , HMGB1 , in the regulation of innate antiviral immunity , in particular during the crosstalk between NK cells and DCs [33] . HMGB1 is an abundant nuclear protein , which acts as a potent pro-inflammatory factor when released extracellularly [34] . The discovery by Wang et al . [35] , in a mouse model of endotoxaemia , that lipopolysaccharide ( LPS ) -activated macrophages release HMGB1 and that protection against endotoxin lethality could be obtained by administration of anti-HMGB1 antibodies , revealed that HMGB1 is a proinflammatory mediator able to alert the immune system to tissue damage and to trigger an immediate response . This was confirmed by a study by Yanai et al . revealing that HMGB proteins function as universal sentinels for nucleic acids , and the absence of HMGBs in Hmgb1 ( -/- ) mice leads to a defect in type-I interferon and inflammatory cytokines induction by DNA or RNA targeted to activate the cytosolic nucleic-acid sensing receptors , and severely impairs the activation of TLR3 , TLR7 and TLR9 by their cognate nucleic acids [36] . Thus , HMGB1 plays a crucial role in TLR9-induced activation by CpG-DNA through its association with CpG-DNA and its receptor RAGE , and this DNA–protein complex preassociates with TLR9 in the ER–Golgi intermediate compartment , thus accelerating the delivery of microbial DNA to TLR9 and leading to the recruitment of the TLR adaptor molecule MyD88 [37] . HMGB1 is essential for DC maturation , migration and Th1 polarization [34] [38] [39] [40] . In particular , during NK-iDC crosstalk , DC-activated NK cells secrete HMGB1 , which induces DC maturation and protects DCs from lysis [41] . In the setting of HIV-1 infection , elevated plasma levels of HMGB1 were detected during progressive HIV-1 infection , positively associated with viral replication [42] . Recently , we analyzed the impact of HMGB1 on the fate of HIV-1-infected mDCs , and we showed that exposure of mDCs to the virus makes them resistant to NK-cell mediated killing due to HMGB1-induced up-regulation of the intracellular cell death inhibitors , c-FLIP and c-IAP2 . Thus , HMGB1 protects HIV-1-infected mDCs from TRAIL-induced apoptosis by NK cells [33] . In addition , we reported that the interaction between NK cells and HIV-1-infected mDCs results in a dramatic increase in viral replication and proviral DNA expression in DCs . This process is mainly triggered by HMGB1 , released both by NK cells and mDCs , and blocking HMGB1 strongly inhibited HIV-1 replication in infected DCs [43] . A single study investigated the impact of HMGB1 on the fate of pDCs , and it reported that HMGB1 and its receptor RAGE are required for pDC maturation , representing a loop that modulates this process [38] . Considering that HMGB1 is a danger signal that triggers innate immunity early after pathogen incoming , that it is required for type-I IFN and proinflammatory response , and that it is produced by activated NK cells , we investigated the possible involvement of HMGB1 during NK-pDC crosstalk and the consequences on the fate and antiviral functions of both NK cells and pDCs in the context of HIV-1 infection .
pDCs were purified from freshly isolated PBMC from healthy donors and their phenotype was characterized . Ex-vivo sorted pDCs were all CD123+ CD303+HLA-DR+ , and they scarcely expressed CD40 , CD80 , CD83 and CD86 , thus exhibiting the phenotype of immature pDCs . In contrast , CD4 and CXCR4 receptors were expressed in almost all pDCs , while CCR5 was detected in 49% of the population ( Fig 1A ) . TLR-9-dependent stimulation of pDCs with CpG ODN 2006 ( 3μg/ml ) for 24 hours induced their maturation , as shown by the increased expression of CD123 , CCR7 , CD40 , CD86 , HLA-DR and CD83 markers ( Fig 1B ) . 18 h-exposure to HIV-1BaL ( 0 . 1 to 40 ng p24/ml ) induced a dose-dependent maturation of pDCs , evidenced by the increased expression of the fore-mentioned maturation markers ( Fig 1C ) . Notably , the highest HIV-1 concentrations were at least as potent as CpG in inducing CCR7 , CD40 and CD83 up-regulation ( Fig 1B and 1C ) . This was confirmed when pDCs were analyzed for the expression of other maturation markers such as CD123 and HLA-DR , mature pDCs exhibiting the CD123highHLA-DRhigh phenotype ( Fig 1D ) . Interestingly , this CD123highHLA-DRhigh mature subset could be easily spotted according to FSC/SSC criteria , corresponding to the red FSChighSSChigh population on FSC/SSC dot plot , in contrast to the immature pDCs , which appears in blue on FSC/SSC dot plot ( Fig 1D ) . Data in S1 Fig confirm that , in response to diverse stimuli , pDC maturation is associated with the emergence of a specific FSChighSSChigh population ( Panel A ) . Analysis of the expression of several maturation markers by FSChighSSChigh pDCs revealed that CD83 , CD86 or CD40 were only partially expressed by mature pDCs in response to CpG ( 40 . 9% , 58 . 1% , 73 . 3% respectively ) ( panel B ) . Therefore , we decided to use the HLA-DR/CD123 combination , the only one able to target the whole population of mature pDCs . Mature CD123brightHLA-DRhighFSChighSSChigh pDC population was induced by HIV-1 at 1ng/ml and 20 ng/ml , and by CpG as well , as shown on dot plots and histograms ( Fig 1D ) . Viral stimulation triggers pDCs to produce vast amounts of antiviral IFN-α [44] [45] . Accordingly , increasing concentrations of HIV-1 induced a dose-dependent IFN-α production by sorted pDCs ( Fig 2A ) , HIV-1 at 20 ng/ml being as efficient as CpG . We also used clobenpropit ( CB ) , a synthetic histamine analogue [46] , to inhibit IFN-α production by pDCs [47] . It was found to be a strong inhibitor of IFN-α release by pDCs in response to both CpG and HIV-1 , thus inducing a 3-log decrease in IFN-α concentration in pDCs supernatants ( Fig 2B ) . Concomitantly , CB strongly inhibited pDC maturation induced by CpG or HIV-1 , mature pDCs being identified as CD123highHLA-DRhigh cells ( Fig 2C ) . HMGB1 was reported to be required for the maturation of human pDCs upon triggering of TLR9 by CpG , but not following LPS stimulation [38] and , in response to CpG , pDCs relocate and secrete HMGB1 [38] . We show herein that HIV-1 also triggers HMGB1 secretion by pDCs , in a dose-dependent manner ( Fig 2D ) , and neutralizing HMGB1 antibodies strongly inhibit pDC maturation , whether induced by CpG or HIV-1 ( 20 ng/ml ) ( Fig 2E ) , thus reducing significantly ( p = 0 . 0005 ) the proportions of mature CD123highHLA-DRhigh CD83+pDCs ( Fig 2F ) . These data indicate that HMGB1 is required for HIV-1-induced pDC maturation . In addition to type I IFNs , pDCs secrete substantial amounts of other inflammatory molecules . Specifically , it has been shown that pDC stimulation with enveloped viruses , such as herpes simplex or influenza , results in the production of the inflammatory cytokines TNF-α and IL-6 , as well as ß chemokines such as MIP-1α and MIP-1ß [48] [49] [50] . However , these studies did not define the extended array of molecules simultaneously produced by pDCs . Such an analytical approach , using multianalyte profiling , was recently used to define the key chemokines and cytokines produced by pDCs in response to TLR-7 and -9 agonists [51] . This approach was not used yet to characterize the pattern of cytokines and chemokines released by pDCs following exposure to HIV . We report herein the extended array of proinflammatory mediators produced by pDCs stimulated with increasing concentrations of HIV-1 ( Fig 3 ) . In addition to a dose-dependent increase in IFN-α production ( Fig 3A ) ( in agreement with recently published data [52] ) , HIV-1 induced the release of other proinflammatory cytokines i . e . TNF-α , IL-6 , IL-13 , IFN-γ and IL-12 p40 ( Fig 3A ) . HIV-1 at 20 ng/ml was particularly potent in stimulating IFN-α , TNF-α and IFN-γ responses ( Fig 3A ) . To investigate the influence of HIV-1 exposure of pDCs on cytokine microenvironment , the relative proportions of cytokines induced by the virus were compared to those induced by CpG . Fig 3B clearly shows that increasing concentrations triggered an important IFN-α response , which was dominant for HIV-1 at 20 ng/ml , while CpG triggered a dominant TNF-α response . The array of chemokines produced upon pDC activation was also determined ( Fig 3C ) . The great majority of chemokines tested were induced by HIV-1 ( i . e . MDC , MCP-1 , IL-8 , IP-10 , MIP-1α , MIP-1β , MCP3 , GRO and GM-CSF ) , in a dose-dependent way . When the relative proportions of chemokines induced by HIV-1 were compared to those induced by CpG , a dominant MDC response was detected upon exposure of pDCs to increasing doses of HIV-1 , while the chemokine response to CpG was spread into MIP-1α , MIP-1β , IP-10 , IL-8 and RANTES ( Fig 3D ) . Notably , RANTES , a potent HIV suppressive factor [53] was spontaneously produced by pDCs incubated in culture medium , and its relative proportion among chemokines strongly decreased following pDC exposure to increasing doses of HIV-1 ( Fig 3D ) . These experiments were repeated on pDCs from seven healthy donors and the pattern of cytokines and chemokines whose mean levels were significantly increased by HIV-1 at 20 ng/ml is depicted in Fig 3E . These data describe for the first time the broad array of inflammatory mediators produced by HIV-1-stimulated pDCs . Interestingly , the array of chemokines induced by HIV-1-stimulated pDCs overlap the one reported at the mRNA level in herpes simplex virus-stimulated pDCs , and shown to induce migration of activated T cells and NK cells in chemotaxis assays [48] . The production of soluble mediators during NK-pDC crosstalk was compared to that of aNK cells or pDCs alone ( S2 Fig ) . In our conditions , levels of IL-12 , TNF-α or IFN-γ were not detectable in supernatants of pDCs or aNK cells cultivated alone during the short-term culture , while their crosstalk led to the production of significant levels of these inflammatory cytokines . An increased production of IL-8 was also detected in NK-pDC cocultures , as compared to aNK or pDCs alone , while MIP1-α and MIP-1β chemokines were already produced by aNK cells and not increased when cocultured with pDCs . When analyzing the impact of various concentrations of HIV-1 on the level of the inflammatory response generated during NK-pDC crosstalk , we discovered that pDCs exposure to HIV-1 at 1 ng/ml for 3 hours prior to their interaction with aNK cells ( ratio 1:5 ) led to a strong increase in the production of all cytokines tested ( n = 11 ) , as compared to the levels of cytokines produced in the absence of aNK cells ( Fig 4A ) . Statistical comparison of mean values obtained for each cytokine from seven experiments performed with cells from independent healthy donors showed that the interaction of pDCs exposed to HIV-1 at 1 ng/ml with aNK cells triggered significant increased levels of IFN-α , TNF-α , IFN-γ , IL-12 and IL-10 ( Fig 4B ) . In contrast , when pDCs were exposed to a higher dose of HIV-1 ( 20 ng/ml p24 ) , their crosstalk with aNK cells had no impact on the levels of cytokines released ( Fig 4A and 4B ) . Similar observations were made regarding the pattern of chemokines produced in aNK-pDC cocultures exposed to the two different doses of HIV-1 ( Fig 4C and 4D ) . Thus , the majority of chemokines tested i . e . IL-8 , IP-10 , MIP-1α , MIP-1β , MCP3 , GRO and GM-CSF were increased in aNK-pDC cocultures after pDC exposure to HIV-1 at 1 ng/ml , and statistical significant differences were found for MIP-1α and MIP-1β levels , while the chemokine levels were unchanged after exposure of pDCs to HIV-1 at 20 ng/ml prior to coculture with aNK cells . These findings show that the inflammatory microenvironment generated when pDCs interact with aNK cells changes depending on the concentrations of HIV-1 that stimulate pDCs , and they also reveal that once pDCs reach a certain activation threshold in response to HIV-1 , they cannot exceed this threshold when interacting with aNK cells . HIV-1 triggers the production of IFN-α by pDCs [54] , which also produce proinflammatory cytokines , such as TNF-α , IFN-γ or IL-6 , and chemokines , including IP-10 , MIP-1α , and MIP-1β ( Fig 3 ) [48] [55] . In further dissecting the mechanisms by which pDCs initiate inflammatory responses and defining IFN-α-driven loops triggered by viral stimulation , we used CB , a potent inhibitor of IFN-α release , as demonstrated in Fig 2B . As shown in Fig 5A , pDC-derived IFN- α mediates the release of IL-12 , IFN-γ , TNF-α , IL-6 , IP-10 , MIP-1α , and MIP-1β after HIV-1 stimulation , as blocking IFN-α release by CB significantly inhibited the expression of these aforementioned mediators . We showed above that TLR stimuli ( HIV-1 and CpG ) induced in pDCs the production of a broad array of inflammatory mediators ( Fig 3 ) , and also the release of HMGB1 ( Fig 2 ) . To investigate how HMGB1 contributes to the initiation by pDCs of an inflammatory milieu , pDCs were stimulated with ODN 2216 ( CpG-A ) or ODN 2006 ( CpG-B ) , potent and weak inducer of IFN-α respectively [56] , in the presence of two concentrations ( 1μg/ml and 2 . 5μg/ml ) of exogenous rHMGB1 . Data in Fig 5B show that , as expected , CpG-B induced the release of low levels of IFN-α while it triggered the production of IL-12 and IFN-γ . Addition of exogenous rHMGB1 at 2 . 5 μg/ml completely abrogated the release of these two cytokines ( Fig 5B ) . In contrast , CpG-A induced in pDCs a potent IFN-α response , while the other cytokines ( IL-12 and IFN-γ ) were not detected . The addition of exogenous rHMGB1 to CpG-A induced a strong inflammatory response , characterized by increased levels of IFN-α and the production of IL-12p70 , IL-12p40 and IFN-γ ( Fig 5B ) . Such a stimulating effect of HMGB1 was not observed on CpG-B-stimulated pDCs ( Fig 5B ) . These findings are consistent with previous observations showing that CpG-A , but not CpG-B , augments the binding of HMGB1 to its receptor RAGE , resulting in a considerable increased inflammatory cytokine production by mean of TLR9 and RAGE [37] . Thus , exogenous HMGB1 strongly contributes to the initiation by pDCs of the inflammatory milieu . As we discovered the pivotal role of HMGB1 in promoting HIV dissemination and latency in mDCs during mDC-NK crosstalk [43] [33] [57] , we decided to decipher the role of HMGB1 in the context of NK-pDC interaction . aNK cells were cocultured with pDCs ( aNK:pDC ratio 1:5 ) previously exposed to HIV-1 at 20 ng/ml p24 , and the influence of HMGB1 antagonists on IFN-α response of pDCs was determined . Glycyrrhizin , which binds to HMGB1 and inhibits its cytokine activity [58] , strongly inhibited IFN-α release by HIV-1 exposed pDCs during their interaction with aNK cells ( Fig 5C ) . N-ethyl pyruvate that inhibits HMGB1 release by preventing its nuclear-to-cytoplasmic translocation [59] was also a potent inhibitor of IFN-α response in the same cultures . Similar effect was obtained with antibodies specific for RAGE , one of the HMGB1 receptors , which totally suppressed IFN-α response to HIV-1 ( Fig 5C ) . As expected , IFN-α Inhibitors , such as anti-IFN-α antibodies or CB , abrogated the IFN-α response of HIV-exposed pDCs during their interaction with aNK cells ( Fig 5C ) . These findings report for the first time that HIV-1 is an inducer of HMGB1/RAGE autocrine loop that is involved in pDC maturation and IFN-α secretion during NK-pDC interaction , thus promoting an inflammatory milieu ( Fig 5D ) . pDC response to TLR9 stimulus was reported to involve such an autocrine loop [38] . We previously showed that HIV-1 virions turn pDC into TRAIL-expressing IFN-producing killer pDC ( IKpDC ) [23] . We also reported that unstimulated pDCs were "dormant" IKpDCs with high levels of intracellular TRAIL that could be rapidly mobilized to cell surface in response to cell-free HTLV-1 [60] or HIV-1 [61] . We show in Fig 6A that 3-hour exposure of pDCs to HIV-1 induces a dose-dependent significant increase of mTRAIL+ pDCs ( p = 0 . 0002 comparing pDCs exposed to HIV-1 20 ng/ml vs uninfected pDCs ) . Notably , TLR-9 ligation by CpG was also a potent inducer of mTRAIL on pDCs ( Fig 6A ) . We performed 3D microscopy experiments and 3D reconstruction analysis to study TRAIL and HMGB1 expression and localization in pDCs stimulated with either HIV-1 or CpG . High levels of intracytoplasmic TRAIL were detected in unstimulated pDCs , associated with nuclear localization of HMGB1 ( Fig 6B , upper panel ) . Following exposure to HIV-1 , mTRAIL was translocated at the membrane of pDCs while nuclear HMGB1 staining was patched ( Fig 6B , middle panel ) . Similar images were observed upon pDC activation with CpG ( Fig 6B , lower panel ) . Flow cytometry analysis after dual staining ( membrane staining with anti-TRAIL antibodies followed by intracellular staining with anti-HMGB1 antibodies ) of pDCs submitted to the same stimuli confirm the lack of expression of mTRAIL in unstimulated pDCs , while their exposure to HIV-1 or CpG induced mTRAIL expression in 44 . 8% and 25 . 8% of pDCs respectively , all of them expressing intracellular HMGB1 , as expected ( Fig 6C ) . The impact of the interaction of HIV-1 exposed pDCs with aNK cells on mTRAIL expression on pDCs was analyzed on cells from twelve healthy donors ( Fig 7A ) . A mean increase in the percentage of mTRAIL+ pDCs was detected for the lower concentration of HIV-1 ( 1 ng/ml p24 ) , although not statistically significant ( Fig 7A ) . For the highest concentration of HIV-1 ( 20 ng/ml ) , the interaction of pDCs with aNK cells did not increase the mean percentage of TRAIL+ pDCs . Notably , in the absence of HIV-1 , aNK:pDC interaction triggered mTRAIL expression on pDCs ( p = 0 . 047 vs pDC ) . We then addressed the question of pDC-triggered mTRAIL expression on NK cells . Resting NK cells ( rNK ) were co-cultured for 24 h in the presence of pDCs previously exposed to HIV-1 at 1 ng/ml or 20 ng/ml . As expected , sorted rNK cells did not express mTRAIL . Their interaction with pDCs , in the absence of virus , triggered mTRAIL expression on NK cell surface ( p<0 . 05 vs rNK cells ) ( Fig 7B ) . Their interaction with pDCs exposed to HIV-1 triggered a strong and significant increase of mTRAIL expression on rNK cells , reaching for the highest HIV concentration a mean of 60% mTRAIL+NK cells ( p = 0 . 02 vs rNK-pDC w/o HIV ) . Moreover , pDC-triggered acquisition of mTRAIL by rNK cells was accompanied by the expression of the activation marker CD69 and the marker of degranulation CD107a , detected both in the absence and in the presence of HIV-1 , and reaching levels comparable to those expressed by aNK cells ( Fig 7C ) . Altogether these observations reveal that , in response to HIV-1 , NK-pDC interaction triggers the generation of cytotoxic cells in both interacting subsets , revealed by the expression of the apoptotic ligand TRAIL and also the lytic granule membrane protein CD107a , whose expression is a functional marker of NK cytolytic activity [62] . We previously reported that HIV-1-mediated expression of mTRAIL on CD4+ T cells [63] and pDCs [23] is regulated by IFN-α . The close link between IFN-α release by pDCs and TRAIL translocation to cell membrane was confirmed using CB , which we showed in Fig 2B to be a strong inhibitor of IFN-α release by pDCs in response to both CpG and HIV-1 . As expected , CB significantly inhibited mTRAIL expression on pDCs stimulated either by CpG ( A ) or CpG ( B ) , or activated by HIV-1 at 20 ng/ml ( Fig 8A ) . Since we found that HMGB1 was required for IFN-α production during NK-pDC crosstalk ( Fig 5C ) , it was of interest to test the possible involvement of HMGB1 on mTRAIL expression on pDCs and NK cells . Data in Fig 8B show that inhibiting HMGB1 with either specific antibodies or N-ethyl pyruvate significantly inhibited the expression of mTRAIL on pDCs exposed to HIV-1 at 20 ng/ml . A weak effect was observed in the presence of anti-RAGE antibodies . In the context of aNK-pDC crosstalk following pDCs exposure to HIV-1 at 20 ng/ml , mTRAIL expression on pDCs was also inhibited by several HMGB1 antagonists , including antibodies specific for HMGB1 or RAGE and N-ethyl pyruvate ( Fig 8C ) . The triggering of mTRAIL expression on NK cells as a consequence of their interaction with HIV-stimulated pDCs required IFN-α release by pDCs , as shown in Fig 8D on dot plots from one representative experiment using CB as an inhibitor . Moreover , inhibiting HMGB1 with N-ethyl pyruvate during NK-pDC interaction significantly decreased the expression of mTRAIL on NK cells ( Fig 8E ) . Altogether , these findings indicate that HMGB1 is essential for the induction of mTRAIL on both pDCs and NK cells in response to HIV-1 ( Fig 8F ) . HMGB1 was originally discovered as a 25 kDa DNA-binding protein that participates in many nuclear functions and , triggered by infection and proinflammatory stimuli , HMGB1 can be released extracellularly and act as a proinflammatory mediator . HMGB1 attached to DNA or by itself is highly immunogenic and stimulates the production of autoantibodies [64] [65] . Serum anti-HMGB1 antibodies have been first reported in SLE patients [66] , but were also detected in healthy individuals [66] [67] . The HMGB1-binding antibodies might play an important physiological role by modulating the proinflammatory activity of HMGB1 , thereby limiting overwhelming inflammatory responses caused by massive HMGB1 release in conditions such as infections or extensive necrosis [68] . It is noteworthy that anti-HMGB1 antibodies , as well as other HMGB1-binding serum proteins , impede the reliable quantification of serum HMGB1 by ELISA , as reported by several groups [69] [66] . Since reliable quantification of circulating HMGB1 remains a problem , we decided to measure instead HMGB1-specific antibodies . We used an in-house ELISA that enables the detection of all HMGB1-specific IgG antibodies i . e . circulating antibodies plus HMGB1-complexed antibodies that have been dissociated from HMGB1 by an acidic treatment ( see M&M ) . Thus the whole range of HMGB1 antibodies that we measure is the direct consequence of the total amount of HMGB1 produced . Data in Fig 9A show that anti-HMGB1 antibodies are detected in serum from healthy donors , and they indicate for the first time that their levels is increased in the setting of HIV-1 infection . An inverse correlation between residual HMGB1 and total anti-HMGB1 IgG antibodies was found , as expected from a putative modulating role of HMGB1-specific antibodies . The level of circulating sTRAIL was assessed in the same cohort of patients and it was strongly correlated with the level of anti-HMGB1 antibodies in viremic patients ( r = 0 . 597 , p = 0 . 003 ) , and with the level of IP10 in the whole group ( r = 0 . 497 , p = 0 . 0002 ) ( Fig 9B ) . These observations suggest for the first time a link between HMGB1-specific antibodies , sTRAIL and IP-10 , whose production by pDCs is strongly triggered by IFNα [70] . The impact of viral load on the levels of circulating sTRAIL in HIV-infected patients is shown in Fig 9C . Increased levels of sTRAIL were detected in viremic patients as compared to aviremic ones , and sTRAIL levels were found strongly correlated with circulating proviral HIV DNA ( r = 0 . 42 , p = 0 . 0004 ) , suggesting that circulating TRAIL might contribute to HIV persistence .
Our study provides evidence that , upon interaction with pDCs , aNK cells promote the release of IFN-α , Th1 cytokines ( IL-12 and IFN-γ ) , CCR5-interacting chemokines ( MIP-1α and MIP-1β ) and IL-8 , in the absence of HIV-1 or upon exposure to low concentration of HIV-1 . In contrast , after exposure of pDCs to higher concentration of HIV-1 , aNK cells do not promote pDC-dependent production of these mediators . We identified HMGB1 , released both by pDCs and NK cells , as being an essential trigger for the secretion of IFN-α and IFN-related soluble mediators ( IFN-γ , TNF-α , IP-10 , MIP-1α , MIP-1β and IL-6 ) during the interplay of HIV-1-exposed pDCs with NK cells . In addition , HMGB1 was found crucial for the expression of mTRAIL on both pDCs and NK cell surface . TRAIL translocation on pDC membrane was induced by HIV-1 in a dose-dependent manner , and the presence of NK cells did not modulate this response . However , the crosstalk between HIV-1-exposed pDCs and activated NK cells promoted the expression of TRAIL on NK-cell membrane , which required HMGB1 , suggesting that HMGB1 is key element between the two innate immune effectors for the triggering of their cytotoxic functions in response to viral infection . The host response to viral infection starts when TLRs recognize Pathogen-Associated Molecular Patterns ( PAMPs ) such as DNA or single strain RNA ( ssRNA ) , then leading to the secretion of proinflammatory mediators . pDCs use TLR9 to detect pathogen-associated DNA ( CpG ) and to trigger the production of type-I interferon as well as other inflammatory cytokines . Regarding the recognition by pDCs of a ssRNA virus , such as HIV-1 , it was shown by Beignon et al . that endocytosis of HIV-1 RNA activates TLR7 and results in the production of IFN-α [71] . Accordingly , silencing TLR7 or inhibiting TLR7 pathway greatly reduces IFNα production in HIV-1-infected pDC cell line [72] . We report here that high concentrations of HIV-1 , but not newly produced virions , are required to induce the secretion of high levels of IFN-α by pDCs , consistent with the observation that the number of HIV-RNA copies are critical to activate pDCs through TLR signalling [71] . The cytokine and chemokine response induced by HIV-1 probably involves TLR7 pathway , although we cannot exclude the involvement of TLR9 signalling . Indeed , the HIV-1 viral cycle includes the formation of a DNA/RNA heterodimer and a double stranded proviral DNA , which represent potential ligands for TLR9 via CpG-rich DNA regions [73] [74] . It would be interesting to evaluate whether fragments of viral DNA included in the virions engulfed by pDCs and processed by the endosomal pathway could trigger TLR9-mediated responses . In the absence of virus , we show that aNK cells trigger the release of cytokines and chemokines by pDCs without inducing their phenotypic maturation . These data suggest that NK-mediated induction of pDC activation in the absence of danger signals may be important in the amplification of ongoing inflammatory responses [75] [76] . Strikingly , NK cells were able to efficiently activate pDCs only in the presence of low but not high concentrations of HIV-1 . Indeed , the phenotypic and functional properties of pDCs exposed to high concentration of HIV-1 were unchanged following their interaction with NK cells . These observations suggest that , once stimulated by high virus concentration , pDCs could not integrate new activating signals delivered by NK cells . This activation threshold may be triggered by IFN-α produced at high levels under these coculture conditions . This is suggested by studies showing that pDCs exposure to high levels of IFN-α may affect their ability to respond to subsequent de novo stimulation [77] , through a negative feedback to limit the extent and duration of IFN-α response . This regulation may involve SOCS proteins that are induced by type I IFNs , and compete with STATs for binding to IFN-α receptor and suppress JAK activity [78] [79] . We cannot completely exclude the involvement of the regulatory cytokines TGFß produced by pDCs exposed to high levels of HIV-1 , which could prevent further release of IFN-α and then activation of pDCs [80] [81] . However , this hypothesis is not supported by the very low levels of IL-10 produced during the coculture with aNK cells of pDCs exposed to high virus concentration ( Fig 4A and 4B ) . Regarding the involvement of TGFβ as a suppressor cytokine induced by high virus levels , our data do not support this hypothesis . Indeed , exposure of pDCs to TGFβ was shown to prevent type I interferon secretion [82] , whereas high levels of IFN-α produced at high virus concentration . Moreover , TGFβ is known to trigger the production by pDCs of proinflammatory cytokines , such as TNF-α or IL-6 , which were not increased during the coculture vs pDCs at high virus concentration ( Fig 4A and 4B ) . pDCs produce IFN-α after HIV-1 exposure , which in turn regulates TRAIL expression by CD4+ T cells , but also by pDCs , thus becoming IKpDCs [23] . We confirmed in the present study that the increase of TRAIL expression on pDC cell surface was due to the translocation of the molecule from the cytoplasm towards cell membrane , as previously shown [61] . We found that TRAIL translocation on pDC membrane was abrogated by the neutralization of IFN-α , suggesting that autocrine IFN-α controls functional activation of pDCs . In contrast to pDCs , exposure of NK cells to high concentrations of HIV-1 did not trigger cell surface expression of TRAIL ( S3 Fig ) . TRAIL translocation on NK-cell membrane required the release of IFN-α by HIV-exposed pDCs , which is consistent with previous observations showing that exogenous NK stimulation derived from pDCs can trigger NK cytotoxicity against HIV-1-infected autologous CD4+ primary T cells [83] . It was reported that , following HIV-1 exposure of the vaginal mucosa , pDCs produce β-chemokines that in turn activate the recruitment of CCR5+ T cells [84] . This observation suggests that pDCs are activated by the microenvironment or other immune mucosal cells , such as NK cells and myeloid DC . A recent study reported that depletion of NK cells during acute SIV infection consecutive to in vivo administration of a JAK3 inhibitor had no impact on plasma viral load in the acute phase but induced an increased viral load in the chronic phase . This treatment also inhibited the redistribution of pDCs from the blood to the GIT , suggesting a protective role of NK-pDC interactions during chronic infection [85] . HMGB1 is abundantly expressed in the nuclei of mammalian cells ( >106 molecules/nucleus ) , facilitating interaction of chromatin with various nuclear proteins [86] . HMGB1 reaches the extracellular environment when innate immune cells secrete it following acetylation [87] , either by passive release from necrotic cells [88] or by active release by apoptotic cells [89] . In our experiments , we observed that HIV-1 had a survival effect ( increased percentage of Bcl2high cells ) rather than a killing effect on pDCs ( S4 Fig ) , excluding that HMGB1 detected in HIV-1 exposed pDCs was the consequence of its release by dead cells . We report that inhibition of HMGB1 activity in NK-pDC co-cultures prevented pDCs maturation and IFN-α release , consistent with the observation that HMGB1 translocation from the nucleus to the extracellular environment of TLR9-activated pDCs is required for pDC maturation and IFN-α secretion [38] . Despite HMGB1 release in NK-pDCs co-culture , it did not modulate cell surface expression of maturation markers such as CD83 , CD86 and CD40 on pDCs ( S5 Fig ) . This is consistent with findings suggesting that two distinct pathways induce pDC maturation and the release of soluble mediators by pDCs harbouring an immature phenotype [12] . However , high concentration of HIV-1 was sufficient to induce full maturation/activation of pDCs and IFN-α production , suggesting that intracellular TLR signalling by HIV-1 is required for HMGB1-mediated phenotypic maturation and activation of pDCs . The bioactivity of extracellular HMGB1 is regulated by tight interactions with soluble effectors ( cytokines and chemokines ) [90] or through chemical modification of the three conserved redox-sensitive cysteines ( C23 , C45 , and C106 ) [34] . Since HMGB1 release in NK-pDC co-culture occurs in the context of the release of many other inflammatory cytokines and chemokines , we hypothesize that HMGB1 activity may be regulated by this microenvironment . The identification of molecules potentially involved in HMGB1 activity in the context of NK-pDC crosstalk is currently under investigation . Several studies showed that pDCs and mDCs exhibit distinct and complementary functions during immune responses against pathogens . Accordingly , we have previously reported that HIV-1 could not by itself induce the maturation of mDC [43] in the absence of NK cells , whereas in the present study we showed that HIV-1 efficiently induced the maturation of pDCs in a dose-dependent manner . Since the maturation process leads to an increased capacity of cells to communicate with others immune cells by enhancing the expression of co-stimulatory receptors and soluble inflammatory molecules , our finding support the hypothesis that pDCs are the first innate cells sensing the virus and initiating the immune response . Circulating levels of HMGB1 are elevated during the course of HIV-1 infection [42] and positively associated with high viral load [91] . HMGB1 can be passively released by virus-infected cells including primary CD4 T cells infected with HIV-1 , and this was associated with both necrotic and apoptotic cell death [92] . HMGB1 can also be released by non-infected apoptotic CD4 T cells that die through a bystander killing process , which is mainly induced by extracellular HIV-1-encoded proteins and by HIV-1-associated chronic immune activation [93] . Increased circulating HMGB1 levels detected in progressive HIV-1 infection , combined with microbial products ( such as LPS ) and TLR ligands , may contribute to gut inflammation and subsequent microbial translocation , suggested to have an important role in HIV pathogenesis [94] . In recent studies using and ex-vivo model of NK-mDC crosstalk , we suggested the contribution of HMGB1 to HIV persistence in mDCs through the upregulation of two apoptosis inhibitors ( cFLIP and c-IAP2 ) in infected mDC , which rendered them strongly resistant to NK killing [33] . Blocking HMGB1 with specific antibodies restored the susceptibility of infected DCs to NK killing , and similar effect was observed knocking down c-FLIP or c-IAP2 by siRNA [33] [57] . In the present study , we found that HMGB1 had a pivotal role on the expression of mTRAIL on both HIV-1-exposed pDC and NK cells , and it required the production of IFN-α by virus-exposed pDCs . This new function of HMGB1 is compatible with previous observations showing that HMGB1 interaction with its receptor RAGE on the surface of pDCs leads to TLR9-dependent IFN-α release [68] , and that IFN-α released by HIV-1 activated pDCs turn them into TRAIL-expressing killer pDCs [95] . Herein , we report that circulating sTRAIL levels were increased in viremic HIV-1+ patients , and strongly correlated with circulating HMGB1-specific antibodies and IP-10 levels , arguing for an interdependency between HMGB1 , IFN-α and TRAIL that is suggested by our ex-vivo model of NK-pDC interaction ( Fig 8 ) . The positive and strong correlation between circulating sTRAIL and HIV-1 proviral DNA is a new finding . It suggests that sTRAIL is a marker of HIV latency in CD4 T cells . Notably , our results are strengthened by studies suggesting that TRAIL contribution to HIV persistence in CD4 T cells from infected patients occurs through the development of CD4 T cell resistance to TRAIL-mediated apoptosis . Indeed , resistance to TRAIL-induced apoptosis may occur through the production of a novel TRAIL splice variant , which can be found in the plasma from infected patients , and which preferentially binds TRAIL-R2 , thus preventing proapoptotic TRAIL signaling [96] . In conclusion , our study reports that the concentration of HIV-1 is critical to sustain the functional activation of both pDCs and NK cells . Low levels of HIV-1 were found to mediate the production of inflammatory Th1 cytokines only when pDCs cells were interacting with NK cells . Considering that a very low number of virus particles cross the genital mucosa [97] , our observations suggest an important role of NK-pDC crosstalk during the first hours following mucosal infection . At high virus concentration , phenotypic and functional pDC maturation occurred and their cytotoxic function was induced . Regarding NK cells , the induction of their cytotoxic function required their crosstalk with pDCs previously exposed to HIV-1 . Several reports showed that the transmitted virus undergoes an amplification step in mucosal tissues before its systemic dissemination . For the design of novel prevention strategies aimed at blocking infection after mucosal HIV-1 exposure , it would be interesting to monitor and to modulate in vivo the impact of early activation of NK-pDC cooperation on HIV-1 mucosal transmission and dissemination .
67 HIV-1 infected patients were tested , included in the Nadis cohort , a large prospective French cohort of HIV-infected patients [98] . They were above 18 years of age , with a median CD4 cell count of 499 /μL ( IQR 370–764 ) , a median nadir CD4 cell count of 272 /μL ( IQR 126–351 ) , and a median CD8 cell count of 832 /μL ( IQR 594–1144 ) . 80% of the patients were on combined antiretroviral therapy ( c-ART ) and the median time on current treatment was 2 years . 57% had undetectable viral load ( < 1 . 6 log10 ) , median plasma HIV-1 RNA was 1 . 6 log10 cp/ml ( IQR 1 . 6–2 . 6 ) and median HIV-1 proviral DNA was 2 . 18 log10 cp/106 cells ( IQR 1 . 66–2 . 71 ) . The Ethics Committee in Montpellier ( France ) approved this study and all subjects gave informed consent prior to screening and enrollment . A control group of healthy adult donors ( n = 12 ) was included . Blood was obtained through the EFS ( Etablissement Français du Sang ) in the setting of EFS-Institut Pasteur Convention . A written informed consent was obtained for each donor to use the cells for clinical research according to French law . Our study was approved by IRB , external ( EFS Board ) as required by French law and internal ( Biomedical Research Committee Board , Institut Pasteur ) as required by Institut Pasteur . Peripheral Blood Mononuclear Cells ( PBMCs ) were separated from the blood of healthy adult donors on a Ficoll-Hypaque density gradient . pDCs were isolated from fresh PBMCs using the Human Plasmacytoid DC Negative Isolation Kit ( StemCell Technologies ) according to the manufacturer’s protocol . The enriched cells were assessed for more than 90% purity using the following antibodies: anti-CD123–APC , anti–BDCA-2–PE ( Miltenyi Biotec ) and anti-CD3-FITC ( Becton Dickinson–Pharmingen ) . pDCs were cultured in RPMI 1640 ( Invitrogen , Gaithersburg , MD , USA ) containing 10% FCS and 1% penicillin-streptomycin at 37°C in a humidified 5% CO2 chamber according to protocol . CD56+ NK cells were isolated by negative selection from fresh PBMCs using the «EasySep NK depletion Kit» ( StemCell Technologies ) . NK cell fraction ( CD3−CD56+ ) was more than 95% pure , as assessed by flow cytometry ( FACScalibur , BD ) using FITC-conjugated anti-CD3 and APC-conjugated anti-CD56 antibodies . Contamination with myeloid cells , assessed with FITC-conjugated anti-CD14 antibodies , was consistently less than 1% . Purified NK cells were activated by a combination of PHA ( 10 μg/ml ) ( Sigma ) and rhuIL-2 ( 10 μg/ml ) ( referred as aNK cells ) , before launching NK-DC coculture experiments . pDC survival was determined with the intracellular Bcl-2 staining , as described previously [99] . Briefly , cultured cells were first stained with CD123 and 20 μg/mL nuclear dye 7-amino-actinomycin D ( 7-AAD; Sigma-Aldrich ) for 30 minutes at 4°C . Cells were then fixed with paraformaldehyde 4% for 20 minutes . To permeabilize cells , perm/wash buffer ( BD Biosciences ) was used before the intracellular staining with Bcl-2-FITC ( clone 124 , Dako Inc . ) . Surviving pDC were identified as CD123+ BCL2med/high 7-AADneg cells . Virus stock was prepared by amplification of R5-HIV-1BaL on Monocytes-Derived Macrophages ( MDM ) . Viral stock was then clarified by centrifugation prior to determination of HIV-1 p24 concentration . CCR5-tropic Ad5 HIV-1 preparations were treated with DNase I ( Takara ) in the presence of 10 mM MgCl2 at 37°C for 30 min and then untracentrifuged ( 17 , 000 g for 1 hour ) . Aliquots were stored at -80°C . Freshly purified pDCs were incubated 3 h with various concentrations of HIV-1 ( 1 , 10 or 20 ng p24/ml ) . pDCs were then plated in 96-well culture plates at 1:5 NK:pDC ratio ( 2x105 NK:106 pDC ) and incubated for 24 hours at 37°C in a 5% CO2 atmosphere . In some experiments , rh-HMGB1 ( 1 μg/ml ) ( HMGBiotech srl , Milano , Italy ) , rabbit anti-HMGB1 Abs ( 10 μg/ml ) ( Abcam , Cambridge , UK ) , Glycyrrhizin ( 10 μg/ml ) or N-ethyl-pyruvate ( 10 μM ) were added at initiation of the coculture . As positive control , pDCs were activated with ODN 2006 ( CpG ) at 3 μg/ml ( InvivoGen , USA ) . As negative control , pDCs were cultivated in the presence of GpC at 3 μg/ml ( InvivoGen , USA ) , LPS at 10 μg/ml ( Sigma-Aldrich ) or trimeric CD40L at 500 ng/ml ( Sigma-Aldrich ) . The phenotype of pDCs was determined with the following primary mAbs and the appropriate isotype controls ( from BD Biosciences , San Jose , CA ) : CCR5 ( clone 2D7 ) , CXCR4 ( clone 12G5 ) , CD40-APC ( clone 5C3 ) ; HLA-DR-APC ( clone L243 ) ; CCR7-FITC ( clone 3D12 ) , CD83-APC ( clone HB15e ) , CD86-APC ( clone 2331 ) and TRAIL-PE ( clone RIK-2 ) . CD4 ( clone 13B8 . 2 ) was purchased from Beckman Coulter . Cells were stained for 30 minutes at 4°C , washed twice in PBS/BSA/NaN3 ( 0 . 5% BSA , 0 . 01% NaN3 ) and fixed with 1% PFA . For intracellular staining , cells were fixed with 4% PFA , permeabilized using 0 . 5% BSA , 0 . 01% NaN3 , 0 . 5% Saponin buffer , stained for 20 minutes at room temperature with FITC-labeled anti-HMGB1 pAbs ( ABCAM ) . At least 5 , 000 events were acquired using a FACScalibur flow cytometer ( BD Biosciences ) , and stained cells were analysed using FlowJo software ( Tree Star , Inc . , Ashland , OR ) . DC survival was assessed using the 7-AAD assay , as described previously [99] . When phenotypic characterization of pDCs was performed in NK-DC cocultures , NK cells were excluded through the gating of CD56neg cells . Surviving pDCs were identified as CD56neg 7-AADneg cells . When phenotypic characterization of NK cells was performed in NK-DC cocultures , NK cells were gated through their expression of CD56 marker . pDCs were cultured overnight in medium ( unstimulated ) , stimulated with CpG ( 3 μg/ml ) or exposed to HIV-1 at 1 ng/ml . pDCs were then plated on poly-l-lysine ( Sigma ) –coated slides and fixed in 4% paraformaldehyde , quenched with 0 . 1 M glycine . pDCs were co-stained with mouse anti-TRAIL ( clone RIK-2 , eBioscience ) and Alexa 488–labeled anti-HMGB1 ( Abcam ) antibodies in permeabilizing buffer containing 1% saponin . TRAIL staining was revealed using a secondary Alexa 547- goat anti-mouse IgG ( Jackson ImmunoResearch ) . Nuclei were stained with DAPI ( Molecular Probes ) . Mounted slides were scanned with a Nikon Eclipse 90i Upright microscope ( Nikon Instruments Europe ) and were subsequently deconvoluted ( Meinel algorithm ) and analyzed using Metamorph ( MDS Analytical Technologies ) . Analysis was performed using a 3D viewer from ImageJ . Forty stacks were compiled to make a 3D view of the cells . pDCs were stimulated for 24 h with ODN 2216 ( CpG-A ) or ODN 2006 ( CpG-B ) at 3 μg/ml ( InvivoGen , USA ) . In some experiments , IFN-α release was inhibited with CB ( Sigma ) at 1 μM . For experiments aiming at neutralizing IFN-α activity , pDCs were incubated for 24 h with antibodies specific for human IFN-α ( clone MMHA-1 ) or human IFN-α/βreceptor ( Clone MMHAR-2 ) ( both from PBL Assay Science ) at 1 μg/ml . IFN-α was quantified in culture supernatants using the Human IFN-Alpha ELISA Kit ( PBL Interferon Source , Piscataway , NJ ) , according to manufacturer's instructions . Circulating sTRAIL concentration was determined with the human CD253 ELISA kit from Diaclone SAS ( Besançon , France ) . Concentrations of the whole range of circulating anti-HMGB1 IgG antibodies were determined with an in house quantitative ELISA assay that enabled the detection of both residual and complexed HMGB1-specific antibodies . 96-well plates were coated overnight at 4°C with recombinant HMGB1 ( HMGBiotech , HM-115 ) in PBS . Simultaneously , coating of serial dilutions of the calibrator human serum IgG ( Sigma ) was performed , as previously reported [100] . After washing the plates , unbound sites were blocked with PBS/2% ( w/v ) BSA . Since anti-HMGB1 antibodies were reported to bind HMGB1 in serum [69] , we dissociated these complexes with glycine before titration of the antibodies . Treated samples were then immediately diluted and dispensed in coated plates . Goat anti-human IgG alkaline phosphatase-conjugated antibodies were added , and detection of HMGB1-specific antibodies was performed after incubation with 100μl pNPP substrate . Their concentration was calculated according to the standard curve obtained from standard immunoglobulin solution absorbance by Ascent software , ThermoElectrocorp . The data are expressed in ng/ml of antibodies detected . Culture supernatants were analysed using the Human Cytokine Milliplex Kit ( Millipore Corporation , Billerica , MA ) and the Luminex LX100 ( Luminex Corporation , Austin , TX ) according to manufacturer’s instructions . The cytokines and chemokines analysed were as follows: IFN-α , TNF-α IL-6 , IL-13 , IFN-γ , IL-12p40 , IL-12p70 , IL-10 , IL-15 , IL-1α , IL-2 , IL-3 , macrophage-derived chemokine ( MDC ) , monocyte chemoattractant protein 1 ( MCP1 ) , MCP3 , IFN-γ-inducible protein 10 ( IP-10 ) , macrophage inflammatory protein 1α ( MIP-1α , MIP-1β , regulated on activation , normal T-cell expressed and secreted ( RANTES ) , growth-regulated oncogene ( GRO ) , granulocyte-macrophage colony stimulating factors ( GM-CSF ) and Eotaxin . Samples were assayed in duplicate . At least 75 events were acquired for each analyte . Values above or below the standard curves were replaced by the lowest or the highest concentrations measured . For some experiments , flow data were formatted with Pestle v1 . 6 . 2 software ( Mario Roederer , Vaccine Research Centre , National Institute of Allergy and Infectious Diseases , National Institutes of Health ) to facilitate the use of SPICE v5 . 2 . Statistical analysis was performed using GraphPad Prism version 5 . 00 ( GraphPad Software , San Diego , CA ) . The data are presented as arithmetic mean ± SD and were compared using Mann-Whitney test and Wilcoxon matched pairs test as appropriate . P-values <0 . 05 were considered to be significant . | Plasmacytoid dendritic cells ( pDC ) are the most potent IFN-α-producing cells and serve as an essential link between innate and adaptive immunity . Exposure of pDCs to HIV-1 triggers IFN-α production , which in turn upregulates TNF-related apoptosis-inducing ligand ( TRAIL ) , turning pDCs into killer pDCs , able to kill infected CD4+ T cells . At sites of infection , pDCs might activate or get activated by Natural killer ( NK ) cells , and pDC-NK cell-cell contact is required to promote the cytolytic potential of NK cells . Functional defects in the pDC and NK cell compartments were reported in the setting of HIV-1 infection , but the precise mechanisms by which HIV impairs NK cell and pDC crosstalk remain to be fully elucidated . To address this question , we developed an ex-vivo model of NK-pDC interaction , based on a short-term contact between sorted peripheral NK cells and purified pDCs exposed to HIV-1BaL . We found that the concentration of HIV-1 is critical to sustain the functional activation of both pDCs and NK cells . Moreover , we identified the alarmin HMGB1 as an essential trigger for the secretion of IFN-α and IFN-related soluble mediators during the interplay of HIV-1-exposed pDCs and NK cells . HMGB1 was also found crucial for HIV-1-induced translocation of TRAIL on both pDC and NK cell membrane . The in vivo relevance of the interdependency between HMGB1 , IFN- and TRAIL is suggested by the strong positive correlations between circulating levels of these mediators in a cohort of 67 HIV-1 infected patients . Altogether these findings highlight a new function for HMGB1 and they suggest a novel mechanism of innate control of HIV infection . | [
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"pa... | 2016 | HMGB1 Is Involved in IFN-α Production and TRAIL Expression by HIV-1-Exposed Plasmacytoid Dendritic Cells: Impact of the Crosstalk with NK Cells |
Upon the ligand-dependent dimerization of the epidermal growth factor receptor ( EGFR ) , the intrinsic protein tyrosine kinase ( PTK ) activity of one receptor monomer is activated , and the dimeric receptor undergoes self-phosphorylation at any of eight candidate phosphorylation sites ( P-sites ) in either of the two C-terminal ( CT ) domains . While the structures of the extracellular ligand binding and intracellular PTK domains are known , that of the ∼225-amino acid CT domain is not , presumably because it is disordered . Receptor phosphorylation on CT domain P-sites is critical in signaling because of the binding of specific signaling effector molecules to individual phosphorylated P-sites . To investigate how the combination of conventional substrate recognition and the unique topological factors involved in the CT domain self-phosphorylation reaction lead to selectivity in P-site phosphorylation , we performed coarse-grained molecular simulations of the P-site/catalytic site binding reactions that precede EGFR self-phosphorylation events . Our results indicate that self-phosphorylation of the dimeric EGFR , although generally believed to occur in trans , may well occur with a similar efficiency in cis , with the P-sites of both receptor monomers being phosphorylated to a similar extent . An exception was the case of the most kinase-proximal P-site-992 , the catalytic site binding of which occurred exclusively in cis via an intramolecular reaction . We discovered that the in cis interaction of P-site-992 with the catalytic site was facilitated by a cleft between the N-terminal and C-terminal lobes of the PTK domain that allows the short CT domain sequence tethering P-site-992 to the PTK core to reach the catalytic site . Our work provides several new mechanistic insights into the EGFR self-phosphorylation reaction , and demonstrates the potential of coarse-grained molecular simulation approaches for investigating the complexities of self-phosphorylation in molecules such as EGFR ( HER/ErbB ) family receptors and growth factor receptor PTKs in general .
Classical polypeptide growth factor receptors are intrinsic membrane proteins , each possessing a ligand binding domain in the N-terminal extracellular portion of the molecule , a protein tyrosine kinase ( PTK ) domain in the C-terminal intracellular portion , and a single transmembrane ( TM ) domain in the intervening sequence [1] . The intrinsic PTK activity of the intracellular domain is activated upon a ligand-dependent dimerization of receptor monomers , with activation resulting in the self-phosphorylation ( autophosphorylation ) of both monomers on multiple tyrosine residues . These tyrosine residue phosphorylation sites ( P-sites ) are typically located within a lengthy phosphorylation domain ( CT domain ) at the receptor C-terminus , but in some cases also within a intracellular juxtamembrane ( JM ) sequence joining the TM domain and PTK core or a “kinase insert” sequence in the PTK domain proper . Receptor self-phosphorylation on P-sites triggers the binding of various signaling effectors via their Src homology 2 ( SH2 ) or phosphotyrosine binding ( PTB ) domains , which both bind phosphorylated tyrosine residues in specific amino acid sequence contexts . Thus , the phosphorylation of specific P-sites within the receptor CT domain or elsewhere results in the activation of specific downstream signaling pathways that can be mitogenic ( inducing of cell division ) , anti-apoptotic ( promoting of cell survival ) , or regulatory of metabolism . While the differing sequences of phosphorylated receptor P-sites results in a selectively in their recruitment and activation of downstream signaling effectors , it is not known what determines the efficiency with which individual P-sites become phosphorylated upon receptor activation . This is in part because the structures of the various receptor CT domains are unknown , perhaps reflective of their generally disordered nature , and because the dynamics of these domains have not been examined . A case in point is that of the prototypical epidermal growth factor receptor ( EGFR , also termed HER1 ) PTK , about which there exists extensive structural information [2] . Here , in terms of the presumed ligand-activated dimeric receptor form , several independent structures of receptor extracellular domain dimers ( with bound growth factor molecules ) and PTK domain dimers have been presented , in addition to a recent structural model of the interaction of the two TM and two intracellular JM domains in the receptor dimer [3] . The elegant and painstaking studies of the Kuriyan lab have led to a generally accepted model in which the latent PTK activity of the monomeric EGFR is activated in the dimeric receptor form by an asymmetric allosteric interaction between PTK domains , in which binding of the C-terminal lobe of one PTK domain ( the “activator” ) to the N-terminal lobe of the other ( the “receiver” ) results in the activation of the receiver but not the activator [4] . Thus , in this model , and assuming no higher-order oligomeric interactions occur , the CT domains of the individual EGFR molecules ( where all direct P-sites are known to reside [5] ) must be phosphorylated either via an intramolecular reaction ( a cis mechanism ) in the case of the receiver molecule or an intermolecular reaction ( in trans ) in the case of the activator molecule . As the roles of the activator and receiver PTK domains might be exchanged by a dissociation and ensuant reassociation of the kinases in the opposite orientation [4] , it is formally possible that the CT domain of an individual receptor molecule is phosphorylated by both cis and trans mechanisms . In contrast to the extensively characterized extracellular and PTK domain structures , no structure for the ∼225-amino acid CT domain of the EGFR is available , except for those of short CT domain sequences that are seen to be ordered in different crystal structures , with variations in the sequences ordered between different crystal forms making the biologic relevance of these CT domain structures unclear ( cf . [6] ) . Of eight candidate P-sites located within the CT domain ( see Table 1 ) , a range of in vitro phosphorylation studies ( employing purified EGFR protein ) and live cell phosphorylation analyses have identified the various major and minor sites of phosphorylation ( Table 2 , reviewed in [7] ) . There indeed is selectivity in phosphorylation site usage , with P-sites including Tyr-1068 , Tyr-1086 , Tyr-1148 and Tyr-1173 ( hereafter designated P-site-1068 , P-site-1086 , etc . ) being consistently identified as major sites of phosphorylation , and P-site-1101 not seen to be significantly phosphorylated . While the effect of P-site sequence on phosphorylation efficiency has been investigated with synthetic P-site-derived peptide substrates [8] , [9] , the sequence specificity seen in such experiments does not recapitulate the pattern of selectively seen in the actual self-phosphorylation reaction . Thus , whereas a P-site-1086-derived peptide was the poorest peptide substrate among those examined ( P-site-992 , -1068 , -1086 , -1114 , -1148 and -1173 ) in one study ( [8] , see Table 1 ) , P-site-1086 has been characterized as a major site of EGFR self-phosphorylation in vitro and in live cells ( see Table 2 ) . For growth factor receptors in general , selectivity in self-phosphorylation is of major significance with regard to signaling outcome . Thus , various laboratories have shown that mutation of individual P-sites ( typically with tyrosine to phenylalanine substitutions ) in growth factor receptor PTKs can alter the downstream signaling effectors activated and the physiologic response ( e . g . induction of proliferation or cellular transformation in the case of oncogenic growth factor receptors ) [10]–[13] . The EGFR and the other EGFR ( HER/ErbB ) family receptors ( HER2 , HER3 and HER4 ) are particularly plastic with regard to their signaling activities , as in addition to homodimeric receptor complexes , various heterodimeric complexes ( e . g . EGFR/HER3 dimers ) are formed . This dramatically alters signaling outcome because of the variety of P-sites presented and the diversity of downstream signaling effectors recruited by such heterodimeric complexes [14] . The biologic significance of HER/ErbB family receptor signaling specificity is evinced by the ongoing development of cancer therapeutic agents that target individual HER/ErbB family receptors [15] . When considering the dimeric EGFR as a molecular unit , the self-phosphorylation reaction , whether occurring by cis or trans mechanisms , is effectively intramolecular nature . We reasoned that the determinants of P-site usage might , in addition to the usual elements of enzyme-substrate recognition , also include topologic/configurational factors that influence the access of individual P-sites to the active site . Thus , because multiple P-sites within the lengthy CT domain of the EGFR can be phosphorylated and one CT domain can indeed be multiply phosphorylated [16] , there must be a large range of CT domain conformations sampled over the time course of self-phosphorylation ( seconds to minutes ) . How such conformational sampling contributes to the efficiency with which individual P-sites are phosphorylated has not to our knowledge been examined in the case of any self-phosphorylating protein kinase . We describe herein the development of a computational coarse-grained simulation method for modeling the EGFR self-phosphorylation reaction and its application in evaluating how the combination of enzyme-substrate recognition elements and the sampling of CT domain conformations effects selectivity in P-site phosphorylation .
For the purpose of simulating the EGFR self-phosphorylation reaction , we first created a complete structural model of the 1 , 186-amino acid EGFR holoreceptor ( i . e . with full-length CT domain ) in the active dimeric form ( see Fig . 1 ) . This structure was built up largely from the known structure of the EGFR extracellular domain with bound EGF in a 2∶2 “dimeric” complex ( 3NJP ) [17] and an asymmetric PTK domain dimer structure formed from individual active ( 2GS6 ) and inactive ( 2GS7 ) conformation kinase structures [4] ( see Materials and Methods ) . Sequences of unknown structure , including the two extracellular and two intracellular JM domain sequences of the dimeric molecule and the 219- and 227-amino acid CT domains of the receiver and activator , respectively , monomers , were structurally modeled using an in-house algorithm in the case of N- and C-terminal extensions and the Loopy algorithm [18] in the case of connecting segments . The active sites of each PTK domain in our model were occupied with an AMPPNP substrate analog and two Mg2+ cations ( i . e . an AMPPNP . 2Mg2+ substrate complex ) ( see Fig . 2 ) . Although there have been exciting recent applications of all-atom molecular dynamics methods in investigating various structural transitions associated with EGFR function ( see Discussion ) , the large number of atoms in our dimeric EGFR structure and the presumed long time scale of receptor CT domain self-phosphorylation events precluded their simulation by all-atom explicit solvent molecular dynamics . Instead , we chose to simulate the EGFR self-phosphorylation reaction with a coarse-grained model of the dimeric EGFR and an implicit solvent Langevin dynamics method [19] . In this coarse-grained model and the associated energetic model , amino-acid residues were represented as pseudo-atoms centered upon their Cα atoms and physical interactions between pseudo-atoms were treated using the approach of Gō [20] , much as implemented by others in studies of protein folding [21]–[25] . Thus , non-electrostatic interactions between non-bonded pseudo-atoms pairs were either short-range attractive for those representing native contacts ( i . e . residues pairs interacting in EGFR elements of known structure ) or strictly repulsive for all others ( see Materials and Methods ) . This treatment of pseudo-atom interactions ensures that the native folds of structured protein domains remain stable in dynamic simulations . With the exception of residues in the CT domain P-sites , residues in modeled structural segments were not considered to make native contacts . By separately docking each of the eight nine-amino acid P-site sequences ( see Table 1 ) in the active site of the active conformation PTK domain structure ( see Fig . 2 ) , we identified a set of “native” contacts characterizing each P-site/active site interaction , with identical contacts used to characterize the active site interactions of the two copies of each P-site present in the dimeric receptor and the potential cis or trans interaction of each P-site with the active site of either the receiver molecule ( referred to hereon as the catalytic site ) or the activator molecule ( see Materials and Methods ) . We envisioned that the inclusion in the energetic model of short-range attractive potentials corresponding to the identified P-site/active site native contacts would allow the formation of stable P-site/catalytic site interactions in our simulations that would mimic those presumed necessary in the course of actual self-phosphorylation events . A principal goal of our study was to assess the relatively frequency with which each of the sixteen P-site elements in the active dimeric EGFR structure interacted with the catalytic site . We reasoned that this might be dependent upon the initial conformations of the CT domains in our model . To avoid biasing our results by use of a single EGFR model with CT domains of arbitrary conformation , we first generated a large set of dimeric EGFR structures with randomized CT domain conformations . This was done by removing the CT domains from our initial EGFR structural model and repeating the CT domain modeling ( see Materials and Methods ) , to generate a total of five EGFR models with distinct randomly generated CT domain conformations . These five models were then used as initial structures in simulations of 10 µsec duration each , such that by sampling structures at 0 . 1 µsec intervals a total of five hundred independent EGFR structures with “randomized” CT domain conformation were obtained . In the energetic model used for these simulations , we removed all short-range attractive potential terms involving P-site residues , which precluded the occurrence of any stable P-site/active site interactions . The apparent randomization of CT domain conformations in the generated structures is exemplified in Fig . 3 . An analysis indicated that the diversity of structures sampled from these trajectories of EGFR motion was sufficient to obviate any potential influence of the choice of initial structures upon the frequencies of P-site binding events in subsequent simulations ( see Supporting Information , Fig . S1 ) . Given this large set of dimeric EGFR structures with randomized CT domain conformations , we performed repeated simulations to assess the relative frequency with which each of the sixteen CT domain P-sites interacted with the catalytic site in a manner consistent with the occurrence of a self-phosphorylation reaction . Thus , in successive simulations , an initial EGFR structure was randomly selected from the set of five hundred , and the motion of the assemblage propagated until a stable P-site/catalytic site interaction occurred ( see Materials and Methods ) , in which case the simulation was terminated , the elapsed time and the identity of P-site encountering the catalytic site recorded , and the process repeated . Running these iterative simulations on five 48-core servers in parallel for 58 days , we accumulated a histogram of the number of catalytic site binding events for each P-site , representing a total of 420 P-site/catalytic site interactions simulated ( see Fig . 4 ) . The average simulated time for a binding event was 0 . 46 µsec , over a total simulated time of 193 µsec . In several of the more protracted simulations , one or more P-site residues bound to the alternate active site of the activator molecule ( events assumed to be nonproductive in terms of P-site phosphorylation ) , which appeared to delay the interaction of P-sites in the same CT domain with the true catalytic site ( see Supporting Information , Videos S1 , S2 , S3 and Fig . S2 ) . Thus , to some extent , the activator molecule competed with the catalytic receiver molecule in P-site binding , a phenomenon potentially relevant in the context of heterodimeric receptors containing the kinase-impaired HER3 protein ( see Discussion ) . Regarding the relatively frequency with which different P-sites interacted with the catalytic site , some general observations can be made . First , the summed frequencies of binding for all P-sites of the receiver molecule ( cis binding events , n = 253 , where n is the number of binding events ) was higher ( p<0 . 0001 ) than that for P-sites of the activator molecule ( trans binding events , n = 167 ) . Interestingly , the frequency of binding for individual P-sites of the receiver molecule ( cis binding events ) was highest ( p<0 . 05 ) for the site most proximal in sequence to the PTK domain ( i . e . P-site-992 , n = 69 ) and lower for more distal sites ( e . g . P-site-1114 and -1148 each had 23 cis binding events each ) . With two exceptions , the frequencies of binding for differing P-sites of the activator molecule ( trans binding events ) mirrored those of the receiver molecule ( cis binding events ) , ranging from 17 to 32 events . The exceptions here were that P-site-1086 of the activator molecule had a lower number of binding encounters ( n = 17 ) than that of the receiver molecule ( n = 25 ) ( although this was not statistically significant ) and that P-site-992 of the activator molecule had no encounters during the simulations . The discrepancy between the frequencies of cis versus trans binding for P-site-992 was responsible for the overall bias in favor of cis binding events , as when P-site-992 was ignored in the analysis , the numbers for cis ( n = 184 ) versus trans ( n = 167 ) binding events were not statistically different . Also , because the unusually high number of P-site-992 cis binding events was negated by a lack of P-site-992 trans binding events , the summed frequencies of cis and trans binding events for each P-site ( see Fig . 4 ) , which should be related to their relative propensities for phosphorylation as determined experimentally , were less variable overall ranging from n = 42 for P-site-1086 to n = 69 for P-site-992 . The relatively low frequency of simulated P-site-1086 binding events is consistent with a previous steady state kinetics investigation of the phosphorylation by the EGFR of synthetic peptide substrates representing individual EGFR P-sites , wherein a P-site-1086-based peptide was found to be the poorest substrate among those examined [8] ( see Table 1 ) . However , P-site-1086 has been identified in various biochemical experiments as either a major or minor site of EGFR self-phosphorylation ( see Table 2 and Discussion ) . The relative frequency of catalytic site interactions for an individual P-site might be dependent upon topologic factors ( e . g . whether its access to the catalytic site is dependent upon its presence in the CT domain of the receiver versus the activator molecule or its location in the CT domain sequence being more proximal versus distal to the PTK core ) . Thus , some P-sites in trajectories of EGFR molecular motion might make more frequent excursions near the catalytic site , independent of conventional enzyme/substrate recognition phenomena , and for this reason be more efficiently phosphorylated . To assess how topologic factors might alter the frequency of catalytic site interactions of individual P-sites , we examined the set of five hundred EGFR structures taken from five simulated trajectories of EGFR motion ( derived using a model without P-site/catalytic site native contacts ) to ascertain the frequency with which individual P-sites were in proximity of the catalytic site ( see Materials and Methods ) . The graphics of Fig . 5 show those P-sites in five hundred EGFR structures that were located within a radius of 40 Å from the γ-phosphate of the AMPPMP substrate in the catalytic site , the numbers of which are quantified in Fig . 6 . Focusing on P-site-922 ( Figs . 5B and 6 ) , it appears that topologic factors potentially had a dramatic influence upon the relative frequency of catalytic site interactions for some P-sites , with P-site-992 of the receiver molecule found much more frequently within the vicinity of the catalytic site ( a cis interaction ) than was P-site-992 of the activator molecule ( a trans interaction ) . However , the relative frequencies of catalytic site interactions of individual P-sites did not seem to be entirely determined by topologic factors . Thus , P-site-1086 had the lowest frequency of catalytic site binding events ( see Fig . 4 ) , although its frequency in the vicinity of the catalytic site was higher than that of four other P-sites ( Fig . 6 ) . Regarding the discrepancy in observed binding events for P-site-992 of the receiver ( 69 of 420 total events ) versus activator ( 0 of 420 total events ) molecule , we considered that it might be physically impossible for P-site 992 to make an in trans interaction with the catalytic site . This issue was explored by examining the conformations of the two CT domains in each of 650 structures sampled from trajectories of EGFR motion to identify those structures in which P-site Tyr-992 of either the receiver ( Fig . 7A ) or activator ( Fig . 7B ) molecule were within 30 Å of catalytic Asp-813 of the receiver . A marked discrepancy in excursions near the catalytic site was evident . The range of P-site-992/catalytic site distances sampled during long trajectories of EGFR structural randomization is shown in Fig . 7C , which indicates that P-site-992 of the activator molecule ( P-site-992B ) was rarely in close proximity to the catalytic site . Thus , it appeared possible that the 24-amino acid sequence connecting Tyr-992 to the PTK domain was not of sufficient length to allow its interaction in trans with the catalytic site . While such an interaction might be physically possible ( indeed a small number of such trans P-site-992/catalytic site encounters were observed in simulations with no CT domain electrostatic interactions as described below ) , it seemed that the lack of occurrences of in trans P-site-992/catalytic site binding events in our simulations ( see Fig . 4 ) was due to the topologic arrangement of the kinase-proximal CT domains in the asymmetric dimer structure ( see discussion ) . In contrast to the apparent inability of P-site-992 of the activator molecule to interact in trans with the catalytic site , P-site-992 of the receiver molecule had the most catalytic site binding events of any P-site . In our randomization of the EGFR CT domain structure , P-site-992 of the receiver molecule ( P-site-992A ) made very frequent excursions near the catalytic site ( see Figs . 7A and C ) . Considering again that the propensity for this cis P-site-992/catalytic site interaction might reflect topological factors , especially its location in sequence being most proximal to the kinase core of the receiver molecule , we modeled the conformation of the CT domain segment connecting an active site-docked P-site-992 to the kinase core ( see Fig . 8A ) . This modeling indicated that the connecting sequence was of a seemingly ideal length to allow a cis interaction of P-site-992 with the catalytic site . To our surprise , we observed that the stretch of residues 968 to 987 in the connecting CT domain sequence was threaded through a cleft in the PTK domain structure formed by an opening between the N- and C-terminal lobes of the kinase . To explore the possibility that this cleft served to enhance the access in cis of P-site-922 to the catalytic site , we examined a set of structures randomly selected from those representing P-site-922 binding events that occurred in our simulations ( see Fig . 8B ) . Likely because of its limited length , the 24-residue CT domain sequence joining P-site Tyr-992 to the kinase core was again found threaded through the same peptide substrate binding cleft identified in our modeled structures . We also noted that the docking of P-site-992 in the active site was attended by favorable electrostatic interactions between Asp-988 and Glu-991 of the P-site sequence and Arg-779 and Arg-817 , respectively , in the active site . The favorability of negative charges in the N-1 and N-4 positions of P-site sequences in terms of their activity as PTK substrates has been previously noted [26] . Thus , it appeared possible that the high frequency of P-site-992 interactions with the catalytic site in cis reflected both its favorable positioning in the CT domain sequence and the favorable charge interactions of P-site-992 residues with the catalytic site . Interestingly , among the three other HER/ErbB family receptors , HER2 and HER4 each possess a candidate P-site homologous to P-site-922 with acidic residues in the N-1 and N-4 positions , despite their CT domains being otherwise highly divergent in sequence ( see Table 3 and Discussion ) . To examine the role that electrostatic interactions might play in facilitating or inhibiting the interaction of individual P-sites with the catalytic site , we performed further simulations using an EGFR model in which the charges on all pseudo-atoms representing CT domain residues ( residues 968–1186 and 960–1186 of the receiver and activator molecules , respectively ) were set to zero ( see Fig . 9 ) . This EGFR model did however include native contacts and associated short-range attractive potentials to stabilize P-site/catalytic site interactions . In agreement with our first simulations with a model including CT domain electrostatic interactions ( compare Fig . 4 ) , the P-site most proximal to the kinase core , P-site-992 , interacted significantly more frequently ( p<0 . 05 ) with the active site than did other more distal P-sites , and this site interacted almost exclusively in cis . This indicated that the high frequency of P-site-992 interactions observed in our first simulations was largely attributed to topological factors that much facilitated the in cis interaction of P-site-992 with the active site , including the short length of the sequence between the catalytic domain and P-site-992 and the substrate binding cleft we identified above ( see Fig . 8 ) . Hence , although the catalytic site interaction of P-site-992 involved residues of complementary charge ( see above ) , the inclusion of electrostatic interactions in our initial simulations ( compare Figs . 4 and 9 ) if anything negated the positive influence that topologic factors imparted upon the frequency of P-site-992 catalytic site binding . This possibly reflected an unfavorable electrostatic interaction of the CT domain sequence elements ( residues 968 to 987 , see Table 3 ) with the peptide substrate binding cleft that otherwise facilitates P-site-992 binding in cis ( see Fig . 8 and Discussion ) . The relatively low frequency of P-site-1086 binding events seen in our original simulations with CT domain electrostatic interactions included was recapitulated in those in which CT domain charges were removed , suggesting that for this P-site electrostatic interactions between the CT domain and the kinase core did not greatly affect its potential interactions with the catalytic site . An as yet unexplained observation was a statistically significant bias ( p<0 . 05 ) in favor of trans ( n = 32 ) versus cis ( n = 8 ) binding events for P-site-1148 in simulations with no CT domain electrostatic interactions that was not evident in our initial simulations . The simulations described above were designed to model the first P-site binding event occurring upon receptor activation , such as would be reflected in an analysis of EGFR self-phosphorylation under initial velocity conditions . Given the possibility that multiple CT domain phosphorylations ( phosphorylation stoichiometries exceeding one ) might occur in vivo , we considered that the pattern of P-site phosphorylation subsequent to an initial phosphorylation event might differ significantly from that of the first . Hence , we simulated P-site binding events subsequent to a first event that resulted in the phosphorylation of a single P-site , using the initial phosphorylation of P-site-992A of the receiver monomer ( the most frequently bound P-site ) as an example . The simulation method applied above was modified so that iterative simulations were initiated with structures randomly chosen from a set representing 59 different P-site-992A binding events that occurred in our initial P-site binding simulations ( see Fig . 8B ) . Also , in these simulations a formal negative charge was given to the pseudo-atom representing Tyr-992 of the receiver molecule to mimic its phosphorylation and all native contacts and corresponding short-range attractive potentials were eliminated for the nine residues of P-site-992A to eliminate its binding interaction with the catalytic site . Thus , each simulation began with the release of “phosphorylated” P-site-992A from the catalytic site , and continued until a second P-site was bound . In the results of these second P-site binding event simulations ( see Fig . 10 ) , we first observed there was a significant bias in favor of cis ( Receiver , n = 138 ) versus trans ( Activator , n = 54 ) binding events ( p<0 . 05 ) . This is noteworthy in that in our earlier simulations ( Fig . 4 ) there was no significant cis versus trans bias except in the case of P-site-992 , whose binding in cis was precluded in second binding event simulations by the mimicking of its phosphorylation in the receiver molecule . Here , a cis versus trans bias was seen in the case of the more kinase proximal P-sites ( P-sites-1045 and -1068 , p<0 . 05 ) and was most obvious in the case of P-site-1045 , with the binding in cis of P-site-1045 being the most frequent binding event overall ( n = 57 ) and its binding in trans occurring much less frequently ( n = 4 ) . Our simulation results , appeared to reflect a potential processivity in EGFR phosphorylation , i . e . the phosphorylation of one P-site ( here P-site-992 of the receiver ) greatly enhanced the likelihood that a neighboring P-site ( P-site-1045 of the receiver ) would be bound to the catalytic site and in turn phosphorylated ( see Discussion ) . An enhanced propensity for the sequential binding of neighboring P-sites was also suggested by an analysis of non-productive P-site binding events ( see Supporting Information , Fig . S2 ) .
The mechanism and P-site selectivity of EGFR PTK multi-site self-phosphorylation was investigated herein through molecular simulations employing a coarse-grained model of the active dimeric EGFR . The all-atom structural model from which this coarse-grained model was derived was unique in representing a dimer of EGFR molecules with bound nucleotide substrates and full-length CT domains , with the 3-D structure of the latter not having been experimentally determined . Whereas several all-atom molecular dynamics and targeted molecular dynamics simulations of the EGFR kinase domain have been published , most were limited to investigating the dynamics of the kinase core either in isolation or in a dimeric form , and focused upon the transition between its inactive and active conformations [27]–[30] or the impact of oncogenic mutations on kinase inhibitor binding [31] . The structural models examined in these investigations included at most only a small segment of the CT domain sequence and shed little light upon the mechanism of self-phosphorylation . The all-atom molecular dynamics study of Mustafa et al . [32] investigated a kinase domain model with a significant segment ( residues 951 to 994 ) of the CT domain included . The structure of the CT domain segment in this model was based on two partial CT domain structures seen to be ordered in different crystal structures of the kinase , although the connectivity of these structured CT domain segments with a single kinase molecule in the unit cell appears subject to uncertainty [6] . The study examined the coordinated movements of the CT and JM domains over a period of ∼10 nsec , and although the structure simulated included P-site-992 , there was no movement of the CT domain on this time scale that approximated a self-phosphorylation event . We envisioned that simulating multiple self-phosphorylation reactions involving P-sites distributed throughout the full-length CT domains of the dimeric EGFR would require a much longer total time of simulation , which because of the large number of atomic coordinates involved would be intractable to standard all-atom molecular dynamics approaches . While Arkhipov et al . [33] , employing a unique special-purpose supercomputer , were able to perform all-atom simulations of a near complete dimeric EGFR structure of ∼5 µsec in duration , the structural model used in this investigation included no CT domain sequence elements and thus the self-phosphorylation reaction was again not illuminated . In this work , using a coarse-grained model of the full-length dimeric EGFR , we were able to observe over 1000 P-site binding events occurring over a total simulated time approaching 200 µsec ( Figs . 4 , 9 and 10 ) , in a set of simulations that even with a coarse-grained model required nearly 150 days of computation . The numbers of catalytic site encounters for the various P-sites of the EGFR CT domain occurring in our simulations should be related to the relative levels of P-site phosphorylation seen in biochemical experiments . We recognize that the frequencies of P-site binding seen in our simulations would not be exact predictors of the relative rates of P-site self-phosphorylation , given that they relate only to the substrate binding event and do not take into account the rate of the subsequent catalytic phospho-transfer reaction ( kcat ) , which is presumably dependent upon the identity of the bound P-site . How the rates of phosphorylation of different P-sites sequences when bound in the catalytic site vary should be reflected in the steady state kinetics parameters reported by Fan et al . [8] , who evaluated both KM and kcat for several 17-amino acid P-site-derived peptide substrates of the EGFR kinase ( see Table 1 ) . We propose that the relative rates of P-site self-phosphorylation ( vphos ) as would be determined biochemically can be predicted from the results of our P-site binding simulations by use of the relationwhere k′intra is the relative frequency of binding site interactions for a given P-site as determined in our simulations performed without CT domain electrostatic interactions and kcat and KM are the catalytic constants for the phosphorylation of the corresponding P-site-derived peptide [8] ( see Supporting Information , Text S1 ) . Predicted values of vphos for those P-sites for which the steady state kinetic parameters of the corresponding peptides are available are given in Table 1 , along with a qualitative characterization of the P-sites as major or minor sites of phosphorylation as based on a survey of the literature . P-sites-992 , -1068 , -1148 and -1173 had the highest vphos values , and thus would be predicted to be efficiently phosphorylated , in agreement with the latter three being identified as major sites of phosphorylation . The low value of vphos for P-site-1086 , in part a reflection of its very low kcat/KM ratio , is not consistent with its prior identification as a major site of phosphorylation . Conversely , while this analysis predicts that P-site-992 would be the most efficiently phosphorylated , it has not been considered a major site of EGFR phosphorylation . It should be recognized that our characterization of major and minor sites of EGFR phosphorylation was based upon the mainly qualitative biochemical analyses summarized in Table 2 , including early phosphorylation site peptide mapping studies . While quantitative analyses have been more recently performed [34] , [35] , these have investigated EGFR sites phosphorylated in the context of live cells , wherein the levels of P-site phosphorylation are almost certainly modulated by the presence of P-site-binding proteins and protein phosphatases . Indeed , P-site-992 of the EGFR is an optimal substrate for the protein tyrosine phosphatase PTP-1B [36] , which has been shown to dephosphorylate the EGFR in the cellular context [37] . Nonetheless , one quantitative study did identify P-site-992 as a major site of EGFR phosphorylation [34] . While the frequencies of P-site binding observed in our studies are thus generally consistent with available phosphorylation data , a more extensive analysis of the predictive capacity of our simulation strategy will require a quantitative analysis of in vitro EGFR phosphorylation under controlled conditions , experimentation that we are now initiating . Awaiting a quantitative assessment of the results of our simulations , we note several intriguing particulars therein . Whereas there was overall a statistically significant increased propensity for cis versus trans phosphorylation , there was no significant difference when P-site-992 binding events were excluded from the analysis . Thus , while it is generally assumed that self-phosphorylation of the EGFR and other receptor PTKs occurs in trans , a belief promulgated by the observation that EGFR dimerization is required for PTK activation ( e . g . [38] ) and direct demonstrations of the phosphorylation of kinase-deficient PTK mutants by their wild-type counterparts ( e . g . [39] , see also [40] ) , our results indicate that cis and trans phosphorylation , at least in the case of the EGFR , should occur with a similar efficiency . This issue is particularly relevant in the context of the larger HER/ErbB family of receptors , in which the kinase-impaired HER3 receptor functions only upon its heterodimerization with the kinase-active EGFR , HER2 or HER4 receptor . Thus , while HER3 must in an EGFR/HER3 or HER2/HER3 heterodimer be phosphorylated primarily in trans [41] , its partner EGFR or HER2 might still be phosphorylated in cis in such heterodimers , adding to the diversity of their downstream signaling . It should be noted that although HER2 is known to be phosphorylated in the context of HER2/HER3 heterodimers , this has also been attributed to the formation of higher-order receptor oligomers [42] , [43] . On the other hand , the observation that P-site-992 interacted with the catalytic site exclusively in cis ( see Fig . 4 ) is particularly provocative . First , this observation highlights the potential importance of topologic factors on EGFR signaling functions , such as would be difficult to investigate by methodologies other than that described herein . Second , this finding is again relevant in the context of heterodimeric HER/ErbB family receptors . With the kinase-proximal CT domain sequence up to and including P-site-992 being strongly conserved in the EGFR , HER2 and HER4 receptors ( see Table 3 ) , the possibility that the homologous P-sites within HER2 and HER4 are phosphorylated exclusively in cis appears strong . It is noteworthy that P-site-992 is not conserved in the HER3 C-terminal sequence . As HER3 possesses an impaired intrinsic kinase activity , this P-site if present would likely not be phosphorylated even in the context of heterodimeric receptors , and hence could have no canonical signaling function . This might explain its loss with the divergent evolution of HER/ErbB family members . Numerous related questions about receptor phosphorylation occurring in the context of homo- and hetero-dimeric HER/ErbB family receptors could be addressed by the simulation methodology described herein . For example , a recent study indicates that significant differences in EGFR phosphorylation site usage might underlie the higher potential for cancerous transformation of EGFR/HER2 heterodimers versus EGFR homodimers [44] . Are such differences in phosphorylation due to differing catalytic site selectivities among HER family receptors , or might they be attributed to topological factors that would differently impact self-phosphorylation occurring in hetero- versus homo-dimeric receptor forms ? Our discovery that the in cis phosphorylation of P-site-922 of the receiver monomer is facilitated by the presence of a cleft between the N-terminal and C-terminal lobes of the PTK domain ( see Fig . 8 ) also suggests that the short CT-domain sequence appending P-site-922 to the kinase core is an element of a conserved mechanism for efficient intramolecular self-phosphorylation . While the presence of this cleft enables the in cis interaction of P-site-992 with the catalytic site , this does not necessarily involve an intimate binding interaction between the kinase-proximal CT domain sequence and PTK domain residues within the cleft . Thus , the threading of this acidic CT domain sequence through the cleft appears not to be stabilized by electrostatic interactions with complementary charged residues therein , consistent with the in cis catalytic site interactions of P-site-992 being markedly enhanced in those simulations in which CT domain charges were removed from the model ( compare Figs . 4 and 9 ) . Possibly , the P-site-992-catalytic site interaction facilitated by the cleft is necessarily transient in nature , to allow the phosphorylation of substrates other than P-site-992 . This might explain why the kinase-proximal CT domain sequence is not seen to be localized within this cleft in any available crystallographic structure of the PTK domain . We note that P-site-992 is of particular biologic significance and the subject of much study . P-site-992 is involved in intracellular calcium signaling [45] and the induction of membrane ruffling in response to EGF [46] ( apparently via its recruitment of signaling effectors including phospholipase C and Src ) , and is a negative regulator of the transforming activity of the EGFR-derived v-ErbB oncogene in vivo [47] . That phosphorylated P-site-992 is an optimal substrate for the protein tyrosine phosphatase PTP-1B [36] , [37] suggests that the control of its phosphorylation level in cells is crucial . Related to the issue of cis versus trans P-site-catalytic site interactions is our observation in some simulations that a productive interaction of a P-site with the catalytic site in the receiver monomer was delayed by a nonproductive interaction of a P-site ( s ) with the active site of the activator ( see Fig . S2 and Videos S1 , S2 , S3 in Supporting Information ) . Thus , if a P-site of either CT domain interacted with the active site of the activator ( located on the face of the PTK domain dimer opposing that of the catalytic site ) , it tended to preclude the interaction of other P-sites in the same CT domain with the true catalytic site . In some cases , a productive P-site/catalytic site binding event was preceded by several nonproductive P-site binding events involving the active site of the activator molecule ( see Supporting Information , Fig . S2 ) . How significantly such competition between alternative active sites impacted the observed frequencies of catalytic site binding events was not examined , and appears to be an unexplored issue regarding the asymmetric dimer allosteric activation mechanism [4] . Such active site competition might be particularly relevant in the case of HER3-containing receptor heterodimers in which the HER3 receptor would provide little if any intrinsic kinase activity but would provide an active site capable of binding ATP [48] and likely also the P-site substrates in the CT domain of its heterodimeric receptor partner . We here also investigated the phenomenon of progressive multi-site self-phosphorylation of the EGFR ( see Fig . 10 ) . In examining the effects of phosphorylation of one P-site on subsequent P-site binding events , we found that mimicking the phosphorylation of one P-site ( specifically P-site-992 of the receiver molecule ) dramatically enhanced the frequency of binding of a P-site adjacent in sequence ( specifically P-site-1045 of the receiver ) . The physical bases for such apparent processivity in phosphorylation could include the fact that the binding of one P-site to the catalytic site places those P-sites closest in sequence within a smaller distance of the catalytic site and , as we observed in several simulations , the possibility that the exchange of one catalytic site-bound P-site for another can occur by a sliding or translation of the CT domain through the catalytic cleft or by a physical displacement of one bound P-site by another adjacent in sequence ( see Supporting Information , Fig . S2 and Video S3 ) . Evaluating the impact of accumulated P-site phosphorylation on subsequent P-site phosphorylation events , such that one could predict the extent to which each P-site is phosphorylated when phosphorylation stoichiometries are high , would require a much longer series of simulations in which the step-wise phosphorylation of several P-sites was modeled . Because such accumulated P-site phosphorylation in the live cell would ultimately be opposed by the action of protein phosphatases and modulated by P-site binding proteins , the results of such modeling might not be predictive of the levels of P-site phosphorylation seen in analyses of EGFR phosphorylation in cultured cells or tissues . Although our coarse-grained modeling of the EGFR self-phosphorylation reaction was necessitated by the large numbers of atoms involved and the limitations of the computational machinery available , we must recognize some limitations of our simulations . In the model employed , amino acid residues were represented by pseudo-atoms centered on the Cα atoms of the receptor polypeptides , and physical interactions between residues , including P-site/active site interactions , were treated with short-range attractive potentials between pseudo-atoms representing native contacts ( residues pairs in close proximity in the original EGFR structures or in our active site-docked P-site structures ) and with or without classical electrostatic interactions in the case of formally charged residues . Obviously , this model might not accurately recapitulate the atomistic steric interactions involved in active site substrate recognition and only approximates the sampling of conformational states of the CT domain polypeptide that occurs in the course of its movement . In terms of the former issue , we attempted to reintroduce those elements of substrate recognition lost in our modeling and the P-site-dependence of the rate of the catalytic phospho-transfer reaction by a simple theoretical treatment that takes advantage of published steady state catalytic constants for P-site phosphorylation . Regarding the latter issue , we must concede that the CT domain conformational sampling occurring in our simulations is an approximation of that occurring in reality , and the conclusions we make concerning the relative propensities of individual P-sites for active site binding are therefore tentative . We do consider that our simulations have identified possible new paradigms with regard to EGFR/HER family receptor signaling that might not otherwise have been discovered . These include the possibilities that cis and trans self-phosphorylation within dimeric receptors might occur with similar efficiencies and that the in cis interaction of P-site-992 with the catalytic site might be facilitated by a substrate binding cleft and could represent a unique evolutionarily conserved mechanism for the self-phosphorylation of this crucial regulatory P-site . In summary , the investigation by simulation of the first step of the EGFR self-phosphorylation reaction we present here provides significant new insights into the mechanism of self-phosphorylation , and raises several intriguing questions about this mechanism in the contexts of the larger HER/ErbB receptor family and growth factor receptor PTKs in general . Issues that remain to be explored include the selectivity of P-site phosphorylation in heterodimeric receptors , such as in the highly transforming HER2/HER3 coreceptor combination that drives breast cancer progression [49] , or how the binding of signaling effectors to phosphorylated P-sites as occurs in the cellular context would alter subsequent P-site phosphorylation . Such questions could be address by straightforward applications of the simulation methodology described herein . In predicting P-site phosphorylation rates from the results of our P-site-catalytic site binding simulations , the known catalytic efficiencies of P-site-derived peptides as derived from steady state kinetic studies were used to introduce elements of atomic-level molecular recognition that were lost in the coarse-grained modeling of EGFR structure . Further methodological developments might include a hybridized procedure in which large-scale movements of EGFR structural domains are treated by a coarse-grained approach and those associated with substrate recognition and catalysis by more refined atomistic methods .
A structure of the full-length active dimeric EGFR was created from known structures of the dimeric extracellular domain ( 3NJP ) [17] and individual active ( 2GS6 ) and inactive ( 2GS7 ) conformation PTK domain structures [4] together assembled in an asymmetric dimer structure . ( We use herein the amino acid numbering of residues in the mature EGFR molecule , i . e . not counting residues in the 24-residue signal peptide . ) It is important to note that while the asymmetric PTK dimer model is reasonably well established , there exists no crystallographic structure for the asymmetric dimer , as the kinase domain apparently crystallizes in lattices of exclusively active or inactive conformation molecules [4] . Adjacent PTK domains in the active conformation crystal lattices do assume “receiver” and “activator” orientations , which led to the asymmetric PTK dimer model . Thus , in this work , the apposition of active ( receiver ) and inactive ( activator ) conformation PTK domains in an asymmetric dimer structure was effected by superpositioning an inactive conformation kinase structure ( 2GS7 , residues 679 to 959 ) upon the activator-orientated kinase ( residue 669 to 967 ) in a kinase dimer generated from the active-conformation crystal structure 2GS6 by a symmetry operation . In this superpositioning , performed with the aid of the Swiss PBD program [50] , only those residues in the C-terminal lobe of the activator kinase near its interface with the N-terminal lobe of the receiver kinase ( residues 904 to 955 ) were aligned . Next , because a related EGFR kinase domain structure ( 3GOP ) from an active conformation-like lattice showed an additional structured portion of the intracellular JM sequence that was demonstrated to be important in stabilizing the asymmetric dimer [3] , [51] , the structure of this so-called “juxtamembrane latch” in 3GOP ( residues 655 to 676 ) was appended to the structure of the active conformation kinase ( residues 677 to 967 ) in our asymmetric dimer structure , after superpositioning of a common structural element of both structures ( residues 675 to 693 ) . Then , as residues 655 to 664 of the JM sequence are believed to assume an α-helical conformation in both activator and receiver kinases [3] but are not included in the inactive conformation kinase structure ( 2GS7 ) used for the activator in our symmetric dimer model , we positioned a copy of this short helical structure from 3GOP near the activator kinase N-terminus , such that it might be joined to the activator kinase by structural modeling of the intervening residues 665 to 678 ( see below ) . Because we wished to have a complete EGFR dimer model with structurally identical extracellular ligand binding domains , versus the nearly identical extracellular domains seen in the crystal structure 3NJP , we superposed a copy of molecule A of the 3NJP dimer structure upon molecule B in the original structure by aligning selected residues in the nearly symmetric contact interface between molecules A and B ( residues 191 to 308 and 573 to 614 ) . Thus , we constructed a nearly symmetrical EGFR extracellular domain dimer composed of structurally identical extracellular domains ( residues 1 to 614 ) each with a bound EGF molecule , specifically residues 5 to 51 of the 53-residue EGF sequence that are ordered in the structure 3NJP . Subsequently , two transmembrane domain structures ( residues 622–644 ) were modeled as α-helices by use of Swiss PDB , and remaining structural elements of the dimeric EGFR holoreceptor were modeled as follows . The several structural elements described above [a dimer of identical EGFR extracellular domains ( residues 1 to 614 ) with bound EGF molecules , two identical transmembrane helical segments ( residues 622–644 ) , an asymmetric dimer of active ( residues 655 to 967 ) and inactive ( residues 679 to 959 ) conformation PTK domains , and a short JM domain helical segment ( residues 655 to 664 ) that would be included in the modeled JM domain of the inactive conformation activator molecule] were manually reoriented with respect to each other in the Swiss PDB viewer such that they might be connected by modeling the missing structural elements , specifically residues 615 to 621 of the two extracellular JM domains , residues 645 to 654 of the intracellular JM sequence proximal to the active conformation kinase domain , residues 645 to 654 and 665 to 678 of the intracellular JM sequence that connect the TM domain , JM domain helical segment , and inactive conformation kinase structures , and lastly , the two CT domains of the active ( residues 968 to 1186 ) and inactive ( 960 to 1186 ) conformation kinases . ( The few disordered residues at the extreme N- and C-termini of the bound EGF molecules were also modeled , as were short disordered sequences within each of the PTK domain structures . ) The several segments connecting known structures were structurally modeled with the Loopy algorithm [18] . The various N- and C-terminal sequence extensions were modeled with an in-house program that sequentially builds up such structures by replacing the terminal amino acid residue of the chain being extended with a dipeptide structure of the appropriate sequence that is randomly chosen from a library of allowable dipeptide structures ( all those found in the PBD structure database ) , aligns the backbone conformation of the first dipeptide residue with that of the terminal amino acid replaced , and checks that the structure of neither dipeptide residue clashes with other structural elements . The result was a structural model of a dimer of full-length EGFR molecules , each with bound EGF and an ∼225 amino acid CT domain in a random conformation ( Fig . 1 ) . As no available active conformation EGFR kinase structure contained a nucleotide substrate complex of ATP ( or the analog AMPPNP ) and two bound Mg2+ cations , as would be presumed to be the relevant substrate for the self-phosphorylation reaction , we docked the nucleotide substrate complex ( AMPPNP with two chelated Mg2+ cations ) from a structure of the active insulin receptor PTK domain with bound nucleotide and peptide substrates ( 1IR3 ) [52] into the active site of each kinase monomer . In the case of the active conformation receiver kinase , this was done by aligning the 1IR3 and 2GS6-derived kinase domain structures , which placed the 1IR3 nucleotide substrate complex into the EGFR active site without clashes and positioned appropriately with respect to critical catalytic residues ( Fig . 2 ) . In the case of the inactive conformation activator kinase , the conformation of which was significantly different from that of 1IR3 , we aligned the AMPPNP of the bound AMPPNP . 2Mg2+ complex in 1IR3 with the AMPPNP in the 2GS7-derived structure , which again appropriately placed the substrate complex in the active site . Each CT domain in the active receptor dimer contains eight candidate P-sites that potentially interact with the active site of the receiver molecule ( referred to herein as the catalytic site ) and become phosphorylated . Our simulation of the self-phosphorylation reaction therefore required the generation of structural models descriptive of the interaction of each P-site with the EGFR catalytic site . Because in most cases the primary determinants of protein kinase substrate specificity are located within four residues on either side of the target residue [53] and because we intended to monitor when the target tyrosine residue of any one P-site would be bound in the catalytic site and positioned appropriately to promote its phosphorylation , we considered that modeling of the catalytic site interactions of relatively short P-site sequences would be sufficient for our purposes . Thus , we created structural models of individual nine-amino acid P-site peptides ( comprising EGFR residues N-4 to N+4 , where residue N is the P-site tyrosine , see Table 1 ) interacting with the active conformation EGFR kinase . As no suitable structure of the EGFR kinase domain with a bound peptide substrate was available , we exploited the structure of the active insulin receptor kinase with a bound IRS-2-derived peptide substrate ( 3BU3 ) [54] . Upon aligning the insulin receptor kinase domain in this structure with that of the active conformation kinase domain in our dimeric EGFR structure , the nine relevant amino acids of the IRS-2 peptide surrounding its target tyrosine were seen to be well accommodated in the EGFR kinase catalytic cleft , and a hybrid structure of the EGFR PTK domain with a bound IRS-2 peptide could be generated . This structure was then used to model with the Swiss PDB program the conformation and EGFR catalytic site interactions of each of the eight , nine-amino acid P-site peptide sequences of the EGFR CT domain . For the purpose of generating a coarse-grained EGFR structural and energetic model that incorporated physical interactions between the P-site sequences and the EGFR active site ( see below ) , the P-site peptides in each of the eight docked structures were linked to the EGFR kinase sequence by a randomly structured CT domain extension ( residues 968 to 987 of the EGFR ) , generated by application of Loopy [18] ( see Fig . 8 ) . In each model structure , the central tyrosine residue of the P-site peptide substrate assumed a conformation identical to that of the docked IRS-2 peptide , thus with its tyrosine hydroxyl in close proximity of the EGFR catalytic aspartate residue ( Asp-813 ) and the γ-phosphate of the bound AMPPMP substrate ( see Fig . 2 ) . The interaction of individual P-site peptides with the kinase active site involved close interactions with active site residues that appeared to be more electrostatically favorable in the case of some P-site peptides . Given an all-atom model of the dimeric EGFR and eight models of the EGFR kinase domain each with a distinct P-site peptide docked in the active site , a coarse-grained structural and energetic model was generated using the approach of Gō [20] as recently implemented [25] , using software generously made available by Dr . Adrian Elcock ( University of Iowa ) . Briefly , each amino acid residue of the peptide components was represented by a pseudo-atom centered on its Cα atom with bonds formed between pseudo-atoms of adjacent residues . Models of the bound AMPPNP . 2Mg2+ complexes comprised pseudo-atoms centered on atoms C2 , C6 and N9 of the adenine moiety , C4′ of the deoxyribose , P1 , P2 and P3 of the α- , β- , and γ-phosphates , and each of the two Mg atoms , with bonds connecting every pseudo-atom pair . The energetic model was the summation of ( 1 ) standard molecular mechanics potential functions to treat interactions between bonded pseudo-atoms , ( 2 ) Gō-inspired potential functions to model short-range attractive interactions between those non-bonded pseudo-atoms representing native contacts , ( 3 ) short-range repulsive potentials to treat interactions between all other non-bonded pseudo-atoms , and ( 4 ) electrostatic potential terms relating to all charged residue/atom pairs ( cf . [25] ) . The values assigned to the various parameters in the energetic model as indicated below were those shown to be optimal for accurate coarse-grained modeling of protein folding thermodynamics and protein diffusion [25] , [55] . In the energetic model , the interactions between bonded pseudo-atoms were described bywhere r , θ , and φ are the bond distances , bond angles , and bond dihedral angles , respectively , req and θeq are the corresponding bond distances and angles in the initial structure , and φ1 and φ3 are phase angles defining the position of the energy maxima of the cosine terms . ( The multiply interconnected pseudo-atoms of the AMPPNP . 2Mg2+ substrate complexes were assigned only harmonic bond distance and bond angle energy terms . ) The force constants kbond and kangle were set to 20 kcal/mol/Å2 and 10 kcal/mol/rad2 , respectively , and the energy maxima V1 and V3 for the dihedral terms were set to 0 . 5 and 0 . 25 kcal/mol , respectively . The interactions between non-bonded pseudo-atoms ( those separated by three or more residues ) were modeled with additional short-range attractive potential functionsin the cases of those non-bonded pseudo-atoms i and j that represented native contacts . A residue was considered to form a native contact if any of its atoms were within a specified distance cut-off ( here 5 . 5 Å ) of any of another residue in the native structure , with exceptions as described below . Here the energy well depth ε was assigned a value of 0 . 6 kcal/mol , and σij and rij are the distances separating the pseudo-atoms in the native and evolving structures , respectively . Non-bonded pseudo-atoms not representing native contacts were modeled with strictly repulsive terms:with σvdw set here at 4 Å and ε at 0 . 6 kcal/mol . In addition to these bonded and non-bonded energy terms , any pseudo-atom representing a residue ( or atom in the AMPPNP . 2Mg2+ substrate complex ) with a significant partial charge at the simulation pH ( here 7 . 6 ) was assigned that partial charge and allowed to interact with other such pseudo-atoms via additional Debye-Hückel electrostatic potential termswhere qi and qj are the charges on pseudo-atoms i and j , κ is the ionic strength ( set at 0 . 15 M ) , is the solvent dielectric constant ( set at 78 . 4 ) , and rij is again the distance between the pseudoatoms . A membrane in which the dimeric receptor was constrained to diffuse laterally was modeled with two planar barriers normal to the z-axis ( with which the EGFR TM domains were aligned ) and separated by 33 Å , and additional terms in the energetic model that held the TM domain residues between the membrane planes and kept extracellular and intracellular EGFR structural elements from penetrating the membrane . Specifically , each pseudo-atom representing one of the TM residues 622 to 644 was subject to a restraining potentialwhere z0 and z are the initial and evolving , respectively , displacements of the TM pseudo-atom along the z-axis and krestraint is a force constant set at 5 . 0 kcal/mol/Å2 . Also , extracellular pseudo-atoms ( those of the receptor extracellular domains and bound EGF molecules ) were subject to half-harmonic repulsive potential termswhere z is the evolving z-coordinate of the extracellular pseudo-atom , zwall indicates the position ( 16 . 5 Å ) of the extracellular membrane , and kwall is a force constant set again at 5 . 0 kcal/mol/Å2 . Analogous repulsive potential terms applied to intracellular pseudo-atoms ( those of the intracellular domains and the nucleotide substrate complexes ) . The all-atom structural model of the dimeric EGFR from which our coarse-grained model was generated contained elements of known structure derived from published crystal structures , as well as some modeled structural elements ( e . g . sequences in the extracellular and intracellular JM domains and the entirety of the two CT domains ) . Only those pseudo-atoms representing residues in elements of known structure were considered able to form native contacts and thus interact with other such non-bonded pseudo-atoms via short-range attractive versus purely repulsive potentials , which ensured that the folding of the structured domains remained intact during dynamic simulations . There were two exceptions . Firstly , those pseudo-atoms of the active site-bound AMPPNP . 2Mg2+ complexes representing atoms within 5 . 5 Å of any atom of an active site residue in the all-atom model were considered to form native contacts and assigned short-range attractive potential terms corresponding to these interactions . Secondly , those residues of the eight different nine-amino P-site sequences that formed contacts with active site residues ( or atoms of the AMPPNP . 2Mg2+ substrate complex ) when docked in the kinase active site were also considered to make native contacts ( again a 5 . 5 Å cutoff was applied ) . In the case of each P-site peptide , the P-site tyrosine in the docked structure formed nine total native contacts with active site residues ( in addition to three involving the γ-phosphate of the bound AMPPNP substrate ) , and the simultaneous formation of these nine contacts in the course of a simulation was used to define a stable P-site/catalytic site interaction . This was analogous to evaluating the extent of protein folding using a reaction coordinate or order parameter ( e . g . Q ) defined as the fraction of native contacts formed in the evolving system [21] . Given the above coarse-grained structural and energetic model , the motion of the dimeric EGFR structure was propagated using a Langevin dynamics algorithm developed by Winter and Geyer [19] and implemented with software provided by Dr . Adrian Elcock ( University of Iowa ) . Simulations used an integration time step of 125 fsec . A list of nonbonded interactions was computed every 6 psec , short-range interactions ( between pseudo-atoms within 12 . 5 Å ) were computed every time step , and medium-range interactions ( between pseudo-atoms greater than 12 . 5 Å and less than 25 Å apart ) every 1 psec . No cutoff was applied to electrostatic interactions . The diffusion tensor descriptive of hydrodynamic interactions was calculated every 24 psec . Hydrodynamic radii of pseudo-atoms representing amino acid residues were set at 5 . 3 Å and those of the somewhat finer-grained AMPPNP . 2Mg2+ substrate complex model set at 3 . 5 Å . In the course of P-site binding simulations , the number of P-site tyrosine/catalytic site native contacts formed was periodically determined , and the simulation terminated if all of the nine possible contacts were simultaneously formed in the case of any P-site . Simulations were performed in sets of five on separate Dell PowerEdge R815 computers , each with four AMD Opteron 6168 12-core processors . Snapshots of simulations were saved every 100 psec for analysis . A multinomial test was used to determine if differences in the frequencies of P-site binding were statistically significant . In structural modeling , homologous structural elements were aligned using the superpositioning functions of the Swiss PDB program [50] . All molecular structure representations were generated with VMD [56] . | The epidermal growth factor receptor ( EGFR ) is one of a large group of cell surface receptors that allow cells to respond to growth-stimulating signals in their environment . Upon sensing of growth factor , the EGFR is activated , which triggers a signaling cascade leading to the cell nucleus and ultimately initiating cell division . The first event following receptor activation is an intramolecular kinase reaction that results in the introduction of phosphate groups onto several specific amino acids ( phosphorylation sites or P-sites ) in the tail of the EGFR protein . Thus , the tail of the receptor undergoes self-phosphorylation , which involves conformational motions enabling the various P-sites to access the catalytic site . The structure of the tail of the receptor is unknown , and hence the mechanism of the self-phosphorylation reaction is not well understood . To investigate this mechanism , we generated a structural model of the EGFR protein and performed computer simulations of EGFR P-site/catalytic site binding reactions . These simulations indicated how the distribution of P-sites along the tail of the receptor and restrictions in molecular movements of the tail lead to selectivity in the phosphorylation of the different P-sites . Our simulations yielded unique insights into the mechanism of EGFR self-phosphorylation that have important biological implications . | [
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] | [
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] | 2014 | Coarse-Grained Molecular Simulation of Epidermal Growth Factor Receptor Protein Tyrosine Kinase Multi-Site Self-Phosphorylation |
Infection by Zika virus ( ZIKV ) is linked to microcephaly and other neurological disorders , posing a significant health threat . Innate immunity is the first line of defense against invading pathogens , but relatively little is understood regarding host intrinsic mechanisms that guard against ZIKV . Here , we show that host tripartite motif-containing protein 56 ( TRIM56 ) poses a barrier to ZIKV infection in cells of neural , epithelial and fibroblast origins . Overexpression of TRIM56 , but not an E3 ligase-dead mutant or one lacking a short C-terminal portion , inhibited ZIKV RNA replication . Conversely , depletion of TRIM56 increased viral RNA levels . Although the C-terminal region of TRIM56 bears sequence homology to NHL repeat of TRIM-NHL proteins that regulate miRNA activity , knockout of Dicer , which abolishes production of miRNAs , had no demonstrable effect on ZIKV restriction imposed by TRIM56 . Rather , we found that TRIM56 is an RNA-binding protein that associates with ZIKV RNA in infected cells . Moreover , a recombinant TRIM56 fragment comprising the C-terminal 392 residues captured ZIKV RNA in cell-free reactions , indicative of direct interaction . Remarkably , deletion of a short C-terminal tail portion abrogated the TRIM56-ZIKV RNA interaction , concomitant with a loss in antiviral activity . Altogether , our study reveals TRIM56 is an RNA binding protein that acts as a ZIKV restriction factor and provides new insights into the antiviral mechanism by which this E3 ligase tackles flavivirus infections .
Zika virus ( ZIKV ) is a small , enveloped RNA virus classified within the family Flaviviridae , genus flavivirus , which also includes medically important pathogens such as dengue virus ( DENV ) , West Nile virus ( WNV ) , Japanese encephalitis virus ( JEV ) , and yellow fever virus ( YFV ) [1] , among others . As with other flaviviruses , ZIKV possesses a ~11-kb long , single-stranded RNA genome of positive polarity . After infecting susceptible cells , the viral genomic RNA is released into the cytoplasm and subsequently translated to a large polyprotein that is cleaved into three structural proteins ( C , prM/M , and E ) and seven non-structural ( NS ) proteins ( NS1 , NS2A , NS2B , NS3 , NS4A , NS4B , and NS5 ) by a combination of viral and host proteases [2] . While the structural proteins make up the virions , viral NS proteins along with the genomic RNA template assemble into replicase complexes on cytoplasmic membrane structures , synthesizing nascent viral RNAs that are packaged into progeny viruses and released from infected cells to go on infecting naïve cells [2] . The replication and pathogenesis of ZIKV depend upon its intricate interplay with host factors , much of which , however , remains to be elucidated . Based on the phylogeny of viral sequences , ZIKV is classified into two major lineages , African and Asian [3] . First isolated in 1947 in Uganda [4] , ZIKVs of African lineage have rarely been associated with human cases , which typically exhibit an acute febrile illness . However , the outbreaks in the past decade of viruses of Asian lineage , not only greatly outnumbered the human infections known to be caused by the African virus but are notorious for their serious complications . Mounting evidence suggests that infection by ZIKV of Asian lineage is linked to congenital defects including microcephaly and spontaneous abortion [5–7] and to Guillain-Barre syndrome and thrombocytopenia in adults [8 , 9] . Precisely how ZIKV causes these congenital and neurological disorders is unclear , but both viral and host factors have been suggested to play a role [10] . Of note , ZIKV replicates in neural progenitor cells , causing cell cycle arrest and/or death of neurons [10 , 11] . Although mosquito bite is the most common route of ZIKV infection , ZIKV can also spread from person to person via sexual contact or vertically from pregnant woman to fetus [12–15] . Consistent with these epidemiological findings , previous studies have revealed that ZIKV infects human skin cells , placental cells , endometrial stromal cells , etc… [11 , 16–18] . Further studies on tissue tropism of the virus and factors regulating viral replication in and host responses of susceptible cell types are warranted , as they will yield novel insights into mechanisms of ZIKV pathogenesis and may identify therapeutic targets . Host cells are equipped with exquisite , intrinsic mechanisms to fend off invading viruses , which could be harnessed for developing antivirals against flaviviruses , including ZIKV . The mammalian innate immune system constitutes the first line of defense against invading microorganisms including viral pathogens . A hallmark of the intrinsic antiviral responses is the rapid induction of interferons ( IFNs ) . Once produced , IFNs act in a paracrine and/or autocrine fashion , signaling through cell surface IFN receptors and downstream Jak-Stat pathway , culminating in the upregulation of hundreds of IFN-stimulated gene ( ISG ) products that collectively establish an antiviral state , reining in viral replication and spread [19] . Flaviviruses are known to subject to innate immune control and ZIKV is no exception [20] . It has been recently reported that mice lacking Ifnar1 or triply deficient for Irf3/Irf5/Irf7 supported heightened ZIKV replication , concomitant with developing neurological diseases [21] . Of the ~ 300 known ISGs , IFITM1 , IFITM3 and Viperin have been shown to inhibit ZIKV [22 , 23] . However , much remains to be learned regarding ZIKV restriction factors—inhibitory host factors against ZIKV , particularly those that are constitutively expressed and whose expression is not or only moderately induced by IFNs . We have previously demonstrated that TRIM56 , a RING-type E3 ligase of the tripartite motif protein family , puts a check on intracellular RNA replication of DENV serotype 2 ( DENV2 ) , YFV , and bovine viral diarrhea virus ( BVDV ) in cell culture [24 , 25] . Although the inhibitory effects on these viruses invariably rely upon the E3 ligase activity as well as the C-terminal integrity of TRIM56 [24 , 25] , the underlying mechanism remains elusive . In addition , whether the antiviral spectrum of TRIM56 can be extended to other viruses within the flavivirus genus , especially ZIKV , is an intriguing and important question to answer . In this study , we demonstrate that TRIM56 exerts a direct antiviral effect on ZIKV infection in human cells of fibroblast- , epithelial- , and neural-origins and that both the E3 ligase activity and C-terminal portion of TRIM56 are critical for restricting ZIKV . Although the C-terminal region of TRIM56 bears sequence homology with the NHL repeat motif of several other TRIM proteins [26] that bind miRNAs and/or mRNAs [27–29] , we found that the inhibition on ZIKV replication by TRIM56 was not undercut in Dicer-deficient cells , indicative of a miRNA-independent antiviral mechanism . Interestingly , we revealed that TRIM56 was associated with ZIKV RNA in infected cells via its C-terminal portion and such capability was required for its antiviral function . Altogether , our work illustrates that TRIM56 is an intrinsic host restriction factor of ZIKV and shed new lights on the mechanism of action by which this E3 ligase curbs flavivirus replication .
The retroviral vectors encoding C-terminally Flag-tagged , wild-type ( WT ) human TRIM56 , the E3 Ub ligase-deficient CC21/24AA mutant , and a deletion mutant lacking C-terminal aa 693 to 750 , respectively , in the pCX4bsr backbone , have been described previously [25] . N-terminally Flag- and HA-tandem tagged human TRIM56 ( FH-T56 ) was inserted into the pCX4pur retroviral vector backbone , to yield pCX4pur-FH-T56 . To express a recombinant TRIM56 protein fragment in E . coli for polyclonal antibody production and protein-RNA interaction assay , we inserted a cDNA fragment encoding the C-terminal 392 aa of human TRIM56 ( T56-C392 ) into pMAL-c4x ( New England Biolabs ) . The resultant plasmid vector , designated pMAL-c4x-T56-C392 , allowed expression and subsequent purification of an MBP-T56-C392 fusion protein in E . coli . The plasmid encoding serotype 1 DENV replicon , pACYC-DENV1-Rluc2A-Rep [30] , was obtained from Ju-Tao Guo and Jinhong Chang ( Baruch S . Blumberg Institute ) with permission of Pei-Yong Shi ( University of Texas Medical Branch ) . The NS4B coding sequence of ZIKV ( MR766 strain ) was amplified by PCR from the cDNA of virally infected Vero cells and ligated into pEF6/V5-His-TOPO ( Invitrogen ) to yield the pEF6-ZIKV-NS4B-V5His6 construct , from which NS4B would be expressed as a protein fused to C-terminal V5-His6 epitope tags . All plasmids were verified by Sanger DNA sequencing [31] . Human embryonic kidney ( HEK ) 293 , HeLa , SV40 T antigen-transformed human fetal glial cell line SVGA ( a gift from Santosh Kumar , University of Tennessee Health Science Center ( UTHSC ) ) , human neuroblastoma SK-N-SH ( kindly provided by Francesca-Fang Liao , UTHSC ) , human hepatoma cell lines Huh7 and HLCZ01 ( kindly provided by Haizhen Zhu , Hunan University , China ) [32] , SV40 T antigen-immortalized human hepatocyte PH5CH8 , African green monkey kidney cell lines Vero and BSC-1 , murine embryonic fibroblasts ( MEFs ) , mouse fibroblast cell line L929 , mouse hepatoma Hepa1-6 cells , and mosquito C6/36 cells were maintained in Dulbecco’s modified Eagle medium ( DMEM ) supplemented with 10% fetal bovine serum ( FBS ) , 100 U/ml of penicillin , and 100 μg/ml of streptomycin . HEK293T and its clonal derivatives deficient in Dicer expression , No-Dice ( 2–20 ) and No-Dice ( 4–25 ) [33] , were kindly provided by Bryan Cullen ( Duke University Medical Center ) . HeLa-Flp-In T-REx-ACE2 ( referred to as HeLa-FitA2 ) cells with tetracycline ( Tet ) -inducible expression of 2xHA-tagged , WT , E3 Ub ligase-deficient CC21/24AA mutant , or C-terminal 693–750 aa deletion mutant of TRIM56 have been described in a previous study [34] . Tet-inducible expression of HA-TRIM56-WT had also been established in HEK293-Flp-In T-Rex ( referred to as HEK293-FIT , Invitrogen ) cells [24] . HEK293-T3Y cells that stably express very low levels of C-terminally YFP-tagged TLR3 were kindly provided by Kate Fitzgerald ( University of Massachusetts Medical School ) . To stably express Flag- and HA-tandem tagged TRIM56 ( FH-T56 ) , we transduced HEK293-T3Y cells with a replication-incompetent retrovirus packaged from pCX4pur-FH-TRIM56 . Following selection with puromycin , surviving cells were pooled , designated as HEK293-T3Y-FH-T56 cells , and used for subsequent analyses . The blasticidin-resistant , HEK293 cells with stable , constitutive expression of Flag-tagged WT TRIM56 or the E3 ligase-dead CC21/24AA mutant have been described [24] . To create HEK293 cells with constitutive expression of the TRIM56-Flag mutant lacking C-terminal 693–750 residues , cells were transduced with a pCX4bsr-derived retroviral vector carrying the mutant TRIM56 cDNA , followed by stable selection with blasticidin . Similar strategy was adopted to establish SK-N-SH- , HEK293T- and No-Dice ( 4–25 ) -derived cell populations with constitutive expression of TRIM56-Flag or the empty pCX4bsr vector , as controls . Generation of primary mouse neuronal cultures was conducted as follows . A pair of combined cortex and hippocampus with subventricular zone was obtained from E18 C57BL/6 mice ( BrainBits , LLC ) . Neuroprogenitor and differentiated cultures were generated based on established protocols [35 , 36] . Specifically , to generate neural progenitor cells ( NPCs ) , tissue was suspended in Dissociation Solution ( DS ) consisting of Hibernate E-Ca without B27 and Papain ( 2 mg/ml ) ( BrainBits , LLC ) for 10 min at 30°C , allow the tissue to settle and replace DS with Hibernate EB ( HEB ) medium ( Invitrogen ) and triturate with pasture pipette for 1 min and allow undispersed pieces to settle . Supernatant was transferred , centrifuged at 200 x g for 1 min , aspirated and the pellet cells resuspended in NpGrow ( BrainBits , LLC ) . Cells were counted and diluted with NpGrow at 2 ml/10 cm2 and plated on ultra-low attachment plates at 2 . 5 x 104 cells/10 cm2 and incubated at 37°C , 5% CO2 , 95% humidity . Half of the media were exchanged with fresh 37°C CO2 equilibrated NpGrow every 3–4 days; neurospheres are present at 4–7 days . To differentiate NPCs into primary neuronal cultures , neurospheres from day 7 cultures were dissociated in DS for 10 min at 30°C , centrifuged at 200 x g , and DS was replaced with NbActiv4 neuron culture medium ( BrainBits ) . Cells were counted and seeded into 24-well plates at 2 x 105 cells/well and incubated at 37°C , 5% CO2 , 95% humidity . Half of the media were exchanged with fresh NbActiv4 media every 3–4 days; axons and dendrites are observable by day 4 while synapses and action potentials have been reported at 7 days [35 , 36] . ZIKV MR766 strain ( BEI Resources # NR-50065 ) and PRVABC59 strain ( kindly provided by Brandy Russel , CDC , Fort Collins , CO ) were propagated in C6/36 cells . For infections of various cell types , cells were inoculated with ZIKV in 2% FBS-containing DMEM at indicated multiplicities of infection ( MOIs ) for 1–2 h . Subsequently , viral inoculum was removed , and cells were washed three times in PBS , refed with complete culture medium and cultured for the indicated time periods . Infectious virus titers in cell-free culture supernatants were determined by an endpoint dilution-based 50% tissue culture infective dose ( TCID50 ) assay in 96-well plates [37 , 38] . Titration of ZIKV was performed on Vero cells , and cytopathic effect ( CPE ) was recorded and used for calculation of viral titers at 7 days post infection ( dpi ) by the Reed and Muench method [39] . Infections of primary mouse neuronal cultures were carried out at day 8 post-differentiation when medium was removed and the cells were washed twice with HBSS . Based on cell counts from 3 wells , neurons were either mock -infected ( controls ) or infected with ZIKV-PRVABC59 at a MOI of 1 in NbActiv4 media for 2 h at 37°C; duplicate wells for each time point were used . Inoculum was then removed , cells washed twice with NbActiv4 , and then incubated in 0 . 5 ml of NbActiv4 media at 37°C , 5% CO2 , 95% humidity . At 24 and 48 h after infection , 0 . 5 ml TRIzol was added to each well for RNA extraction and qPCR analysis . HeLa cells with stable , shRNA-mediated knockdown of TRIM56 ( HeLa-shT56 ) and control cells stably transduced with a non-targeting shRNA ( HeLa-shCtrl ) were described in a previous study [24] . To deplete TRIM56 expression in SVGA cells , we transduced the cells with lentiviral shRNA-094 specifically targeting human TRIM56 [24] and selected cell populations resistant to puromycin . For comparison , control cells were generated by transduction with a non-targeting control lentiviral shRNA packaged from pLKO . 1-shRNA-scramble ( Addgene# 1864 ) , followed by selection in puromycin-containing medium . For transient knockdown of TLR3 , a synthetic siRNA specifically targeting human TLR3 ( siTLR3 ) [40] was transfected into cells by Lipofectamine 2000 for the indicated times as per manufacturer’s instructions ( Invitrogen ) . As a control for comparison , a non-targeting negative control siRNA ( Invitrogen AM4636 ) was used in lieu of siTLR3 . Extraction of total cellular RNA by TRIzol ( Invitrogen ) , cDNA synthesis by reverse transcription , and quantitative PCR ( qPCR ) were implemented as described elsewhere [25 , 41] . The following primers were used to detect ZIKV RNA ( specifically recognizing the NS4B-coding region ) : ZIKV-4B-7260F , 5’-GCACTACATGTACTTGATC-3’; and ZIKV-4B-7367R , 5’-ACCACTATTCCATCCACAAC-3’ . Primers specific for human innate immune genes including TLR3 , IFNB , IL29 , RANTES and ISG56 have been described [34 , 42] . The relative abundance of each target was normalized to that of 28S rRNA [34] . Copy numbers of ZIKV RNA were calculated based on standard curves generated using serially diluted pEF6-ZIKV-NS4B-V5His6 DNA that ranged from 104 to 107 copies/ml . For miRNA detection , we synthesized oligonucleotide primers and adopted the protocol as described in the qSTAR miRNA qPCR detection system ( Origene ) . In brief , after a poly ( A ) tailing procedure , a miR-oligo-dT primer with a sequence of 5’-GAACATGTCTGCGTATCTCAGACTTCTGATTCACGCTTTTTTTTTTTTTTTTTTTVN-3’ , was used for reverse transcription of total small RNAs . Subsequently , SYBR green-based qPCR was performed to measure the levels of miRNAs of interest relative to that of an internal control , i . e . , U6 snRNA . The primers for qPCR detection included a miRNA/snRNA-specific forward primer and a universal reverse primer , as follows . miR-92a , 5’-TATTGCACTTGTCCCGGC-3’ ( forward ) ; miR-21 , 5’-TAGCTTATCAGACTGATGTTG-3’ ( forward ) ; U6 snRNA , 5’-CTGCGCAAGGATGACACGC AA-3’ ( forward ) ; and miR-Rev-universal , 5’-GAACATGTCTGCGTATCTC-3’ ( reverse ) . Cell lysates were prepared in RIPA buffer and subject to SDS-PAGE and immunoblotting analysis were described previously [25 , 43] . Immunofluorescence staining were performed as previously described [25 , 43] . The following monoclonal ( mAb ) and polyclonal ( pAb ) antibodies were utilized: mouse anti-Flag-tag M2 mAb ( Sigma ) ; mouse anti-HA-tag mAb ( Invivogen ) ; rabbit anti-maltose-binding protein ( MBP ) pAb ( New England Biolabs ) ; mouse anti-flavivirus envelope protein ( 4G2 ) mAb ( Millipore ) ; mouse anti-ZIKV NS5 mAb , clone 8B8 ( Biofront Technologies , kindly provided by Hengli Tang , Florida State University ) ; mouse anti-β-actin mAb ( Sigma ) ; rabbit anti-TRIM56 pAb ( Bethyl Labs ) or rabbit anti-TRIM56 S4091 pAb ( generated by immunizing rabbits at Proteintech Group Inc . with a recombinant protein comprising the C-terminal 392 aa of human TRIM56 fused to MBP that was expressed and purified from E . coli ) ; peroxidase-conjugated secondary goat anti-rabbit and goat anti-mouse pAbs ( Southern Biotech ) ; FITC-conjugated secondary goat anti-mouse pAb ( Southern Biotech ) . Specifically , FH-T56 was detected by mouse anti-Flag-tag M2 mAb ( Sigma ) ; rabbit anti-TRIM56 S4091 pAb was used to detect endogenous T56 protein in HeLa cell lysates; and rabbit anti-TRIM56 pAb ( Bethyl Labs ) was used for other T56 immunoblotting experiments . ZIKV-infected HEK293 cells expressing control vector , or Flag-tagged , WT or mutant TRIM56 , respectively , were washed twice with ice-cold PBS and lysed in a buffer containing 50 mM Tris-HCl ( pH 8 . 0 ) , 150 mM NaCl , 1% NP-40 , 0 . 25% sodium deoxycholate , and protease inhibitor cocktail ( Sigma ) . The cell lysates were incubated at 4°C for 30 min , clarified by centrifugation at 12 , 000 g at 4°C for 15 min , and their protein concentrations were adjusted to 1 μg/μl . For RNP-IP , 400 μg of cell lysates were incubated with the anti-Flag mAb at 1:500 at 4°C overnight . Subsequently , RNA-protein-antibody complexes were captured by incubation with protein A/G agarose beads ( Santa Cruz Biotech ) . After three washes , the beads were divided equally into two fractions , which were subjected to RNA isolation by TRIzol and protein extraction by boiling in SDS-sample buffer , respectively . The qPCR analysis of ZIKV RNA levels and immunoblotting of TRIM56 were performed as described above . To assess whether TRIM56 directly interacts with ZIKV RNA , we first purified MBP-tagged T56-C392 ( comprising the C-terminal 392 aa of human TRIM56 ) protein from E . coli using the pMAL Protein Fusion and Purification System ( New England Biolabs , E8000S , USA ) . To produce a control protein , we purified MBP along with a short stretch of the downstream polylinker ( MBP-polylinker ) from E . coli transformed with the empty vector pMAL-c4x . ZIKV RNA was extracted using TRIzol from virions in high-titer ZIKV stocks . Subsequently , two micrograms of MBP-polylinker or MBP-T56-C392 protein were incubated with 0 . 5 μg of ZIKV RNA at room temperature for 30 min , followed by pull-down of MBP-tagged proteins using amylose resin . After three washes , the resins were divided equally into two fractions , which were subjected to RNA isolation by TRIzol and protein extraction by boiling in SDS sample buffer , respectively . Quantification of ZIKV RNA levels was performed by qPCR as described above . MBP-polylinker and MBP-T56-C392 in the protein samples were probed by immunoblotting using rabbit anti-MBP pAb ( New England Biolabs ) and rabbit anti-TRIM56 pAb ( Bethyl Labs ) . All results are presented as means ± standard deviations . For analysis of statistical differences , two-tailed student t-test ( Excel 2016 , Microsoft , USA ) was used to compare the means of two groups and one-way ANOVA followed by Tukey or Dunnett test ( GraphPad Prism 5 . 0 , USA ) was applied for comparing the means of more than two groups . Differences with a P value of < 0 . 05 were deemed statistically significant .
To study host factors that impact ZIKV replication , we first surveyed a number of human and animal cell lines of various tissue origins for ZIKV susceptibility . Cells were infected by MR766 , a prototype African strain of ZIKV , followed by immunofluorescence staining of viral E protein expression at 72 h . p . i . Three human hepatocyte cell lines , including Huh7 , PH5CH8 , and HLCZ01 , were all susceptible to ZIKV infection , albeit to different degrees ( S1 Fig ) . The hepatoma Huh7 cells were previously reported to support ZIKV infection [44] . We found Huh7 cells supported the highest efficiency of ZIKV infection , with 100% of cells infected and exhibiting significant cytopathic effect ( CPE ) at 72 h . p . i . HLCZ01 , a recently established human hepatoma line , was also highly susceptible , with >90% cells infected by ZIKV . In comparison , PH5CH8 , a non-neoplastic hepatocyte line that harbors intact innate immune responses resembling primary human hepatocytes [38 , 40 , 45] , was permissive for ZIKV but less susceptible than Huh7 and HLCZ01 . HeLa-FitA2 cells , which were derived from the human cervical epithelial carcinoma cell line HeLa and stably express the Tet repressor and harbors a single integrated Flp recombination target site allowing for rapid generation of Tet-inducible stable cell lines , also supported robust ZIKV infection ( S2 Fig ) . The human embryonic kidney HEK293 cells had been reported to support low efficiency of ZIKV infection [11] . We examined three different HEK293-derivatives , HEK293-FIT , HEK293-T3Y and 293T cells , and found they supported varying degrees of ZIKV replication ( see below ) . A notable feature of ZIKV infection in humans is its neurotropism [46–48] . We thus examined two human cell lines of neural origin , i . e . , the neuroblastoma line SK-N-SH and SVGA , a fetal glial cell line , and found both to be permissive for ZIKV infection ( S3 Fig ) . Apart from the human cell lines , we found the African green monkey kidney cell line BSC1 and the Madin–Darby canine kidney ( MDCK ) cell line both to be susceptible to ZIKV infection ( S4 Fig ) . We also examined the susceptibility of three commonly used mouse cell lines , Hepa1-6 , L929 and MEF , to ZIKV infection and compare these lines with Huh7 and HEK293 , which represent human cell lines with relatively high and low permissiveness for ZIKV infection , respectively . Cells were infected with ZIKV-MR766 for different time periods , followed by quantification of intracellular viral RNA abundance by qPCR that sensitively detects viral RNA replication . As shown in S5 Fig , Huh7 cells were highly permissive , harboring 108 copies of ZIKV RNA per microgram of total RNA already at 24 h . p . i . There was a further , ~4-fold increase in viral RNA levels at 48 h . p . i . , which reached sub-109 copies/μg total RNA range . In contrast , HEK293 cells were much less permissive , harboring mid-106 copies/μg total RNA range of viral RNAs at 24 h . p . i . ZIKV RNA replication increased by ~18-fold in HEK293 cells at 48 h . p . i , approaching that of Huh7 cells at 24 h . p . i . However , all three murine cell lines examined ( hepa1-6 , L929 and MEF ) exhibited limited permissiveness , if any , for ZIKV , harboring mid-105 to ~106 copies/μg total RNA range viral RNA at 24 h . p . i . Moreover , viral RNA levels did not increase in hepa1-6 and MEFs , while had a mere ~2-fold uptick in L929 cells , at 48 h . p . i . Collectively , these data demonstrate that ZIKV infects and replicates in a broad range of human and animal cell lines , although the susceptibility of different cell lines vary considerably . Our previous studies have shown that TRIM56 is an antiviral host factor against several different RNA viruses , among which include 3 members of the Flaviviridae , DENV2 , YFV , and BVDV [24 , 25] . To determine whether TRIM56 impacts ZIKV fitness , we conducted infection experiments in HeLa-FitA2-T56 cells with Tet-inducible expression of HA-tagged TRIM56 we developed previously [34] . In these cells robust expression of HA-TRIM56 protein could be turned on upon addition of doxycycline ( Dox ) to culture medium ( Fig 1A , compare lanes 2 vs l ) . When infected with ZIKV-MR766 , cells without HA-TRIM56 expression harbored abundant viral E protein at 72 h . p . i . ( Fig 1A , lanes 3 and 5 ) . In contrast , the level of ZIKV E protein was profoundly reduced in cells with Dox-induced HA-TRIM56 expression ( Fig 1A , lanes 4 and 6 ) . Consistent with the viral protein data , progeny virus titers in culture supernatants were significantly lower in cells induced for HA-TRIM56 expression than those cultured in the absence of Dox ( Fig 1B , compare bars 4 vs 3 , a 11 . 7-fold decrease at MOI 0 . 5 ) . Immunofluorescence staining revealed that the percentage of cells positive for ZIKV E antigen was substantially lower in cells with Dox treatment than those without ( Fig 1C ) . To ensure this is not a cell-type-specific phenomenon , we assessed the anti-ZIKV activity of TRIM56 in HEK293-FIT-T56 cells that express HA-TRIM56 in a Tet/Dox-inducible manner [24] . As shown in Fig 1D , the results mirrored those obtained in HeLa-FitA2-T56 cells . In addition to the Tet-inducible expression system , a constitutive overexpression strategy was also utilized to examine the impact of TRIM56 on ZIKV infection . Retrovirus-mediated ectopic expression of Flag-HA-tagged TRIM56 ( FH-T56 ) curtailed viral E protein expression in HEK293-T3Y cells infected by ZIKV ( Fig 1E , compare lanes 4 vs 3 , and lanes 6 vs 5 ) . Taken together , these results reveal that ectopic expression of TRIM56 inhibits the propagation of an African strain of ZIKV . Since the recent ZIKV outbreaks were caused by viruses of Asian lineage , we sought to verify the anti-ZIKV effect of TRIM56 using the PRVABC59 strain , an Asian lineage ZIKV [49] . When challenged with this virus , control cells without FH-TRIM56 expression supported abundant expression of ZIKV E protein at 72 h . p . i . ( Fig 2A , lanes 3 and 5 ) , whereas cells constitutively expressing FH-TRIM56 had diminished viral protein levels ( lanes 4 and 6 ) , even when infected at a high MOI ( MOI = 2 , compare lanes 6 vs 5 ) . Data on progeny virus production in culture supernatants ( Fig 2B ) also confirmed the inhibitory effect of TRIM56 on ZIKV-PRVABC59 propagation . To determine whether TRIM56 expressed at physiologic levels impedes propagation of ZIKV , we depleted endogenous TRIM56 in HeLa cells by shRNA-mediated knockdown and evaluated the changes in viral protein expression and progeny virus production . As shown in Fig 3A , the abundance of ZIKV E protein was higher in cells with efficient TRIM56 knockdown ( shT56 ) than in cells bearing a nontargeting , scrambled control shRNA ( shCtrl ) ( compare lanes 4 vs 3 , and lanes 6 vs 5 ) . Moreover , shT56 cells consistently yielded ~ 1-log more progeny virus than did shCtrl cells , when infected at two different MOIs ( Fig 3B ) . These data establish TRIM56 as a restriction factor of ZIKV when expressed at physiologically relevant levels . To understand how TRIM56 exerts its antiviral action , we determined the domains or activities that are critical for TRIM56 to restrict ZIKV infection . The main functional domains of TRIM56 and its various mutants are illustrated in Fig 4A . We first utilized three cell lines derived from HeLa-FitA2 that stably express , in a Dox-inducible fashion , HA-tagged wildtype TRIM56 ( WT ) , the E3 Ub ligase-deficient CC21/24AA mutant ( AA ) , and the C-terminal aa 693–750 deletion mutant ( Mut N ) , respectively [34] . As shown in Fig 4B , comparable expression of WT TRIM56 and mutant proteins was achieved following Dox treatment . When challenged with ZIKV ( MOI = 0 . 5 ) , cells in which WT TRIM56 was induced harbored substantially lower level of ZIKV E protein than cells without Dox induction ( Fig 4B , upper panels , compare lanes 4 vs 3 ) . In contrast , intracellular ZIKV E protein abundance was unaffected following Dox induction of either the E3 Ub ligase-dead AA mutant or the C-terminal deletion mutant ( Mut N ) ( Fig 4B , middle and lower panels ) . To corroborate this finding , we evaluated ZIKV propagation efficiency in HEK293 cells stably transduced with an empty retroviral vector ( Bsr ) , WT or TRIM56 mutants ( AA and Mut N ) , respectively . Compared with that in Bsr control cells , ZIKV E protein abundance was reduced in cells overexpressing WT TRIM56 ( Fig 4C , compare lanes 6 vs 5 ) but not in cells overexpressing either TRIM56 mutant ( Fig 4C , compare lanes 7 and 8 vs 5 ) . The AA and C-terminal aa 693–750 deletion mutants also lost their abilities to limit progeny ZIKV production , as compared with WT TRIM56 ( Fig 4D , compare bars 3 and 4 vs 2 , increases of ~12-19-fold ) . In aggregate , these results suggest that the E3 Ub ligase activity and the C-terminal 693-750aa portion are both indispensable for TRIM56-mediated restriction of ZIKV . TRIM56 targets certain RNA viruses for inhibition at different steps of viral life cycle . It inhibits influenza viruses , YFV and DENV2 by impeding viral RNA synthesis [24 , 26] but curbs human coronavirus ( HCoV ) -OC43 by acting at the stage of viral packaging/release [24] . To pinpoint where TRIM56 exerts its antiviral action during ZIKV life cycle , we compared the kinetics of intracellular viral RNA accumulation at different times following infection between control and TRIM56-overexpressing cells . At 2 h . p . i . , a time point immediately after viral entry and prior to initiation of viral RNA replication , intracellular viral RNA levels were comparable between control HEK293-T3Y cells and cells expressing FH-T56 , suggesting that TRIM56 does not affect cellular entry of ZIKV ( Fig 5A ) . Intracellular viral RNA abundance began to climb at 24 h . p . i . in both cells but did so much more quickly and robustly in cells without FH-T56 expression . For all three time points examined at/after 24 h . p . i . , ZIKV RNA replicated to significantly lower levels in FH-T56 cells than in control HEK293-T3Y cells ( Fig 5A ) . Confirming the effect was not virus strain-specific , we obtained similar results from experiments using ZIKV-PRVABC59 in lieu of ZIKV-MR766 ( Fig 5B ) . Importantly , the inhibitory effect of TRIM56 on viral RNA replication was lost upon mutations that abolish the E3 ligase activity or delete its C-terminal portion ( Fig 5C ) . Furthermore , ZIKV RNAs replicated to significantly higher levels in HeLa-shT56 cells with stable TRIM56 knockdown than in HeLa-shCtrl cells ( Fig 5D ) , suggesting that endogenous TRIM56 shares the effect with overexpressed TRIM56 . We conclude from these data that TRIM56 restricts ZIKV infection by inhibiting viral RNA replication , and that such capacity depends on both its E3 ligase activity and the integrity of C-terminal portion . Of note , these observations are reminiscent of the effects of TRIM56 on BVDV , YFV , and DENV2 [24 , 25] , suggesting a shared antiviral mechanism against flaviviruses . To determine whether this can be said with another flavivirus , we electroporated a luciferase-encoding DENV1 replicon into HEK293-FIT-T56 cells that were repressed ( -Dox ) or induced ( +Dox ) for HA-TRIM56 expression and followed up the viral RNA replication kinetics ( S6 Fig ) . Similar to the effects of TRIM56 on BVDV and DENV2 replicons [24 , 25] , the result showed that while translation of the input DENV1 replicon RNA ( at 3 h post electroporation ) was not affected by TRIM56 , viral RNA replication at later time points was always significantly lower in cells with HA-TRIM56 induction than those not induced . Data from the current study on ZIKV as well as from several others on BVDV , YFV , DENV2 and influenza viruses , all underscore the importance of the C-terminal portion of TRIM56 for restricting RNA viruses [24–26] . While TRIM56 is classified within the subgroup C-V that consists of TRIM proteins without known functional domains in their C-terminal regions , the C-terminal portion of TRIM56 exhibits sequence homology with the NHL repeat of several TRIM-NHL proteins including TRIM2 , TRIM3 , TRIM32 and TRIM71 [26] . Of these , TRIM71 and TRIM32 were reported to bind to miRNAs and/or mRNAs and regulate their function or metabolism [27–29] . Thus , we tested the possibility that the NHL-like repeat in the C-terminal portion of TRIM56 may contribute to antiviral activity via a miRNA-dependent mechanism . To this end , we investigated the impact of TRIM56 on ZIKV infection in 293T-derived cells deficient in Dicer expression thus lacking the biogenesis of miRNAs [33] . First , we confirmed the Dicer deficiency phenotype in two clonal cell lines ( referred to as No-Dice ( 2–20 ) and ( 4–25 ) ) by showing the absence of miR-92a and miR-21 expression , as opposed to control 293T ( referred to as 293T-WT ) cells ( Fig 6A ) . It should be noted that total small RNA deep sequencing analysis had shown these two Dicer-knockout 293T cell lines were deficient in miRNA expression globally , as opposed to 293T-WT cells , in a previous study [33] . Our miRNA qPCR data thus confirmed the reported phenotype of these cells . Next , we wondered if Dicer deficiency affected ZIKV fitness in these 293T-derived cells . Although No-Dice ( 2–20 ) cells were slightly impaired for supporting intracellular ZIKV RNA replication ( Fig 6B ) and progeny virus production ( Fig 6C ) , No-Dice ( 4–25 ) cells permitted ZIKV propagation at efficiencies comparable to 293T-WT cells ( Fig 6B and 6C ) . These data suggest that host miRNAs are not essential for ZIKV replication . In subsequent experiments , we utilized No-Dice ( 4–25 ) and 293T-WT cells for comparison to dissect the possible role of host miRNAs in the anti-ZIKV action of TRIM56 . Ectopic expression of Flag-tagged TRIM56 or empty control vector was achieved in 293T-WT and No-Dice ( 4–25 ) cells by retroviral-mediated gene transfer , and there was comparable expression of Flag-TRIM56 between the two cell lines ( Fig 6D , compare lanes 4 vs 2 ) . Following ZIKV infection , profound reductions in ZIKV E and NS5 protein levels were observed in TRIM56-overexpressing cells as compared with control vector-transduced cells , irrespective of Dicer deficiency ( Fig 6D ) . The same could be said of intracellular viral RNA replication ( Fig 6E ) or progeny virus production ( Fig 6F ) . Collectively , these data show that the deficiency of Dicer does not undermine the inhibitory effect of TRIM56 on ZIKV infection , suggesting an antiviral mechanism independent of the biogenesis of or regulation by host miRNAs . Since the NHL repeat motif of TRIM71 has been shown to bind to mRNA[28] , we investigated whether TRIM56 interacts with ZIKV RNA to hamper viral RNA replication . HEK293 cells expressing control vector ( Bsr ) , WT TRIM56 , the E3-ligase-dead AA mutant , or Mut N ( delta-aa 693–750 ) lacking a portion of the C-terminal NHL-like repeat sequence were infected by ZIKV , followed by RNP-IP of TRIM56-RNA complexes and qPCR quantifying the viral RNAs associated with TRIM56 . The ectopically expressed , Flag-tagged WT and mutant TRIM56 proteins were all efficiently immunoprecipitated by the anti-Flag antibody ( Fig 7A , lanes 2–4 ) , and no background was detected in negative control ( Bsr ) cells ( lane 1 ) . Immunoblotting detection of ZIKV NS5 protein in input cell lysates confirmed the successful infection of ZIKV ( lanes 6–9 ) and the antiviral effect imposed by WT TRIM56 ( lane 7 ) . Immunoblotting of the IP-enriched protein complexes showed no association of TRIM56 with viral NS5 protein or cellular actin ( Fig 7A , lanes 1–4 ) , demonstrating the specificity of the RNP-IP assay . Next , we quantified the ZIKV RNA levels co-immunoprecipitated with TRIM56 or the two TRIM56 mutants . Strikingly , significant enrichment of ZIKV RNAs was observed with WT TRIM56 or the AA mutant , but not Mut N lacking C-terminal aa 693–750 ( Fig 7B , compare bars 2 and 4 vs 1 , increases of ~6-8-fold ) . These data suggest that TRIM56 binds to viral RNA in ZIKV-infected cells via its C-terminal portion . Because the AA mutant was as efficient as WT TRIM56 in co-precipitating ZIKV RNAs ( Fig 7B ) , we conclude that the E3 ligase activity , although indispensable for the antiviral function against ZIKV , is not required for the ability of TRIM56 to associate with viral RNAs . To determine whether TRIM56 can directly interact with ZIKV RNA , we set up additional binding experiments in cell-free reactions using recombinant TRIM56 protein and viral RNA purified from virions . We expressed and purified from E . coli a TRIM56 fragment comprising the C-terminal 392 aa fused to MBP ( MBP-T56-C392 ) , and as a negative control , an MBP-polylinker protein . Their quality and purity were verified by Coomassie blue staining ( Fig 7C , left panel ) and immunoblotting ( right panel ) following SDS-PAGE . The recombinant proteins were incubated separately with ZIKV RNA , and thereafter the protein-RNA complexes were recovered by the MBP-tag-binding amylose resin , whereby the associated protein and RNA were isolated . Immunoblotting analysis indicated successful pull-down of the bait proteins , MBP-polylinker and MBP-T56-C392 , with comparable efficiency ( Fig 7D ) . qPCR analysis revealed that , compared with amylose resin alone or the MBP-polylinker control protein pull down groups , there was significant enrichment of ZIKV RNA by recombinant MBP-T56-C392 protein , regardless of viral RNA origin ( be it MR766 or PRVABC59 ) ( Fig 7E ) . These data demonstrate that TRIM56 can directly associate with ZIKV RNA via its C-terminal portion . This result is also in line with our earlier data obtained from ZIKV-infected cells ( Fig 7A and 7B ) . The association of ZIKV infection with fetal microcephaly highlights the importance of neurotropism in ZIKV pathogenesis . In earlier work we found two human cell lines of neural origin , SK-N-SH and SVGA , to be permissive for ZIKV ( S3 Fig ) . These offered tractable in vitro systems for evaluating the impact of TRIM56 on ZIKV infection in neural cells . SK-N-SH cells were transduced with control vector ( Bsr ) or Flag-TRIM56 , followed by infection by ZIKV . Immunoblotting revealed viral E protein expression was substantially reduced in cells ectopically expressing Flag-TRIM56 , as compared with control Bsr cells ( Fig 8A , compare lanes 4 vs 3 , and 6 vs 5 ) . In agreement with this , progeny virus titers were significantly curtailed by TRIM56 overexpression ( Fig 8B , compare bars 2 vs 1 , and 4 vs 3 , decreases of ~6-8-fold ) . To determine if endogenous TRIM56 expression restricts ZIKV in neural cells , we performed shRNA knockdown experiments in the human fetal astrocyte cell line SVGA , which expressed readily detectable TRIM56 protein . In comparison with a non-targeting control shRNA , TRIM56 shRNA significant decreased TRIM56 protein abundance ( Fig 8C , compare lanes 2 vs 1 ) . Upon infection by ZIKV , intracellular viral E and NS5 proteins accumulated to higher levels in TRIM56 knockdown SVGA cells than in cells bearing control shRNA ( Fig 8C , compare lanes 4 vs 3 , and 6 vs 5 ) . Consistent with the immunoblotting data , TRIM56 depletion led to increased percentage of viral E protein-positive cells ( Fig 8D ) , heightened intracellular viral RNA replication ( Fig 8E ) , and elevated progeny virus production ( Fig 8F ) . Taken together , the data gleaned from SK-N-SH and SVGA cells demonstrate that TRIM56 also functions in human cells of neural origin to impede ZIKV replication . To investigate if TRIM56 expression changes in neural cell types following ZIKV infection , we first examined the abundance of endogenous TRIM56 protein in SVGA cells by immunoblotting ( Fig 9A ) . At 72 h . p . i , cells infected with increasing doses of ZIKV-MR766 all exhibited a ~3-fold increase in TRIM56 protein , compared with mock-infected cells ( compare lanes 2–6 vs lane 1 ) . This was not secondary to an overt IFN response , as minimal induction of IFIT3 , a well-characterized ISG and sensitive marker of IFN production , was merely observed at high MOIs ( lanes 5 and 6 ) , and high concentration IFN treatment ( lane 7 ) did not give rise to more robust uptick of TRIM56 expression than ZIKV , despite being a much stronger inducer of IFIT3 expression . Subsequently , qPCR of TRIM56 and ISG56 mRNA expression confirmed the protein data and indicated that the ZIKV upregulation of TRIM56 occurred at mRNA level ( Fig 9B and 9C ) . To determine whether this is the case in primary cells of neural origin , we examined Trim56 expression in primary mouse cortical neurons . Because it was challenging to obtain large number of neurons for immunoblotting , we utilized qPCR assay to quantify the mRNA levels of Trim56 prior to and at 24 and 48 h . p . i . , respectively , of ZIKV-PRVABC59 ( MOI = 1 ) . As shown in Fig 9D , these cells were found to have basal expression of Trim56 . In line with the expression pattern of TRIM56 transcript in SVGA cells , Trim56 mRNA abundance was upregulated by ~ 2-fold at 48 h . p . i . In aggregate , the experiments show that TRIM56 is expressed in human and mouse neural cell types , which moderately upregulate the expression of this antiviral protein in response to ZIKV infection .
In this study we demonstrate that TRIM56 , an E3 ubiquitin ligase ubiquitously expressed in human tissues [25] , poses a barrier to ZIKV infection in human cells . Ectopic expression of TRIM56 inhibited ZIKV propagation , while depletion of endogenous TRIM56 had the opposite effect . Importantly , this was demonstrated in cells of fibroblast , epithelial and neural origins , representative cell types targeted by ZIKV in vivo . No less important , we confirmed that TRIM56 restricts both African and Asian lineages of ZIKVs . To our knowledge , this is the first study that identifies a host restriction factor of ZIKV in the TRIM protein family . Taken into account the neurotropism of ZIKV infection in vivo , our revelations that TRIM56 was basally expressed in human brain [25] , human astrocytes SVGA and primary mouse neurons ( this study ) and that its expression was elevated following ZIKV infection in neural cell types support the biological relevance of TRIM56 as a host restriction factor of ZIKV . In agreement with our previous observation [25] , TRIM56 abundance was only moderately upregulated by IFN-α or ZIKV and the increase in its expression following ZIKV infection was not strictly correlated with induction of classical ISGs ( IFIT3 and ISG56 ) , indicating different mechanism of gene expression regulation that warrants future investigation . Very recently , Seo et al . reported that in mouse macrophages Trim56 deletion did not significantly alter viral RNA levels following ZIKV infection [50] . It should be noted that mouse is not a natural host of ZIKV and not susceptible to the virus unless with deficiencies in IFN induction and/or signaling [20 , 21] . Consistent with this , our in vitro infection experiments demonstrated that three murine cell lines , MEFs , L929 and hepa1-6 , all had limited permissiveness for ZIKV infection , as compared with human cell lines . Whether TRIM56 exhibits different roles in antiviral immunity between host species and/or between tissue origins in mouse will require additional study . A subset of TRIM proteins has been shown to modulate innate immune signaling during viral infections [51] . Flavivirus replication generates dsRNAs , a major pathogen-associated molecular pattern that are sensed by two classes of pattern recognition receptors ( PRRs ) , i . e . , the RIG-I-like receptors ( RLRs , RIG-I and MDA5 ) and TLR3 [52] . While TRIM56 is dispensable for RLR signaling and its overexpression per se does not trigger IFN production , it promotes TLR3-dependent innate immune response via interacting with the adaptor protein TRIF , independent of its E3 ligase activity [34] . It should be noted , however , that several lines of evidence suggest that the antiviral effect of TRIM56 against ZIKV revealed in this study is direct and not secondary to a regulation of IFN antiviral responses . First , ZIKV , like other flaviviruses , is not a strong inducer of IFN responses . Flaviviruses replicate their RNAs within re-arranged membrane structures that shield and delay viral dsRNAs from innate immune recognition [53] . ZIKV encodes multiple IFN antagonists including NS1 , NS4A , NS5 , among others , that inhibit IFN production and/or signaling [54–56] . Consistent with this , we found almost no induction of IFN-β , IFN-λ1 and ISGs by ZIKV in cell culture within 48 h . p . i ( S7 Fig ) . Second , TRIM56 did not augment , but rather decreased , the minimal induction of IFNs and ISG56 late post ZIKV infection ( S7 Fig ) , which may be explained by TRIM56-mediated reduction in ZIKV RNA ( i . e . , the immune stimulus ) levels . Third , although ZIKV may elicit TLR3 signaling , as suggested by a recent study showing that ZIKV could disrupt neurogenesis through TLR3 activation [57] , and TRIM56 promotes TLR3 signaling [34] , the E3 ligase-deficient TRIM56 mutant ( AA ) with intact function in augmenting TLR3/TRIF signaling [34] was incapable of suppressing ZIKV propagation . Furthermore , we demonstrated that efficient knockdown of TLR3 by siRNA ( S8A Fig ) had no impact on the anti-ZIKV activity of TRIM56 in HEK293 cells ( S8B Fig ) . Altogether , the anti-ZIKV action of TRIM56 is not attributed to its effect on TLR3 signaling . At present , we cannot fully exclude the possibility that in certain cell types such as immune cell subsets , TRIM56 may promote innate immune activation and fine-tune host responses , thereby adding to its direct antiviral effect . Whether this mechanism operates will require future studies . Previously , TRIM56 has been shown to possess direct antiviral activities against distinct RNA viruses , including BVDV , YFV and DENV2 from the family Flaviviridae , HCoV-OC43 from the family Coronaviridae [24 , 25] , and influenza A and B viruses from the family Orthomyxoviridae [26] . However , the underlying mechanisms are unclear . It should be noted that TRIM56 is not a universally antiviral host factor against RNA viruses , as it had no inhibitory effects on several negative-stranded RNA viruses , including Sendai virus , vesicular stomatitis virus , and human metapneumovirus [25 , 26] . In addition , TRIM56 was not found to impact replication of hepatitis C virus , a hepatotropic RNA virus classified in the family Flaviviridae , in permissive hepatoma Huh7 cells [25] . The present study expands the antiviral spectrum of TRIM56 to two more classical flaviviruses , ZIKV and DENV1 . Mirroring the molecular determinants required for TRIM56-mediated restriction of BVDV , YFV and DENV2 [24 , 25] , the E3 ligase activity and the integrity of C-terminal portion were found to be both essential for the anti-ZIKV action of TRIM56 . Interestingly , the very C-terminal region is also a prerequisite for TRIM56’s antiviral action against influenza viruses , although it alone was found to be sufficient in this case [26] . Notably , intracellular viral RNA replication is the molecular stage in the life cycle of all five flaviviruses at which TRIM56 targets for inhibition ( [24 , 25] , and this study ) , arguing strongly a shared mechanism underlying the broad anti-flavivirus activity . As proposed previously [24 , 25] , TRIM56 may act on shared proviral host factor ( s ) for post-translational modifications such as attachment of ubiquitin or ubiquitin-like modifier linkages via its E3 ligase activity , in ways that alter their turn-over rates , trafficking to and/or incorporating into viral replicase complexes . The possibility that TRIM56 binds to viral RNAs via its C-terminal region to hinder viral RNA replication was also raised [25] . As discussed below , we favor the hypothesis that these two scenarios are not mutually exclusive and they operate concurrently and/or in concert to implement viral restriction . Importantly , the current study significantly advances our understanding of the antiviral mechanism of TRIM56 by showing that TRIM56 is an RNA-binding protein and that its C-terminal region mediates the association with ZIKV RNA in infected cells . In addition , we revealed that host miRNAs are not essential for ZIKV replication and that the restriction of ZIKV by TRIM56 is independent of miRNA biogenesis or its regulation . TRIM56 is currently classified within the subgroup C-V of TRIM proteins [58] , because its C-terminal region was not found to contain a defined protein domain structure . However , we previously found aa 521–748 in the C-terminal portion of TRIM56 constitutes a NHL-like domain that shares homology and conserved residues with the NHL repeat domain of several TRIM-NHL proteins such as TRIM2 , TRIM3 , and TRIM71 [26] . The NHL repeat domain folds into a six-bladed beta-propeller resembling that of WD40 domains [59] . Part of its surface is positively charged and may potentially interact with nucleic acids , whose backbone is negatively charged . In line with this model , TRIM71 associates with miRNA and Argonaute2 via its NHL domain , thereby regulating miRNA function and gene expression [60] . The NHL domain also targets TRIM71 to cellular mRNAs , leading to repression of gene expression [28] . Additionally , NHL repeats confer binding affinity of other TRIM-NHL proteins to cellular miRNAs and mRNAs [27–29] . Altogether , these prompted us to determine if miRNA regulation and/or RNA-binding play a part in the anti-ZIKV action of TRIM56 . To assess the potential connection of TRIM56 with miRNA , we investigated the impact of TRIM56 on ZIKV infection in Dicer knockout cells that lack the biogenesis of mature miRNAs . We did not observe any influence of host miRNA deficiency on TRIM56-imposed suppression of ZIKV RNA replication , viral protein expression , or progeny virus production , suggesting a miRNA-independent antiviral mechanism . We subsequently tested if TRIM56 could bind to viral RNA in infected cells . Indeed , a specific association between TRIM56 and ZIKV RNA was detected in RNP-IP analysis . In contrast , a TRIM56 mutant lacking aa 693–750 , the very C-terminal end portion of the NHL-like domain , was no longer able to capture viral RNA , concomitant with a loss in antiviral activity . Importantly , we have demonstrated that a recombinant TRIM56 fragment composed of the C-terminal 392 aa was able to efficiently capture ZIKV RNA in cell-free reactions , suggesting the TRIM56-viral RNA interaction is direct and does not depend on cellular factors , although we cannot exclude the possibility that certain cellular proteins may fine-tune this interaction in infected cells . Interestingly , the E3 ligase activity , another key anti-ZIKV determinant , was found to be dispensable for TRIM56-ZIKV RNA association . Taken together , these observations imply that the full anti-ZIKV action of TRIM56 necessitates not only the C-terminal RNA-binding region to recognize ZIKV RNA , but also the activity of E3 ligase that catalyzes post-translational modification ( s ) of pro-viral host ( or viral ) factors to impede their involvement/function in viral RNA replication ( S9 Fig ) . Given previous reports that TRIM5 , TRIM25 , and TRIM6 , can synthesize unanchored polyubiquitin chains via their E3 ligase activities [61–63] and that unanchored polyubiquitin chains play a role in the life cycle of an RNA virus ( influenza A virus ) [64] , there is a possibility that TRIM56 may catalyze the synthesis of unanchored polyubiquitin chains to regulate the flaviviral RNA replication process . We also cannot rule out the possibility that TRIM56 promotes ubiquitin-like modifications , such as sumoylation and ISGylation that were reported with TRIM28 and TRIM25 [65 , 66] , respectively , to execute its antiviral function . While future studies are needed to define the boundary of the RNA-binding motif ( s ) within TRIM56 , we favor a model in which the structured NHL-like domain mediates the interaction with viral RNA . Lending support to this notion , our previous studies have shown that the integrity of the TRIM56 C-terminal portion is essential for inhibiting viral RNA replication of BVDV , DENV2 and YFV , and that two different C-terminal deletion mutants , Δaa 621–695 and Δaa 693–750 , each lacking a separate portion of the NHL-like domain , invariably lost antiviral activity [24 , 25] . Whether TRIM56 binds to these flaviviral RNAs via its C-terminal NHL-like domain to exert the observed antiviral effects , as it does with ZIKV RNA , warrants further investigation . In summary , the present study identifies TRIM56 as a novel restriction factor of ZIKV , deepening our understanding of host intrinsic mechanisms that fend off this medically important flavivirus and broadening the antiviral spectrum of TRIM56 . Moreover , the revelation that TRIM56 is an RNA-binding protein that associates with viral RNAs in ZIKV-infected cells and in cell-free reactions unravels new insights into the molecular mechanisms by which TRIM56 restricts flaviviruses . Further investigations that elucidate the precise action mechanism of TRIM56 may expose therapeutic targets that can be harnessed to combat ZIKV and possibly other flavivirus infections . | The E3 ligase TRIM56 was previously shown to inhibit the replication of several viruses in the family Flaviviridae , including dengue virus serotype 2 , yellow fever virus and bovine viral diarrhea virus , but had not demonstrable antiviral effect against hepatitis C virus , a hepatotropic virus in the same family . Nonetheless , the antiviral mechanism remains unclear and whether TRIM56 restricts other flaviviruses remains to be determined . In this study we demonstrated that TRIM56 inhibits ZIKVs of Asian and African lineages and a dengue virus serotype 1 replicon . We additionally uncovered that TRIM56 is an RNA-binding protein and that a portion of the C-terminal NHL-like domain mediates the association of TRIM56 with ZIKV RNAs in infected cells . Importantly , the RNA-binding activity of TRIM56 was found to be required for its antiviral function , although it alone is insufficient . In contrast , TRIM56 restricted ZIKV in Dicer-deficient cells , indicating an antiviral mechanism independent of miRNA regulation , a function known to be associated with NHL-containing proteins . In aggregate , our work identifies TRIM56 as a novel restriction factor of ZIKV and sheds new lights on the antiviral mechanism of TRIM56 against flaviviruses . | [
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... | 2019 | The E3 ligase TRIM56 is a host restriction factor of Zika virus and depends on its RNA-binding activity but not miRNA regulation, for antiviral function |
Parasite loads were quantified in repeated skin biopsies from lesions of 2 patients with Old-World cutaneous leishmaniasis ( CL ) caused by Leishmania major and L . infantum during and after treatment with miltefosine . Miltefosine induced a rapid therapeutic effect on both infections with an initial decline of parasites of ∼1 log/week for the L . major infection . These observations illustrate the usability of quantifying parasite loads in skin lesions as a pharmacodynamic measure and quantitative descriptor of drug effect for CL supporting clinical assessment .
Miltefosine is an oral antileishmanial drug widely used in the management of visceral leishmaniasis ( VL ) in the Indian subcontinent [1] . There is increasing evidence on the efficacy of miltefosine in New-World cutaneous leishmaniasis ( CL ) [2] . In Old-World CL the applicability of miltefosine is documented in a few reports with Leishmania major as causative species [3]–[6] . The miltefosine dosage regimens used in CL are based on those established in Indian VL patients and lack a rational background [7] . Treatment can be given to speed up spontaneous healing of CL . Systemic treatment is indicated in ‘complex’ disease in patients with multiple lesions ( >5 ) , with lesions in cosmetically or functionally delicate areas , with lesions which are non-responsive to intralesional treatment or when mucocutaneous leishmaniasis may develop . Clinical assessment of the progress and healing of CL lesions remains difficult , certainly in complex CL cases , where definitive cure from a clinical perspective is determined up to 6 months post-treatment . Nothing is known about the dose-effect relationship of miltefosine in CL , mainly because a good quantitative descriptor of drug effect has not yet been established . In an attempt to rationalize the treatment of CL and to support the clinical examination and follow-up , two patients with Old-World CL for whom systemic miltefosine treatment was indicated were followed-up by measuring the Leishmania parasite load in repeated skin biopsies during and after therapy .
Two patients with cutaneous leishmaniasis described in this report both presented to the Unit of Tropical Medicine at the Academic Medical Center ( AMC ) , Amsterdam , the Netherlands and were treated with miltefosine at the currently advised maximal total daily dose of 50 mg three times daily for a total of 28 days ( Paladin Labs Inc , Montreal , Canada ) . Informed consent was obtained from both patients concerning the miltefosine treatment , the procedures described here and publication of the clinical descriptions and photographs . In both patients , full thickness skin biopsies with the same diameter ( 2 mm ) were taken repeatedly with a sterile disposable biopsy puncher from the active border of the same lesion at approximately the same location: at the edge of the inflammatory zone bordering the necrotic ulcer and always adjacent to the scar of the previous biopsy site . Biopsies were lysed using 950 µL L6-buffer , prior to extraction of RNA and DNA [8] . Parasite loads were measured by quantitative reverse-transcriptase real-time PCR ( qRT-PCR ) based on the detection of Leishmania 18S ribosomal RNA , which may allow for the detection of viable parasites [8] . Leishmania species were identified using the mini-exon repeat PCR method described by Marfurt et al . [9] , with minor modifications [10] . Miltefosine plasma concentrations were measured by a validated liquid chromatography-coupled to tandem mass spectrometry method ( LC-MS/MS ) [11] .
The time courses of Leishmania parasite loads in the skin biopsies quantified with qRT-PCR are shown in Figure 2 . The initial parasite load for L . major at start of treatment was 1 . 5×106 , while the first recorded parasite load for L . infantum at day 15 of treatment was 1 . 1×104 . At the end of treatment ( day 28 ) , skin biopsies taken from both patients still revealed the presence of parasite RNA/DNA as measured by qRT-PCR: 104 and 34 parasites per skin biopsy in the lesions of the L . major and L . infantum infection , respectively . The lesion of Patient 1 ( L . major ) was parasite-free after 50 days after start of treatment with a rapid parasite clearance rate of ∼1 log/week and remained parasite-free during follow-up examinations . The initial parasite clearance rate for Patient 2 ( L . infantum ) between day 15 and 22 of treatment appeared to be lower ( <0 . 5 log/week ) compared to the L . major infection , although on average in the last two weeks of treatment ( day 15–29 ) a similar clearance rate of ∼1 log/week was observed in the two infections . Patient 2 , however , had a slight upsurge of parasites on day 50 after start of treatment , despite clinical improvement of the lesions . Unfortunately , no skin biopsy of Patient 2 was available from the follow-up period . Miltefosine kept accumulating until the end of treatment and plasma trough concentrations had increased on day 28 ( last day of treatment ) to 38 µg/mL and 29 µg/mL for Patient 1 and Patient 2 , respectively ( Figure 2 ) .
This report describes two patients with extensive Old-World CL: one with L . major infection which suggested dissemination or at least multiple inoculations and the other with L . infantum infection of facial skin with collateral swelling of the nasal mucosa . Both patients were treated with miltefosine and both showed a parallel log-linear decline of the parasite load in skin biopsies . The described cases of Old-World CL originated from Northern Africa and the Mediterranean Basin caused by L . major and L . infantum , respectively . L . infantum has been described as a sporadic cause of CL [12]–[14] . To our knowledge , this case is the first reported L . infantum-related CL primarily treated with miltefosine . The initial parasite load in the skin lesions was high but probably in the same order of magnitude for both patients ( parasite load on day 0 was missing for the L . infantum infected patient ) . The repeated skin biopsies showed a rapid log-linear decline after start of miltefosine treatment , comparable to the decline rate of parasites in the blood of a previously miltefosine-treated VL patient [15] . The reproducible nature of the results was not completely expected , since we had anticipated a greater variability of parasite density among the repeated biopsies . This suggests that the parasite density is rather homogenously spread in the inflammatory zone that surrounds the necrotic ulcer and that the depth of the biopsy does not require calibration , as long as the diameter of the punch hole is calibrated and includes the full-thickness skin , since parasites accumulate in the upper layer of the dermis . Therapeutic effect of miltefosine was thus already noticeable directly after start of treatment , although miltefosine-levels had not reached steady-state yet . Surprisingly , at the end of treatment ( day 28 ) , the examined skin biopsies still revealed the presence of Leishmania parasites in the lesions of both patients , indicating that complete elimination of the parasites in the lesion does not occur within the period of drug administration . After an initial decrease of the parasite load of L . infantum , this slightly increased again on day 50 , possibly indicating a slower parasite clearance rate in the lesion of this patient . This may also have been caused by variability in parasite densities among the biopsy sites . Unfortunately , no further biopsies were taken to confirm complete parasite clearance from the lesion . Old-World CL is a slow healing condition of which clinical evaluation can pose difficulties in the first 3 months and is also known to be self-healing . The purpose of treatment of CL is to speed up spontaneous cure . Here we illustrate that parasite clearance rates from skin lesions , which can be assessed with qRT-PCR , can be used as a pharmacodynamic measure or ‘biomarker’ in future randomized clinical trials on CL . Miltefosine has an extremely long terminal half-life of over 30 days , resulting in high plasma concentrations of miltefosine for a prolonged period of time after ending the 28-day therapy [7] . Whether the continued parasite clearance after discontinuation of drug administration is due to long-time residence of miltefosine concentrations after end of therapy , due to induced immunological response prompted by the therapy or rather natural evolution , is unknown . It is important to note that CL can be caused by a wide variety of Leishmania species and a high variability in sensitivity to miltefosine has previously been shown in vitro between these different species [16] , however various host-related factors make it difficult to extrapolate these observations to the in vivo sensitivity of the parasite . The most commonly seen side-effects of miltefosine , mild to moderate vomiting and diarrhea , are probably related to drug-intake and not to drug plasma concentrations . Concurrent intake of a fatty meal largely reduced gastro-intestinal complaints in both patients . In conclusion , a rapid therapeutic effect was observed in two patients with respectively L . major- and L . infantum-related CL after initiation of miltefosine treatment . Repeated quantifications of the parasite load in skin biopsies showed a rapid , log-linear , decline of approximately 1 log/week in the L . major-related CL and lesions were free of parasites 50 days after start of miltefosine treatment . The L . infantum-related CL seemed to respond more slowly , possibly due to mucosal involvement , but definite cure could eventually be determined at 7 months follow-up . To establish the role of miltefosine in the treatment of Old-World CL , more and better designed randomized controlled clinical trials employing pharmacokinetics and pharmacodynamics are needed . | The clinical evaluation of the ulcerated lesions in cutaneous leishmaniasis ( CL ) is both difficult and subjective . As a result , the evaluation of therapeutic efficacy of drugs for CL remains complicated . The relationship between dose and effect of antileishmanial drugs in CL is unclear and a good quantitative descriptor of drug effect has not yet been established . This report describes the use of quantifying the parasite load in repeated full-thickness skin biopsies from lesions of two patients with extensive Old-World CL ( Leishmania major and L . infantum ) who both were treated with miltefosine to demonstrate the dynamics of parasite clearance within CL lesions . Therapeutic effect of miltefosine was already noticeable directly after start of treatment , with a rapid , log-linear decline in parasite load in the skin biopsies of approximately 1 log/week for the L . major infection . These observations illustrate the applicability of quantifying parasite loads as a pharmacodynamic measure for CL supporting clinical assessment . The methodology described here might enable better evaluation and comparison of standard and new therapeutics in future randomized clinical trials for CL . | [
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] | 2011 | Dynamics of Parasite Clearance in Cutaneous Leishmaniasis Patients Treated with Miltefosine |
Mutations that result in amino acid changes can affect both pre-mRNA splicing and protein function . Understanding the combined effect is essential for correct diagnosis and for establishing the most appropriate therapeutic strategy at the molecular level . We have identified a series of disease-causing splicing mutations in coagulation factor IX ( FIX ) exon 5 that are completely recovered by a modified U1snRNP particle , through an SRSF2-dependent enhancement mechanism . We discovered that synonymous mutations and missense substitutions associated to a partial FIX secretion defect represent targets for this therapy as the resulting spliced-corrected proteins maintains normal FIX coagulant specific activity . Thus , splicing and protein alterations contribute to define at the molecular level the disease-causing effect of a number of exonic mutations in coagulation FIX exon 5 . In addition , our results have a significant impact in the development of splicing-switching therapies in particular for mutations that affect both splicing and protein function where increasing the amount of a correctly spliced protein can circumvent the basic functional defects .
Exonic mutations represent by far the most frequent cause of human genetic disorders , [1] and their pathogenic effect is usually attributed to alterations of the amino acid code . However , the exons contain also an intricate series of splicing regulatory elements ( “splicing code” ) that are essential for their recognition and overlap with the aminoacid code . In fact , the correct selection of canonical splice sites that define the exon boundaries ( 3’ss and 5’ss , respectively , which include the polypyrimidine tract and the branch site at the 3’ss ) requires a series of auxiliary regulatory elements . According to their location and activity , the auxiliary elements are known to function as exon splicing enhancer and silencers ( ESEs and ESEs , respectively ) or intron splicing silencers ( ISSs and ISSs , respectively ) . Typically , in the exon , Serine/arginine-rich ( SR ) proteins[2] recognize ESE whereas heterogenous nuclear RiboNucleoProteins ( hnRNP ) interact with ESS , [3] inducing exon inclusion or skipping , respectively . Due to the presence of these regulatory elements , exonic mutations are strong candidates to affect splicing and the most frequent defect they produce is exon skipping , as shown in several disease genes and model systems . [4–8] However , in affected genes , the relative contribution of splicing and protein function in the disease pathogenesis is largely unknown . Intriguingly , while correction strategies for missense mutations are far from being proved , there are several tools enabling the rescue of splicing and proposing them as innovative therapeutic strategy , which include antisense oligonucleotides , chemical compounds and modified U1 small nuclear RNAs ( U1 snRNA ) . [9–12] Moreover , these strategies can be exploited in combination . [13] In exon skipping mutations , modification of the U1 snRNA , which is the key component of the spliceosomal small nuclear ribonucleoprotein ( U1 snRNP ) , is able to rescue exon skipping . The engineered U1 snRNPs defined as Exon Specific U1s ( ExSpeU1 ) bind in the intron downstream of the 5’ splice site and rescue exon skipping variants in Spinal Muscular Athrophy[14] , Netherton syndrome[15] Cystic fibrosis and Hemophilia B . [9] The activity of these molecules in primary cells derived from patients[14 , 15] and in vivo in mouse models through AAV delivery[14] suggests that ExSpeU1s have a strong therapeutic potential . In factor IX ( FIX ) exon 5 , ExSpeU1s rescued exon-skipping mutations at the 5'ss and at the polypyrimidine tract with a complete recover of the functional factor IX activity . [9] Mutations is FIX exon 5 are associated to Hemophilia B , a rare X-linked hemorrhagic disorder ( 1/35000 males ) with reduced levels of factor FIX , a key coagulation protein of liver origin . [16] The level of FIX antigen or clotting activity in the plasma determines the variability of the disease severity . [17] Hemophilia B represents a paradigmatic example of human disease with a heterogeneous mutational pattern [18] and even a small increase of FIX levels ( >2% ) would significantly ameliorate the clinical phenotype . The FIX gene contains eight exons and seven introns and transcribe 2 . 8 kb long mRNA [19] . Exonic mutations ( missense , nonsense and synonymous ) represent the first cause of coagulation factor deficiencies , thus providing ideal models to address the relationship between splicing and protein function . As a matter of fact , in several cases the results from the expression of the missense coagulation factor variants did not recapitulate the residual expression levels in the affected patients and disease causing mechanism of synonymous mutations is frequently unclear . In this study , we focused on exonic mutations in FIX gene that have been found in Hemophilia B patients , and particularly on exon 5 that encodes for EGF2 domain , which is crucial for the coagulant activity in the intrinsic coagulation pathway . [20] This exon contains several missense , nonsense and synonymous mutations , [21] whose potential effect on pre mRNA splicing has not been studied so far . Here , we show for the first time that FIX exon 5 contains dense splicing regulatory information that overlap with the aminoacid code and that several FIX exonic mutations result in exon skipping . Furthermore , a unique ExSpeU1 , through an SRSF2-mediated mechanism , rescues all FIX splicing defects . Most importantly , through complementary expression studies with minigene splicing assays and full-length protein constructs we dissected for each mutation the relative contribution of splicing alteration , defective protein secretion or abnormal coagulant activity . Lastly , this relationship allows us to select those exonic mutations that most likely will have a therapeutic benefit from splicing correction .
To map exonic splicing regulatory elements in FIX exon 5 we initially performed multiple deletions analysis . We created eleven 10 bp-long deletions distributed throughout the entire exon ( from Δ1 to Δ11 ) ( Fig 1A ) . These deletions were tested in the previously validated FIX exon 5 minigene system[9] , where the WT exon is not completely defined , showing ~80% of exon inclusion as reported in human liver [9] . Consistent with the presence in the exon of dense splicing regulatory information , most deletions affected splicing . Based on the splicing changes , we identified two ( Δ4 and Δ5 ) and eight ( Δ2 , Δ3 , Δ6 , Δ7 , Δ8 , Δ9 and Δ10 ) regions with silencer and enhancer properties , as their deletion resulted in exon inclusion or skipping , respectively ( Fig 1B ) . As the most striking and deleterious effect on splicing was observed with the Δ9 deletion and neighboring Δ10 deletion , we evaluated more in detail these regions by creating 3 bp long deletions . Splicing assays showed that Δ9 . 2 , Δ10 . 1 and Δ10 . 3 induced significant exon skipping ( <10% ) ( Fig 1C ) , which suggests the presence of multiple and overlapping exonic regulatory sequences in this region . To further clarify the role of exonic elements , we mapped binding sites of SR-proteins according to ESE finder , [22] hnRNPA1 motifs[23] and predicted ESE and ESS [24 , 25] . In silico analysis showed the presence in the exon of multiple and frequently overlapping silencers and enhancers ( Fig 1A ) . Interestingly , the Δ9 and Δ10 regions , associated to severe exon skipping , contain several potential ESEs . In addition , among the SR proteins and according to ESE finder , SRSF2 is the most represented factor with four potential binding sites . Moreover , the exon contains also 5 recently identified consensus SRSF2 binding motif SSNG[26] ( S = C/G , N = any ) ( Fig 1A ) . Thus , both in silico and experimental data indicate that FIX exon 5 contains several splicing regulatory elements with both enhancer and silencer function . The presence of these dense splicing regulatory elements suggests that FIX exon 5 may be extremely susceptible to mutation-induced splicing derangement . The FIX mutation database reports several missense , nonsense or even synonymous mutations in exon 5 that are associated with Hemophilia B . [21] To understand the contribution of exonic mutations on splicing , we focused on changes with unclear disease-causing mechanism ( synonymous or conservative amino acid substitutions ) and/or on variants located in strongest splicing regulatory elements ( i . e . in the Δ9 and Δ10 elements ) . Overall , we evaluated 16 disease-causing mutations: 2 synonymous ( V107V and R116R ) , 11 missense and 3 nonsense ( Table 1 ) . We also include in the analysis the artificial R116G variant . Strikingly , in splicing assay , 9 mutations induced significant exon 5 skipping ( Fig 1D and Table 1 ) . In this experimental system , R116R and Q97Stop showed complete or nearly complete exon skipping ( below 5% of exon inclusion ) suggesting that in these cases the primary disease-causing mechanism is the severe splicing defect . Interestingly , 6 mutations maintained some residual levels of normal splicing: V170V , R116Stop and Q121H showed between 5–10% of exon inclusion whereas R116G , L117F , A118V and Q121Stop showed ~ 25% of exon inclusion . The presence of some degree of leaky splicing indicates that in these cases the splicing defect contributes partially to the disease pathogenesis . These results clearly indicate that a significant proportion of exon 5 mutations negatively affect the splicing process . To identify splicing factors potentially involved in regulating exon 5 splicing , we performed overexpression experiments . Wild-type ( wt ) and two mutated minigenes ( V107V and R116R ) were co-transfected with different splicing factors followed by analysis of the splicing pattern ( Fig 2A ) . Several factors including most of the Serine/arginine-rich proteins ( SRSF3 , SRSF4 , SRSF7 and SRSF1 ) along with ESRP1 and hnRNP A1 had a negative effect on splicing , reducing the percentage of exon inclusion in the wild-type minigene . In contrast , Polypyrimidine tract binding protein 1 ( PTBP1 ) , Cytotoxic granule-associated RNA binding protein ( TIA1 ) , and Serine/arginine-rich splicing factor 2 ( SRSF2 ) induced exon inclusion . This enhancing effect was also evident for the two synonymous exonic variants . Since SRSF2 was the most active factor , and the only serine/arginine-rich splicing factor with enhancing activity on exon 5 , we evaluated more in detail its role through silencing experiments . Indeed , in the wt context , SRSF2 silencing reduced the percentage of exon 5 inclusion , strongly suggesting that this factor is crucial for its definition ( Fig 2B and 2C ) . In parallel , to understand the potential regulatory sequences that mediate the SRSF2-dependent splicing enhancement , we tested the effect of SRSF2 overexpression on the deletion mutants ( S1 Fig ) . The transfection experiments showed that SRSF2 significantly improves exon skipping in almost all deletion mutants , suggesting that this factor promotes exon 5 definition through its binding throughout the entire exon . We previously reported that modified U1 snRNA binding in FIX intron 5 rescued exon skipping caused by mutations located at the polypyrimidine tract or at the 5'ss . [9] To evaluate the potential therapeutic effect of ExSpeU1s on the exonic mutations , we initially tested a panel of ExSpeU1s that bind at different intronic positions ( Fig 3A ) . These molecules were evaluated on R116R that , by remarkably affecting exon definition ( Fig 1D ) , is one of most severe exonic mutation identified . Out of 9 ExSpeU1 FIX tested , 6 rescued exon 5 inclusion at least to the level of the wild-type condition ( Fig 3B ) . The lower or absent rescue activity in FIX13 , FIX16 and FIX 22 ExSpeU1s could be due either to their lack of interaction with the corresponding intronic sequences or to their reduced expression levels . One of the active ExSpeU1s was then evaluated on the natural splicing mutations ( Fig 3C ) and on the artificial deletion mutants ( S2 Fig ) . The splicing assays showed that cotransfection of ExSpeU1 completely rescued all exon-skipping events . Therefore , loading of modified U1 snRNA on FIX intron 5 represents a therapeutic strategy for splicing correction not only for polypyrimidine and 5'ss mutations[9] but also for mutations that affect splicing regulatory elements located in the exon . To address whether the ExSpeU1-mediated splicing enhancement requires SRSF2 , we performed silencing experiments . The SRSF2-silenced cells were cotransfected with ExSpeU1 and R116R variant . As expected , ExSpeU1 completely restored exon inclusion , but the concomitant silencing of SRSF2 remarkably reduced its activity ( Fig 3D , compare lanes 3 with lane 4 ) . In a second set of experiments , by exploiting U1 decoy molecules ( Fig 3E ) , we evaluated whether SRSF2-mediated splicing improvement requires the endogenous U1 . The U1 decoy D1 is an antisense RNAs that when trasfected in cells it functionally inhibits the normal U1 snRNP activity by complementarity[27] . Cotransfection of D1 , but not the D3 control , was previously shown to induce exon skipping in several gene systems [14] [27] . As the ExSpeU1-mediated splicing enhancement on R116R is not appreciably affected by the U1 decoy ( D1 treatment , Fig 3D , compare lanes 8 and 9 ) , ExSpeU1 can functionally overcome the absence of the endogenous U1 snRNP . This result is consistent with previous data obtained in other gene systems . [14] When we tested the effect of the U1 decoy on the SRSF2-mediated splicing improvement , we observed that functional inhibition of the endogenous U1 with the D1 treatment has a minimal effect on the splicing pattern ( Fig 3E , compare lanes 11 and 12 ) . Similar result was obtained in the wild-type context where D1 treatment reduces splicing efficiency in WT minigene ( Fig 3E , compare lanes 1 with 2 ) but has no effect in the presence of SRSF2 ( Fig 3E , compare lanes 4 with 5 ) . All together these results suggest that ExSpeU1 promotes splicing facilitating loading of SRSF2 on the defective FIX exon 5 sequences . To understand how the exonic mutations negatively affect splicing we focused on the two splicing severe synonymous V107V and R116R variants , on which we performed protein pull-down experiments followed by mass-spectroscopy . This analysis identified three major splicing factors with splicing inhibitory activity that bind to the mutated sequences: DAZ associated protein 1 ( DAZAP1 ) , Heterogeneous nuclear ribonucleoprotein H1 ( hnRNP H1 ) and Heterogeneous nuclear ribonucleoprotein A1 ( hnRNP A1 ) ( S3 Fig ) . Upon western blotting we confirmed increased hnRNPA1 and DAZAP1 binding on V107V and increased binding of DAZAP1 on R116R ( Fig 4A ) . In contrast , hnRNP H1 did not show any differential binding between wt and mutant sequences . As hnRNP A1 is a well known splicing inhibitor [28–31] , and DAZAP1 contributes to hnRNPA1 to exon skipping in another disease-causing mutation , [28] we evaluated their contribution with silencing experiments ( Fig 4B ) . Silencing of HNRNPA1/2 slightly increased the percentage of exon inclusion in V107V ( from 8 . 1±1 . 5 to 15 . 0±2 . 5 , t-test p≤0 . 05 ) whereas R116R was not appreciably affected . In contrast , DAZAP1 did not promote exon inclusion and unexpectedly it reduced splicing in the V107V mutant ( from 8 . 1±1 . 5 to 1 . 0±0 . 2 , t-test p≤0 . 01 ) ( Fig 4C ) . These data suggest that the formation of the novel binding sites for hnRNPA1 and DAZAP1 in the mutants partially contributes to exon skipping . To explore those exon 5 mutations that could benefit from ExSpeU1- mediated splicing correction , we measured the effect of 2 synonymous and 6 missense mutations associated with exon skipping on the secreted FIX protein and coagulant activity levels ( Table 1 ) . Expression studies with the exonic variants in the FIX cDNA context , which is not influenced by splicing , showed that the synonymous V107V and R116R substitutions did not affect the protein biology nor influence or pause the ribosomal translation due to codon preferences[32] ( Table 1 , lanes cDNA FIX:Ag and cDNA FIX:C ) ( Fig 5 ) . The R116G , A118V and Q121H missense mutations resulted in reduced secretion but strikingly , the lower amounts of the secreted proteins have a normal specific coagulant activity ( Table 1 , lanes cDNA FIX:Ag and cDNA FIX:C ) ( Fig 5 ) . In contrast , L117F variant strongly impaired the FIX secretion , as indicated by the barely detectable FIX antigen in medium ( Table 1 ) ( Fig 5 ) . Lastly , we included in the analysis as controls two mutations that do not affect splicing , Q97K and Q97E . These variants were efficiently secreted but displayed an impaired coagulant activity ( Table 1 ) .
Exons are known to accommodate two complementary and overlapping information: splicing signals and amino acids code . Exonic mutations are first checked by the spliceosome and then any residual amount of normally spliced transcript is evaluated for protein functionality . The final effect of a mutation on gene expression is the result of the severity of aberrant splicing along with the functional consequence of the amino acid substitution . In the paradigmatic model of FIX exon 5 we demonstrate that the final outcome , as well as the possible therapeutic rescue , depends on the impact of each mutation on mRNA splicing and/or protein biology . Our results clearly indicate that understanding the combined effect of exonic mutations on splicing and protein function is fundamental for correct diagnosis at the molecular level and for establishing the therapeutic feasibility of a splicing rescue strategy . In normal conditions FIX exon 5 is correctly recognized by the spliceosome and mostly included in the final transcript . This leads to the production of a normal FIX protein that folds correctly in the ER , it is efficiently secreted and , when present in the blood , activates properly coagulation ( Fig 5 , WT ) . Based on the effect on splicing , secretion and coagulant activity , exonic splicing mutations ( ESMs ) can be divided into three major groups which define their molecular basis and potential therapeutic splicing intervention . In the first case , the disease-causing mechanism is entirely due to aberrant splicing and therefore splicing correction is therapeutic ( Fig 5 , class I ) . The two synonymous V107V and R116R variants belong to this class: they affect binding of splicing factors and induce severe exon skipping with the production of a non-functional mRNA . Their ExSpeU1-mediated splicing improvement result in the production of the correct mRNA and normal protein . Affected patients have mild/ moderate phenotype and the residual splicing levels well correlate with the reported FIX:ag and FIX:C activities ( Table 1 ) . In the second type of mutations ( Fig 5 , ESM class II ) , the presence of two defects , one on splicing and the other on secretion determines at the end the lack or very reduced amounts of the protein . In this case , the partial splicing defect produces low amounts of normally spliced transcript that , when translated , result in a defective FIX protein with a significantly reduced , but not completely abolished , secretion . However , as the protein once secreted maintains a normal coagulant activity , an efficient ExSpeU1-mediated correction is expected to partially rescue its function . Natural mutations A118V and Q121H , and the artificial R116G belong to this class ( Fig 5 , ESM class II ) ; A118V and Q121H are associated to a mild phenotype and to residual FIX:C levels that well correlates with the presence of partial defects in splicing and secretion ( Table 1 ) . Lastly , mutations like L117F ( Fig 5 , ESM class III ) show a splicing defect but , as the resulting amino acid change severely affects secretion , splicing improvement would not recover FIX function . In this case , splicing correction should be complemented by additional strategies to bypass the secretion defect and possibly improve misfolding . Interestingly , some nonsense mutations partially ( Q121Stop ) or severely ( Q97Stop and R116Stop ) affect splicing ( Fig 1D ) ( Table 1 ) . These nonsense ESMs might also indirectly benefit from ExSpeU1 splicing rescue . In the experimental system we have used , which is NMD-independent [33] , the consequence of nonsense mutation on pre mRNA processing can be exclusively attributed to their effect on splicing . In these cases , the splicing correction will not have any direct positive effect by itself but become mandatory for subsequent read-through therapies . In principle , exonic mutations can create a splicing silencer or disrupt a splicing enhancer . However , in several cases , due to the promiscuous composition of the regulatory elements and the intrinsic combinatorial control of splicing , [25 , 34] both mechanisms are involved . [35 , 36] Consistent with a major role of silencers in FIX exon 5 , we show , using protein pull-down analysis coupled by mass-spectroscopy , that two synonymous mutations ( V107V and R116R ) increase binding to a negative splicing regulator hnRNP A1[28] and also to DAZAP1 ( Fig 4A ) . DAZAP1 associate with hnRNPA1 and was previously implicated in exon skipping . [28] However , their silencing have a small effect on splicing ( Fig 4C ) , suggesting that additional factors are involved . In any case , notwithstanding the exon skipping mechanism , ExSpeU1 rescued all nine exonic mutations ( Fig 3B ) . Overall , including the previously reported five variants at the polypyrimidine tract and at the 5'ss , [9] a unique ExSpeU1 can rescue 14 different splicing mutations , increasing the number of affected individuals that would benefit from this therapeutic strategy . This effect on different types of mutations is probably due to the presence in the exon of several SRSF2 binding sites . SRSF2 is the most active factor promoting exon 5 inclusion ( Fig 2 ) and its silencing inhibits the ExSpeU1-mediated splicing rescue ( Fig 2C ) . Thus , binding of ExSpeU1 in the intron downstream the 5'ss facilitates recruitment of SRSF2 on the exon , compensating the negative effect of the exonic and intronic mutations . This mechanism is consistent with the fact that SRSF2 is known to facilitate interaction between U1 and U2 snRNP . [37] The ExSpeU1 interaction with intronic sequences is also expected to reduce possible off targets with the advantage , in common with splicing correction strategies and in contrast to classical gene therapy approaches , of maintaining expression of the gene in the normal chromosomal context . In the perspective of a therapeutic intervention , AAV vector represents a reliable system to deliver ExSpeU1s , as we have recently demonstrated [14] , and liver is a well established target tissue for this vector . [38] In conclusion , our result establishes that mutations in FIX exon 5 can contribute to the disease combining splicing and protein dysfunctions and identifies those variants eligible for splicing-switching therapeutic molecules . In exons , dissection of the relative contribution of splicing versus amino acid dysfunction is critical for making a correct diagnosis at the molecular level and for establishing the therapeutic feasibility of a splicing rescue strategy . The splicing correction approach based on precise engineering of the U1 core spliceosomal RNP can be easily applied to different type of defective exons and diseases increasing the potential therapeutic spectrum of these novel class of molecules .
For the reporter minigene splicing assay , we have used the previously described pTBFIX exon 5 minigene . [9] Overlapping PCR approach was used for introducing disease-causing point mutations and oligonucleotides are listed in S1 Table . The minigenes of FIX exon 5 deletions ( Δ1 to Δ11 and Δ9 . 1 to Δ10 . 4 ) were commercially synthesized ( GenScript , NJ , USA ) . Expression vectors for the recombinant FIX variants were produced by site-directed mutagenesis using the QuickChange II Site-Directed Mutagenesis Kit ( Stratagene , La Jolla , CA , USA ) . The mutations were introduced into the human FIX cDNA cloned into the pCMV5 vector[39] , using primers listed in S1 Table . Exon-specific U1 snRNAs were created by replacing the sequence between the sites BclI and BglII with oligonucleotides as done previously . [9] Oligonucleotides are listed in S1 Table . All the clones were verified by sequencing . Splicing Assays: HeLa cell line was grown in Dulbecco's modified Eagle's medium with Glutamax I ( Gibco ) ( DMEM with glutamine , sodium pyruvate , pyridoxine and 4 . 5 g/l glucose ) supplemented with 10% fetal calf serum ( Euro Clone ) and Antibiotic Antimycotic ( Sigma ) . HeLa cells grown on six well plates were transfected with Effectene reagents ( Qiagen ) according to the manufacturer's instructions . 500 ng of FIX exon 5 minigenes were transfected either alone or with 500 ng of ExSpeU1-encoding plasmids and the same was performed for co-transfection of splicing factors as previously reported [28 , 40 , 41] . GFP expression was routinely assessed in cotransfection experiments and showed more than 80% of transfection efficiency . Cells were incubated for 24 hours and then collected for RNA analysis . Total RNA extraction was performed using TRIreagent ( Invitrogen ) and cDNA was generated using 2 μg of total RNA and M-MuLV Reverse Transcriptase ( NEB , UK ) . Alpha2 , 3 and Bra2 oligonucleotides were used for PCR amplification of pTBFIX exon 5 minigenes , as described previously . [9] PCR products were resolved on 1 . 5% agarose gel electrophoresis . Quantification of exon inclusion was performed using the ImageJ software . Protein assays: Expression vectors for FIX exonic variants were transiently transfected in HEK293 cells , and secreted FIX antigen ( ELISA ) and coagulant activity ( aPTT-based assays ) were evaluated as previously described . [39] For the pull-down analysis , the RNA templates were short RNA oligonucleotides , listed in S2 Table and the protocol was previously described . [29] Briefly , 10 μg of RNA oligo treated with m-periodate were mixed with dehydrazide agarose beads ( Sigma ) equilibrated with NaOAc and incubated on a rotator at 4°C overnight . After washing with Solution D ( 20 mM Hepes pH = 7 . 9 , 100 mM KCl , 0 . 2 mM EDTA pH = 8 . 0 , 100 mM DTT , 6% v/v Glycerol ) , the RNA-beads complex was incubated with HeLa cell nuclear extract ( C4 , Biotech ) and 6 mg/mL of heparin . The beads were then washed six times with Solution D and the samples were loaded on 12% SDS-polyacrylamide gels . Gels were stained with Coomassie brilliant blue R250 . The protein bands were excised and analyzed with mass spectrometer ( LCQ DECA XP-ThermoFinnigam ) and proteins were identified by analysis of the peptide MS/MS data with Turbo SEQUEST ( Thermo Finnigam , CA , USA ) and MASCOT ( Matrix Science , UK ) . For the validation , protein samples were separated by NuPAGE 4%–12% Bis-Tris precast gels ( Life Technologies , CA , USA ) and electroblotted onto nitrocellulose membranes . The primary antibodies that were applied in western blotting analysis are: rabbit polyclonal anti-hnRNPA1 antibody ( 1:1000 , Santa Cruz ) and rabbit polyclonal anti-DAZAP1 antibody ( 1:1000 ) . Silencing for HNRNPA1/2 , DAZAP1 and SRSF2 was performed twice after 24 and 48 hours using Oligofectamine ( Invitrogen , CA , USA ) , according to the manufacturer's instructions . The sense strands of RNAi oligos ( Dharmacon , CO , USA and Sigma Aldrich , MO , USA ) , which were used to silence the target genes , are listed in the S3 Table . 24 hours after the second treatment with siRNA the cells were transfected with the minigenes , as described above . After additional 24 hours , cells were collected and divided in two equal fractions for RNA and protein depletion analysis . Confirmation of HNRNPA1/2 and DAZAP1 silencing was done using Western blot ( antibodies listed above ) and for the SRSF2 using Sybr Green qPCR and ΔΔCt relative quantification with GAPDH as an endogenous control . The primers are listed in S4 Table . U1 snRNA 5′ functional inhibition was achieved by co-transfection of D1 plasmid as previously described . [27] The in silico splicing analyses were performed using Human Splicing Finder ( HSF ) [23] with implementation of ESS Hexamers[25] , predicted sites of SR proteins binding with ESE finder [22] and SRSF2 consensus [26] . | Clarification of if an exonic variant has an effect on splicing and/or on protein function is an important aspect in clinical genetics and in development of appropriate therapeutic strategies , and most of published evidence consider splicing and protein function separately . In exons , the presence of dense splicing regulatory and amino acidic coding information implies that mutations may have a double pathogenic effect acting on splicing and/or on protein function . To address this issue we focused on coagulation factor IX ( FIX ) exon 5 , where we identified natural mutations that induce different degree of exon skipping . All exon skipping mutations were completely corrected by a novel splicing-switching therapeutic approach based on modified U1 snRNP . To detect the substitutions that might benefit from this correction , we investigated splicing recovered mutations for FIX protein secretion and specific activity . This analysis identified synonymous mutations causing remarkable exon skipping and missense mutations with a partial effects on both splicing and secretion , but compatible with normal FIX coagulant properties , as target variants for the splicing-switching therapy . | [
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"compleme... | 2016 | Molecular Basis and Therapeutic Strategies to Rescue Factor IX Variants That Affect Splicing and Protein Function |
Most cell surface receptors for growth factors and cytokines dimerize in order to mediate signal transduction . For many such receptors , the Janus kinase ( Jak ) family of non-receptor protein tyrosine kinases are recruited in pairs and juxtaposed by dimerized receptor complexes in order to activate one another by trans-phosphorylation . An alternative mechanism for Jak trans-phosphorylation has been proposed in which the phosphorylated kinase interacts with the Src homology 2 ( SH2 ) domain of SH2-B , a unique adaptor protein with the capacity to homo-dimerize . Building on a rule-based kinetic modeling approach that considers the concerted nature and combinatorial complexity of modular protein domain interactions , we examine these mechanisms in detail , focusing on the growth hormone ( GH ) receptor/Jak2/SH2-Bβ system . The modeling results suggest that , whereas Jak2- ( SH2-Bβ ) 2-Jak2 heterotetramers are scarcely expected to affect Jak2 phosphorylation , SH2-Bβ and dimerized receptors synergistically promote Jak2 trans-activation in the context of intracellular signaling . Analysis of the results revealed a unique mechanism whereby SH2-B and receptor dimers constitute a bipolar ‘clamp’ that stabilizes the active configuration of two Jak2 molecules in the same macro-complex .
Non-receptor protein tyrosine kinases of the Janus kinase ( Jak ) family play an essential role in signal transduction mediated by a host of cell surface receptors that lack intrinsic enzymatic activity . As a prominent example , the receptor for growth hormone ( GH ) , a therapeutically important cytokine that modulates an array of cellular processes , including metabolism , proliferation , and survival [1] , constitutively associates with intracellular Jak2 [2]–[4] . The ordered binding of the bivalent GH ligand results in the formation of active cell surface complexes comprised of one GH and two receptor molecules , a process that is understood in exquisite mechanistic detail [5] . The dimerized receptors juxtapose two associated Jak2 molecules , facilitating transphosphorylation of both Jak2 and the receptor [2] . Phosphorylation of Jak2 further activates the enzyme , and receptor phosphorylation sites foster recruitment of the signal transducer and activator of transcription ( STAT ) variants STAT3 and STAT5b , which are phosphorylated by Jak2 [6] . Given the central role of Jak2 in GH receptor signaling , it is not surprising that its function is modulated by other proteins . A prominent negative regulator is suppressor of cytokine signaling ( SOCS ) -1 , which binds phosphorylated Tyr1007 in the activation loop of Jak2 and elicits degradation of the kinase [7] , [8] . Conversely , the ubiquitously expressed adaptor protein SH2-Bβ also binds Jak2 but instead enhances its function [9]–[12] . The core structure of SH2-Bβ contains an N-terminal dimerization domain ( DD ) , a pleckstrin homology ( PH ) domain , and a C-terminal Src homology-2 ( SH2 ) domain . Among the multiple Jak2 sites phosphorylated in response to GH stimulation , Tyr813 is specifically recognized by the SH2-Bβ SH2 domain [13] . SH2-B also dimerizes by homotypic association of the DD , which has led to a conceptual model in which SH2-Bβ facilitates Jak2 autophosphorylation through formation of a heterotetrameric Jak2- ( SH2-Bβ ) 2-Jak2 complex [14] . In support of this mechanism , purified SH2-Bβ enhances Jak2 phosphorylation in solution with a biphasic dose response , consistent with saturation of Jak2 at high SH2-Bβ concentrations to form dead-end Jak2- ( SH2-Bβ ) 2 complexes; in the same study , it was further shown that either the SH2 domain or DD expressed alone can antagonize GH-stimulated Jak2 and STAT5b phosphorylation in cells [14] . There is also evidence to the contrary , as the SH2 domain of SH2-Bβ was sufficient to activate Jak2 in a different experimental context 15 , 16; if so , the biphasic dependence of Jak2 autophosphorylation on SH2-Bβ concentration might be attributed to a second , inhibitory interaction involving the PH domain . Although the PH domain has not yet been characterized fully , it has a speculated role in targeting SH2-Bβ to the plasma membrane , based on the established interactions of other PH domains with specific phosphoinositide lipids . Clearly , the two proposed mechanisms of SH2-Bβ function highlighted here present opposing views regarding the importance of DD dimerization . In this work , we apply computational modeling to critically analyze the role of SH2-Bβ in Jak2 activation , revealing a novel mechanism . The model accounts for GH/GH receptor dynamics and Jak2/GH receptor , SH2/Jak2 , DD/DD , and PH/lipid interactions in cells ( Figure 1 ) . As demonstrated in our previous domain-based models of Shp2 [17] and phosphoinositide 3-kinase regulatory subunit [18] , this small number of interactions can produce thousands of distinct molecular species , and we manage this combinatorial complexity using the rule-based modeling approach [19] . Whereas our results challenge the notion that SH2-Bβ dimerization is sufficient for significant Jak2 association in solution or in cytosol , they also show that SH2-Bβ can significantly enhance Jak2 activation stimulated by GH . Dimerized receptors on the one hand , and dimerized SH2-B on the other , are proposed to act as a bipolar clamp that promotes Jak2 transphosphorylation by holding two Jak2 molecules in the same complex ( Figure 1 , top right ) .
Nishi et al . [14] purified Jak2 and SH2-Bβ and showed that SH2-Bβ enhances Jak2 autophosphorylation in solution . They obtained results with 14 pM Jak2 and SH2-Bβ concentrations in the range of 0 . 01–100 nM , which were incubated along with excess ATP for 10 minutes at 25°C in a total volume of 150 µL . The greatest change in Jak2 phosphorylation was seen as the SH2-Bβ concentration increased from 0 . 1 to 1 nM , and the effect of SH2-Bβ decreased at higher concentrations [14] . We recapitulated those conditions in our In Vitro Model ( Methods ) , with the affinities of the SH2-Bβ ( SH2 ) /Jak2 and SH2-Bβ dimerization ( DD/DD ) interactions varied systematically ( Figure 2 ) . The SH2 domain affinity , characterized by KD , JS , was assigned values in the range of 1–100 nM , which are at the low end of KD values ( high affinity ) measured for single SH2 domains [20] , [21] . Indeed , although the KD of the interaction between full-length SH2-Bβ and Jak2 is not known , the isolated SH2 domain binds to a Jak2-derived phospho-peptide with KD = 80–550 nM [22] , [23] . For DD dimerization , we considered an even wider range of KD , SS values , from 0 . 1 nM to 10 µM . Because there are no phosphatases present , the dephosphorylation reactions are turned off in the In Vitro Model , and as a best-case scenario , we assume that the SH2-Bβ binding site of Jak2 ( Tyr813 , or Y1 ) is pre-phosphorylated . In this context , phosphorylation of the Jak2 activation site ( Tyr1007 , or Y2 ) is the readout of the model , which serves as a surrogate for the modification of multiple Jak2 autophosphorylation sites . The results show that , if Jak2 autophosphorylation were to proceed by the proposed heterotetramer ( JS2J ) formation mechanism , the extent of phosphorylation is at most ∼0 . 3% , or <0 . 01 fmol , of Jak2 ( Figure 2A–C ) . Analysis of the model indicates that the rate of phosphorylation is limited by the rate of exchange between phosphorylated and unphosphorylated Jak2 in the heterotetrameric complex , which is most affected by the rate of Jak2-SH2-Bβ association . The values of the association rate constants , kon , JS and kon , SS , are fixed at 0 . 06 nM−1 min−1 ( 1 µM−1 s−1 ) in the model , and therefore similar levels of Jak2 phosphorylation are predicted over multiple decades of KD ( koff ) values . These results are difficult to reconcile with the experimental observations for the following reasons . First , to produce optimal phosphorylation at SH2-Bβ concentrations of ∼1–10 nM , extremely high-affinity interactions are required for both the SH2 domain and DD of SH2-Bβ ( KD values ∼1 nM ) . Second , the predicted amount of phosphorylated Jak2 is probably too low to be detected by immunoblotting . Even if it were 10-fold higher , as by assuming kon = 10 µM−1 s−1 ( quite high for protein-protein interactions ) , it is unclear whether or not it would be detectable . The in vitro role of SH2-Bβ dimerization is even more difficult to reconcile if we relax the assumption that the SH2-Bβ binding site ( Y1 ) is pre-phosphorylated . Indeed , an alternative model was considered that includes SH2-Bβ-independent Jak2 dimerization and phosphorylation of Y1 as a prerequisite for SH2-Bβ binding , and we found that very high concentrations of SH2-Bβ ( ≫100 nM ) are needed to enhance Jak2 phosphorylation , even when the binding affinities are arbitrarily high; even then , the magnitude of the enhancement is quite small ( Figure S1 , Supporting Information ) . In that model , SH2-Bβ must associate rapidly with Jak2 dimers that happen to have catalyzed the phosphorylation of Y1 on both Jak2 molecules , but not of the activating site , Y2; Y2 phosphorylation on either Jak2 molecule leads to rapid phosphorylation of available sites , in which case SH2-Bβ binding has no bearing on the Jak2 phosphorylation status of that complex . With a total Jak2 concentration of 14 pM , the overall concentration of monomeric Jak2 with Y1 phosphorylated never achieves an appreciable concentration for dimerization of Jak2/SH2-Bβ complexes in solution . Based on this analysis , the formation of JS2J heterotetramers cannot adequately explain how SH2-Bβ apparently enhances Jak2 phosphorylation in this assay . The aforementioned alternative mechanism , whereby SH2-Bβ binding stabilizes Jak2 in a more active conformation [16] , is more plausible in the context of Jak2 autophosphorylation in solution . In the rest of this paper , we focus on the more pertinent question of how SH2-Bβ dimerization might enhance Jak2 phosphorylation in cells . Whereas it seems unlikely that SH2-Bβ-mediated heterotetramers could form to a significant extent in solution to explain the activation of Jak2 in vitro , Jak2 kinase activity is normally associated with cytokine receptor signaling at the plasma membrane in vivo . Using our Simplified Cellular Model ( Methods ) , we quantified activated ( receptor-bound and Y2-phosphorylated ) Jak2 stimulated by varying doses of GH at steady state , relative to the number of cell-surface GH receptors in the absence of GH ( Figure 3 ) ; as explained previously [24] , maximal GH receptor activation is accompanied by significant downregulation from the surface , so a relative value of ∼0 . 05 by this measure is the maximum . The Simplified Cellular Model does not allow for membrane localization of SH2-Bβ through its PH domain . In the absence of SH2-Bβ , or ( equivalently ) with SH2-Bβ lacking the DD , the Jak2/receptor binding may be estimated in a straightforward manner . For the parameter values assumed , with total Jak2 expression in excess over receptors and equal to the KD of Jak2/receptor binding , roughly half of the dimerized receptors are bound with Jak2 , and so roughly 1/4 of the receptor dimers have two Jak2 molecules bound and phosphorylated at steady state ( Figure 3A ) . It is noted that , for the parameter values assumed , the two Jak2 molecules remain almost fully phosphorylated on Y1 and Y2 while in the same receptor complex; therefore , allowing SH2-Bβ binding to further enhance Jak2 catalytic activity [16] is of little consequence in this context ( Figure S2A , Supporting Information ) . By comparison , the presence of dimerization-competent SH2-Bβ ( with the reasonable assumption that STot = JTot = KD , JS = KD , SS ) increases by ∼3-fold the number of receptor dimers with two Jak2 bound ( Figure 3A ) and , accordingly , the number of Jak2 molecules with Y2 phosphorylated ( Figure 3B ) . Analysis of the model shows that it does so by forming stable , seven-member “macro-complexes” containing GH , two receptor , two Jak2 , and two dimerized SH2-Bβ molecules , as depicted in Figure 1 . Thus , dimerized receptors serve as a template for Jak2 recruitment , and , once Jak2 has been autophosphorylated , the SH2-Bβ dimer clamps the active Jak2 molecules in place . To further characterize this hypothetical mechanism , the intracellular concentration and dimerization affinity of SH2-Bβ were varied for a constant GH concentration of 10 nM ( Figure 4 ) . Although a broad range of SH2-Bβ concentrations was tested in order to evaluate the full spectrum of behaviors , it is noted that the endogenous SH2-Bβ expression level is not expected to be above the nanomolar range . Given a constant Jak2/SH2-Bβ affinity ( KD , JS = 100 nM ) , the SH2-Bβ concentration should be of a similar magnitude or somewhat higher for near maximal enhancement of Jak2 phosphorylation; extremely high SH2-Bβ concentrations , similar in magnitude to χr ( 100 µM; see Methods ) are needed to antagonize the formation of the stable macro-complex , leading instead to formation of less stable , nine-member S2J ( RLR ) JS2 complexes ( Figure 4A ) . Analysis of the GH receptor/Jak2 complexes formed reveals that , as expected , SH2-Bβ stabilizes complexes with two Jak2 molecules while increasing the total Jak2 recruitment only modestly ( Figure 4B ) . In the absence of SH2-Bβ , approximately half of all GH receptors are Jak2-bound , and this constitutive binding accounts for a significant fraction of the total at all SH2-Bβ concentrations . We next considered the role of the SH2-Bβ PH domain , which is thought to mediate binding with phosphoinositides and thus plasma membrane localization [25] , in our Extended Cellular Model ( Figure 5 ) . Based on physical principles , membrane localization increases the rate of association between complexes containing receptor or/and phosphoinositide molecules by roughly two orders of magnitude , enhancing the binding of SH2-Bβ with receptor-bound Jak2 ( Methods ) . In fact , we find that the addition of the PH domain interaction broadens the efficacy of SH2-Bβ-mediated Jak2 activation down to low nanomolar SH2-Bβ concentrations , well below the assumed KD of the Jak2/SH2-Bβ interaction in solution ( Figure 5A ) . As in the Simplified Cellular Model , this enhancement is not accompanied by dramatic gains in overall Jak2/receptor binding ( Figure 5B ) . Membrane localization of SH2-Bβ facilitates binding to receptor-bound Jak2 and SH2-Bβ dimerization , and therefore it stabilizes signaling-competent macro-complexes at the expense of other receptor/Jak2 complexes . To probe this mechanism further , we repeated the analysis with the DD of SH2-Bβ removed . Intuitively , one might expect that membrane localization of SH2-Bβ would drive significantly more Jak2 into complex with receptors; however , this was not the case with the DD present ( Figure 5B ) , and accordingly , Jak2 autophosphorylation was not dramatically enhanced by SH2-Bβ with the DD absent , even with arbitrarily high SH2-Bβ and phosphoinositide concentrations ( Figure 5C and 5D ) . Variation of the other parameters , such as the Jak2 concentration and binding affinities , did not qualitatively affect the outcome ( results not shown ) . Why is SH2-Bβ dimerization predicted to be so important in the cellular context ? A key insight is that Jak2 must be phosphorylated on Y1 , by associating with dimerized receptors , before it can bind membrane-localized SH2-Bβ . Phosphorylated Jak2 might even associate with SH2-Bβ quite readily , but the lifetime of the receptor/Jak2 interaction is not affected as a result . The association of JSP complexes with free receptors is modest because this pool of Jak2 is small; once formed , the JSP complex is more likely to dissociate via one of its two linkages than to associate with a free receptor site , and when it does bind free receptors , it does not discriminate between dimerized and inactive receptor molecules . By comparison , SH2-Bβ dimerization specifically stabilizes Jak2 interactions with dimerized receptors; this is the essence of the bipolar clamp mechanism . To further evaluate the roles of the functional SH2-Bβ domains , we assessed the ability of different domain mutants to antagonize the function of wild-type SH2-Bβ in cells , i . e . , to act as a dominant negative ( Figure 6 ) . The Extended Cellular Model was used with the addition of the mutant SH2-Bβ species . The SH2 domain alone competes with wild-type for Jak2 binding and is an effective inhibitor at concentrations of at least 1 µM ( for nanomolar concentrations of endogenous SH2-Bβ , as expected ) , which is 10-fold higher than the assumed value of KD , JS ( Figure 6A ) . Inhibition by the DD alone is through dimerization with wild-type SH2-Bβ and is somewhat less effective ( Figure 6B ) , which might be attributed to the partial neutralization of the DD through homo-dimerization . The addition of the PH domain to either the SH2 domain ( functionally equivalent to the DD-mutated SH2-B analyzed in Figure 5C and 5D ) or the DD results in membrane localization of the mutant SH2-Bβ and , accordingly , more potent disruption of receptor/Jak2/SH2-Bβ macro-complexes when it is expressed in excess compared with wild-type SH2-Bβ; comparing PH-SH2 and DD-PH , the former construct shows the more robust inhibition of SH2-Bβ function ( Figure 6C and 6D ) . The predicted efficacies of these two dominant-negatives reflect the gamut of effects , both strong and subtle , discussed previously: 1 ) the effect of SH2-Bβ concentration , relative to its Jak2-binding affinity , on macro-complex formation; 2 ) antagonism of macro-complex formation at extreme SH2-Bβ concentrations , exceeding the value of χr; and 3 ) the ability of phosphoinositides to enhance the effective concentration of SH2-Bβ , which facilitates macro-complex formation at low SH2-Bβ concentrations and also a modest degree of Jak2-receptor association at high SH2-Bβ concentrations that is independent of SH2-Bβ dimerization .
This is the third system we have studied using the rule-based modeling approach to specifically address the concerted binding of multiple , modular domains in signaling proteins . This aspect of signal transduction is a recognized source of complexity in the signal transduction field [26] , [27] , yet it is commonly side-stepped in the formulation of mathematical models of signaling pathways . At the level of pathways and networks , we recognize and espouse that the finer molecular details , while important to consider , must be simplified ( or “lumped” , in the mathematical sense ) . The rule-based approach addresses the problem of combinatorial complexity [28] , its main strength being that it allows the modeler to invoke more mechanistic or biologically plausible assumptions [29]; however , it cannot ease the burden associated with specifying a large number of model parameters , which becomes increasingly problematic at the pathway/network level . For this reason , we apply rule-based modeling to subsystems that involve only a handful of interactions yet give rise to combinations of complexes that could not readily be enumerated in the classical way . Indeed , in this work , models with as many as 3 , 821 differential equations were generated . Despite their large size and complex structure , these models were generated with a small number of generating equations ( “rules” ) and are governed by only a handful of parameters . We analyzed the receptor-mediated activation of Jak2 and the role of the adaptor protein SH2-Bβ , which contains three modular domains ( DD , PH , and SH2 ) , and demonstrated how modeling can be used to evaluate the integration of domain functions as they affect receptor-mediated signaling in cells . In particular , we sought to clarify the role of SH2-Bβ dimerization . Protein homodimerization , or dimerization of structurally homologous proteins , is a ubiquitous process in molecular biology and permeates signal transduction from the receptor level ( e . g . , cytokine receptors , receptor tyrosine kinases ) to the activation of transcription factors ( e . g . , STATs , Smads ) . Ligand-induced dimerization of the GH receptor is necessary but not sufficient for intracellular signaling , requiring also the juxtaposition of two Jak2 molecules; this theme is common to ( and our conclusions are predicted to be applicable to ) signaling mediated by dimers of the closely-related erythropoietin receptor [30] . Dimerization of SH2-B isoforms , and of the closely related APS proteins , is unique because they are considered adaptors or modulators of , not executors of , intracellular signaling . Our results suggest that dimerization of SH2-B goes hand in hand with the binding of Jak2 to dimerized receptors , which template the assembly of the JS2J heterotetrameric unit . Thus , dimerized receptors and SH2-Bβ together coordinate the recruitment of two Jak2 molecules . At least in the context of our models , it is incorrect to characterize SH2-Bβ dimerization as a means of bringing two Jak2 molecules together , as might be inferred by the ability of the adaptor to enhance Jak2 autophosphorylation in solution; rather , we suggest that it acts as a clamp that stabilizes existing J ( RLR ) J complexes . This is because Jak2 must already be autophosphorylated , at least on Tyr813 , for SH2-Bβ to bind . Accordingly , enhancing the association rates of the RJ/S or R/JS linkages , as by membrane localization of SH2-Bβ , is insufficient for significant enhancement of Jak2 phosphorylation if SH2-Bβ cannot dimerize . This work puts forward a number of testable predictions . One concerns the mechanism by which SH2-Bβ dimerization affects Jak2 autophosphorylation , as outlined above . We anticipate that testing the bipolar clamp mechanism concept would be a challenge , because for any mechanism involving SH2-Bβ , enhancement of GH-stimulated Jak2 phosphorylation hinges upon Jak2 binding to GH receptors and its subsequent phosphorylation on Tyr813 . In principle , however , one could express the following Jak2 mutants in cells , in parallel experiments: 1 ) a phosphorylation-mimicked ( Y813D ) Jak2 mutant , 2 ) a phosphorylation-deficient ( Y813F ) Jak2 mutant , and 3 ) a variant of mutant 1 that cannot interact with GH receptor . If over-expressed at a level that is sufficient to saturate constitutive binding to GH receptors , where applicable , then cells expressing mutant 1 or mutant 2 ( or wild-type Jak2 ) would be expected to show similar levels of GH-stimulated autophosphorylation , greater than those expressing mutant 3 , because the stabilizing effect of SH2-Bβ would be superfluous . In contrast , if mutant 1 and mutant 2 were co-expressed in cells at similar levels , SH2-Bβ should stabilize mutant 1 ( or wild-type Jak2 ) relative to mutant 2 , which would be reflected by their differential autophosphorylation . Other predictions consider the potential role of the SH2-Bβ PH domain ( or whichever structural motif is responsible for the observed membrane localization ) . In a cellular context where endogenous SH2-Bβ expression is lacking or repressed , comparison of wild-type SH2-Bβ and a mutant defective in lipid binding might only show moderate differences , and in fact the mutant might outperform the wild-type adaptor if the adaptor concentration is in the high nanomolar range ( as is often the case for expression plasmids; Figure 5 ) . The model results suggest that the role of membrane localization is to broaden the efficacy of SH2-Bβ to low or sub-nanomolar concentrations of the adaptor . But by the same token , we show that membrane localization of SH2-Bβ should enhance the inhibitory properties of constructs that lack either the SH2 domain or the DD , and thus the importance of the membrane localization effect might be more effectively interrogated through such inhibition experiments . To put these predictions in the proper context , it will be important to identify the sequence ( s ) of SH2B-β responsible for its apparent membrane localization , whether in the PH domain or elsewhere in the molecule . Besides the bipolar clamp mechanism explored here , it has also been postulated that SH2-Bβ binding is sufficient for enhancing Jak2 catalytic efficiency [16] , and this alternative mechanism might account for the apparent ability of SH2-Bβ to enhance Jak2 autophosphorylation in solution and in unstimulated cells ( with Jak2 overexpressed ) [14] . This alternative mechanism might also complement the bipolar clamp function of SH2-Bβ , but only in cells where Jak2 in J ( RLR ) J complexes ( without SH2-Bβ bound ) tends to be dephosphorylated , at least on certain sites ( it is important to bear in mind that Tyr813 must be autophosphorylated in order for SH2-Bβ to bind; see Figure S2B , Supporting Information ) . Likewise , there are intracellular conditions in which the clamping function would have no apparent effect; as implied above , saturation of constitutive Jak2-receptor binding renders the mechanism unnecessary . As we have found for other proteins with multiple binding domains , there is a clear indication that the function of SH2-Bβ , and even the dominant mechanism by which it functions , is context-dependent . Finally , we speculate that the bipolar clamp mechanism studied here for the Jak2/SH2-B system will be applicable to analogous signal transduction processes . A striking example is that of 14-3-3 proteins [31] . Like proteins of the SH2-B/APS family , 14-3-3 proteins lack catalytic function , they homo- and hetero-dimerize , and they simultaneously engage certain phosphorylated proteins ( on phospho-serine/-threonine rather than phospho-tyrosine ) . Indeed , 14-3-3 proteins are thought to promote the formation of complexes containing two isoforms of Raf [32] , serine/threonine kinases that function in the most prominently studied of the mitogen-activated protein kinase cascades . This provides a clue that functionally similar mechanisms might be at play at multiple points of signal transmission from the cell surface to the nucleus .
Where applicable , we build upon a previous model of GH/GH receptor interactions and trafficking [24] and use the same parameter values for wild-type human GH . Briefly , the GH ligand concentration [L] is fixed and is an input variable to the model , and unbound GH receptors ( R ) are present at a level of 2×103 molecules/cell initially . Receptor expression is determined by the ratio of the synthesis rate [Vs = 10 ( #/cell ) /min] and basal turnover rate constant ( kt = 0 . 005 min−1 ) . Ligand-receptor complexes ( C ) form with site 1 forward rate constant kf1 = 0 . 1 nM−1 min−1 and reverse rate constant kr1 = 0 . 15 min−1 and are subject to basal turnover . Receptor dimers ( D ) , which are competent for signaling , form from C and R with site 2 forward rate constant kx2 = 2 . 42×10−3 ( #/cell ) −1min−1 and reverse rate constant k−x2 = 0 . 016 min−1 , and they can also dissociate via the site 1 linkage with rate constant 1 . 5×10−3 min−1 ( as noted previously , setting this rate equal to zero does not affect the results for wild-type human GH ) , leaving the ligand to dissociate rapidly via the unstable site 2 linkage . Dimers are endocytosed and degraded at an enhanced rate , with rate constant ke = 0 . 1 min−1 . Secondary effects of Jak2 and SH2-Bβ interactions on GH/GH receptor dynamics are discussed below . Our models are based on mass-action kinetics , with bimolecular ( association of two species ) and unimolecular ( dissociation or change in state of a complex ) transitions . Definitions and ranges of values for the model parameters are given in Table S1 ( Supporting Information ) . For all bimolecular interactions where one or both of the species is in the cytosol , the association rate constant kon was assigned a typical value of 0 . 06 nM−1 min−1 ( or 1 . 0 µM−1 s−1 ) [33] , and the dissociation rate constant koff is calculated from koff = kon KD , where KD is the specified equilibrium dissociation constant . The total intracellular concentrations of Jak2 , SH2-Bβ , and phosphoinositide ( JTot , STot , and PTot , respectively ) are conserved and are specified alternatively in units of molar concentration or molecules/cell; these units are interconverted by assuming a volume of 0 . 52 pL , equivalent to that of a sphere with 5 µm radius . Jak2 binds receptors , regardless of their ligand-bound status and the phosphorylation status of Jak2 , with a KD defined as KD , RJ . The model considers phosphorylation of two Jak2 tyrosine sites , Y1 and Y2 , corresponding to Tyr813 and Tyr1007 , which are responsible for SH2-Bβ association and stimulated activation of Jak2 kinase activity , respectively . Consistent with the current understanding of GH receptor activation , Jak2 can be phosphorylated on Y1 and Y2 only when two Jak2 molecules are associated with the same complex ( receptor or/and SH2-Bβ mediated ) . Once Y2 is phosphorylated , the catalytic efficiency of that Jak2 molecule increases substantially . Accordingly , we model Jak2 phosphorylation as a pseudo-first order process , and once Y2 of the Jak2 molecule acting as the enzyme is phosphorylated , its phosphorylation rate constant towards both Y1 and Y2 of the other Jak2 molecule increases from 6 min−1 ( 0 . 1 s−1 ) to 60 min−1 ( 1 s−1 ) . Jak2 dephosphorylation is also modeled as a pseudo-first order process , with a rate constant of 6 min−1 for both Y1 and Y2; phosphorylated Y1 that is bound to SH2-Bβ is protected from dephosphorylation . SH2-Bβ participates in as many as three interactions , with KD values defined as follows: its SH2 domain binds to Jak2 molecules with Y1 phosphorylated ( KD , JS ) , its DD dimerizes ( KD , SS ) , and its PH domain binds phosphoinositides ( KD , SP ) . The introduction of SH2-Bβ in the system gives rise to interactions in the plane of the membrane or within a multi-molecular complex , and these occur at accelerated rates in the forward direction as compared to the situation where one or both of the interacting species is in the cytosol . Dissociation of such a linkage is assumed to occur with the same rate constant as when one or both of the dissociating components is/are released into the cytosol . Interactions between two membrane-associated species arise as a consequence of SH2-Bβ binding to phosphoinositide lipids ( PS ) or to receptor-bound Jak2 ( RJS , with or without ligand ) , which can subsequently form complexes such as PS2P , RJSP , RJS2JR , etc . To simplify the model in a manner that satisfies detailed balance , interactions in the membrane are assigned a forward rate constant that is calculated as χmkon , with kon = 0 . 06 nM−1 min−1 = 1 . 91×10−4 ( #/cell ) −1min−1 and χm defined as a common , dimensionless enhancement factor; as considered in previous signal transduction models [34] , [35] , its value is based on a confinement layer ( reduced volume ) with 10 nm thickness at the membrane , yielding χm = [ ( 5 µm ) /3 ( 10 nm ) ] ( 103 nm/µm ) = 167 . The corresponding dissociation rate constant is assumed to be the same as for release of one or both species to the cytoplasm; this assumption could be relaxed if diffusion limitations were to be considered . Interactions within a complex ( ring closure ) include the association of two SH2-Bβ molecules with dangling DDs , as in the species SJ ( RLR ) JS , or association of SH2-Bβ and Jak2 in the J ( RLR ) JS2 complex , for example . Ring closure is a unimolecular transition with forward rate constant calculated as χrkon , where χr is the effective concentration of an unbound site within the complex , assumed to be the same for all such interactions ( the notation is from [35] , referring to interactions within a receptor complex ) . A conservative value of χr = 100 µM was used ( see [18] for a detailed discussion ) . Ring closure also affects GH binding , because of the ability of the JS2J heterotetramer to dimerize receptors without ligand present . Thus , the model accounts for closure of species such as LRJS2JR via the GH ( site 2 ) /GH receptor linkage; because GH-induced receptor dimerization normally occurs in the plane of the membrane , the association rate constant for this ring closure transition is calculated as ( χr/χm ) kx2 . To avoid the formation of potentially infinite chains at the membrane , which would occur if GH/GH receptor dimers were clustered via JS2J linkages ( which would be a rare occurrence if accounted for ) , the model is constrained so that complexes may contain no more than 2 receptor molecules . All complexes containing 2 receptors , whether they contain ligand or not , are considered receptor dimers and are subject to enhanced endocytosis , with rate constant ke = 0 . 1 min−1 . Internalized receptors cannot associate with Jak2; any Jak2 and SH2-Bβ in complex with a receptor when it is internalized ( whether endocytosed by the induced or basal turnover pathway ) dissociate at the normal rate . Our simplest model is the so-called In Vitro Model , which contains only Jak2 and SH2-Bβ molecules , and therefore the largest complex in this model is the heterotetramer , JS2J . It considers the best-case scenario where all Y1 sites are pre-phosphorylated and thus generates only 11 species ( state variables ) ( Figure 1 ) . The dephosphorylation reactions are turned off in the In Vitro Model , because phosphatases are not present . The Simplified Cellular Model considers all of the interactions except those with phosphoinositides , generating 470 species ( 5 , 033 reactions ) . The Extended Cellular Model adds the influence of phosphoinositides and generates 2 , 561 distinct species ( 41 , 233 reactions ) . In variations of this model , we also considered the influence of a SH2-Bβ mutant lacking one or two of its domains , acting as a dominant negative , alongside the wild-type SH2-Bβ species; these yielded even more species and reactions , according to the complexity of the dominant negative construct considered: SH2 , 2 , 849 species; DD , 3 , 154 species; PH-SH2 , 3 , 152 species; DD-PH , 3 , 821 species . Our rule-based model was developed using the software program BioNetGen2 , which is freely available through http://bionetgen . org . As discussed in detail elsewhere [17] , [36] , the user defines the biochemical network in terms of molecules , their interaction domains , and context-dependent rules for association/dissociation or covalent modification . Based on those rules , an exhaustive search is performed to automatically generate all possible species ( combinations of interactions and modification states ) and their corresponding conservation equations ( differential equations in time ) , which are numerically integrated using a standard stiff solver up to time = 103 min , by which time the system was confirmed to have reached steady state . For the In Vitro Model , a time of 10 min was used , corresponding to the experimental conditions . The BNGL files specifying the rules for the In Vitro , Simplified Cellular , and Extended Cellular models have been included in the online Supporting Information ( Text S1 , Text S2 , and Text S3 , respectively ) ; note that some of the parameter values were varied as indicated in the figure legends . | Janus kinases ( Jaks ) interact with and activate receptors on the cell surface that mediate changes in gene expression . How these interactions are promoted and regulated is of central interest in fields such as cellular endocrinology and immunology . Here , detailed computational models of Jak activation are offered at the level of protein modification states and interaction domains , wherein the specification of only a handful of binding/reaction rules can produce networks comprised of thousands of differential equations . Specifically , we investigated the role of an adaptor protein , SH2-B , revealing a novel mechanism whereby it cooperates with receptors to form a stable complex that juxtaposes two Jak molecules for efficient activation . We refer to this mode of molecular assembly as the bipolar clamp mechanism . | [
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"signaling"
] | 2009 | A Bipolar Clamp Mechanism for Activation of Jak-Family Protein Tyrosine Kinases |
Aquaporins of the TIP subfamily ( Tonoplast Intrinsic Proteins ) have been suggested to facilitate permeation of water and ammonia across the vacuolar membrane of plants , allowing the vacuole to efficiently sequester ammonium ions and counteract cytosolic fluctuations of ammonia . Here , we report the structure determined at 1 . 18 Å resolution from twinned crystals of Arabidopsis thaliana aquaporin AtTIP2;1 and confirm water and ammonia permeability of the purified protein reconstituted in proteoliposomes as further substantiated by molecular dynamics simulations . The structure of AtTIP2;1 reveals an extended selectivity filter with the conserved arginine of the filter adopting a unique unpredicted position . The relatively wide pore and the polar nature of the selectivity filter clarify the ammonia permeability . By mutational studies , we show that the identified determinants in the extended selectivity filter region are sufficient to convert a strictly water-specific human aquaporin into an AtTIP2;1-like ammonia channel . A flexible histidine and a novel water-filled side pore are speculated to deprotonate ammonium ions , thereby possibly increasing permeation of ammonia . The molecular understanding of how aquaporins facilitate ammonia flux across membranes could potentially be used to modulate ammonia losses over the plasma membrane to the atmosphere , e . g . , during photorespiration , and thereby to modify the nitrogen use efficiency of plants .
Nitrogen is a macronutrient that is often limiting for plant growth . Hence , efficient channeling and storage of ammonia , a central molecule in nitrogen metabolism , is of fundamental importance . Tonoplast Intrinsic Proteins ( TIPs ) belonging to the Major Intrinsic Protein family , also known as the aquaporin ( AQP ) superfamily , have been shown to conduct both water [1] and ammonia [2–4] . TIPs are present in all land plants , but whereas primitive plants like mosses only have one type of TIP ( TIP6 ) , five specialized subgroups ( TIP1‒5 ) have evolved in higher plants [5] . TIPs may constitute up to 40% of the protein in the vacuolar membrane , i . e . , the tonoplast [6] , and have been suggested to enhance nitrogen uptake efficiency and detoxification by acid entrapment of ammonium ions in vacuoles [3] . Furthermore , TIP-mediated increase of ammonia permeability was proposed to play a role in remobilization of vacuolar ammonia during nitrogen starvation [2] and in reallocation of nitrogen at senescence [7] . Recently , TIPs were included in a revised model of futile cycling under high ammonia conditions [8] . Sequence similarities to TIPs are observed in mammalian AQP8s [9] , which are also ammonia-permeable [10] and have been implicated in pathological conditions like hyperammonemia and hepatic encephalopathy [11] . Crystal structures have established that AQPs are homotetramers , where each of the monomers holds a functional pore created by six membrane-spanning helices ( helix 1‒helix 6 ) , five connecting loops ( loop A‒loop E ) , and two shorter helices ( helix B and helix E; Fig 1 ) , both displaying the AQP-signature motif Asn-Pro-Ala ( NPA ) [12–16] . Helices B and E connect at the NPA motifs in the middle of the membrane , thus forming a bipartite transmembrane segment . Different AQP isoforms facilitate permeation of a variety of small uncharged polar molecules , while protons are efficiently excluded from the pore in part by the positive charge , which is focused at the NPA region by the macro dipoles of the short helices [17] . Substrate specificity is thought to be achieved by the aromatic/arginine selectivity filter [18] , which has been defined as four residues located at the noncytosolic end of the pore [19] . Of these residues , an arginine is conserved in the short helix E of most AQPs and contributes to the exclusion of protons [20] , whereas a histidine in helix 5 is associated with water specificity [13] . AQPs permeable to ammonia and water called aquaammoniaporins , including the human HsAQP8 , typically lack the histidine in helix 5 but instead have a histidine in helix 2 [2–4] . However , all previously published AQP structures represent either water-specific channels ( true AQPs ) or the water- and glycerol-conducting aquaglyceroporins , so a further understanding of the structural features that confer ammonia selectivity has been missing . To close this gap in knowledge , we set out to crystallize the aquaammoniaporin AtTIP2;1 from A . thaliana . Here , we present the crystal structure of AtTIP2;1 determined at atomic resolution ( 1 . 18 Å with partial twinning ) . Combined with molecular dynamics ( MD ) simulations and functional studies of mutants , the structure provides new insight into the molecular basis of substrate selectivity in the AQP superfamily .
Heterologously expressed AtTIP2;1 yielded up to 1 . 1 mg of purified and concentrated protein per g of wet Pichia pastoris cells . Purified AtTIP2;1 was verified as a functional water channel , inhibited by mercury , and also permeable to ammonia ( Fig 2 ) . AtTIP2;1 , solubilized by n-octyl-β-D-glucoside , was crystallized at pH 5 . 0 and the structure determined at 1 . 18 Å resolution ( Table 1 ) . In the reported structure , 238 amino acid residues are resolved , and only the N-terminal tag and 12 native residues at the C-terminus are not included in the model . In contrast to other AQPs ( e . g . , SoPIP2;1 [14] , HsAQP5 [15] ) , neither loops nor the resolved parts of terminal regions overlap with neighboring monomers in the tetramer . Loop A and loop D fold back on their own subunit and the N- and C-terminal regions meet at the outer edge of the cytoplasmic vestibule without restricting the pore ( Fig 1C and 1D ) . Accordingly , the structure of AtTIP2;1 constitutes an open channel where the cytosolic and vacuolar vestibules are connected by a pore lining eight water molecules in a single file . Interestingly , the pore diameter of AtTIP2;1 at the NPA region is smaller than in other AQPs , and it remains constant at around 3 Å throughout the pore ( Fig 3A and 3B ) . This is unusual , since in other structures of open AQPs , the aromatic/arginine selectivity filter constitutes the narrowest part of the pore . As mentioned earlier , amino acid residues at the four positions of the pore selectivity filter in helix 2 , helix 5 , loop E , and helix E ( specifically denoted H2P , H5P , LEP , and HEP ) are thought to determine the substrate specificity ( Figs 1A and 3C ) . In line with this , TIP2s deviate from other AQPs ( Fig 3D ) , and as expected from mutational studies and modeling [2 , 22] , the wider selectivity filter is mainly due to an isoleucine ( Ile 185 ) at position H5P in helix 5 , replacing a histidine that is conserved in the water-specific AQPs . However , the most striking feature of the AtTIP2;1 selectivity filter arises from an unpredicted positioning of the arginine at HEP in helix E ( Arg 200 ) , a conserved residue in nearly all AQPs . In AtTIP2;1 , the arginine side chain is pushed to the side of the pore by a histidine located in loop C ( His 131 ) , which now appears as a fifth residue ( LCP ) of an extended selectivity filter . The novel position of the arginine is further stabilized by a hydrogen bond to the histidine ( His 63 ) at position H2P in helix 2 , which occupies essentially the same space as corresponding aromatic residues of water and glycerol channels ( e . g . , Phe 81 in SoPIP2;1 [14] , Trp 48 in EcGlpF [12] ) without direct effects on the pore aperture . The close interaction with Arg 200 at position HEP in helix E suggests a shift in the pKa of His 63 at position H2P , which is likely to stay unprotonated also in the acidic environment of the vacuole . In contrast to His 63 , the additional His 131 at position LCP in loop C points to the center of the pore and forms a hydrogen bond to a pore-water ( Wat 2; Fig 3B ) . Hence , AtTIP2;1 represents the first AQP structure where a residue in loop C ( His 131 ) directly participates in interactions with the substrate in the selectivity region , defining an extended selectivity filter with five positions . The histidine residue at position H2P in helix 2 is conserved in all TIPs , whereas the histidine at position LCP in loop C is only maintained in some types of TIPs , including the TIP2 isoforms , and appears to have been replaced by phenylalanine in a common ancestor of TIP1s and TIP3s ( Fig 3D ) [5] . A phenylalanine at position LCP in loop C is also capable of sterically directing the arginine at position HEP in helix E to the side of the pore , but provides a more hydrophobic environment at the selectivity filter . Worth noting , similar to TIP3s , the mammalian AQP8s [9 , 10] also possess a histidine at position H2P in helix 2 , lack a conserved histidine in loop C , and can be aligned with a phenylalanine at position LCP in loop C ( Fig 3D ) . Thus , a histidine at position H2P in helix 2 and an aromatic residue at position LCP in loop C seem to be a common feature among ammonia-permeable AQPs both in plants and animals . This suggests that the derived phenylalanine at LCP in loop C of some plant TIPs , which supports ammonia permeability without the ability to form hydrogen bonds to the substrate , reflects an adaptation to a different milieu , e . g . , regarding pH or alternatively altered requirements on permeation rate and selectivity . Measurements of absolute water permeabilities [23] and relative permeabilities to other substrates [24] reveal large variations among water-specific AQPs . For example , mammalian AQP4 has a 4-fold higher single channel water permeability compared to AQP1 , although they share identical or similar residues at the four canonical positions of the selectivity filter . Our structure led us to re-examine the amino acid residue at position LEP in loop E , which contributes with its carbonyl to the selectivity region . Comparison of AtTIP2;1 with other AQP structures identifies two spatial groups ( I and II ) of the carbonyls from residues at position LEP in loop E as illustrated in Supporting Information ( S1 Fig; top view in Fig 3C ) . AtTIP2;1 groups together with aquaglyceroporins and the water-permeable human AQP4 ( group I ) , whereas the majority of water-specific AQPs constitutes group II due to an asparagine at position LCP in loop C ( Fig 3D ) , which can form a hydrogen bond to the backbone carbonyl of the amino acid residue at position LEP . This group II-specific interaction directs the LEP carbonyl to the center of the pore and quasiparallel to the membrane . In this conformation , the peptide bonds of this and the two preceding residues are contorted . In contrast , the AQPs lacking an asparagine at position LCP place the backbone carbonyl of the amino acid residue at position LEP ( Gly 194 in AtTIP2;1 ) —and preceding residues of loop E—in a more relaxed conformation , resembling that of a corresponding carbonyl ( Gly 80 ) in the quasisymmetry-related loop B ( Fig 1A ) . The variation of the carbonyls of the amino acid residues at position LEP is spatially small and hence has little effect on the pore diameter per se , but it significantly affects hydrogen bonding to substrates . Thus , the identity of the amino acid residue at position LCP in loop C appears to be relevant for substrate specificity , not only in ammonia permeable AQPs but also in water-specific isoforms . To investigate the contribution of the amino acid residues in the extended selectivity filter to substrate specificity , we analyzed the permeability of the water-specific human AQP1 ( HsAQP1 ) and AtTIP2;1 using in vivo and in vitro assays . Both these channels have the conserved arginine at position HEP of the selectivity filter but , as established by our structure , the spatial location of its side chain relative to the pore differs . Expectedly , substituting all four deviating residues of the extended selectivity filter in a quadruple mutant of AtTIP2;1 to the corresponding residues of HsAQP1 abolished complementation in a yeast growth assay probing for ammonia permeability ( Fig 4 ) . Single point mutations at each of the four positions specify that all of the individual substitutions except the exchange of histidine for phenylalanine at H2P are compatible with ammonia permeability in AtTIP2;1 . The incompatibility of the phenylalanine may be explained by its different electrostatics and slightly larger size , which would be in conflict with the orientation of the arginine at position HEP in the selectivity filter of AtTIP2;1 . The spatial orientation of the arginine at position HEP in AtTIP2;1 is constrained by loop C and the histidine at position LCP , hence the introduced phenylalanine at H2P is likely to adopt an alternative position where it occludes the pore . The compatibility of the three other single mutations may at first appearance be explained by their polar nature , allowing formation of hydrogen bonds to ammonia . However , as mentioned above , at position LEP , the hydrogen bond to the substrate is offered by the backbone carbonyl rather than the side chain . Interestingly , a double mutation in helix 5 ( position H5P ) and loop E ( position LEP ) of the selectivity filter failed to demonstrate ammonia permeability in the growth assay . This may indicate that a small and flexible residue at position LEP of AtTIP2;1 is required to allow its carbonyl to interact with the substrate in the presence of a histidine at position H5P . The identification of determinants for substrate specificity based on functional knockout mutants is complicated by possible trivial explanations for loss of function , such as misfolding of the protein or failure to reach the plasma membrane . Therefore , rather than just trying to eradicate the ammonia specificity of AtTIP2;1 by mutations , we chose to focus on gain-of-function mutants of the water-specific HsAQP1 in an attempt to mimic the ammonia and water permeability of AtTIP2;1 . Hence , the four deviating residues in the extended selectivity filter of HsAQP1 were mutated in a stepwise fashion to probe if a histidine at LCP , which may force the arginine at LEP of HsAQP1into an AtTIP2;1-like position , was enough to achieve a matching substrate selectivity , or if additional substitutions were required . Unexpectedly , only the quadruple HsAQP1 mutant ( H2P-F56H , LCP-N127H , H5P-H180I , LEP-C189G ) showed significant growth compared to the empty vector control in the yeast complementation assay for ammonia permeability ( Fig 5 ) . Stopped-flow experiments were conducted to quantitate ammonia and water permeability rates of the HsAQP1 mutants and the controls . To compare relative specificities independently of differences in expression levels of the correctly folded protein in the yeast plasma membrane , the ratios of these rates were calculated ( Table 2 ) . Albeit the two rates represent alkalization and swelling rates in two different arbitrary units , their ratios offer an informative measure of the relative specificity in comparisons between mutated and wild type proteins . Whereas wild type HsAQP1 only conducts water , AtTIP2;1 showed a significant ammonia permeation rate , corresponding to approximately 3% of the water permeation rate in this strain . Interestingly , the same ratio was only obtained for the quadruple mutant of HsAQP1 , showing that these four substitutions are sufficient to reproduce an AtTIP2;1-like specificity in HsAQP1 . Two other mutant forms of HsAQP1 , namely the H5P-H180I single mutant and the triple mutant ( H2P-F56H , LCP-N127H , H5P-H180I ) , exhibited a higher specificity for ammonia than AtTIP2;1 and similar absolute ammonia rates as the quadruple mutant , but surprisingly failed to complement the ammonia transport deletion strain in the growth assay . Insufficient water permeability of these mutants may explain these observations , implying that a dual permeability might be necessary for an efficient ammonia uptake in vivo . The impaired water permeability is likely to be a result of the higher hydrophobicity of isoleucine replacing the histidine at position H5P of HsAQP1 . Apparently , water permeability is restored in the quadruple mutant by the LEP-C189G mutation . This is consistent with conservation of a small amino acid residue ( alanine or glycine ) at LEP of TIPs . A small residue may allow the carbonyl oxygen in LEP—not hydrogen bonded to an amino acid at position LCP—to adopt the relaxed position and interact with waters in the selectivity filter more efficiently . In contrast to earlier mutational studies [4] , our new structural insight has allowed us to rationally design and implant a TIP2-like substrate profile to a water-specific AQP . Although the high resolution structure of AtTIP2;1 allowed us to discriminate between nitrogen and carbon atoms in side chains of histidines ( Fig 3B ) , it would not be possible to distinguish nitrogen of ammonia from oxygen of water in the pore of AtTIP2;1 due to their similar electron density and expected low ammonia occupancy . To get a more detailed view of the substrate specificity in AtTIP2;1 , we therefore employed MD simulations . Water permeation was seen at high frequency ( pf ± SD ) corresponding to approximately 25 ± 4 × 10−14 cm3 s−1 ( S2 Fig ) , which is about four times as high as estimated for human AQP1 . The high water permeation in AtTIP2;1 is consistent with its low free energy for water ( S3 Fig ) . Notably , spontaneous ammonia permeation events were observed in unbiased simulations with a length of 400 ns ( S1 Movie ) and verified by umbrella sampling simulations yielding a free energy barrier of approximately 15 kJ/mol ( Fig 6A ) in line with a high ammonia permeability . Further analysis shows that desolvation effects are compensated for by several hydrogen bonding residues at the selectivity filter ( Fig 6A–6C ) , substantially lowering the energetic barrier in this region , where it peaks for the water-specific HsAQP1 [25] . In contrast to a simple model membrane ( S3 Fig ) , the tonoplast contains sterols [26] , which increase the impermeability to polar molecules . Therefore , the ammonia permeability of AtTIP2;1 is compared to a cholesterol containing model membrane with a free energy barrier for ammonia of 20 kJ/mol ( Fig 6A ) . Due to these differences in energy barriers , the permeability of AtTIP2;1 is an order of magnitude higher . It is debated whether aquaammoniaporins are permeated by ammonium ions or not [27] . This has physiological relevance , since an effective exclusion of ammonium ions is necessary to acid-trap it in the vacuole . MD simulations containing ammonium ions showed no spontaneous permeation events , as expected due to electrostatic repulsion and desolvation effects in the pore . This brings to mind the ammonia transporter AmtB , which is generally considered to have a similar ammonium/ammonia selectivity , but where ammonia permeation has been proposed to be stimulated by ammonium recruitment to the noncytosolic vestibule [28] . We therefore investigated if ammonium ions accumulate on the vacuolar surface of AtTIP2;1 . A comparison of the electrostatics reveals that the vacuolar surface of AtTIP2;1 is distinctly more negative than the corresponding surface of another plant AQP , the extracellular surface of the plasma membrane located water-specific SoPIP2;1 that we established the structure of at 2 . 1 Å resolution [14] ( S4 Fig ) . The negative vacuolar surface of AtTIP2;1 is predominant at exposed acidic residues ( Asp 48 , Asp 52 , Asp 210 ) . Indeed , our MD simulations show a local enrichment of ammonium ions at these acidic residues ( Fig 7A ) . Although the exact positions of acidic residues vary , a negative vacuolar surface constitutes a conserved feature among TIPs , which implies a generality of this finding . Considering the low pH in some vacuoles and accumulation of ammonium on the vacuolar surface of AtTIP2;1 , the permeation efficiency would clearly benefit if ammonium contributes to channeling of ammonia . How then is ammonium deprotonated ? The distal position of the second vacuolar loop ( loop C ) relative to Arg 200 at position HEP in helix E hints at an explanation . In fact , structure and MD simulations concur that loop C leaves enough space for a continuous side pore reaching from the selectivity filter all the way to the vacuolar surface ( Fig 7A–7D ) . In all previously reported AQP structures , the HEP-arginine is directly hydrogen bonded to the backbone carbonyl oxygen of the residue corresponding to Pro 129 of loop C . In AtTIP2;1 , this contact is instead mediated via a water molecule ( Wat 10; Fig 7D ) occupying a similar position as the arginine-binding carbonyl oxygen in other AQP structures . Interestingly , the peptide bond preceding the TIP2-specific histidine ( His 131 ) at position LCP in loop C retains an unusually large dihedral angle ( 19° ) [29] , and this contortion would be even larger with a deeper position of loop C . To explore if His 131 at position LCP and the side pore could play a role in facilitating deprotonation of ammonium , we conducted further MD simulations . Ammonium deprotonation via LCP-His 131 through the side pore most likely requires a positional shift of this residue away from ammonia and towards a water molecule in the side pore ( Wat 11 ) to get in hydrogen bond distance . The simulations indicate that the angle of the His 131 side chain ( chi 1 ) remains as in the crystal structure when neutral ( Fig 7B and S5 Fig ) , whereas in a protonated , i . e . positively charged state , an alternative orientation towards Wat 11 is indeed observed ( Fig 7C , S5 Fig and S6 Fig ) . Furthermore , as simulations support the side pore as being continuously solvated , from His 131 to the vacuolar exit , it offers a proton wire for return of protons to the vacuolar environment , as ammonium is transferred to the pore as ammonia . These findings lead us to speculate about a possible TIP2-specific mechanism ( Fig 7E ) where histidine ( His 131 ) at position LCP in loop C shuttles protons from the main pore to the vacuolar surface via the side pore , using a Grotthuss mechanism , putatively enhancing the permeation rate of ammonia from the vacuole under nonequilibrium flux conditions .
The atomic structure of the water and ammonia permeable AtTIP2;1 provides new insights into the substrate selectivity of AQPs . The structure reveals an extended selectivity filter , including a fifth amino acid residue at position LCP in loop C that also may play a role in defining substrate profiles of the entire AQP superfamily . The importance of the extended selectivity filter is demonstrated by mutational studies in vivo and in vitro , showing gain-of-function of AtTIP2;1 substrate selectivity in the water-specific human AQP1 . MD simulations support ammonia conductance and a lack of ammonium permeability . As expected from the structure , ammonia interacts with LCP-His 131 and behaves similar to water in the pores of AQPs [18] , reorienting in the NPA region at the N-termini of helix B and helix E due to their macro dipoles ( S1 Movie ) . Based on structural analyses and simulations , we describe a selectivity filter that is highly permeable to ammonia due to its width and many potential polar contacts with the substrate and speculate on a mechanism in which ammonia permeation may be further increased by ammonium accumulation at the vacuolar protein surface , deprotonation through the TIP2-specific LCP-His 131 , and proton transfer via a previously unidentified water-filled side pore . It should be stressed that there is only limited support from simulations for this speculation , and without confirmatory structures it is difficult to specifically target the side pore by mutations or to predict if it is a conserved feature of other TIPs and AQP8s . However , we find it most likely that the conserved arginine at position HEP in helix E of other TIPs and AQP8s adopt the same conformation , as shown here for AtTIP2;1 , due to aromatic residues at position LCP in loop C and hydrogen bonding residues at position H2P in helix 2 . The conservation patterns in the selectivity filter of AQP8s and separate subgroups of TIPs indicate that a functional differentiation has evolved among aquaammoniaporins . TIP2s and TIP4s of higher plants are similar to TIP6s found in primitive plants like mosses and are therefore likely to represent original functions and mechanistic features of TIPs . In contrast , TIP1s and TIP3s , which appear with the emergence of seed plants , as well as AQP8s in animals , lack the histidine at position LCP , which is proposed here to enhance deprotonation of ammonium . Such variations among aquaammoniaporins may relate to pH-dependent properties , which however remains to be investigated . In this context , it should be mentioned that both AQP8 and an AtTIP2;1 mutant lacking the histidine at position LCP complemented ammonia permeability in the growth assay equally well or better than the wild type AtTIP2;1 , demonstrating that a histidine at this position is not essential for efficient ammonia uptake under these conditions . The AtTIP2;1 structure will facilitate modeling of other AQPs including human AQP8 and may therefore also help to elucidate the molecular basis of ammonia permeation in man . From our results , it is clear that AtTIP2;1 can enhance the ammonia permeability of membranes , but conditions linking an ammonia-related phenotype to TIPs have so far not been reported in plants [30] . Plants emit significant amounts of ammonia from their leaves , and ammonia generated by photorespiration further accentuate losses , implying a limited capacity of ammonia reassimiliation enzymes [31] . We expect that high ammonia permeability of the tonoplast and rapid acid-entrapment of ammonium in the vacuole is especially important under transient periods of photorespiration when it counteracts high levels of ammonia in the cytosol and thereby reduces losses over the plasma membrane , giving reassimilation pathways time to incorporate more of the generated ammonia . Hence , we propose that expression of ammonia-permeable mutant AQP isoforms in the plasma membrane , such as PIP2 mutants having a TIP2-like extended selectivity filter , combined with regulation of TIP expression can be used to vary the relative ammonia permeability of the plasma membrane and the tonoplast to explore effects on ammonia emission under these conditions . Control of ammonia emission by regulation of ammonia permeability in membranes could potentially open up a new way to improve the nitrogen use efficiency in plants .
For details , see Supporting Information S1 Text . AtTIP2;1 with an N-terminal deca-His-tag was expressed in Pichia pastoris and purified as previously described [32] . Briefly , membranes were urea washed and solubilized with 10% n-octyl-β-D-glucoside ( OG ) . The protein was purified by nickel affinity chromatography , employing a 100 mM imidazole wash , followed by size exclusion chromatography using an S200 column . Functionality was verified by stopped-flow analyses of proteoliposomes . Hanging drop vapor-diffusion crystallization was performed at room temperature using a reservoir solution consisting of 50 mM magnesium/sodium acetate pH 5 . 0 and 28% ( v/v ) PEG 400 and crystals were flash-cooled in liquid nitrogen . Data were collected at 1 Å wavelength on the X06SA ( PXI ) beamline at the Swiss Light Source , Villigen , Switzerland . The structure was solved by molecular replacement and the model built manually . Refinement excluded 3% reflections , including twin-mates , and resulted in a twin fraction of 40 . 7% , reaching Rwork and Rfree-values of 10 . 2% and 11 . 2% , respectively . Ramachandran outliers and residues in unfavored regions were manually inspected . Mutant studies of AtTIP2;1 and HsAQP1 were executed using protoplasts and intact cells from Saccharomyces cerevisiae , as previously described [33] . The simulations were conducted with the GROMACS 4 . 5 software [34] using the CHARMM36 forcefield [35] . To study the properties of AtTIP2;1 , the protein was embedded in a 1-Palmitoyl-2-oleoylphosphatidylcholine ( POPC ) bilayer . Three unbiased 500 ns simulations were conducted to study the equilibrium behaviour of AtTIP2;1 , in the presence of water , ammonia or ammonium ions . Umbrella sampling was employed to calculate the PMF for permeation of water and ammonia [36 , 37] . | Ammonia is a central molecule in nitrogen metabolism . Aquaporins are integral membrane proteins that form channels that accelerate the passive permeation of small polar uncharged molecules , like water and ammonia , across lipid membranes of the cell . Structural information of ammonia-permeable aquaporins has been lacking . Here , we report a high-resolution structure of the ammonia-permeable aquaporin AtTIP2;1 and explore it by functional assays of mutants and by molecular dynamics simulations . Our data uncover unexpected features of the substrate selectivity filter , including a conserved arginine in a new orientation that is stabilized by interactions to a histidine that is linked to ammonia specificity . An additional histidine in a different part of AtTIP2;1 fortifies the position of the arginine and interacts directly with the substrate in the channel . This histidine is therefore included in an extended selectivity filter , which should prompt a reinterpretation of the determinants of specificity in all types of aquaporins . We speculate that an intriguing water-filled side pore , next to the substrate-binding histidine , participates in deprotonating ammonium ions , which could increase the net permeation of ammonia . Understanding the principles of ammonia permeability may , in the future , allow us to modulate the passage of ammonia and generate crops with higher nitrogen-use efficiency . | [
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... | 2016 | Crystal Structure of an Ammonia-Permeable Aquaporin |
Constraint-based modeling ( CBM ) is increasingly used to analyze the metabolism of complex microbial communities involved in ecology , biomedicine , and various biotechnological processes . While CBM is an established framework for studying the metabolism of single species with linear stoichiometric models , CBM of communities with balanced growth is more complicated , not only due to the larger size of the multi-species metabolic network but also because of the bilinear nature of the resulting community models . Moreover , the solution space of these community models often contains biologically unrealistic solutions , which , even with model linearization and under application of certain objective functions , cannot easily be excluded . Here we present RedCom , a new approach to build reduced community models in which the metabolisms of the participating organisms are represented by net conversions computed from the respective single-species networks . By discarding ( single-species ) net conversions that violate a minimality criterion in the exchange fluxes , it is ensured that unrealistic solutions in the community model are excluded where a species altruistically synthesizes large amounts of byproducts ( instead of biomass ) to fulfill the requirements of other species . We employed the RedCom approach for modeling communities of up to nine organisms involved in typical degradation steps of anaerobic digestion in biogas plants . Compared to full ( bilinear and linearized ) community models , we found that the reduced community models obtained with RedCom are not only much smaller but allow , also in the largest model with nine species , extensive calculations required to fully characterize the solution space and to reveal key properties of communities with maximum methane yield and production rates . Furthermore , the predictive power of the reduced community models is significantly larger because they predict much smaller ranges of feasible community compositions and exchange fluxes still being consistent with measurements obtained from enrichment cultures . For an enrichment culture for growth on ethanol , we also used metaproteomic data to further constrain the solution space of the community models . Both model and proteomic data indicated a dominance of acetoclastic methanogens ( Methanosarcinales ) and Desulfovibrionales being the least abundant group in this microbial community .
Microbial communities are of major importance for human health [1 , 2] , geochemical cycles [3 , 4] and biotechnological processes [5–7] . Despite of their importance , most microbial communities are still poorly understood due to their complex nature . Mathematical modeling can help to uncover the interactions and dependencies of the members of these communities . Different modeling formalisms have been used to simulate microbial communities including stoichiometric models , which can be analyzed by constraint-based methods [8–18] . An increasing number of stoichiometric community models considers balanced growth as a key assumption stating that all organisms must grow with the same growth rate in a stable community [11 , 15 , 16] . One central goal of these models is the characterization and prediction of possible community compositions and the analysis of the different modes of cross-feeding between the involved organisms . Stoichiometric models of microbial communities with balanced growth usually result in bilinear models , where , in some equations , independent variables are multiplied with each other . Thus , apart from their increased size , these models have a more complex nature than the linear metabolic models of single species . To make bilinear models amenable to established constraint-based modeling approaches , they can be linearized by fixing either the community growth rate [16] or the community composition [11 , 15] . In this study , we first provide a unified framework for setting-up , analyzing , and linearizing community models . Even in linearized community models , the application of certain constraint-based techniques becomes quickly infeasible with an increasing number of organisms . Furthermore , one shortcoming of existing methods for modeling of communities is that the solution space often contains unrealistic solutions ( where , for example , a species behaves unrealistically altruistic to produce substrates needed by other community members ) . We therefore introduce a new approach , RedCom , to build reduced community models . The main principle of RedCom is similar to what has been suggested by Taffs et al . [10] , namely to compute , in a first step , relevant net conversions of the single-species models which serve as reactions for the reduced model . This reduced model can then be used to identify suitable combinations of single-species net conversions to obtain community-level conversions . However , while Taffs et al . [10] used elementary modes to describe the single-species net conversions , RedCom is based on the more general concept of elementary flux vectors [19 , 20] . This will be required to ensure balanced growth in the community model and to appropriately account for flux bounds and other ( e . g . proteome allocation ) constraints . Reduced community models obtained with RedCom do not only focus on most relevant solutions but allow for a comprehensive characterization of solution spaces also for communities with more than only two or three species . In the following , we apply the proposed techniques for different community models with increasing complexity from three up to nine species . The investigated communities are capable of degrading different substrates to biogas , a renewable energy source . Community models of the biogas process give insights on interdependencies and feasible community compositions and may contribute to increase productivity and stability of this process . As one of the first studies , we also compare simulation results from the community models with experimental data of laboratory-scale biogas reactors for growth on ethanol and glucose-cellulose media .
Constraint-based ( stoichiometric ) modeling of metabolic networks [21] relies on the assumption of a steady-state for internal metabolite concentrations leading to the mass balance equation: Nr=0 ( 1 ) The structure of the network is captured by the stoichiometric matrix N storing the stoichiometric coefficients of the metabolites ( rows ) in the metabolic reactions ( columns ) . As consequence of eq . ( 1 ) , steady-state flux vectors r fulfill the condition that no net accumulation or depletion of internal metabolites occurs . Additionally to the steady-state condition , reversibility constraints ( 2 ) , flux bounds ( 3 ) and other types of inhomogeneous linear constraints ( 4 ) can be included: rj≥0forj∈Irrev ( 2 ) αj≤rj≤βj ( 3 ) Ar≤b . ( 4 ) The set Irrev contains the indices of irreversible reactions . If only the steady-state ( 1 ) and the irreversibility constraints ( 2 ) are taken into account , the solution space forms a polyhedral ( flux ) cone; with any constraint of type ( 3 ) or ( 4 ) its shape becomes a ( flux ) polyhedron . In order to create a community model combining all ( n ) single-species models , herein referred to as full model , a compartmented approach is usually employed [9 , 11 , 12 , 15 , 22 , 23] . Each organism represents one compartment and an additional exchange compartment allows for exchange of metabolites ( substrates/products ) between organisms and with the medium ( Fig 1 ) . With the new exchange compartment , the former external ( unbalanced ) metabolites become now internal ones and must be balanced in eq . ( 1 ) . Exchange metabolites used by several species are combined such that they exist only once in the community model . As described in [15] the units of the ( specific ) single-species reaction rates must be adapted to refer to the total community ( instead of single-species ) biomass . Accordingly , the units of all reaction rates change from mmol/gDWi/h to mmol/gDWc/h . Exceptions are the n biomass synthesis ( growth ) reactions producing the species biomasses BMi from a ( species-specific ) set of precursors: γi , 1pi , 1+γi , 2pi , 2+⋯+γi , qpi , q→1BMi[gDWi] ( i=1…n ) ( 5 ) In the single-species models , the specific ( growth ) rates μi ( i = 1…n ) of these n reactions referred to unit 1/h , which is now changed to gDWi/gDWc/h . We indicate the changed units of these reaction rates in the community model by the symbol μ˜i ( i=1…n ) . Furthermore , n new pseudo-reactions are introduced in the community model to describe the integration of the n species biomasses into the community biomass BMc ( Fig 1 ) : 1BMi[gDWi]→1BMc[gDWc] ( rate:rBMi→BMc[gDWi/gDWc/h] ) ( i=1…n ) ( 6 ) Finally , a new community growth reaction is introduced “exporting” the synthesized community biomass to the medium ( Fig 1 ) ; the rate of this reaction is the community growth rate μc [1/h]: 1BMc[gDWc]→ ( rate:μc[1/h] ) ( 7 ) Note that , in steady state , μ˜i=rBMi→BMc and ∑i=1nμ˜i=∑i=1nrBMi→BMc=μc . The obtained structure of the whole community network is captured in the community stoichiometric matrix Nc and the reaction rates in the community flux vector rc ( with units as described above ) . As for the single-species models , we demand steady-state for the metabolites ( including all metabolites in the exchange compartment ) : Ncrc=0 . ( 8 ) In a stable continuous culture , the growth rate of microorganisms is typically equal to the dilution rate . We assume that the same is true for a microbial community cultivated in a continuous process . In that case , the growth rates μi of all organisms ( each normalized to the respective specific biomass ) must be identical and equal the community growth rate μc: μ1=μ2=⋯=μn=μc . ( 9 ) This concept of balanced growth of microbial communities has previously been proposed by Khandelwal et al . ( 2012 ) and is also an underlying principle of the OptDeg [15] and the SteadyCom [16] approach . It has been argued that , even if there is no steady state in a continuous cultivation , the specific growth rates of the organisms need to be the same on average because otherwise the fastest organism would outgrow the others . With constant growth rates , also the fractional biomass abundances Fi=BMiBMc ( 10 ) of each species i in the community biomass BMc must be constant . The fractions Fi define the community composition F = ( F1 , … , Fn ) and sum up to unity: ∑i=1nFi=1 . ( 11 ) With balanced growth , the fraction Fi of species i is given by the ratio of the specific biomass production rate of species i ( normalized to the community biomass ) and the community growth rate: Fi=BMiBMc=rBMi→BMcμc=μ˜iμc ( 12 ) ) Note that the fractional contributions to the synthesis of the community biomass ( μ˜i=rBMi→BMc; normalized to BMc ) are not identical over the species , hence , the μ˜i need not fulfill ( 9 ) . However , for the specific growth rates μi ( referring to BMi ) it holds that μi=μ˜i/Fi=μc and thus ( 9 ) is indeed satisfied . For each species , we can rewrite ( 12 ) to the following constraint: rBMi→BMc=Fiμc . ( 13 ) ( Alternatively we could also use μ˜i instead of rBMi→BMc in this equation ) . In the optimization problems considered below , constraints of type ( 13 ) need to be included only for n−1 species , because ( 6 ) , ( 7 ) , and ( 11 ) already imply ( 13 ) for the n-th species: rBMn→BMc=μc−∑i=1…n−1rBMi→BMc=μc−∑i=1…n−1Fiμc=μc− ( 1−Fn ) μc=Fnμc . Due to the re-normalization of the reaction rates from specific to community biomass , as the last step in assembling the community model we also need to adjust the normalization of the original flux bounds ( 3 ) and other inhomogeneous conditions ( 4 ) by multiplying them with the fractional abundances: Fiαij≤rijc≤Fiβij ( 14 ) where αij and βij are the lower and upper bounds for reaction j in organism i and rijc is the reaction rate of reaction j in organism i in the community model . Likewise , constraints ( 4 ) are adjusted for each organism to Airic≤Fibi ( 15 ) ( Ai , bi correspond to the respective variables in ( 4 ) for species i ) . The irreversibility constraints for the reaction rates are kept from the single-species models: rijc≥0forj∈Irrevi . ( 16 ) To exclude solutions with non-zero fluxes rijc≠0 in organisms that are not present in the community ( Fi = 0 ) , we assume that every flux in species i is bounded ( i . e . , αij and βij are bounded ) . With ( 14 ) , a non-zero flux rijc then implies Fi>0 . In principle , with the chosen constraints , one can also consider the case where the community is not growing ( μc = 0 ) , i . e . , where dependencies arise exclusively from the maintenance metabolism of the participating species . However , if the community is growing ( μc>0 ) , a non-zero flux rijc≠0 in species i implies again Fi>0 and , due to ( 13 ) , then also rBMi→BMc=μ˜i>0 . In analogy to classical flux balance analysis ( FBA ) in single organisms , we may formulate a ( linear ) objective function maximizing certain ( combinations of ) reaction rates in the community model: MaximizecTrcs . t . ( 8 ) , ( 11 ) , ( 13 ) − ( 16 ) ( 17 ) Due to the multiplication of ( independent ) variables in constraints ( 13 ) , the community model and the associated optimization problem become bilinear . While non-linear solvers can be employed to solve the optimization problem ( e . g . , to search for maximum community growth rates or to scan feasible ranges of fluxes or community compositions; see below ) , a linearization can be applied to enable application of standard linear programming solvers and methods routinely used in ( linear ) constraint-based modeling . Two approaches have been used to linearize bilinear community models and to simplify its analysis ( Fig 2 ) . In the first approach ( utilized in SteadyCom [16] ) , the community growth rate μc is fixed to a constant ( known ) value . The constraints ( 13 ) become then linear and the optimization problem ( 17 ) thus treatable with standard linear programming ( LP ) solvers . Linearization by fixing the community growth rate is useful , for example , to analyze which community compositions are feasible for a given community growth rate . Repeating these analyses ( in discrete steps ) for the feasible range of community growth rates yields a more complete picture of the whole solution space . An alternative linearization method was used in community FBA [11] and in the OptDeg approach [15] . Here , instead of the community growth rate , the community composition , i . e . all the fractional abundances Fi , are fixed . Eq ( 13 ) becomes then again linear allowing the utilization of LP solvers . With given fractional abundances , constraint ( 11 ) can be removed from the optimization problem ( 17 ) . This second linearization approach is useful to scan , for example , the feasible community flux space for a given community composition . However , with a growing number of organisms , this scanning becomes very expensive in terms of the number of linear programs to be solved [16] . In this study , we therefore linearize community models by fixing μc as proposed in the SteadyCom approach . We used an iterative approach to find the maximum community growth rate μc , max in these linearized models . First , we set μc to a value of 0 . 005 h-1 . If a feasible flux distribution exists ( here , any ( including a zero ) objective function can be used in Eq ( 17 ) ) , we double μc and check again for a feasible flux distribution . We repeat these steps until no feasible flux distribution is found . We then take the average of this μc and the last feasible μc ( or zero if the first μc did not yield a flux distribution ) . These steps are repeated ( check for feasibility , use average of latest feasible and infeasible μc as new constraint and check again for feasibility ) until the difference between the last feasible and infeasible μc is smaller than 0 . 00001 h-1 . Generally , for both linearization variants , apart from the FBA-like optimization in ( 17 ) , other constraint-based methods like flux variability analysis ( FVA ) or metabolic pathway analysis based on elementary flux modes or elementary flux vectors can be carried out ( see below ) . The described approaches for modeling communities under balanced growth can be used to define and analyze community solution spaces . However , these solution spaces often include unrealistic solutions on the species-level ( e . g . , a species synthesizes , without any benefit for its own growth , products required by another species in the community [15] ) . Consequently , the predicted ranges for community compositions or growth rates may become very large as they include many non-relevant phenotypes . FBA could be used to find community compositions fulfilling certain optimality criteria , but the question of suitable objective function in communities arises . In single-species models , a typical objective function is maximization of the growth rate . In community models we can also maximize the community growth rate [11] . But , again , even these optimal solutions may represent unrealistic community compositions in which some organisms waste substrate to ensure survival of the others [15] . We therefore proposed previously an optimization approach to minimize a weighted sum of substrate uptake rates to find community compositions in which all organisms grow with their maximum biomass yields [15] . This approach enabled us to narrow down the solution space to community compositions in which all organisms grow optimally with their maximum biomass yields at a given community growth rate . When introducing our model reduction approach below , we will use a similar method to exclude unrealistic community flux distributions . Elementary flux modes ( EFMs ) are non-decomposable flux vectors fulfilling Eqs ( 1 ) and ( 2 ) [24] . EFMs represent balanced pathways or cycles and have become an important tool for exploring metabolic networks [20 , 25–28] . However , one shortcoming of EFMs is that inhomogeneous constraints ( Eqs ( 3 ) and ( 4 ) in the single-species models and ( 14 ) and ( 15 ) in the community model ) , such as non-growth associated ATP maintenance demand and substrate-uptake limits , cannot be considered . We therefore make use of the concept of elementary flux vectors ( EFVs ) , a generalization of EFMs which can account for inhomogeneous constraints [19 , 20] . From the theory of EFVs , it is known that the flux polyhedron P resulting from a set of linear constraints is generated by convex combinations of bounded EFVs pk and conic linear combinations of unbounded EFVs xi and yj: P={r∈ℜn|r=∑k∈Kγkpk+∑i∈Iαixi+∑j∈Jβjyj , γk≥0 , ∑k∈Kγk=1 , αi≥0} ( 18 ) Due to combinatorial explosion , EFVs can usually only be calculated in medium-scale metabolic networks and , thus , only in smaller community models combining the central metabolism of two or three species . We present RedCom , a new method to generate community models of reduced size and with reduced solution spaces excluding unrealistic community behaviors . The main idea of the reduction approach , which has some similarities with but is not identical to an approach presented by Taffs et al . [10] , is to describe the metabolism of each organism by certain net conversions taken from the EFVs of the single-species models ( Fig 2 ) . Since we are mainly interested in community compositions and metabolic interactions ( exchange reactions ) between the community members , it is often sufficient to focus only on net conversions of the respective species instead of taking its whole metabolic reaction network explicitly into account . Furthermore , from the list of all net conversions of a species we select only those that obey certain optimality criteria avoiding unrealistic phenotypes in the community model . The selected net conversions are used as reactions in the reduced community model to be built . The construction of reduced community models with the RedCom approach is described in the following , a detailed example is given in S1 Text in the Supplements . All models presented in the Results section were implemented and analyzed with CellNetAnalyzer version 2018 . 1 , a MATLAB package for structural and functional analysis of metabolic and signaling networks [29 , 30] . CPLEX was used as a solver for linear optimizations and efmtool for computation of EFVs . For solving bilinear problems , we used the fmincon solver for nonlinear optimization in MATLAB . The solver needs an initial flux distribution that we retrieved from the linearized model . Experimental data from a laboratory-scale biogas reactor on a defined glucose-cellulose medium were published earlier [31] and used for a comparison with predictions from the nine-species biogas producing community ( see Results ) . The data were taken from steady-state conditions [31] . We calculated the average methane and CO2 production rates over a course of 100 days . To achieve steady-state conditions , the reactors were operated under similar conditions for 190 days prior to this time period . Additionally to the data already published , we estimated biomass dry weights by measuring protein concentrations with the Lowry Assay [32] and dividing them by the factor 0 . 64 ( assumed fraction of protein of the total biomass in the model ) . We used these data to calculate specific production and consumption rates for comparison with simulation results . A detailed description of the procedures applied for inoculation , feeding , and sample analyses along with cultivation setup and parameters is given in the S6 Text . Briefly , two 1 . 5 L bioreactor systems were inoculated with sludge from the aforementioned enrichment and fed with the same medium containing 14 . 6% ( v/v ) ethanol as main carbon source instead of glucose and cellulose . After an adaption period , continuous cultivation mode was initiated using constant feeding rates and volume control . In the following , different dilution rates were sampled at steady-state conditions , starting from 5 . 3∙10−4 h-1 further increasing until the biogas production collapsed . Sampling and subsequent analyses comprised pH , biomass protein content , biogas composition and biogas volume produced . In addition , samples were analyzed for residual ethanol and accumulated organic acids . Finally , taxonomic analysis was carried out using an established MS-based metaproteomic workflow ( see S8 Text ) .
Using KEGG [33] and MetaCyc [34] as well as various literature references we manually constructed single-species models of the central metabolism of nine different organisms all being relevant for the biogas process: four primary fermenting bacteria ( Acetobacterium woodii , Escherichica coli , Clostridium acetobutylicum , Propionibacterium freudenreichii ) , three secondary fermenting bacteria ( Syntrophomonas wolfei , Syntrophobacter fumaroxidans , Desulfovibrio vulgaris ) and two methanogenic archaea ( Methanospirillum hungatei and Methanosarcina barkeri ) . As suggested by Taffs et al . [10] , we consider each of these organisms as one functional guild in the biogas process with certain metabolic properties . More specifically , under anaerobic conditions , E . coli produces ethanol as well as different organic acids like formate , lactate , acetate and succinate from glucose , glycerol and gluconate . A . woodii is an homoacetogenic organism that can either ferment sugars like glucose and fructose but also lactate , formate or hydrogen and CO2 to produce acetate via the Wood-Ljungdahl pathway [35 , 36] . P . freudenreichii can ferment glucose , glycerol and lactate to succinate and propionate . The organism uses the methyl-malonyl-CoA pathway to produce propionate . Some organisms using the methyl-malonyl-CoA pathway like Pelobacter propionicus are also capable of using ethanol as a substrate [37] . Since we aimed to represent the functional guild of propionate producing bacteria using the methyl-malonyl-CoA pathway , we also added ethanol oxidation to propionate to the model . C . acetobutylicum ferments glucose and glycerol to different organic acids and solvents like acetate , butyrate , ethanol , butanol and aceton . The organism is known to grow in two different phases [38] . In the first phase , the organism produces organic acids like acetate and butyrate . These pathways have high ATP yields but the acids produced lower the pH in the medium . In the second phase , acids are taken up and solvents like butanol and aceton are the main product . C . acetobutylicum represents primary fermenting bacteria in our community model and we assumed that mainly production of formate , acetate , butyrate and ethanol is relevant in anaerobic digestion . We therefore disabled production of the other solvents in the community model . D . vulgaris is a sulfate-reducing bacterium that can grow on organic substrates like pyruvate , lactate and ethanol using sulfate or thiosulfate as an electron acceptor . In the absence of electron acceptors , the organism can also grow in syntrophic associations with hydrogen utilizing organisms . The products formed by D . vulgaris are either acetate and hydrogen plus CO2 or formate ( in syntrophic cultures ) or acetate plus hydrogen sulfide ( when sulfate is present ) . Additionally , the organism can utilize hydrogen with acetate as a carbon source and sulfate as an electron acceptor . S . fumaroxidans can grow on propionate in syntrophy or with sulfate as an electron acceptor [39] . In pure culture the organism can grow on fumarate , fumarate plus propionate or succinate , formate or hydrogen plus sulfate [39] . S . wolfei is a secondary fermenting bacterium that can degrade saturated fatty acids from butyrate through octanoate either to acetate and hydrogen ( even number of C-atoms ) or to acetate , propionate and hydrogen ( odd number off C-atoms ) in syntrophic cultures [40] . Growth of S . wolfei is also possible on crotonate in monoculture [41] . The methanogenic organism M . hungatei ( cytochrome-free ) produces methane from formate or from hydrogen plus CO2 while M . barkeri ( possesses cytochromes ) can use hydrogen plus CO2 , acetate , methanol and methylamines for methanogenesis . In addition to different substrates utilized by the methanogens they also differ in ATP yields and substrate affinities . M . barkeri has higher ATP yields but lower substrate affinity for hydrogenotrophic methanogens . In our M . barkeri model we only implemented methanogenesis from acetate , methanol , and hydrogen with CO2 . A summary of the single-species models with model dimensions ( number of metabolites and reactions ) and constraints is given in Table 1 . The models of D . vulgaris , M . barkeri and M . hungatei were published before [15] . We estimated flux bounds for substrate uptake and product formation from experimental data or existing models , partially also from closely related organisms ( see S3 Text ) . Maintenance coefficients ( rATPmaint ) were taken from literature data but the reported values varied by more than one order of magnitude between the different species ( Table 1 , S3 Text ) . Below we will therefore carry out a sensitivity analysis to investigate the influence of the maintenance coefficients on simulation results . For model validation , we also compared model predictions with measured biomass yields reported in the literature ( see S4 Text ) . All models are listed ( and also provided in SBML format ) in S1 Table in the Supplements . For the simulations performed in this work , we focused on ethanol ( three and six-species community ) and glucose ( nine-species community ) as the only available substrates and switched the uptake of other substrates ( glycerol , gluconate , methanol , fructose , sulfate ) off to reflect the composition of media used in the experiments . We investigated a three-species community model ( Table 2 ) consisting of D . vulgaris , M . hungatei and M . barkeri . This community can convert ethanol to methane , CO2 , and acetate and thus covers the last two steps of anaerobic digestion . A similar community was experimentally investigated by Tatton et al . [42] and simulated with FBA in a previous study [15] . In analogy to the study of Tatton et al . [42] , the uptake of external CO2 was allowed to also include solutions in which the acetoclastic methanogen is non-essential . We extended the three-species community model to a model with six of the nine model organisms by additionally integrating A . woodii , P . freudenreichii , and S . fumaroxidans ( Tables 1 and 2 ) . The three additional organisms were chosen according to their potential of being part in an ethanol-degrading community; they represent functional guilds that extend the capability of the three-species community investigated above by additional pathways for homoacetogenesis and propionate fermentation . Growth of the other ( remaining ) three organisms ( CA , SW , EC; see Table 1 ) is not supported with ethanol as substrate and they have therefore not been included yet . Note that , at this initial point , no experimental data have been used yet to adjust the composition of the community model; this will later be done when including metaproteomic data from a concrete enrichment culture . We finally simulated a community capable of growth on glucose . Here , all of our nine guild organisms can potentially be involved in the process and are thus part of the community model ( Tables 1 and 2 ) . In addition to the six-species community studied above , this model included E . coli , C . acetobutylicum and S . wolfei . We first simulated the community with the bilinear model to predict the maximum community growth rate as well as ranges for substrate uptake , product excretion , biogas composition and methane yield ( Table 5 ) . As already observed for the six-species model , a reliable prediction for μc , max was thus not possible with this model ( with the iterative approach in the linearized models we found that μc , max = 0 . 23 h-1 ) In contrast , the predicted ranges for reaction rates and yields seem reasonable . We then compared predictions of the linearized full model and the reduced model with experimental data ( Table 5 ) from an enrichment culture grown on glucose-cellulose medium ( [31]; see also Methods ) . Data were available for two duplicate experiments with identical dilution rate . Since hydrolysis of cellulose is not included in the model , we used glucose as a starting point and assumed that cellulose is converted to glucose by hydrolytic enzymes . We set the community growth rate to 0 . 00067 h-1 , which corresponds to the dilution rate of the experiment and derived the corresponding linearized full community model and the reduced community model . EFV computation was possible with the reduced model ( 213689 EFVs ) but not with the full model where we computed only ranges for biomass compositions , exchange rates , and methane yield via flux variability analysis ( Table 5 and Fig 8 ) . Confirming findings from the three- and six-species models , we observed that the predicted ranges , especially of exchange rates and community compositions , are again considerably smaller in the reduced model compared to the linearized full model . In fact , the calculated ranges of exchange rates of the linearized full model are almost identical to the ones from the bilinear model , although the latter did not consider a fixed growth rate . The measured exchange rates were only slightly smaller than the minimum rates predicted by the models . The predicted ranges of the reduced model lie on the lower end of the range of the linearized full model and are thus closer to the experimental data indicating that the organisms use their substrate efficiently as assumed by our model reduction approach ( Table 5 and Fig 8 ) . The slight overestimation of the rates could again be a consequence of overestimating maintenance coefficients or an underestimation of ATP yields in the models . Furthermore , we noticed a relatively high variance of the measurements for the exchange rates which may partially explain deviations between data and model predictions . We also measured higher methane to CO2 ratios and lower methane yields than predicted by the models . Typically , we would expect a ratio of 1 methane to one CO2 for carbohydrates like glucose . However , some of the released CO2 might have been lost due to its better solubility in water ( compared to methane ) .
Microbial communities are of major importance for health , nature , and biotechnological applications . Constraint-based stoichiometric modeling helps to obtain a better understanding of interrelationships in these communities and to make quantitative predictions . However , compared to classical constraint-based modeling of single-species metabolic networks , analysis of community models based on the favored concept of balanced growth is hampered by four major technical difficulties: Our introduced RedCom approach , where reduced community models are constructed from net conversions of the linear single-species models , addresses three of the above four issues ( ( 2 ) - ( 4 ) ) . Taffs et al . [10] also published an approach where EFMs ( instead of EFVs ) of single-species models were used as input for the community model ( “nested pathway consortium analysis approach” ) . While the basic principle is the same , our RedCom approach uses EFVs instead of EFMs which is mandatory to guarantee balanced growth of the community and to allow the consideration of flux bounds , maintenance coefficients , and other inhomogeneous constraints . A necessary pre-processing step is the calculation of EFVs in the single-species models for the fixed community growth rate followed by the selection of relevant EFVs projected onto their exchange fluxes . Different optimization or selection criteria can be used for selecting the relevant single-species behaviors . We decided to use all EFVs representing minimal conversions of exchange metabolites , which , as one particular advantage , ensures exclusion of unrealistic ( altruistic ) community behaviors of the respective species ( see point ( 3 ) ) . Dependent on the application , other criteria could be used as well . In the three- , six- , and nine-species community models considered herein , the RedCom approach led to community models with desired properties: the models ( a ) are much smaller than the full ( linearized ) models , ( b ) exclude many spurious solutions , and ( c ) are amenable for detailed EFV analysis enabling the extraction of many important features of the community while avoiding an elaborate scanning of the solution space . There are two potential disadvantages of the reduction approach . First , the reduced community model contains information on the exchange fluxes while the internal flux distributions are not visible . However , in most applications of community models , the focus is indeed on predictions on the exchange fluxes , product yields , and feasible community compositions , which can all be derived from the respective flux vector of the reduced model . Furthermore , internal flux distributions of single-species could be “unpacked” from particular community net conversions whenever needed . A second potential disadvantage concerns the calculation of EFVs from the single-species models , which is usually not feasible if the latter are at genome-scale . However , with the typical application focus on exchange fluxes , single-species metabolic network models at the level of the central metabolism seem to be sufficient in many cases . Third , since the reduced community model requires eventually only the ( minimal ) net conversions of the single-species models , the ( direct ) calculation of elementary conversions might be a feasible approach even in genome-scale models [45] . We applied our RedCom approach to build community models of up to nine species relevant for the biogas process . We used a compartmented approach where each functional guild in anaerobic digestion is represented by a core model ( central metabolism ) of one organism . For the respective communities , we analyzed the maximum community growth rate and the feasible ranges of exchange rates , yields and fractional abundances of the involved species—with the bilinear as well as with the linearized and the reduced community model . Results were always consistent ( in bilinear models , as long as the solver could reliably compute the respective minima and maxima ) . However , the reduced models obtained with the RedCom approach show a significantly narrower solution space by excluding solutions from the single-species models that are physiologically very unlikely resulting in more conclusive model predictions . While bilinear community models are usually linearized to make them amenable to constraint-based analysis techniques , we found that they can , in principle , be used to roughly gauge the community’s solution space . However , in larger models , some solutions found by the solver , especially for the determined maximum community growth rate , depended on starting values used for the solver pointing to potential issues with finding the global optimum in this non-linear optimization problem . Whenever the community growth rate can be fixed ( e . g . , to the maximum growth rate or to the dilution rate used in an experiment ) , the bilinear model becomes linear making its analysis and calculations simpler . With increasing numbers of organisms the computational costs , e . g . for an FVA-based scanning of the solution space , increase drastically also in the linearized ( full ) community model and EFVs could only be computed for models of up to four organisms . With the reduced community models , we were able to compute and analyze EFVs also for the largest community consisting of nine organisms . In order to compare simulation results with experimental data from biogas communities and to investigate which solutions of the solution space are the most relevant in a concrete culture , we carried out experiments with an ethanol enrichment culture for different dilution rates . First , we compared experimental data with predicted ranges for specific substrate uptake and product formation rates as well as for biogas composition and methane yields obtained from the linearized full and the reduced six-species model . The predicted ranges of the specific rates covered the measured values but were very large and thus of low predictive power , especially in the full model . Confirming earlier findings [15] , the maintenance coefficient of the different species has a tremendous impact on many properties of the community , especially on the predicted rates and community composition . Therefore , the maintenance coefficient should be determined as precisely as possible to obtain valid community models . As many other microbial communities , anaerobic digestion communities usually have a low growth rate implying that a relatively large fraction of the metabolism is devoted to maintenance processes . Generally , our results for the anaerobic digestion community indicate that the best agreement of model predictions and experimental data can be achieved when the maintenance coefficients of all species are approximately set to 1 mmol/ ( gDW∙h ) . In contrast to rates and community compositions , the predicted ranges for methane yields and biogas composition were much smaller and appeared to be less sensitive to the maintenance coefficients making these model predictions generally more reliable . In fact , methane yields and biogas compositions from the experiments were close to the predicted values for both the reduced and the full model . The predicted maximum growth rate of the full and the reduced six-species community model were identical but considerably higher than the maximum dilution rates that supported a stable process with the enrichment culture . Here , maximization of the community growth rate might not be a suitable objective function for communities in a realistic continuous process . In particular , maximum substrate uptake rates used in the models are usually derived from single-species cultures under their respective optimal conditions and it is likely that process conditions do not support optimal conditions and maximum growth rates for all organisms . The slowest ( essential ) species will then limit the overall community growth rate . We used metaproteomic data from enrichment cultures for growth on ethanol to find out , which taxonomies and pathways were present in these cultures and to use this information to build a more constrained community model for this culture . The most abundant taxonomic orders identified in the experiments were Methanosarcinales , Methanomicrobiales , Methanococcales , Methanobacteriales and Desulfovibrionales , which correspond to the guilds represented by M . barkeri , M . hungatei and D . vulgaris in our six-species model . Furthermore , we found enzymes for ethanol oxidatation in Desulfovibrionales , acetoclastic and hydrogenotrophic methanogenesis in the archaeal superkingdom . There was little to no evidence for syntrophic acetate oxidation , homoacetogenesis and ethanol oxidation to propionate , which agrees well with the taxonomic analysis . In a last step , we used that information to further constrain the reduced six-species model and explored options to predict community compositions from the remaining solution space . The model predicted M . barkeri to be the dominant species in the community and D . vulgaris to be the least abundant organism . In fact , Methanosarcinales was also the taxonomic order with the highest spectral count abundance in the experiments while D . vulgaris had the lowest abundance confirming the model predictions . The experimental data also indicated that mainly Methanosaeta species were involved in acetoclastic methanogenesis . These organisms grow with lower biomass yield but higher substrate affinity compared to Methanosarcina species . Therefore , Methanosaeta should be added as a separate guild to the community model for future studies . Overall , to the best of our knowledge , the presented model-driven analysis of metaproteomic data from communities involved in anaerobic digestion is the biggest of its kind reported so far and demonstrates the high potential of a computer-aided approach to investigate properties and to assess experimental data of microbial communities . | Microbial communities are involved in many fundamental processes in nature , health and biotechnology . The elucidation of interdependencies between the involved players of microbial communities and how the interactions shape the composition , behavior and characteristic features of the consortium has become an important branch of microbiology research . Many communities are based on the exchange of metabolites between the species and constraint-based metabolic modeling has become an important approach for a formal description and quantitative analysis of these metabolic dependencies . However , the complexity of the models rises quickly with a growing number of organisms and the space of predicted feasible behaviors often includes unrealistic solutions . Here we present RedCom , a new approach to build reduced stoichiometric models of balanced microbial communities based on net conversions of the single-species models . We demonstrate the applicability of our RedCom approach by modeling communities of up to nine organisms involved in degradation steps of anaerobic digestion in biogas plants . As one of the first studies in this field , we compare simulation results from the community models with experimental data of laboratory-scale biogas reactors for growth on ethanol and glucose-cellulose media . The results also demonstrate a higher predictive power of the RedCom vs . the full models . | [
"Abstract",
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... | 2019 | RedCom: A strategy for reduced metabolic modeling of complex microbial communities and its application for analyzing experimental datasets from anaerobic digestion |
What is the relationship between the complexity and the fitness of evolved organisms , whether natural or artificial ? It has been asserted , primarily based on empirical data , that the complexity of plants and animals increases as their fitness within a particular environment increases via evolution by natural selection . We simulate the evolution of the brains of simple organisms living in a planar maze that they have to traverse as rapidly as possible . Their connectome evolves over 10 , 000s of generations . We evaluate their circuit complexity , using four information-theoretical measures , including one that emphasizes the extent to which any network is an irreducible entity . We find that their minimal complexity increases with their fitness .
What is the relationship between complexity and the fitness of evolved organisms , whether natural or artificial ? It is often assumed [1]–[4] that while evolving organisms grow in fitness , they develop functionally useful forms , and hence necessarily exhibit increasing complexity [5] . Some , however , argue against this notion [6] , [7] , pointing to examples of decreases in complexity , while others assert that any apparent growth of complexity with fitness is an admixture of chance and necessity [8] , [9] . One reason behind this absence of a consensus is the lack of formal or analytical definitions that permit relating complexity and fitness within a single framework . While many context-dependent definitions of complexity exist [3] , [10]–[13] , fitness has been less frequently formalized into an information-theoretic framework [14] . One such attempt [15] showed analytically that the fitness gain due to a predictive cue was tightly related to the amount of information about the environment carried by the cue . Another study using an artificial life setup demonstrated that the observed evolutionary trends in complexity , measured as in [16] , could be associated with a systematic driving force such as natural selection , but could also result from an occasional random drift away from the equilibrium [17] . Recently , a computer model of simple animats evolving in an environment with fixed statistics , randomly generated mazes that they had to traverse as quickly as possible ( Fig . 1 ) , reported [18] that the complexity of their brains was strongly correlated with their fitness . Using integrated information of the main complex , ( defined in the latter part of this work ) , as a measure of complexity , Spearmans rank correlation coefficient between complexity and fitness was . However , no specific relation between these two quantities was established . In all experiments - and also in our setup - the evolutionary change takes place via two mutually disjoint processes , namely a purely stochastic mutation of the genome followed by a selection process . The stochastic nature of the genetic mutation allows us to equate ensemble-averages over many evolutionary histories to the time-averages over a single history , provided sufficient time has passed for an equilibrium to be established locally . By exploiting this ergodicity , we could greatly scale up the statistic from our evolutionary runs . This enabled us to reproduce the simulations of Edlund et . al . [18] for 126 new evolutionary histories ( see below ) for a more extensive analysis . We obtained a very broad distribution of Spearmans rank correlation coefficients between fitness and , with a mean of 0 . 69 and a variance of 0 . 24 ( Fig . 1 ) . Even though the distribution shows a tendency for high values , the broad variance hints towards the presence of an uncontrolled , noisy factor that lessens the correlation . Most information-theoretic definitions of functional or structural complexity of a finite system are bounded from above by the total entropy of the system . The law of requisite variety of Ashby [19] connects the notion of complexity in a control system with the total information flowing between sensory input and the motor output , given by the corresponding sensory-motor mutual information ( SMMI ) [20] . This relation provides a convenient tool for studying the connection between evolved complexity and fitness . Here , we probe the relationship between fitness and the SMMI in the context of 10 , 000s of generations of evolving agents , or animats , adapting to a simulated environment inside a computer [18] . In addition to SMMI , we compute three other measures of complexity: the predictive information [12] , the state-averaged version of integrated information ( or [21] ) of a network of interacting parts using the minimal information partition ( MIP ) as well as the atomic version of , also known as stochastic interaction [22] , [23] . We relate all four measures to the extent to which these artificial agents adapt to their environment .
The mutual information between two variables and is given by ( 1 ) and is a measure of statistical dependence between the two variables [26] . Note , that throughout this work , a boldface symbol such as signifies a system ( or subsystem ) variable , while a particular state of the variable is denoted as a regular-face-type , sometimes subscripted as per context as . In particular , the SMMI for an agent connectome is evaluated as ( 2 ) This corresponds to the average information transmitted from the sensors at time , affecting the motor state at one time step later . Our definition of SMMI is a variant of the predictive information used in studies [27] , [28] involving a Markovian control system or autonomous robots where sensory input variables and motor or action variables can be distinguished [18] . Depending on whether or not the state-update mechanism uses feedback or memory , these definitions may differ from each other . Fig . 3 shows the distribution of SMMI calculated for 126 evolutionary histories after every 1000th generation . The data shows increasing lower SMMI values as the fitness of the agents increase . The predictive information of a time series , as defined in its original form [12] , is a characteristic of the statistic , which quantifies the amount of information about a future state of a system contained in the current state assumed by the system . It can be loosely interpreted as the ability of an external user - as opposed to the intrinsic ability of the system - to predict a future state of a system , based on its current state , hence the name predictive information . Considering the system as a channel connecting two consecutive states , the predictive information has been proposed as a possible measure of functional complexity of the system . The predictive information of a system being observed during a time interval of is defined as ( 3a ) where and denote the entire past and entire future of the system with respect to an instance at time . We here consider the predictive information between one discrete time step , and , that is for above , or ( 3b ) Fig . 4 shows the distribution of estimated for the evolved agent connectomes along the LODs of the best fit agent at the 60 , 000th generation in each of the 126 evolutionary histories . Similar to SMMI , too shows a boundary on the lower side , confirming our expectation of an increasing minimal bound on the complexity with increasing fitness . Indeed , a lower boundary was observed ( not shown here ) in all cases when we calculated ( an approximate ) between two states up to 8 time-steps apart . We use the state-averaged version of integrated information or [21] of a network of interacting variables ( or nodes ) as a measure of complexity and relate it to the degree to which these agents adapt to their environment . The state-averaged version of the integrated information measure is defined as the minimal irreducible part of the information generated synergistically by mutually exclusive non-overlapping parts or components of a system above the information generated by the parts themselves . One proceeds by defining a quantity called the effective information ( 4 ) where is the whole system and its parts belonging to some arbitrary partition . The subscript indices represent temporal ordering of the states . The function represents the probability of the system making a transition from a state to a state . In other words , indicates the probability that a variable takes a state immediately following . is the Kullback-Leibler divergence or the relative entropy between two probability distributions and , given by ( 5 ) The partition of the system that minimizes the effective information is called minimal information partition or MIP . The effective information , defined over the MIP , is thus an intrinsic property of the connectivity of the system and signifies the degree of integration or irreducibility of the information generated within the system . This quantity is called and is given by ( 6 ) Note that the effective information minimization has a trivial solution , whereby all nodes are included in the same part , yielding a partition of the entire system into a single part . This uninteresting situation is avoided by dividing by a normalization factor , given by ( 7 ) in eq . 4 , while searching for a MIP [21] . , however , is the non-normalized as defined in eq . 6 . here denotes the number of parts in the partition , while is the maximum entropy . By definition , of a network reduces to zero if there are disconnected parts , since this topology allows for a method of partitioning the network into two disjoint parts across which no information flows . That is , the system can be decomposed into two separate sub-systems , rather than being a single system . For each agent , we then find the subset of the original system , called the main complex ( MC ) , which maximizes over the power-set of the set of all nodes in the system . This is done by iteratively removing one node at a time and recalculating for the resulting sub-network . The corresponding maximal value of the is denoted as . Fig . 5 plots against fitness . As for the two other complexity measures ( SMMI and ) , shows a broadly increasing trend with . Yet this curve also displays a very sharp lower boundary . That is , the minimal irreducible circuit complexity of our animats , for any one level of fitness , is an increasing but bounded function of the animat's fitness . Evaluating for a system requires searching for MIP of the system - partition that minimizes the effective information for the given dynamical system . MIP search , in turn , necessitates iterating over every possible partition of the system and calculating the as given in eq . 4 . This is computationally very expensive , as the number of possible partitions of a discrete system comprised of components is given by the Bell number , , which grows faster than exponentially . As a consequence , determining is , in general , only possible for small systems , excluding any realistic biological network [29] . In such cases , a method for approximating either MIP or needs to be used . We denote the effective information calculated over the atomic partition - the finest partition , in which each singleton or elementary unit of the system is treated as its part - by . This completely eliminates the need for iterating over the set of partitions of a system . Thus , ( 8a ) For a system comprised of binary units - as is the case with our agents ( ) - reduces to ( 8b ) a measure of complexity , previously introduced as the stochastic interaction [22] , [23] with the conditional entropy function defined as ( 9 ) The against fitness calculated for the same networks as in Fig . 3 is shown in Fig . 6A . Note , that , i . e . the integrated information when considering a partition with each node as its own part , is always larger than that of the main complex , , as seen from Fig . 6B . This is expected , since is defined as the minimum over all partitions , which includes the atomic partition over which is calculated . In other words , will be necessarily as large as or larger than . To confirm that selection by fitness is actually necessary to selectively evolve high creatures , we carried out two control experiments in which selection by fitness was replaced by random selection followed by stochastic mutation of the parent genome . In a first control experiment , agents never experienced any selection-pressure , as each new generation was populated by randomly selecting agents from the previous one . Animats unsurprisingly failed to evolve any significant fitness - maximal fitness was with . In a second control experiment , organisms evolved as usual for 45 , 000 generations . This selected for agents able to rapidly traverse through the maze . The resulting along the LODs over 64 independent runs show a broad distribution , with a maximum of 1 . 57 bits . The maximal fitness obtained in these runs was 91 . 27% ( Fig . 7A ) . We then turned off selection via fitness as in the previous experiment . The population quickly degenerated , losing any previously acquired navigational skills within 1 , 000 generations due to genetic drift - the highest fitness was 0 . 03% , with an associated of 0 . 12 bits ( Fig . 7B ) .
Analyzing various information-theoretical measures that capture the complexity of the processing of the animats as they evolve over 60 , 000 generations demonstrate that in order to achieve any fixed level of fitness , a minimum level of complexity has to be exceeded . It also demonstrates that this minimal level of complexity increases as the fitness of these organisms increase . Not only SMMI , but also predictive information and integrated information show features similar to SMMI . Indeed our numerical experiments replicate those of [18] . There is a clear trend for integrated information of the main complex , ( and also the and the predictive information ) to grow with fitness , computed relative to a perfectly adapted agent ( with ) . By way of comparison , the fitness of Einstein , a near-optimal hand-designed agent within the constraints of our stochastic Markov network , is plotted as a magenta asterisk in Figs . 3–5 . It should be noted , that our terminologies differ slightly from those in [18]; we preserve the original definition of the predictive information [12] , termed in [18] , while our SMMI was originally named predictive information . Even a cursory inspection of the plots of SMMI , and versus fitness reveal a lower boundary - most evident in case of - for any fitness level . The complete absence of any data points below these boundaries , combined with the high density of points just above them , implies that developing some minimal level of complexity is necessary to attain a particular level of fitness . The existence of such a boundary had been previously surmised in empirical studies [1] , [2] , where complexity was measured crudely in terms of organismal size , number of cell-types , and fractal dimensions in shells . Conversely , no upper value for complexity is apparent in any of the plots ( apart from the entropic bounds of 2 bits for SMMI and 12 bits for and ) . That is , once minimal circuit complexity has been achieved , organisms can develop additional complexity without altering their fitness . This is an instance of degeneracy , which is ubiquitous in biology , and which might even drive further increases in complexity [30] . Degeneracy , the ability of elements that are structurally different to perform the same function , is a prominent property of many biological systems ranging from genes to neural networks to evolution itself . Because structurally different elements may produce different outputs in different contexts , degeneracy should be distinguished from redundancy , which occurs when the same function is performed by identical elements . Degeneracy matters not with respect to a particular function , but more generally with respect to fitness . That is , there are many different ways ( connectomes ) to achieve the same level of fitness , which is exactly what we observe . This provides enough diversity for future selection to occur when the environment changes in unpredictable ways . Curiously , the hand-designed agent , Einstein , has little degeneracy , lying just above the minimal complexity level appropriate for its fitness level . In our simulations , any additional processing complexity did not entail any cost to the organisms . This is not realistic as in the real world , any additional processing will come with an associated metabolic or other costs [31]–[33] . We have not considered such additional costs here . In two control experiments , we showed that selection by fitness is necessary to attain fitness and high circuit complexity . Yet complexity and fitness were neither explicitly connected by construction nor measured in terms of each other . Hence , any network complexity evolved in this manner must be a consequence of the underlying relationship between fitness and complexity . While this complexity is completely determined by the transition table associated with the brain's nodes , its fitness can only be evaluating by monitoring the performance of the agent in a particular environment . This and the fact that all complexity measures studied in this work show similar behaviors support the notion of a general trend between fitness and minimal required complexity . Thus , complexity can be understood as arising out of chance and necessity [8] . The additional complexity is not directly relevant for survival , though it may become so at a later stage in evolution . On the other hand , a certain amount of redundancy [34] , even though not useful for enhancing fitness at any stage , may be necessary for evolutionary stability by providing repair and back-up mechanisms . The previously reported correlation between integrated information and fitness [18] should be understood in this light . High correlation values correspond to data points close to the lower boundary . This strong correlation deteriorates as more and more data lies away from the boundary .
Our maze is a two-dimensional labyrinth that needs to be traversed from left to right ( Fig . 2A ) and that is obstructed with numerous orthogonal walls with only one opening or door bored at random . At each point in time , an agent can remain stationary , move forward or move laterally , searching for the open door in each wall in order to pass through . Inside each doorway , a single bit is set that contains information about the relative lateral position of the next door ( for e . g . arrows in Fig . 2A; a value of 1 implies that the next door is to the right , i . e . , downward , from the current door , while a value of 0 means the next door could be anywhere but to the right , i . e . , either upward or straight ahead ) . This door bit can only be read by the agent inside the doorway . Thus , the organism must evolve a simple one-bit memory that would enable it to efficiently move through the maze and it must evolve circuitry to store this information in a 1-bit memory . The maze has circular-periodic boundary conditions . Thus , if the agent passes exit door before its life ends after 300 time steps , it reappears on the left side of the same maze . Fig . 2B shows the anatomy of the agent's brain with a total of twelve binary units . It comprises a three bit retina , two wall-collision sensors , two actuators , a brain with four internal binary units , and a door-bit sensor . The agent can sense a wall in front with its retina - one bit in front of it and one each on left and right front sides respectively - and a wall on the lateral sides via two collision sensors - one on each side . The two actuator bits decide the direction of motion of the agent: step forward , step laterally right- or left-ward , or stay put . The four binary units , accessible only internally , can be used to develop logic , including memory . The door bit can only be set inside a doorway . While the wall sensors receive information about the current local environment faced by the agent at each time-step , the information received from the door bit only has relevance for its future behavior . During evolution of the brain of these animats , they have to assimilate the importance of this one bit , store it internally and use it to seek passage through the next wall as quickly as possible . The connectome of the agent , encoded in a set of stochastic transition tables or hidden Markov modeling units [18] , [35] , is completely determined by its genome . That is , there is no learning at the individual level . Each evolutionary history was initiated with a population of 300 randomly generated genomes and subsequently evolved through 60 , 000 generations . At the end of each generation , the agents ranked according to their fitness populate the next generation of 300 agents . The genome of the fittest agent , or the elite , from every generation is copied exactly to the next generation without mutation , while those of other agents selected with probabilities proportional to their fitness are operated over by mutation , deletion and insertion . The probabilities that a site on the genome is affected by these evolutionary operators are respectively 2 . 5% , 5% and 2 . 5% . Evolutionary operators are applied purely stochastically and the selection acts only after the random mutations have taken place . This allows us to relate the fitness-complexity data sampled along each evolutionary line after every 1000th generation - similar to time averaging - to that sampled only after 50 , 000th generation over 64 evolutionary histories - or ensemble averaged - as in [36] , provided that each evolutionary trial has been run over large enough times confirming exploration of a significant part , if not the entire , of the genomic parameter-space . Fig . 1 shows the distribution of 126 such Spearman rank correlation coefficients calculated per evolutionary trial , with respect to that reported with a red arrow for the 64 evolutionary histories in [18] . The green arrow indicates the rank coefficient value obtained in the same manner for the 126 evolutionary trials from this study . The fitness of the agent is a decreasing function of how much it deviates from the shortest possible path between the entrance and exit of the maze , calculated using the Dijkstra search algorithm [36] . To assign fitness to each agent as it stumbles and navigates through a maze during its lifetime ( of 300 time steps ) , its fitness is calculated as follows: first , the shortest distance to exit , is calculated for every location in the maze that can be occupied using the Dikjstra algorithm . Each position in the maze receives a fitness score of ( 10 ) where is the maximum of shortest path distances from all positions in . The fitness of an agent over one trial run of time-steps through is given by ( 11 ) where is the position occupied by the agent at time-step and we use the convention in eq 12 , which accounts for the offset due to a non-zero fitness score at the start of the trial , when agent begins navigating from an arbitrary position , but not necessarily at corresponding to . counts how many times the agent has reached the exit in its life and reappeared on the left-extreme of the maze . To reduce the sampling error , final fitness of the agent is then calculated as the geometric mean of its fitness relative to the optimal score from 10 such repetitions . ( 12 ) To avoid adaptation bias to any particular maze-design , the maze was renewed after every generations . | It has often been asserted that as organisms adapt to natural environments with many independent forces and actors acting over a variety of different time scales , they become more complex . We investigate this question from the point of view of information theory as applied to the nervous systems of simple creatures evolving in a stereotyped environment . We performed a controlled in silico evolution experiment to study the relationship between complexity , as measured using different information-theoretic measures , and fitness , by evolving animats with brains of twelve binary variables over 60 , 000 generations . We compute the complexity of these evolved networks using three measures based on mutual information and one measure based on the extent to which their brain contain states that are both differentiated and integrated . All measures show the same trend - the minimal complexity at any one fitness level increases as the organisms become more adapted to their environment , that is , as they become fitter . Above this minimum , there exists a large degree of degeneracy in evidence . | [
"Abstract",
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] | [
"evolutionary",
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] | 2013 | The Minimal Complexity of Adapting Agents Increases with Fitness |
The growth plate mediates bone growth where SOX9 and GLI factors control chondrocyte proliferation , differentiation and entry into hypertrophy . FOXA factors regulate hypertrophic chondrocyte maturation . How these factors integrate into a Gene Regulatory Network ( GRN ) controlling these differentiation transitions is incompletely understood . We adopted a genome-wide whole tissue approach to establish a Growth Plate Differential Gene Expression Library ( GP-DGEL ) for fractionated proliferating , pre-hypertrophic , early and late hypertrophic chondrocytes , as an overarching resource for discovery of pathways and disease candidates . De novo motif discovery revealed the enrichment of SOX9 and GLI binding sites in the genes preferentially expressed in proliferating and prehypertrophic chondrocytes , suggesting the potential cooperation between SOX9 and GLI proteins . We integrated the analyses of the transcriptome , SOX9 , GLI1 and GLI3 ChIP-seq datasets , with functional validation by transactivation assays and mouse mutants . We identified new SOX9 targets and showed SOX9-GLI directly and cooperatively regulate many genes such as Trps1 , Sox9 , Sox5 , Sox6 , Col2a1 , Ptch1 , Gli1 and Gli2 . Further , FOXA2 competes with SOX9 for the transactivation of target genes . The data support a model of SOX9-GLI-FOXA phasic GRN in chondrocyte development . Together , SOX9-GLI auto-regulate and cooperate to activate and repress genes in proliferating chondrocytes . Upon hypertrophy , FOXA competes with SOX9 , and control toward terminal differentiation passes to FOXA , RUNX , AP1 and MEF2 factors .
In the formation and longitudinal growth of endochondral bones , committed mesenchymal cells condense and differentiate into chondrocytes to form a growth plate , within which chondrocytes undergo coordinated and sequential differentiation phases of proliferation , cell cycle exit and hypertrophy , resulting in longitudinal bone growth[1 , 2] . Endochondral bone formation requires tightly controlled proportions of the different chondrocyte populations , recognized by their distinct morphology , characteristic gene expression patterns , and organization into different zones . Firstly , round chondrocytes become proliferative , flattening to form columns . As proliferating chondrocytes ( PCs ) mature , they exit the cell cycle and enter a prehypertrophic phase . This phase is an important transition , which produces signals for maintaining proliferation on the one hand and on the other , to regulate the progression from proliferation to cell cycle exit , entry into a prehypertrophic state , followed by the final stages of differentiation in which the cells enlarge to form hypertrophic chondrocytes ( HCs ) and then become osteoblasts [1–4] . Disruption of the progression from one differentiation state to the next and the relative proportions results in skeletal defects such as chondrodysplasia [5 , 6] . The sophisticated program of chondrocyte differentiation requires the activation or repression of many genes , which is strictly mediated by transcription factors ( TFs ) , including the SOX ( SOX5 , SOX6 and SOX9 ) [7–13] , GLI ( GLI1 , GLI2 and GLI3 ) [14–17] , RUNX ( RUNX2 and RUNX3 ) [18 , 19] , MEF2C[20] , AP1[21] and FOXA[22] family members[23] . SOX9 is the master regulator of chondrocyte differentiation . Chondrocytes cannot form in Sox9 null mutants and heterozygous mutations in SOX9 severely disrupt skeletal development , causing campomelic dysplasia [7 , 8 , 10 , 11 , 13] . Shortly after mesenchymal condensation , SOX9 cooperates with SOX5 and SOX6 to activate the expression of cartilage matrix genes , e . g . , Col2a1 and Aggrecan , positively regulating chondrocyte proliferation , while inhibiting both the progression of these cells to hypertrophy and the osteogenic program [11 , 24–26] . The Hedgehog signaling pathway regulates chondrocyte proliferation and hypertrophy through a complex negative feedback loop with the PTHrP signaling pathway [27] . IHH secreted from prehypertrophic chondrocytes ( PHCs ) activates HH signaling and GLI transcription factors ( GLI1/2/3 ) in proliferating chondrocytes ( PCs ) via its receptor PTCH1 , and stimulates the expression of Pthrp . GLI1 functions as an activator which is highly expressed in PCs and perichondrium adjacent to the prehypertrophic and hypertrophic zones . GLI2 has both activator and repressor forms and GLI3 acts as a repressor . GLI2 is expressed in most chondrocytes , but at a lower level in HCs [28] . Binding of PTHrP to its receptor PPR results in activation of PKA and phosphorylation of SOX9 that enhances its transcriptional activity [29] , indicating crosstalk between SOX9 and IHH signaling in regulation of chondrocyte proliferation and differentiation . RUNX and FOXA transcription factors are critical regulators of hypertrophic chondrocyte maturation . Mutations in RUNX2 cause the skeletal disorder , Cleidocranial dysplasia [30] . In mice , inactivation of Runx2 or FoxA2/FoxA3 causes severe defects in chondrocyte hypertrophy and bone formation [18 , 22] . Although these TFs have been studied individually for their importance in chondrocyte differentiation , understanding of how they interact and integrate into a gene regulatory network ( GRN ) that acts genome wide , is still only emerging and largely limited to addressing control of individual gene expression ( reviewed in [23] ) . For example , in vitro transactivation assays in cultured chondrocytes highlight a potential in vivo cooperation between GLI1/2 and RUNX2/SMADs in activating Col10a1 via interaction with its promoter [31] . The Notch signaling pathway transcriptional co-activator , Mastermind-like 1 ( MAML1 ) , was reported to enhance the transcriptional activity of RUNX2[32] . Recently genome-wide analyses of SOX9 binding peaks in chondrocytes [33] assisted the discovery that SOX9 and AP1 factors ( Jun ) co-activate Col10a1 in prehypertrophic chondrocytes to promote hypertrophy [21] . The SOX proteins are characterized by their dependence on partner factors in controlling cell differentiation [34] . Cooperation of the SOX trio proteins ( SOX9 , SOX5 and SOX6 ) controls sequential differentiation of chondrocytes [9 , 11 , 12 , 35–37] . Cooperative interaction between SOX9 and other factors such as AP-1 , NFAT , FOXA , RUNX and HOX has also been implicated because their binding motifs are enriched in the SOX9 peaks [21 , 33] . It has been reported that GLI1 can regulate Sox9 via a far upstream enhancer [38] and SOX9 can regulate its own expression via another far upstream enhancer [39] . We previously implicated cooperation between SOX9 and GLI factors in repressing Col10a1 expression in proliferating chondrocytes [40] . Despite the wealth of information about the individual roles of SOX9 and GLI in regulating chondrocyte differentiation , it is not fully understood about how these TFs together mediate the transition from PCs and PHCs where SOX9 and GLI factors dominate , to HCs controlled by FOXA , RUNX and other factors [40] . Also little is known whether these two factors cooperate to activate chondrocyte genes and if they do , the breadth of potential genes that are cooperatively regulated by these factors . The need to understand the regulatory mechanisms driving the phases of chondrocyte differentiation in the growth plate has prompted investigations to establish global transcriptomic analyses for gene signatures for the different populations . Prior transcriptomic studies on mouse chondrocyte populations had been narrowly focused on chondrogenic cell lines [41] , early stage limb mesenchyme and E13 . 5 chondrocytes before hypertrophy [42] , manually dissected tibia segments [43] , or postnatal proliferating and hypertrophic chondrocytes without transition zones [44 , 45] . A recent study on the transcriptomes of 217 single cells from the growth plate aimed to reconstruct the spatial and temporal pattern gene expression of individual chondrocytes [46] . However only a limited number of genes were mapped in that dataset and many genes important for chondrocyte differentiation were not detected ( e . g . Ctnnb1 , Gli2 , Wnt5a , Wnt5b , etc . ) . The spatial information on gene expression is also lacking since the cells were not fractionated according to zones . The limited information on the integration of the GRN that controls the important transitions from one differentiation phase to the next within the growth plate motivated us to develop a comprehensive atlas of gene expression for finely fractionated chondrocyte subpopulations in growth plate . We aimed to use this resource for the analyses and discovery of the complex molecular signatures , differential gene expression patterns , biological processes and pathways operating during the phases of chondrocyte differentiation , especially in the transition into prehypertrophy . We created a searchable library , GP-DGEL ( URL: http://www . sbms . hku . hk/kclab/gp . html ) , that provides sequential and dynamic gene expression information encompassing growth plate chondrocytes at different stages from proliferative to prehypertrophy , early and late hypertrophy . By integrative analysis of the transcriptome and chondrocyte ChIP-seq datasets coupled with functional tests , we find evidence for a dominant role for SOX9-GLI cooperation in proliferating chondrocytes and identify new SOX9 GLI targets . Importantly we find evidence for a model of phase transition of the gene regulatory program from SOX9-GLI cooperation to SOX9-FOXA competition directing chondrocyte differentiation . In this SOX9-GLI-FOXA centric model GRN , SOX9-GLI preferentially activates sets of genes in proliferating chondrocytes . In maturing prehypertrophic chondrocytes the SOX9-GLI nexus begins to fade , so that in full hypertrophy , control is relayed to an alternative set of transcription factors including FOXA2 , RUNX2 , AP1 and MEF2C .
We generated GP-DGEL , by fractionating the mouse proximal tibial growth plate at postnatal day 10 ( P10 ) into four zones representing chondrocyte sub-populations: PCs in the proliferating zone ( PZ ) , PHCs in the pre-hypertrophic zone ( PHZ ) , early HCs in the upper hypertrophic zone ( UHZ ) and late differentiated HCs in the lower hypertrophic zone ( LHZ ) ( Fig 1A ) . Navigating the location and identity of these zones was guided by the morphologies and RT-PCR analyses for the expression of zone-characteristic markers ( Col2a1 , Col10a1 , Ppr , Ihh and Mmp13; S1A–S1C Fig ) . Gene expression profiling data from the 4 fractions of biological triplicates were generated for further analysis ( S1D Fig ) . We defined the set of genes expressed at each stage: a total number of 4799 , 4811 and 4879 genes were expressed in PZ , PHZ and HZ ( average of the gene expression in UHZ and LHZ ) respectively . We categorized all the expressed genes that were commonly or uniquely expressed in PZ , PHZ and HZ ( see Methods and S1E Fig ) . Here , 4792 out of 4886 genes ( 98% ) are commonly expressed showing the “On” state in all three zones . The remainder 94 genes are expressed in only one or two zones ( S1 Table ) . Only one gene , Fzd9 , was expressed only in PCs . No genes were specifically expressed only in PHCs , as might be expected for cells in transition from proliferation to hypertrophy , but 73 genes were HC-specific . GP-DGEL allows the detection of variation in gene expression across different zones . Amongst all the expressed genes , 1891 genes ( ~37% ) were differentially expressed with the Coefficients of Standard Deviation ( CSD ) over the 4 zones greater than 0 . 15 ( S2A Table ) . The differentially expressed transcription factors included Sox9 , Sox5 , Gli1 , Gli2 , Runx3 and Mef2c in PZ and PHZ , which is consistent with their known roles in regulating endochondral ossification , affirming the reliability of the dataset . The remaining genes were constantly expressed across the zones , including Hif1a , which plays an important role in bone development but is regulated through post-translational modification [47–49] . Using k-means clustering analyses to categorize the patterns of the 1891 differentially expressed genes ( DEGs ) , we identified four major clusters: Cluster I genes exhibit decreasing expression from PZ to HZ ( 654 genes ) ; Clusters II to IV genes are typically most highly expressed in PHZ ( 299 genes ) , UHZ ( 31 genes ) and LHZ ( 907 genes ) , respectively ( Fig 1B; S2A Table ) . This categorization formed the basis for genome-wide discovery and identification of biological processes , pathways and GRNs that underlie these transition patterns . We tested GP-DGEL for its capacity as a resource for the discovery and functional analyses of signaling pathways , biological processes and transcriptional regulators of chondrocyte differentiation as follows . To identify the enriched biological processes and signaling pathways for each cluster , we performed Gene Ontology enrichment analysis . Genes associated with biology process of “skeletal system development” were enriched in PZ , PHZ and HZ , establishing the authenticity of our data ( Fig 1C; S3 Table ) . Genes associated with the processes of “regulation of Smoothened signaling pathway” , “transcription” , and “cell proliferation” were significantly enriched in the PZ ( cluster I ) and supported a significant role for IHH signaling ( mediated by Smoothened and Gli ) in proliferating chondrocytes . Genes associated with the processes of “sterol metabolic process” , “cell motility , “actin cytoskeleton regulation” and “cell growth” were most common in the PHZ ( cluster II ) . This agrees with the dramatic changes in chondrocyte size and morphology observed during hypertrophy . Sterol ( cholesterol ) biosynthesis is required for the processing and maturation of hedgehog ligands and Hedgehog signaling [50] . In explant organ cultures , cholesterol was found to stimulate chondrocyte hypertrophy and bone growth through regulating the expression of Rora . Inhibition of cholesterol biosynthesis attenuates chondrocyte enlargement [51] and results in growth retardation with decreased chondrocyte proliferation and Ihh expression [52] . The UHZ ( cluster III ) was enriched for genes associated with the process of “extracellular matrix ( ECM ) organization” , which is consistent with the transition from synthesis of an ECM rich in collagen II to one where collagen X is the major component [53] . The LHZ ( cluster IV ) was enriched for “hydrogen transport” , “vascular development” , “cell redox homeostasis” , “phosphate metabolic process” , “ossification” and “regulation of apoptosis” , consistent with late-stage differentiation , cartilage calcification , degradation , vascular invasion , bone formation and chondrocyte to osteoblast trans-differentiation that occur at the chondro-osseous junction[2 , 3] where cell cycle re-entry in the process has been implied[4] . The enrichment for “regulation of apoptosis” is intriguing: of the 37 genes highlighted , 16 genes were classed as contributing to "negative regulation of apoptosis" ( p-value = 3 . 5e-18 ) and 9 genes as contributing to "positive regulation of apoptosis" ( p-value = 1 . 9e-13 ) such as Cdkn1a ( p21 ) a cell cycle regulator and its interacting pro-apoptotic factor Trp53inp1 which may imply complex control that balances apoptosis and survival and control of cell cycle re-entry in the transition from hypertrophic chondrocytes to osteoblasts . Overall the corroboration of genes associated with processes that occur in the relevant zones attests to the quality of the library . Many signaling pathways are known to play key roles in coordinating chondrocyte proliferation and differentiation but their relative importance in each phase and sub-population is unclear . A gradient of BMP pathway gene expression has been reported for the rat postnatal growth plate [45] but how this compares with other pathways is not known . To gain global mechanistic insight into the relative scope of signaling action in each zone , we computed the enriched GO terms for the genes involved in canonical signaling pathways: WNT , BMP/TGFβ , FGF , Notch , IGF , Hippo and Hedgehog ( S4 Table ) . We found the expression of the components in WNT , BMP/TGFβ , Hippo , FGF and Notch pathways was not significantly enriched in particular regions over the 4 different zones ( Fisher’s exact test p-value > 0 . 05 ) . In contrast , genes of Hedgehog and IGF pathways were preferentially expressed in PZ and PHZ , suggesting distinctive roles of these signaling pathways in regulating cell cycle progression and the initiation of chondrocyte hypertrophy . We tested the capacity of GP-DGEL to identify potential associations of the differentially expressed genes with those implicated in mouse skeletal phenotypes and human skeletal diseases in MGI and OMIM databases . A subset of 396 genes , accounting for 20% of the whole list , was associated with abnormal skeletal phenotypes in mouse ( S2B Table ) . 93 genes were associated with human skeletal disorders ( S2C Table ) . To infer the functional significance of the phase-specifically expressed TFs on skeletal development , we ranked the TFs according to the CSD values over 4 zones ( S5 Table ) . Of 76 phasic-specific TFs , 46 ( including Sox9 and Trps1 ) were associated with human skeletal disorders and/or mouse skeletal defects . GP-DGEL can therefore be used to identify new candidate genes of skeletal disorders . An example worthy of further investigation is Srebf1 , not known to cause skeletal disorders but is implicated as a regulator of cholesterol metabolism and apoptosis ( OMIM 184756 ) [54] . The large sets of genes sharing phasic-specific expression patterns imply the presence of a coordinated transcriptional program at each phase . For an unbiased identification of phase specific transcriptional regulators , we performed de novo motif enrichment analysis in the promoter regions for the genes in each cluster using the computer program Discriminating Motif Enumerator ( DME ) , MotifClass and MatCompare [55–58] . The most enriched TF binding motifs include SOX9 and GLI ( GLI1 , GLI2 and GLI3 ) in the PZ and PHZ; SOX9/FOXA and KLF4 in the UHZ; and MEF2C and FOXA motifs in the LHZ ( Fig 2A ) . The core consensus binding motifs for FOXA factors ( FOXA1 , FOXA2 and FOXA3 ) and SOX9 comprise highly similar AT-rich sequences ( ACAAA-like for FOXA; ATTGT-like for SOX ) , raising the possibility that these factors compete for binding in regulating gene expression [22] . The over-representation of a GLI motif in the PZ and PHZ genes agrees with the known action of Hedgehog signaling in PCs . Gli1 , itself a target of Hedgehog signaling , is most highly expressed in the PZ , while the cytoplasmic GLI3 repressor may transform into an activator in the presence of IHH [59] . KLF4 motif enrichment in the UHZ cluster could imply a role in promoting hypertrophy which would be consistent with its capacity to reprogramme dermal fibroblasts in concert with SOX9 and cMYC [60] . In the LHZ cluster , we detected enrichment for the binding motif for MEF2C , a vital regulator of chondrocyte hypertrophy that is required for the proper expression of Col10a1 , Runx2 and Vegf [20] . Beyond the chondrocyte fate , KLF4 and MEF2C may prime the lineage progression of hypertrophic chondrocytes to osteoblasts [3 , 61 , 62] . SOX9 functions as a dimer in chondrocyte differentiation [63] . To predict the degree to which SOX9 dimer/monomer binding motifs were utilized in chondrocyte gene regulation , we screened for evolutionarily conserved SOX9 binding sites located within 10kb from the transcriptional starting site ( TSS ) . Using the MEME program [64] for long consensus motif analysis , we identified SOX9 dimer motifs with varied length of spacer sequences ( Fig 2B ) . The most enriched SOX9 dimer motifs were identified for PZ and PHZ genes ( p-value<1 . 0e-5 ) . In proliferating chondrocytes , SOX9 dimer motifs were associated with the genes which it activates ( e . g . Col2a1 ) or represses ( e . g . Col10a1 ) [24 , 40 , 65] . The length of the spacer sequences in the dimer motifs ranges from 4 to 13-bp in the PZ and 4 to 16-bp in the PHZ genes ( S6 Table ) , raising the question whether the variation in the linking sequences could confer different specificity of co-binding of partner factors with SOX9 dimers [66 , 67] . For LHZ where SOX9 protein level dropped to undetectable level , no significant SOX9 dimer binding motifs were identified . The enrichment of SOX9 binding motifs in the DEGs ( Fig 2A and 2B ) is consistent with the vital roles of SOX9 in regulating phasic gene expression and helps identification of target genes regulated by SOX9 at each stage of chondrocyte differentiation . We searched the evolutionarily conserved noncoding DNA elements across 30 vertebrates in gene promoter , intergenic , intronic and 3’- UTR regions for putative SOX9 monomer and dimer binding sites ( S7A–S7D Table ) . To identify functional binding sites , we integrated the bioinformatics predictions with the SOX9 ChIP-seq dataset from mouse neonatal rib chondrocytes [33] . Overall , 503 genes out of 654 in the PZ cluster , 250 out of 299 in the PHZ cluster , 24 out of 31 in the UHZ cluster and 664 out of 907 in the LHZ cluster were found to harbor at least one SOX9 binding region ( SBR ) ( S8A1–S8B2 Table ) , consistent with the major role of SOX9 in regulating chondrocyte differentiation . SOX9 can act as both an activator and a repressor in PCs [40] . Therefore the genes identified by this analysis could be either activated or repressed by SOX9 . We selected those genes with predicted SOX9 binding sites located within 250-bp from the SOX9 ChIP peaks as potential SOX9 targets . Multiple copies of monomer and dimer sites near the SOX9 binding peaks were identified for known SOX9 targets Sox9 , Sox5 , Sox6 , Col2a1 , Acan and Col10a1 ( Fig 2C; S7E and S7F Table ) . In the Col10a1 locus , we found a SOX9 binding peak 4 . 4 kb upstream of the TSS ( Fig 3H ) , where an element has been shown to mediate repression by SOX9 in non-hypertrophic chondrocytes [22 , 40] . These data affirm the validity of our approach . We identified several potential SOX9 targets ( Zbtb20 , Wwp2 , Foxp2 , Ppa1 , Slc8a3 , Bnip3 and Wnk4 ) . By in situ hybridization or antibody staining on proximal tibia growth plate , we confirmed the expression patterns of these targets ( Fig 3A–3I ) as corresponding with the regions identified in GP-DGEL . These potential SOX9 targets may function in different steps of chondrocyte differentiation and endochondral bone formation . For instance , Wwp2 ( Fig 3B ) has been identified as a direct SOX9 target during palatogenesis [68] . Interestingly many genes encoding major components of the IHH signaling pathway were identified as potential SOX9 targets in our study , including Ihh , Ptch1 , Gli1 , Gli2 and Gli3 ( Fig 2C ) , which is consistent with the enrichment for GLI binding motifs for the PZ and PHZ clusters ( Fig 2A ) . To validate SOX9 binding under the SOX9 peaks , we performed in vivo ChIP-qPCR assays on three candidates: Cyr61[69 , 70] , Trps1 [59 , 71] and Ptch1 ( S9 Table ) , with an Aggrecan enhancer situated 10kb up-stream of TSS as a positive control [12] . The ChIP-qPCR results showed that SOX9 binds to the promoters of Cyr61 and Ptch1 , and the intron 1 of Trps1 ( S2A–S2D Fig ) . Within the regions covering the validated binding sites , we detected SOX9 binding peaks ( Fig 4A–4C ) , indicating that these genes are direct SOX9 targets . To test whether the expression of these candidates is associated with SOX9 activity , we compared their expression levels in wild type ( Sox9+/+ ) and heterozygous null ( Sox9+/- ) mutant littermates in embryonic day 13 . 5 ( E13 . 5 ) limbs , when the limb abnormality is minimal [72] . Expression of known SOX9 targets ( Sox9 , Sox5 , Sox6 and Col2a1 ) was down regulated in Sox9+/- mutants compared with wild type littermates ( Fig 5A ) , consistent with the dosage requirement for SOX9 . Expression of Cyr61 , Trps1 , Ptch1 , Gli1 and Gli2 was reduced by approximately 50% in Sox9+/- mutants ( Fig 5A ) , and Gli3 expression has been reported to be reduced in Sox9+/- mutants [35] , indicating that SOX9 positively regulates these genes . Ihh is expressed in PHCs [14] , and its expression is not significantly changed in Sox9+/- mice , consistent with the previous finding that the expression of Ihh is not affected by Sox9 heterozygous mutation [72] . Since Ptch1 is down-regulated in Sox9+/-mutants , we tested whether Ptch1 is transcriptionally regulated by SOX9 . Using the chondrogenic cell line ATDC5 , we found SOX9 transactivated a luciferase reporter driven by regulatory sequences in the Ptch1 promoter region containing a SOX9 binding peak in a dosage-dependent manner in ( Fig 5C ) , indicating that SOX9 directly regulates the expression of Ptch1 . The enrichment for SOX9 and GLI motifs in the PZ and PHZ clusters ( Fig 2A ) , the co-expression of Sox9 and Gli1 in the PCs ( S3A and S3B Fig ) , the known GLI activation of Ptch1 [73] and the cooperative repression of Col10a1 by SOX9-GLI3R [40] raise the possibility of a substantial role for cooperation of SOX9 with GLI in activating gene expression . To investigate whether SOX9 and its targets are co-regulated by GLI factors , we screened the phasic DEGs for putative GLI binding sites ( S7E Table ) . Abundant GLI consensus motifs were found near SOX9 peaks in Sox9 itself and SOX9 target , in particular Sox9 , Sox5 , Sox6 and Col2a1 ( Fig 2C ) . Since it has been reported that cells derived from a common progenitor lineage share similar genome-wide epigenetics and TF binding profiles[74] , we integrated the bioinformatics predictions with the SOX9 ChIP-seq from newborn rib chondrocytes ( S8A1 and S8A2 Table ) , GLI1 ( S8C1 and S8C2 Table ) and GLI3 ( S8E1 and S8E2 Table ) ChIP-chip datasets from E11 . 5 developing limbs[73] to check whether in principle , these TFs could bind to the putative common target genes . Binding regions for SOX9 ( SBR ) , GLI1 and GLI3 ( GBR ) were found in 1426 , 699 and 1421 phasic DEGs respectively ( SOX9 , S8B1 and S8B2 Table; GLI1 , S8D1 and S8D2 Table; and GLI3 , S8F1 and S8F2 Table ) . Among these , 721 of 1426 SOX9-targeted DEGs ( 51% ) harbored at least one SOX9/GLI linked binding region ( SGBR ) with an inter-peak distance shorter than 250-bp ( S10A–S10D Table ) . The genes that were most enriched for SGBRs include the known SOX9 targets Sox9 , Sox5 , Sox6 and Col2a1 . Interestingly substantial over-representation of putative SOX9/GLI common targets was found for the PZ , PHZ and UHZ clusters compared to genes in the LHZ cluster ( p-value<0 . 01 ) , consistent with the expression pattern of SOX9 protein which spans the PZ , PHZ and persists into the UHZ [26] . The correlation of SGBRs with phasic gene expression decreased as the inter-peak distance increased ( S10A1 , S10D2 and S10D2 Table ) . Correlation was also found between phasic-specific genes and SGBRs that were located in the intergenic regions ( p-value = 0 . 0032 , S10A3 Table ) , suggesting SOX9-GLI may also mediate long-range regulation . These bioinformatics predictions suggest that SOX9 and GLI factors cooperate to regulate common targets in PCs and PHCs . To test these predictions we examined the SOX9 ChIP-seq data and found that the SOX9-bound regions in Trps1 and Ptch1 loci were co-localized with the GLI1 binding peaks ( Fig 4B and 4C ) . We tested the ability of SOX9 and GLI singly and in combination to transactivate expression of the Ptch1-luciferase reporter vector . Both SOX9 and GLI1 could drive the expression of the Ptch1-luciferase reporter ( Fig 5C and S3C–S3F Fig ) . We also detected SOX9 binding peaks in the Gli1 and Gli2 loci ( Fig 4D and 4E ) . In Sox9+/- mutants , the expression of Gli1 and Gli2 was down-regulated ( Fig 5A ) , indicating that these genes may be regulated by SOX9 . Firstly we tested for the cooperative control of GLI on SOX9 targets . We found GLI1 peaks in SOX9 target genes , including Sox9 , Col2a1 , Sox5 and Sox6 , which are close to SOX9 binding regions ( Fig 4F–4I ) . To test whether the expression of SOX9 targets is affected by removal of the GLI activator , we compared their expression levels in Gli2+/+ and Gli2-/- littermates . In Gli2-null mutants , with the exception of Cyr61 , the SOX9 targets Sox9 , Col2a1 , Sox5 , Sox6 and Gli1 , Trps1 and Ptch1 were markedly downregulated ( Fig 5B ) , consistent with cooperative regulation by SOX9 and GLI . We next tested the cooperative activity of SOX9 and GLI in regulating Sox9 , Col2a1 , Ptch1 , Gli1 and Gli2 by transactivation assays using luciferase reporters driven by genomic fragments containing at least one SGBR . GLI1 and GLI2 transactivated the Ptch1 , Gli1 and Gli2 reporters . This transactivation activity was significantly enhanced by SOX9 ( Fig 5D–5F ) . SOX9-dependent transcriptional activation of Sox9 and Col2a1 reporters was enhanced by GLI1 and GLI2 ( Fig 5G and 5H ) , confirming the addictive action of SOX9 and GLI activators in the regulation of common target genes . While SOX9 and GLI factors play key roles in the GRN of PCs and PHCs , they are not expressed in hypertrophic chondrocytes in the LHZ ( Fig 6A , S3A and S3B Fig ) . We therefore sought to gain insight into the GRN that mediates the transition from proliferation to prehypertrophy and hypertrophy . It is notable that Foxa2 , a critical regulator of hypertrophy [22] , is mainly expressed in PHCs and HCs ( Fig 6B ) . Interestingly , SOX9 and FOXA2 are co-expressed in PHCs and early HCs ( Fig 6C ) . We analyzed the published FOXA2 ChIP-seq dataset [75] which has been used in other studies to identify multiple target genes in the notochord [76] , and found multiple FOXA2 binding peaks in the Sox9 promoter and distal gene regulatory elements ( S8G Table ) . As Sox9 expression diminishes in the UHZ whilst Foxa2 is robustly expressed , it is possible that FOXA2 represses Sox9 expression . As FOXA2 and SOX are co-localized in PHCs and early HCs and bind closely related AT-rich DNA elements [22] , FOXA2 and SOX9 could compete for binding sites and alter the dynamics of the regulatory phase . To study the relationship between these two factors , we expressed SOX9 and FOXA2 in ATDC5 cells and examined their impact on the transactivation of two established SOX9 targets , Col2a1 and Col10a1 , using promoter/enhancer-driven luciferase reporter expression . As expected , SOX9 transactivated the expression of a Col2a1-luciferase reporter ( Fig 7A ) . However , this transactivation was progressively weaker with increasing amounts of FOXA2 . Notably , FOXA2 alone did not transactivate the Col2a1 reporter . The reporter was progressively activated with increasing amounts of SOX9 ( Fig 7B ) . SOX9 represses the expression of Col10a1 by direct binding to the conserved regulatory region located between −4 . 3 and −3 . 6 kb of the mouse Col10a1 gene [40] . We tested the ability of FOXA2 to activate the expression of luciferase driven by the Col10a1 promoter ( -832bp to +68bp ) with the enhancer region ( -4433bp to -3780bp ) . FOXA2 alone could transactivate the Col10a1-luciferase reporter and this activation was gradually dampened with increasing amounts of co-transfected SOX9 ( Fig 7C ) . SOX9 alone did not transactivate the Col10a1 reporter and the repression was released with increasing amounts of FOXA2 ( Fig 7D ) . To test further the transcriptional competition between SOX9 and FOXA , we selected known regulatory regions from the Col2a1 and Col10a1 loci [24 , 40 , 77] containing SOX9/FOXA binding motifs and carried out electrophoretic mobility shift assay ( EMSA ) with homogenously purified FOXA and SOX9 protein constructs ( Fig 7E , Gel I-IV ) . We found that both FOXA and SOX9 effectively associated with the Col2a1 and Col10a1 sequences with FOXA migrating as a monomer ( Fig 7E , Gel I-IV , lane 7 ) whereas SOX9 migrated as a monomer or dimer under equilibrium conditions ( Fig 7E , Gel I-IV , lane 8 ) . The SOX9 monomer fraction predominates at the tested concentration suggesting that the homodimer cooperativity is profoundly weaker than for canonical SOXE DNA elements in the reverse-forward ( ACAATGN3-5CATTGT ) configuration [66] . On the Col2a1 element , SOX9/FOXA heterodimer fractions appeared under conditions when the FOXA monomer is also formed suggesting that FOXA is able to interact with DNA bound by SOX9 monomers ( Fig 7E , Gel I , lane 2 and 3 ) . At high FOXA concentration when DNA probes become limiting , the SOX9 monomer disappear and the FOXA monomer and SOX9/FOXA heterodimer become dominant ( Fig 7E , Gel I , lane 1 ) . The dimeric SOX9/DNA complex persisted even at very high FOXA concentrations suggesting a highly stable association of dimeric SOX9 . In the inverse experiment when the FOXA concentration is fixed and SOX9 is increased , the SOX9/FOXA heterodimer is formed equally well and at high Sox9 concentration the Sox9 homodimer is formed at the expense of the FOXA monomer ( Fig 7E , Gel II ) , suggesting that SOX9 and FOXA can from heterodimers on Col2a1 DNA in an un-cooperative fashion but SOX9 homodimers and FOXA monomers are incompatible and compete . On the Col10a1 element , SOX9 also forms a homodimer with similar efficiency as on Col2a1 DNA whilst FOXA binds monomerically . However , a SOX9/FOXA heterodimer is barely visible on this element ( Fig 7E , Gel III and IV , lane 1–3 ) . Interestingly , the presence of FOXA counteracts the formation of monomerically bound SOX9/DNA complexes but favors the formation of dimeric SOX9/DNA complexes ( Fig 7E , Gel III and IV , compare lanes 1–3 with lanes 8 ) . This indicates that dimeric SOX9 more effectively resists competition by FOXA than monomeric SOX9 . Together , these findings demonstrate that FOXA and SOX9 possess the capacity to associate with highly similar DNA sequences and indicate that competition between SOX9 and FOXA is a plausible mechanism for the transcriptional switches during chondrogenesis .
In our study we have aimed to provide insights into the gene expression dynamics and gene regulatory network that guide chondrocytes through their phases of differentiation in the growth plate . Although the cells in each region were not pure populations , especially in the LHZ which is adjacent to the primary ossification center with vascular invasion , the expression profiles of many chondrogenic markers ( Sox9 , Sox5 , Sox6 , Wwp2 , Col2a1 , Col9a1 , Acan , Comp , Ihh , Ptch1 , Gli1 , Gli2 , Ppr , Fgfr3 , Igf1 , Bmp6 , Wnt5b , Dkk1 , Cdkn1c , Mef2c , Bmp2 , Col10a1 , Mmp9 , Mmp13 , et al ) did show high consistency with the published data . Cognizant of the potential limitations we have validated the expression of the novel genes ( Zbtb20 , Foxp2 , Slc8a3 , Ppa1 and Bnip3 ) . Therefore analysis of these microarray data still provides vast transcriptomic information on chondrocyte differentiation . Towards that end , we developed a library of differentially expressed genes , GP-DGEL that has fine spatial resolution and global transcriptomic coverage , allowing systematic analyses of the genes that regulate transition between these phases . GP-DGEL is a valuable resource to complement efforts to identify causative mutations in skeletal dysplasia and predict the underlying GRN . This is illustrated by our correlative analyses of the 1891 DEGs with the MGI and OMIM databases , which identified genes associated with mouse and human skeletal disorders and additional candidates ( S2 Table ) . GP-DGEL has also enabled the identification of new gene signatures . Many of the DEGs remain poorly studied in chondrocytes . Integration of the dataset with global ChIP-seq data allows the identification of target genes for TFs , singly and in combination , thereby revealing cooperative activities . Using this approach we identified new targets for SOX9 and evidence for SOX9-GLI cooperation . We validated several of the predicted SOX9 targets ( Cyr61 , Trps1 , Ptch1 , Gli1 and Gli2 ) by functional assays . The downregulation of Cyr61 , Trps1 and Gli2 in Sox9+/- chondrocytes in a recent report [35] is in agreement with our data . The presence of SOX9 peaks associated with these genes in SOX9 ChIP-seq data from rat chondrosarcoma cells is also consistent with direct regulation [37] . We also confirmed the expression patterns of other potential SOX9 targets ( Zbtb20 , Wwp2 , Foxp2 , Ppa1 , Bnip3 , Slc8a3 and Wnk4 ) that were identified based on the presence of associated SOX9 binding peaks ( Fig 3 ) . These genes are candidates for functional studies . An example is Wnk4 , which is expressed in late PHCs and early HCs ( Fig 3G ) . WNK4 is the major regulator of the Na-Cl co-transporter in the kidney , a regulator of adipogenesis and energy metabolism and a causal gene for pseudohypoaldosteronism type II [78–80] , but has no known role in chondrocyte hypertrophy . A major outcome of the integrated approach is the identification of genes that are co-regulated by both SOX9 and GLI factors . Zbtb20 , highly expressed in PHCs and downregulated in the UHZ , is a potential SOX9-GLI target since SOX9 and GLI binding peaks were identified in the locus ( Fig 3A ) . Ablation of Zbtb20 in chondrocytes results in an expanded HZ , and delayed vascularization [81] , consistent with a role downstream of SOX9 in regulation of the transition from prehypertrophy to hypertrophy [26] . This function may additionally require co-regulation of SOX9-GLI . Other predicted targets that can be followed up in functional analyses include Fgfr3 , Igf1r , Bmp6 , Wnt5a and Ror2 , which are the direct targets of GLI1 and/or GLI3 , among which Fgfr3 , Igf1r and Bmp6 are also targeted by SOX9 ( S8 Table ) , indicating SOX9-GLI interacts with FGF , IGF , BMP and WNT signaling for regulating chondrocyte proliferation and differentiation . Although in the luciferase assays , SOX9 show higher transactivation potential on Ptch1 reporter comparing to GLI1 and GLI2 ( Fig 5D ) , their transcriptional activities are comparable on other Hedgehog targets including Gli1 and Gli2 ( Fig 5E and 5F ) . Since the regions used in these experiments only comprise a short representative fragment containing SOX9 and GLI binding regions , the level of transactivation achieved should not be taken as an absolute quantification of the degree of activation of the gene in vivo . Therefore we cannot conclude that SOX9 is a more potent factor for Ptch1 then GLI1 and GLI2 . But we can confirm that Ptch1 is a common target of SOX9 and GLI . Our analyses of the transcriptome and functional assays have implicated SOX9 , GLI and FOXA as key regulators mediating differentiation transitions in the growth plate . Building on this finding , we went further to infer the wider interaction network mediated by these factors . By integrating data in GP-DGEL with SOX9 , GLI1 and GLI3 ChIP-seq datasets , we found evidence for a regulatory network centered on SOX9-GLI-FOXA ( Fig 8A ) . The GRN presents the progressive changes in expression of TFs as chondrocyte transition from proliferating to late differentiated states . Since SOX9 and GLI are highly expressed and their DNA binding regions are most enriched in the cluster of PZ genes ( Fig 2A ) , we placed SOX and GLI families in the center of the network ( Fig 8A ) . The common targets of SOX9 and GLI harboring at least one SGBR were placed in the inner circle of the network , while the genes that were targeted singly by SOX9 or GLI were arranged in the outer rim . Multiple binding regions for SOX9 and GLI factors were identified in the promoters and the intergenic regions of Gli1 and Gli2 and also the SOX trio of Sox9 , Sox5 and Sox6 ( S8 Table ) . The model therefore predicts the existence of regulatory feedback loops in which Gli1 and Gli2 are the targets of SOX9 and vice versa . We next focused on the key transition from PHZ to UHZ and LHZ . The expression of SOX9 single target genes without GLI binding peaks in this outer rim , including transcription factors Mef2c , Rora , and Tcf3 , showed little change in the transition from PHZ to UHZ . Exceptions included Irx3 , Irx5 , Cebpb , Ets-1 , Atf6 , and Egr1 , which were not expressed in the PZ and PHZ but progressively increased in the UHZ and LHZ as SOX9 expression was down-regulated in the LHZ . These genes may be negatively regulated by SOX9 since SOX9 is both an activator and a repressor . SOX9 represses Col10a1 expression in PCs[40] and osteoblastic gene expression in HCs[26] . The coincident increasing gradient of expression of Foxa2 in the PHZ and UHZ , and the shared binding consensus of SOX9 and FOXA2 , are consistent with the role of FOXA2 in activating the hypertrophic program[22] . Whether the same motif mediates transactivation and repressor activities is unclear . The SOX9 motif was also identified in the LHZ where SOX9 protein is absent , suggesting this motif may mediate the activity of the FOXA factors ( FOXA2 and FOXA3 ) , which are expressed in HCs and may compete with SOX9 for the SOX/FOXA motif[22] . SOX9 is known to auto-regulate itself through several long-range enhancers [82] and GLI factors can activate Sox9 through direct binding to a far upstream enhancer[38] . From ChIP-chip data , GLI1 input was found in the Sox9 promoter region ( Fig 4F ) , suggesting that proximal chromatin interaction by GLI activator may also contribute to Sox9 expression . Our data indicate that in the context of growth plate cartilage , IHH signaling targets Sox9 as well as its transcriptional targets through GLI factors , and vice versa , to promote stage-specific chondrocyte differentiation , consistent with previous studies which found that the expression of Sox9 was induced by HH signaling articular cartilage chondrocytes[83] and retinal explants[84] . The cooperative action of SOX9 and GLI factors may reflect a wider application of cooperation between SOX and GLI factors in other systems . It is interesting that in neural tube patterning , cell fate determination requires both SOX2 and GLI1 inputs [85] . In pancreatic ductal adenocarcinoma , both SOX9 and GLI1 are important to maintain the malignant phenotype of cancer stem cells . Suppression of either SOX9 or GLI1 by siRNA reduces the expression of Sox9 , Gli1 and Gli2 [86] . In primary chondrocytes , SOX9 up-regulates the expression of PthrP through direct binding to its promoter region via collaboration with GLI2 [87] . Even when Hedgehog signaling was blocked by cyclopamine , overexpression of Sox9 could still increase the expression of Ptch1 , which is consistent with our finding that SOX9 positively regulates the transcription of Ptch1 without affecting Ihh expression . This report also demonstrated that SOX9 can directly bind to GLI2 in vitro[87] , thus their direct interaction for cooperation in vivo warrants further investigation . We propose a SOX9-GLI-centric model in which SOX9 and IHH signaling initiate control of chondrocyte differentiation phases , especially in the PZ and PHZ ( Fig 8B ) . Upon transition to PHC , down-regulation of GLI repressor ( due to Hedgehog signaling ) allows higher levels of activator SOX9 together with RUNX2 , and increased expression of MEF2C and FOXA2/3 which promote hypertrophic differentiation exemplified by Col10a1 expression[26] . We demonstrated that FOXA proteins compete with SOX9 for the binding to regulatory elements derived from Col2a1 and Col10a1 . These data imply FOXA2 competition with SOX9 is important for the switch to the down-regulation of Col2a1 and activation of Col10a1 in HCs . We propose that in the presence of high SOX9 level , particularly in its highly stable homodimeric form , FOXA is precluded from accessing this regulatory element ( Fig 7E ) . However , when SOX9 level declines and FOXA is elevated , FOXA2 competes off SOX9 , accesses this binding site and activates Col10a1 expression . The data are consistent with a model where FOXA2 and FOXA3 de-repress Col10a1 by binding the regulatory regions bound by SOX9[22] . FOXA has been proposed to act as a pioneer factor to displace the linker histone and keep the enhancer accessible for specific TFs to activate gene expression in liver cells[88] . FOXA factors might initiate the hypertrophic program by out competing with repressive SOX factors to keep the chromatin accessible for other synergistic factors including RUNX2 , MEF2C and SMAD1/4[22] . We surmise that SOX9 expression levels need to be lowered for this competition to take place as SOX9 forms a stable dimer at high concentrations that cannot be easily displaced . RUNX2 interaction with SOX9 depletes the effective level of SOX9 [89] . Furthermore , ZBTB20 and TRPS1 , which are induced by SOX9 , may repress SOX9 in the HZ in a negative-feedback loop [81 , 90] . In view of the reported SOX9/AP1 cooperation in transactivating Col10a1 expression , it is interesting to note that AP1 binding sites were also present close to the SOX9/FOXA binding motifs in the Col10a1 enhancer[21] . This raises questions about whether FOXA2 could also cooperate with AP1 factors in promoting hypertrophy and if FOXA factors can compete to modulate the cooperative action of SOX9-AP1 . We have illustrated the powerful utility of integrating GP-DGEL with other databases as a discovery strategy to determine which biological processes and pathways , transcriptional regulators and their potential cooperating partners are active , in the growth plate . Our phasic GRN featuring SOX9 , GLI and FOXA represents an initial template for the construction of a more complete model of chondrocyte differentiation that should incorporate a more complete set of TFs . In particular , it would be important to understand how SOX9-FOXA competition integrates with the SOX9-AP1 cooperation in promoting hypertrophy[21] . The protein interactome , non-coding RNAs , epigenetic status and chromatin/super-enhancer organization should also be taken into account in the future . It would be too great a task for a single study to investigate and functionally validate all the different target genes and processes identified . Therefore the gained information is shared as a public resource to facilitate and inspire additional discovery ( S4 Fig ) . Mining and integration of the information in GP-DGEL with other emerging genome-wide data on the binding profiles of other transcription factors will be essential to extend our understanding of the complex and dynamic GRN mediating the transition steps in chondrocyte differentiation .
Transverse sections of the proximal tibia of 10-day-old female F1 ( offspring of CBA and C57BL/6 ) mouse were obtained from chondrocyte sub-populations by cryosectioning . 5-micron sections were prepared and pooled into fractions consisting of 10 sections per fraction to ensure separation of each cell type in the growth plate . Samples were dissolved in Trizol reagent ( Invitrogen ) for RNA extraction . To guide the sub-division of chondrocyte populations into zones , every 10th section was examined histologically and 10% of RNA was isolated from selected sections at regular intervals for RT-PCR analyses of known growth plate markers ( S1C Fig ) , to guide the sub-division of chondrocyte populations into zones . Sox9 heterozygous null ( Sox9+/- ) mutants were generated by crossing Sox9-flox mice ( gift of Andreas Schdel ) [10] with Protamine-Cre transgenic mice ( gift of Yelena Marchuk ) [91] . Gli2 null ( Gli2-/- ) mice were a gift from C . C Hui[92] . Total RNA was isolated according to the instructions for RNA isolation ( Invitrogen ) . Prior to microarray analysis , 50ng total RNA was used to generate cDNA from each fraction by reverse transcription using SuperScript II reverse transcriptase and random hexamers . Semi-quantitative PCR analysis was performed to detect the expression of chondrogenic markers to identify the subpopulations of chondrocytes . Quantitative PCR was performed using Syber-Green master mixture to compare the expression levels of SOX9 target genes in the chondrocytes of wild type and Sox9+/- mice . RNA quality and integrity were analyzed using RNA 6000 Nano Kit ( Agilent ) . The pooled RNA was amplified for one cycle using MessageAmpTM II-Biotin Enhanced Kit , then labeled and hybridized to Mouse Genome 430 2 . 0 GeneChip containing 45101 probe sets ( Affymetrix ) in the Centre for Genomic Sciences ( the University of Hong Kong ) . All primary microarray data are deposited in the GEO website ( GSE99306 ) . Gene expression data for each zone in triplicate were normalized by using RMA algorithm in BioConductor software package [93] . The k-Means Clustering algorithm [94 , 95] and Eisen software tools [96] were used to identify the distinct expression patterns of genes with Coefficient of Standard Deviation ( C . S . D . ) > 0 . 15 across 4 zones . For each gene , the C . S . D . value was calculated with the formula: C . S . D . =S/X¯ , where S is the standard deviation and X¯ is the mean expression level of the samples over the 4 zones . The Gene Ontology analysis was performed for each cluster of genes by using the Gene Ontology database [97] and the David Web Tools [98] . To identify differentiation phase-specific genes and differential patterns of expression across different zones , we defined a set of “On” and “Off” genes in the dataset . Sox9 mRNA is expressed in the PZ and PHZ and is down-regulated in UHZ and LHZ . In contrast , Col10a1 is exclusively expressed in PHCs and HCs during hypertrophic differentiation in the PHZ , UHZ and LHZ . Therefore , we used the expression level of Sox9 in HZ ( 356 , the average from UHZ and LHZ ) and that of Col10a1 in PZ ( 511 ) to set the threshold of “On-Off” states for each zone ( S2A Table ) . The DME analysis were performed by using the Software package CREAD [99] with input from the promoter sequences extracted from 1k upstream and 200 bp downstream of TSS of the genes in each cluster . The background sequence file was generated by using the computer program ‘fasta-shuffle-notryptic . pl’ in the Bioinformatics CPAN Perl module of InSilicoSpectro-Databanks version 0 . 0 . 43 . The Matcompare program in the CREAD package was used to compute the similarity between the identified DME motif and those in the TRANSFAC , JASPAR and ENCODE databases [100–103] . The Position Frequency Matrices and the consensus DNA binding sequences of the transcription factors were compiled from TRANSFAC database and the literature . Foreground ( FG ) represents the number of occurrences of the identified DNA motifs in the set of promoter DNA sequences . Background ( BG ) represents the number of occurrences of the motifs in the randomly generated DNA sequences . The ratio of FG/BG indicates the fold enrichment of the identified motifs in the foreground over the background set of sequences [104] . Position Weight Matrix identified in DME promoter analyses and the functional SOX9_COL2C1 , COL2C2 [24] , COL2C3 [65] binding consensus ( S7 Table ) were utilized as the seed motif for screening of SOX9 monomer binding sites in the genomic region within 10kb from TSS of the zone DEGs . The DNA sequences of 25-bp flanking the identified SOX9 monomer site on both sides were retrieved from Reference Genome ( mm9 ) after removing DNA repeats . The MEME program was run with the command: $meme monomer_site_flanking_sequence . fq -dna -mod anr -nmotifs 2 -w 30 -oc meme_out_30bp -pal The parameter on motif length was set to range from 10 to 30-bp with the palindrome search mode activated . The genomic sequences of evolutionarily conserved non-coding elements in the promoter and intergenic regions of each gene were retrieved from the Mouse Reference Genome Sequence of NCBI build 37 , mm9 . The conservation scores of DNA sequences for 30-vertebrates and the genomic coordinates of the non-coding elements were obtained from UCSC Genome Browser Database [105] . The algorithm [106] was implemented to match the Position Weight Matrix of the transcription factors with the genomic DNA sequences for screening of their binding elements . A match between the TF and the target sequence was accepted if the sequence similarity score was over 85% and the UCSC phastCons DNA conservation score was over 300 . For prediction of SOX9 COL2C1 , COL2C2 and COL2C3 binding elements , we searched for the exact matches of the binding motifs in the sequence of evolutionarily conserved non-coding DNA elements . The Position Weight Matrices used for identification of SOX9 dimer and GLI binding elements were constructed from the SOX9 binding HMG core sequence and the GLI binding consensus in TRANSFAC database respectively . Detailed methods of rib chondrocyte isolation and SOX9 ChIP-seq experiment were described as reported [33] . The GLI1 promoter and GLI3 Genome-Wide ChIP-chip datasets were downloaded from GEO database ( GSE11062 and GSE11063 ) [73] and converted from mm8 to mm9 assembly by using the UCSC Toolkit [107] . The SOX9 and GLI binding regions were identified by applying the procedure for local maxima finding [108] with 25- and 50-bp genomic windows respectively . Fisher’s exact test on two-tailed P value was performed for a 2x2 contingency table with GraphPad Software ( GraphPad Software , Inc . ) , where group 1 represents the PZ , PHZ and HZ DEGs in the clustering heatmap , and group 2 represents the DEGs containing SGBR . The Odds Ratio number was calculated with the formula , OddsRatio=PZ , PHZSGBR/HZSGBRPZ , PHZtotal/HZtotal where PZ , PHZSGBR is the number of SOX9/GLI common target in the PZ , PHZ gene sets , and PZ , PHZtotal is the total number of the PZ , PHZ genes in the heatmap . Fisher’s Exact Test was performed with: ( i ) varied inter-peak distances between SOX9 and GLI binding regions; ( ii ) varied genomic distance between SGBRs and the target gene TSS as in previous studies[109 , 110] . The intergenic region was defined by the two nearest genes located at the 5’-end and 3’-end of the gene in query . The gene annotation information was downloaded from UCSC Genome Database . In situ hybridization was performed as previously described[111] . Hind limbs dissected from 10-day-old F1 littermates were fixed in 4% paraformaldehyde overnight at 4°C and decalcified in 0 . 5M EDTA for 24h before embedding in paraffin . [35S]UTP labeled probes for the selected genes were hybridized on the paraffin sections of the hind limbs . The paraffin sections were dewaxed and rehydrated . For cryosection , tissues were fixed in 4% PFA overnight before immersed in 30% sucrose . Sections were blocked with blocking buffer ( 5% BSA or goat serum , 0 . 5% Tween20 ) for 1 hour at room temperature . The primary antibodies of rabbit anti-Foxp2 ( 1:400; Abcam ) , rabbit anti-SOX9 ( 1:500 , Millipore ) , guinea pig anti-SOX9 ( 1:2000 , gift from V . Lee , STEMCELL Technologies ) and rabbit anti-FOXA2 ( 1:500 , Millipore ) were diluted in blocking buffer and applied on the sections at 4°C overnight . The signal was visualized by using 1:500 goat-anti-rabbit or donkey-anti-guinea pig antibodies and mounting with Vectashield® mounting medium containing DAPI . EMSAs were performed as described [66] . DNA probes were prepared with cy5-label at the 5’ end of the forward strand and reverse strand unlabeled . Equimolar amounts of complementary strands were annealed at 95°C for 5 min and subsequent cooling to 4°C at 1°C /min . Reaction mixtures ( 60nM probes and varying concentrations of proteins ) were incubated at 4°C in the dark for 4h and electrophoresed at 200V for ~40min at 4°C in the dark . The gels were imaged with a Typhoon FLA-7000 PhosphorImager ( FUJIFILM ) . ATDC5 cells were grown in DMEM/F12 supplemented with 5% fetal bovine serum , human transferrin ( 10μg/ml ) and sodium selenite ( 5ng/ml ) , and seeded in 12-well plates . With ~70%–80% confluency on the following day , the cells were transiently transfected with pGL3-basic luciferase reporters containing different regulatory elements using Lipofectamine 2000 ( Invitrogen ) . Luciferase activity was measured using the Dual luciferase reporter assay kit ( Promega ) according to the manufacturer's instructions . Luciferase expression is given as a fold-change relative to the activity of Renilla luciferase . The work with the use of mice and their care was approved in accordance with our institutional guidelines ( Committee for the Use of Live Animals in Research , the University of Hong Kong ) . | In the development of the mammalian growth plate , while several transcription factors are individually well known for their key roles in regulating phases of chondrocyte differentiation , there is little information on how they interact and cooperate with each other . We took an unbiased genome wide approach to identify the transcription factors and signaling pathways that play dominant roles in the chondrocyte differentiation cascade . We developed a searchable library of differentially expressed genes , GP-DGEL , which has fine spatial resolution and global transcriptomic coverage for discovery of processes , pathways and disease candidates . Our work identifies a novel regulatory mechanism that integrates the action of three transcription factors , SOX9 , GLI and FOXA . SOX9-GLI auto-regulate and cooperate to activate and repress genes in proliferating chondrocytes . Upon entry into prehypertrophy , FOXA competes with SOX9 , and control of hypertrophy passes to FOXA , RUNX , AP1 and MEF2 factors . | [
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"seque... | 2018 | Synergistic co-regulation and competition by a SOX9-GLI-FOXA phasic transcriptional network coordinate chondrocyte differentiation transitions |
Heritable microbial symbionts have profound impacts upon the biology of their arthropod hosts . Whilst our current understanding of the dynamics of these symbionts is typically cast within a framework of vertical transmission only , horizontal transmission has been observed in a number of cases . For instance , several symbionts can transmit horizontally when their parasitoid hosts share oviposition patches with uninfected conspecifics , a phenomenon called superparasitism . Despite this , horizontal transmission , and the host contact structures that facilitates it , have not been considered in heritable symbiont epidemiology . Here , we tested for the importance of host contact , and resulting horizontal transmission , for the epidemiology of a male-killing heritable symbiont ( Arsenophonus nasoniae ) in parasitoid wasp hosts . We observed that host contact through superparasitism is necessary for this symbiont’s spread in populations of its primary host Nasonia vitripennis , such that when superparasitism rates are high , A . nasoniae almost reaches fixation , causes highly female biased population sex ratios and consequently causes local host extinction . We further tested if natural interspecific variation in superparasitism behaviours predicted symbiont dynamics among parasitoid species . We found that A . nasoniae was maintained in laboratory populations of a closely related set of Nasonia species , but declined in other , more distantly related pteromalid hosts . The natural proclivity of a species to superparasitise was the primary factor determining symbiont persistence . Our results thus indicate that host contact behaviour is a key factor for heritable microbe dynamics when horizontal transmission is possible , and that ‘reproductive parasite’ phenotypes , such as male-killing , may be of secondary importance in the dynamics of such symbiont infections .
Heritable symbionts are common in natural populations of arthropods [1] , where they affect the biology of their host individual in diverse ways . They can be obligatory , providing physiologically crucial functions to their host , such as amino acid or vitamin anabolism [2] , or alternatively provide ecologically contingent benefits , such as conferring the ability to resist natural enemy attack [3 , 4] . Finally , they can be parasitic , spreading through their host population via the distortion of host reproductive biology towards the production and survival of infected females [1] . These impacts on the individual host can have consequences for ecology and evolution at the host population level when the symbionts spread efficiently [5–9] . Thus , it is important to understand the factors that contribute to symbiont epidemiology . Past work on heritable symbiont epidemiology has emphasized vertical transmission ( VT ) through maternal inheritance as the dominant means by which new infections are established [10–16] . Within this framework , aspects of host ecology , such as contact structure , are commonly ignored as they are not thought to influence microbe transmission . However , a number of heritable microbes that infect insects readily combine VT with horizontal transmission ( HT ) , creating symbionts with a mixed-mode of transmission [17] . Unlike VT , HT rates are dependent on the degree of contact between hosts and thus may represent a means through which host ecology and behaviour can drive symbiont spread . However , HT and host contacts have not been empirically explored in heritable symbiont epidemiology . Heritable symbionts can horizontally transmit via several mechanisms , including passing through the phloem of a host’s food plant [18] , being vectored via parasitoid wasp ovipositors [19 , 20] , or by being transmitted when mating [21] . Notably , several symbionts that infect parasitoid wasps achieve HT when infected and uninfected host females share an oviposition target–a phenomenon termed superparasitism when occurring between females of the same species , and multiparasitism when a host is shared by two or more parasitoid species [22–26] . For heritable microbes such as these , the contact structure of the host is likely to be an important determinant of symbiont dynamics , as each contact represents a transmission opportunity . Specifically for parasitoid species , superparasitism rates will impact on the probability of acquiring a symbiont , thus linking host reproductive behaviour and symbiont epidemiology . Remarkably , this host behavioural trait has also been shown to be manipulated by a virus to promote its HT [25 , 26] . Here , we use an experimental epidemiology approach to directly evaluate the impact of host contact structure on the dynamics of a reproductive parasite with mixed-mode transmission . We manipulate the host contact structure ( opportunity for superparasitism ) in populations of parasitoid chalcid wasps and subsequently track the dynamics of the male-killing bacteria Arsenophonus nasoniae . A . nasoniae was originally described in the wasp Nasonia vitripennis , where it kills c . 80% of the male offspring of infected females [23 , 27–29] . A . nasoniae is unusual among male-killers as it is not directly transmitted within the host’s egg . Rather , this bacterium achieves VT when a female wasp oviposits into a fly pupa and the bacterium ( which is extracellular ) is inoculated into the fly host with the wasp’s venom . A . nasoniae then infects the wasp larvae that hatch as they feed on the fly cadaver [30] . If an uninfected and infected female N . vitripennis superparasitise a fly pupa , the progeny of both females become infected , creating horizontal transmission [23 , 24] . Despite this capacity for HT , the dynamics of A . nasoniae and other male-killers have only been described with VT-only models , wherein male death increases the fitness of their infected sisters; a mechanism termed ‘fitness compensation’ [10 , 11] . Empirical evidence for such benefits in this system is weak and/or lacking [31] , indicating other factors may be key for A . nasoniae spread . We further explore whether host contact behaviours , and thus opportunities for HT , can influence symbiont dynamics in multiple , related host species . N . vitripennis shares its filth-fly niche with a number of other parasitoids that can acquire the symbiont through interspecific multiparasitism in the laboratory [24] . However , surveys of natural parasitoid communities have found little or no A . nasoniae infection in many of these species [24 , 32] . We hypothesise that the ability of A . nasoniae to spread and be maintained in new parasitoid host species is dependent upon HT through superparasitism . In these multi-species experiments , we track symbiont dynamics , VT efficiency and cost of infection in a number parasitoid species that vary in their superparasitism propensity . From this we determine the potential importance of superparasitism behaviour in driving variable patterns A . nasoniae dynamics between host species .
We first compared the dynamics of A . nasoniae in populations of N . vitripennis when superparasitism was permitted compared to populations where solitary parasitism was enforced . Following this , we examined quantitatively the impact of different opportunities for superparasitism ( i . e . varying degrees of host contact ) on A . nasoniae dynamics . Our hypothesis was that host contact through superparasitism would drive A . nasoniae into populations through HT , and that higher rates of superparasitism would be associated with higher prevalence of the symbiont . Within this , we also investigated whether pupal host resource level ( number of fly pupae offered per wasp ) influenced A . nasoniae dynamics . N . vitripennis shares its filth fly niche with several other parasitoids , including N . longicornis , N . giraulti , Trichomalopsis sarcophagae and Muscidifurax raptorellus [33] . Previous work has shown that N . longicornis , N . giraulti and M . raptorellus can acquire A . nasoniae when multiparasitising with infected N . vitripennis under controlled conditions [24] , though this mode of interspecific transfer has not been previously tested with T . sarcophagae . In nature , all Nasonia species used here maintain the symbiont , but it has not been found in surveys of M . raptorellus or T . sarcophagae [24 , 32] . We examined the dynamics of A . nasoniae in these parasitoid wasp species to investigate whether close relatedness of a wasp species to N . vitripennis predicted A . nasoniae maintenance and to determine the biological basis of the pattern . We established replicated populations for each of the five wasp species at 100% initial A . nasoniae prevalence . Superparasitism was permitted in all populations .
The ability of heritable symbionts to horizontally transmit is increasingly being appreciated [18–22 , 24 , 26 , 34 , 35] , but its importance in symbiont epidemiology within host populations has not been empirically investigated . Here , we show that host-host contact during superparasitism , a behaviour that permits HT in our system , is necessary for the spread of a heritable male-killer . Further , the extent of symbiont spread relates closely to the frequency of host contacts in the population . We also demonstrate that variation in symbiont maintenance between host species may be partly explained by the differing superparasitism behaviours exhibited between wasp species . These results have implications for our understanding of heritable symbiont epidemiology , the evolution of reproductive manipulation and the consequences of superparasitism in parasitoids . In our experiments , A . nasoniae was lost from wasp populations unless HT was permitted through superparasitism . This suggests that the dynamics of symbionts with mixed-mode transmission are strongly reliant upon host contact rates . From this observation , we predict that variation in A . nasoniae prevalence across host populations and species will be driven by both variation in contact rates/behaviours , and by the reproductive rate of infected females as presumed under VT-only models . For example , for species such as N . vitripennis , wasp density ( and therefore superparasitism intensity ) vary greatly over population ranges [36 , 37] , with up to 40% of fly pupae being parasitized by more than one female wasp [38] . Our results suggest that this variation will impact on the frequency of A . nasoniae infection . These findings echoes theoretical and field studies of an insect virus that exhibits a similar mixed-mode of transmission [39 , 40] , in which spatially heterogeneous host and parasitoid densities have been linked with variation in symbiont prevalence . Because HT breaks down the link between host relatedness and symbiont transmission , our findings present an interesting exception to the current view on the spread of male-killing symbionts . Under VT-only models , male-killing is thought to be most common when competition is between siblings ( i . e . no superparasitism ) , as the death of males directly benefits the fitness of their infected sisters and thus promotes symbiont VT ( fitness compensation ) [10 , 11 , 41] . Our observation that A . nasoniae is lost from host populations when superparasitism is prevented indicates that reproductive parasitism phenotypes alone are not sufficiently strong to maintain infection in this system . Indeed , A . nasoniae was maintained in N . giraulti , which readily superparasitizes , despite an observed 10–20% cost of symbiont carriage , measured in terms of daughter production . Furthermore , our VT-only model predicts higher symbiont prevalence after 4 generations than we see in our populations of N . vitripennis kept under solitary parasitism conditions . This result may indicate that A . nasoniae is exerting an additional cost on its host that we do not detect and allows us to exclude any advantage of male-killing as sufficient for maintenance of A . nasoniae in these species . The observation that there is only a weak benefit of male-killing to symbiont transmission is echoed in other studies of male-killing [15 , 31 , 42] . In some instances such ‘weak’ male-killing microbes have also additional phenotypes such as protecting their host against natural enemy attack [15 , 43] . Furthermore , where male killing efficiency is compromised by host evolved suppression , additional reproductive manipulation phenotypes have been revealed [44] . Our results , in combination with these previous studies , indicate that male-killing may evolve and be maintained as an additional , rather than primary , driver of heritable symbiont fitness in a number of cases . Unlike these other systems where additional phenotypes also promote VT , our findings show that for A . nasoniae any benefit of male-killing is supplementing obligate HT . Indeed , A . nasoniae is unusual among male killers in that it may be grown in cell-free culture and requires HT in order to persist as we have revealed . As such , male-killing in A . nasoniae may have arisen through an entirely different evolutionary trajectory to that in more ‘traditional’ heritable symbionts where male-killers show greater linkage with their host line [45 , 46] . Nevertheless , we would note that the use of HT in addition to sex ratio distortion is not unique to our system , but is additionally shown by parthenogenesis inducing Wolbachia in trichogrammatid wasps [22] , although the dynamical importance of HT has not been determined in this system . Our experiments and measurements beg the question as to why male-killing has been maintained or evolved at all in this system . We suggest that although fitness compensation whilst is not sufficient to enable spread of the symbiont on its own , it provides enough of a marginal benefit to be advantageous over non-male-killing mutants . Indeed , contrary to the current paradigm that male-killing is most favoured when competition is primarily between siblings[12] , the impact of male-killing on A . nasoniae transmission may actually be stronger under HT than under VT-only . This arises because resource competition in a superparasitised pupae will likely be more intense due to crowding [47] , and the infection transfers to both sibships within the host pupa . Balas et al [31] proposed a verbal model of a similar mechanism called the ‘incremental gains hypothesis’ to explain the marginal benefits of male-killing observed in wild caught A . nasoniae-infected wasps . The data presented here empirically support this otherwise neglected model . Interestingly , this predicts that solitary parasitoids should be less likely to harbour male-killing , horizontally transmitted symbionts because there are no brood-mates to horizontally transmit into . Indeed , to our knowledge , the male-killing phenotype has not been described in any symbiont infection of a solitary parasitoid . We observed that A . nasoniae maintenance varied between parasitoid species , an outcome that correlated positively with variation in superparasitism rates . This result strongly supports the hypothesis that superparasitism propensity may contribute towards the persistence of A . nasoniae after host shift events . Host-symbiont interactions in novel hosts may be disrupted compared to that in ancestral hosts in a variety of ways , e . g . by increased infection costs or low VT efficiency [48 , 49] . These disruptions may prevent spread . In contrast , we observed maintenance of the infection in N . giraulti , a host which readily superparasitised , despite a considerable cost to infection . Thus , high rates of superparasitism are able to compensate for a symbiont misfit and permit symbiont maintenance . In our experiments , individuals were maintained under conditions in which superparasitism would be necessary if every female were to oviposit . Thus they represent an ecological extreme of host parasitoid density and fly pupal scarcity that leaves only wasp behavioural variation as the determinant of superparasitism rate . In nature , spatiotemporal heterogeneity in wasp and fly densities are likely to be the major determinants of superparasitism rate and , so too , A . nasoniae epidemiology in these species . This effect has been explored both in theoretically and field studies of LbFV where viral prevalence is low or absent in less dense populations at the host-species range [39 , 40] . We thus argue that once extrinsically determined host contact networks are accounted for , the intrinsic effect of host propensity to superparasitise may play a role in the host range for this symbiont in nature . Potentially , certain arthropod groups may act as hotspot reservoirs of symbiont infection by virtue of their oviposition behaviour . Our results also add a new facet to our understanding of the link between superparasitism and population sex ratio . N . vitripennis is a model organism linking parasitism behaviour to individual brood sex ratio using the conceptual framework of local mate competition [50–53] . Where females of this species oviposit singly they produce c . 80% daughters , in accordance with Local Mate Competition theory ( LMC ) . In contrast , a superparasitising female will lay a male-biased brood to exploit the fitness opportunity from males being rare in the local mating pool . This effect is well established at an individual level in N . vitripennis under laboratory and field conditions [50 , 51] and is expected to generate a relationship between high superparasitism levels and more equal population sex ratio in natural populations [37 , 54] . Conversely , we have demonstrated that superparasitism promotes the spread of male-killing A . nasoniae through the population , which will act to inhibit the return of the population sex ratio towards parity . By facilitating the transmission of a sex-ratio distorting parasite , superparasitism reduces and destabilizes the male frequency compared to uninfected lineages , which would adhere to LMC predictions . We also observed that high rates of superparasitism created a new condition under which male-killers could drive host extinction . Theoretical work has implicated sex ratio distortion in host extinction [52] , including male-killing and feminizing endosymbionts [55 , 56] , but has suggested they would only cause extinction where VT was near perfect and males mate globally ( rather than locally ) . This possibility has been verified in laboratory populations of Drosophila innubila , where male-killing Wolbachia under strong VT conditions and local sibling competition caused host extinction due to virginity [41] . In our experiment , we observed population extinction when fly host resources are scarce and the male killer is driven to high prevalence through wasp superparasitism . Thus , the requirement of perfect VT for population extinction is relaxed when superparasitism occurs because mixed-mode transmission allow rapid spread of the microbe before deleterious virginity effects manifest at the population level . Superparasitism can impact key ecological and evolutionary traits of a species . We show for the first time that this oviposition behaviour can drive remarkable changes in symbiont epidemiology , with consequences for host sex ratios and even host population survival . Additionally , these findings illuminate a previously unconsidered facet of the evolution and ecological impact of parasitoid superparasitism behaviour . Furthermore , our results challenge a major assumption in existing models; that heritable symbiont dynamics and the evolution of reproductive manipulation phenotypes are largely shaped within a framework of VT . In light of these data , we should work to incorporate the occurrence and frequency of mixed-mode transmission into a framework of symbiont epidemiology and host-symbiont interactions to capture the dynamics of symbionts such as A . nasoniae .
Arsenophonus nasonia is a γ-proteobacteria originally found infecting the parasitoid wasp Nasonia vitripennis where it distorts the secondary sex ratio towards a female bias by killing c . 80% of male embryos [23 , 27 , 30] . VT efficiency of A . nasoniae in N . vitripennis has been estimated at 95% [23] and so generates a relatively minor rate of segregational loss which has been supposed , but not demonstrated , to be offset through fitness compensation through male-killing . Surveys of A . nasoniae in natural populations of N . vitripennis have found that among population infection prevalence varies between 5–47% [31 , 32] , and several populations have failed to show any infection [31 , 32] . The factors causing this variation are unknown , but it can be assumed that there are barriers preventing its spread to certain populations to the high prevalences observed in some other male-killer systems [e . g . 57] . N . vitripennis is a model organism for sex ratio research and has been shown to adhere to LMC theory predictions in both laboratory and natural studies [36 , 50 , 53] . N . vitripennis’s utility as a study organism for sex ratio research has stemmed from its haplodiploid sex determination system . Female wasps can produce haploid sons from unfertilized eggs and so can alter their clutch sex ratio by controlling sperm access to their ova . Superparasitism is common in natural populations of N . vitripennis with up to 40% of broods founded by multiple mothers and up to nine mothers can contribute offspring to a single pupa [37 , 38] . The wasp’s dipteran hosts are typically aggregated around bird’s nests and animal corpses and so encourage high densities of wasps to congregate [38] . HT of A . nasoniae is readily achievable due to the per-oral transmission of the bacterium from maternal calyx fluid to offspring gut , therefore all larvae present in the host can acquire the infection [23 , 30] . Previous work has demonstrated that this HT is possible through interspecific multiparasitism , with success negatively correlated with genetic distance between species pairs [24] . The A . nasoniae strains used in N . vitripennis—only density experiments derive from wild caught N . vitripennis from Canada isolated by Graeme Taylor in 2010 [32] , ( CAN1 ) . The male killing efficiency of this strain in N . vitripennis is c74% ( see S1 Fig ) . The A . nasoniae used in multispecies experiments was isolated from an infected N . vitripennis caught in Marbury , Cheshire , UK by the authors ( UK1 ) . The male-killing efficiency of UK1 is given in S2 Fig and S2 Fig . Both strains were cultured on GC media supplemented with 3ml/L IsoVitalex ( Applied Biosytems ) at 25°C . All wasp lines were derived from isofemale laboratory cultures originally isolated in the Netherlands by the group of Leo Beukeboom . Wasps had been maintained , A . nasoniae-free for at least one year prior to experimentation . All wasps were maintained on Sarcophaga bullata pupae at 25°C , 12:12 L:D . S . bullata were obtained as larvae , allowed to pupate at 20°C and then either presented to wasps within 48 hours of pupation , or kept as pupae for up to one week at 4°C before use . A . nasoniae infected lines of N . vitripennis were established by injecting 5μl of Arsenophonus cells suspended in PBS at 105 CFU ml-1 into a surface-sterilized fly pupa and then allowing female wasps to oviposit . Offspring from these broods were then allowed to mate and oviposit individually before being screened for infection as below . The F2 offspring of infected females were then established as infected ( A+ ) isofemale lines kept en masse . A+ lines had been maintained in this way for at least two generations before experimentation . The uninfected line ( A- ) was retained as a comparator . Infection prevalence in experimental populations was scored by diagnostic PCR following the protocol of [24] . Briefly , DNA was extracted from whole wasps using Chelex 100 [58] . PCR amplification specific to A . nasoniae based on the 16S ribosomal RNA gene was used to detect infected individuals ( primers: Arse16S–F:GGG TTG TAA AGT ACT TTC AGT CGT/Arse16S-R: CGC AGG CTC GCC TCT CTC [1] ) . Approximately 1% of DNA samples in the first set of N . vitripennis-only experiments , showed inconclusive positive amplification of the Arse16S amplicon ( weak or under/over sized bands ) . To correct for false positives these were further verified by performing an additional screen for the more specific , but type-II error prone , metallaprotease-1 gene ( primers M1-F: GGGTCACATACCTATTTT , M1- 473 R: GTAGTCGCCTGGGTGGG , ( GenBank accession: CBA72251 . 1 , [52] ) . In all cases this verified that Arse16S primers had correctly identified an infected individuals and so this verification step was not used when screening samples from the multiple-species experiments . DNA quality was verified for each sample by amplifying a portion of the insect cytochrome oxidase gene ( CO1 ) , ( Primers: LCO . 5' GGT CAA CAA ATC ATA AAG ATA TTG G 3 , HCO . 5' TAA ACT TCA GGG TGA CCA AAA AAT CA 3' [59] ) , or the insect 18S rRNA gene ( NSF4/18: CTG GTT GAT YCT GCC AGT , NSR399/19: TCTCAGGCTCCYTCTCCGG ) [60] . Any samples that failed to amplify a CO1 product visible through gel electrophoresis were discarded from further analysis . When necessary , DNA samples were stored for short periods at -20°C or , for periods >1 week , at -80°C Here , we test whether relatedness of a wasp species to N . vitripennis predicts A . nasoniae dynamics and determine the biological basis of the observed pattern . All statistical analyses were conducted in R using the lme4 , fBasic , binom and multcomp packages ( R Core team 2014 ) . Where measured , sex ratio was recorded as the proportion of males in a clutch/population . Analyses of proportional data such as sex ratio and prevalence of Arsenophonus were carried out using generalised linear models with assumption for binomial distribution of errors and a logit link function . Where necessary , experimental block was included as a random factor ( experiment set 1 ) . Any overdispersion was accounted for by fitting either quasibinomial in GLMs or an observation-level random effect to account for high residual variation in GLMMs . All models were simplified to the minimum adequate form through pairwise maximum likelihood tests and AIC selection . Unless otherwise stated , statistics reported for fixed effects are generated by comparing models with/without the given effect . Multiple comparisons of factor levels within significant main effects were conducted with Tukey HSD obtained from the ‘multcomp’ package . Where applicable , Fisher’s exact tests and exact binomial tests were used to compare observed responses to predicted values or probability distributions . All data used in analyses and figures are available in the Dryad repository ( doi:10 . 5061/dryad . 60ff8 ) [62] . | Most insects house heritable symbionts and these represent an important component of their biology , both as partners conveying beneficial traits such as defence against natural enemies , or as antagonists manipulating their hosts’ reproduction . Work on these bacteria mostly assumes that such phenotypes have evolved primarily to facilitate the symbiont’s vertical transmission from parent to offspring . However , several such bacteria also move horizontally between unrelated individuals . Here , we show that a male-killing symbiont actually depends upon horizontal transmission for its spread and maintenance . We observed Arsenophonus nasoniae was only maintained in parasitoid wasp populations when the route enabling horizontal transmission , superparasitism of fly pupae , was allowed . When superparasitism was common enough to cause epidemic spread of A . nasoniae , host population extinction occurred due to lack of males . Our study indicates that superparasitism behaviour is likely to be the key element determining which wasp species maintain this symbiont in nature . This provides new insights into the factors determining heritable symbiont frequency within and amongst species , and highlights the extreme effects such symbionts can have on their host populations . The data also indicate that male-killing may evolve and be maintained as an additional , rather than primary , driver of heritable symbiont fitness . | [
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... | 2016 | Superparasitism Drives Heritable Symbiont Epidemiology and Host Sex Ratio in a Wasp |
Retinal ganglion cells receive inputs from multiple bipolar cells which must be integrated before a decision to fire is made . Theoretical studies have provided clues about how this integration is accomplished but have not directly determined the rules regulating summation of closely timed inputs along single or multiple dendrites . Here we have examined dendritic summation of multiple inputs along On ganglion cell dendrites in whole mount rat retina . We activated inputs at targeted locations by uncaging glutamate sequentially to generate apparent motion along On ganglion cell dendrites in whole mount retina . Summation was directional and dependent13 on input sequence . Input moving away from the soma ( centrifugal ) resulted in supralinear summation , while activation sequences moving toward the soma ( centripetal ) were linear . Enhanced summation for centrifugal activation was robust as it was also observed in cultured retinal ganglion cells . This directional summation was dependent on hyperpolarization activated cyclic nucleotide-gated ( HCN ) channels as blockade with ZD7288 eliminated directionality . A computational model confirms that activation of HCN channels can override a preference for centripetal summation expected from cell anatomy . This type of direction selectivity could play a role in coding movement similar to the axial selectivity seen in locust ganglion cells which detect looming stimuli . More generally , these results suggest that non-directional retinal ganglion cells can discriminate between input sequences independent of the retina network .
Regardless of their classification , virtually all ganglion cells receive input from multiple bipolar cells . For example , in the cat and guinea pig retina , as many as 150 bipolar cells synapse onto a single On type ganglion cells [1]–[3] . With the exception of the soma and very proximal dendrites , bipolar cells contact ganglion cells uniformly throughout the dendritic tree [1] , [4] . Thus ganglion cells have the task of integrating synaptic input from multiple bipolar cells whose synapses are distributed throughout the entire dendritic tree before a decision to fire an action potential can be made [5] , [6] . Much of our current knowledge of the dendritic integration of multiple excitatory inputs comes from modeling studies [7]–[10] as well as analysis of conductance changes evoked by illumination of ganglion cell receptive fields [11] . However , these studies do not address the rules regarding summation of closely timed inputs along single or multiple dendrites . Similarities in the local statistics of light in natural scenes [12] result in highly correlated activity of neighboring bipolar cells . Thus , as the eyes sample the visual world , individual ganglion cell dendrites are likely to be activated by cohorts of bipolar cells whose input with spatio-temporally correlated synaptic output . Theoretical work has suggested that there is a directional component to dendritic integration [13] , and this has been confirmed in cortical neurons where activation sequence toward the soma ( centripetal ) produced more summation than the sequence directed away from the soma ( centrifugal ) [14] . However , it is unclear whether the same rules for dendritic integration apply to retinal ganglion cells . To test for direction-dependent summation of excitatory postsynaptic potentials ( EPSPs ) in On ganglion cells , we activated multiple distinct loci along a dendrite with targeted local photolysis of caged glutamate . Surprisingly , we found that summation was supralinear for centrifugal input , while centripetal activation resulted in linear summation . Hyperpolarization activated cyclic nucleotide-gated ( HCN ) channels have been shown to modulate summation and firing in a number of brain regions making them excellent candidates for modulators of dendritic integration in ganglion cells [15]–[20] . We found that blockade of HCN channels increased overall summation , and eliminated the directional component of summation . Further , the effect of blockade was most pronounced in the distal dendrites suggesting that the density of HCN current increases with distance from the soma . Our results show that intrinsic properties of the ganglion cell allow non-direction selective cells to code specific input sequences . This directional summation of EPSP is similar to that seen in starburst amacrine cells where centrifugal stimuli produce larger Ca2+ responses [21] , [22] . Our results suggest that this fundamental mechanism for directional summation is an essential building block across multiple cell types and species for generating a traditionally direction selective cell .
Sprague Dawley rats were purchased from Charles River Laboratories ( Wilmington , PA , U . S . A . ) and housed in the Albert Einstein College of Medicine Animal Care Facility . Rats were subjected to 12 h light/12 h dark cycles , and were fed a standard chow diet . All procedures were performed according to a protocol approved by the Institute for Animal Studies of the Albert Einstein College of Medicine . Whole mounts of rat retina ( P14–18 and P21–35 ) , were prepared from dark adapted ( 1 hr ) animals . Animals were anesthetized with isoflurane applied to gauze attached to the top of a closed chamber . This results in loss of consciousness within a minute . Animals were then decapitated , and subsequent surgery and cell recordings were performed in dim red light . The eyes were enucleated and retinal dissections were performed in oxygenated physiological saline ( in mM: 119 NaCl , 2 . 5 KCl , 1 . 3 MgCl2 , 2 . 5 CaCl2 , 1 NaH2PO4 , 26 . 2 NaHCO3 , 11 glucose ) . The eyecup was cut into two pieces , the retina removed with forceps , and transferred to a recording chamber . Whole-cell recordings were made from ganglion cells using a patch pipette filled with ( in mM ) : 135 KMeSO3 , 5 KCl , 1 CaCl2 , 5 EGTA , 10 glucose , 2 ATP , 0 . 3 GTP , 10 HEPES ( pH to 7 . 2 ) and 100 µM Oregon-green BAPTA . The retina was superfused at 30°C with oxygenated physiological saline . Recordings were performed under infrared conditions using a CCD video camera fitted with IR filter and macro lenses . A halogen source was used for light stimuli . Cells were selected for patching based on large soma size . This approach yielded ∼85% On cells . At the beginning of each experiment ganglion cell subtype was identified using a light stimulus in the dark adapted retina . Subsequent use of the 405 nm uncaging or 488 imaging laser changed adaptation state . The use of caged compound allowed us to isolate the postsynaptic response independent of presynaptic circuitry . Only one cell was obtained from a preparation insuring all cells were initially dark adapted . Light evoked EPSP responses were kept subthreshold by placing 3 or 4 log unit neutral density filters in the light path , and varying stimulus duration ( range 10–200 ms ) . Individual ganglion cells were then imaged with an Olympus Fluoview 1000 confocal microscope using a 40× water-immersion objective [23] . Laser light was limited to brief exposures sufficient to identify the dendritic target for the uncaging beam . If holding current exceeded 100 pA the experiment was terminated . A Z-series of confocal images were collected at the completion of the experiment . 200–500 µM MNI-caged-glutamate ( Tocris , Ellisville , MO ) was added to the external solution . ( 2R ) -amino-5-phosphonovaleric acid ( APV; 50 µM; Tocris , Ellisville , MO ) , and ZD7288 ( 100 µM; Tocris , Ellisville , MO ) were used to block NMDA and HCN channels , respectively . Recordings were accepted only if the holding current was less than −150 pA when ganglion cells were voltage clamped at −67 mV . Electrophysiology data were collected and analyzed off line with pClamp ( Molecular Devices , Sunnyvale , CA ) . The “HCN isolation solution” contained ( in mM ) : TTX 0 . 1 , BaCl2 1 , NiCl2 0 . 1 , D-AP5 0 . 05 , NBQX 0 . 01 , picrotoxin 0 . 05 , TPMPA 0 . 05 , strychnine 0 . 001; NaH2PO4 was omitted when Ba2+ and Ni2+ were present to avoid precipitation . TTX , BaCl2 , NiCl2 , APV , NBQX , picrotoxin , TPMPA , and strychnine were used to block sodium , inward rectifying potassium , calcium , N-methyl-d-aspartate ( NMDA ) , AMPA receptor , GABAA , GABAC and glycine receptors , respectively [24] , [25] . An Olympus Fluoview 1000 confocal microscope equipped with a 405 diode laser ( 1–3 ms pulses ) was used for uncaging MNI-caged glutamate ( 200–500 µM ) at multiple targeted regions of the dendrite ( 0 . 5 ms minimum delay between locations ) . Laser power and uncaging duration were kept constant for the duration of an experiment . For all experiments we obtained 8 trials for each uncaging sequence . Retinas were isolated from newborn ( P0 ) rats after cryoanesthesia and were incubated for 45 min at 37°C in DMEM with HEPES ( Mediatech , Washington , DC ) , supplemented with 6 units/ml papain ( Worthington , Freehold , NJ ) and 0 . 2 mg/ml cysteine . Papain was then inactivated by replacing the enzyme solution with complete medium composed of DMEM , 5 mM HEPES , 0 . 1% Mito+ serum extender ( Collaborative Research , Bedford , MA ) , 5% heat-inactivated fetal calf serum ( HIFCS ) , 0 . 75% penicillin-streptomycin-glutamine mix ( Life Technologies ) . Osmolarity was adjusted to 300 mOsm by addition of distilled water . Retinas were triturated through a fire-polished Pasteur pipette , plated onto glass coverslips pretreated with poly-D-lysine ( 0 . 1 mg/ml ) , and maintained in complete medium supplemented with 15 mM KCl . 72 hr after plating , cells were treated with the antimitotics 5-fluoro-2-deoxyuridine ( 0 . 01 mg/ml ) and uridine ( 0 . 026 mg/ml ) for 24 hrs . Cells were used for recording or immunohistochemistry at 11–14 days in vitro . Cultured retinal ganglion cells were fixed for 10 min in 4% paraformaldehyde ( PFA ) , rinsed with Tris-buffered saline ( TBS ) , and blocked for one hour with TBS containing 4% bovine serum albumin ( BSA ) , and 0 . 1% Triton-X 100 . After blocking , cells were incubated with primary antibodies ( chicken anti-MAP2 , 1∶1000 , Millipore , Billerica , MA; rabbit anti-GluR2 , 1∶200 , Millipore; anti-HCN1 , anti-HCN2 , and anti HCN4 raised in mouse , 1∶200 , NeuroMab , Davis , CA ) for 1 hour . Cells were washed in TBS and incubated with fluorescently-conjugated secondary antibodies ( Alexa Fluor 488 goat anti-chicken IgG , 1∶1000 , Life Technologies , Grand Island , NY; Cy-3 conjugated donkey anti-mouse IgG ( H+L ) , 1∶500; Cy-5 conjugated donkey anti-rabbit IgG ( H+L ) , 1∶500 , Jackson ImmunoResearch Laboratories , West Grove , PA ) . After washing , coverslips were mounted for imaging . Retinal ganglion cells were imaged using a Hamamatsu Orca ER camera mounted on an inverted Nikon fluorescent microscope with a 60× Plan Apo lens . The strength of the excitation light and length of exposure time were kept constant for precise comparisons . Images were background-subtracted , and analyzed using Metamorph software ( Molecular Devices , Sunnyvale , CA ) . No labeling was observed in control slices in which the primary antibody was omitted . HCN1 immunoreactivity was quantified by comparing the average intensity value for HCN1 labeling in proximal ( 0–50 µm ) and distal ( >100 µm ) dendrites . Morphologies for ganglion cells were derived from live images of ganglion cells taken after recordings . An image of the dendritic arbor was digitized by tracing each dendrite manually or by a semi-automatic algorithm , similar to previous models [10] , [26] . The dendritic arbor was divided into regions ( proximal , middle , and distal ) that could be assigned different biophysical properties . For some simulations , the cell was given ion channels defined by Markov sequential-state models , with various combinations of densities of 3 channel types ( KHCN , Na , Kdr ) . Channel densities and other biophysical properties ( Rm , Ri ) were specified for each region of the cell . The standard set of biophysical properties is listed in Table 1 . For some models , we set the channel densities uniform across the cell , but for others , we specified a gradient of channel densities as a function of distance from the soma . In addition , an offset voltage and kinetic rate multiplier could be specified for the rate functions for each channel type . The model of the HCN channel was taken from the literature [27] , [28] . Further modifications were performed to match results obtained from voltage step responses and tail current experiments . The rate of activation and deactivation for HCN were decreased by a factor of 0 . 33 and 2 respectively , to approximately match the data in whole cell recording . We simulated the uncaging of glutamate with a synaptic model connected to a presynaptic compartment that was voltage-clamped . The voltage clamp was directly set by the intensity of a light stimulus at the local ( X , Y ) location . The background light “intensity” was set to the resting potential for the pre-synaptic compartment , and the stimulus “contrast” generated a depolarizing pulse of several mV . This voltage controlled an exponential release function with 2 mV per e-fold change . To approximate the time course of the real responses , the released neurotransmitter was passed through a first-order temporal filter with separate time constants for rise and fall , set to 75 ms and 125 ms respectively . The filtered glutamate release bound to post-synaptic AMPA receptors modeled using a markov diagram , derived from [29] . The reversal potential for the AMPA conductance was set to 0 mV . Thus , synaptic conductance was modulated by the “contrast” of the stimulus to the presynaptic compartment , to vary the amplitude of EPSPs recorded in the simulated ganglion cell . Synapses were positioned at regular intervals ( ∼10 µm , with regularity ( m/SD ) = 8 ) over the dendritic tree by an automatic algorithm . Stimulus pulses were given to specific points on a dendrite for a specified duration ( 3 ms ) and inter-pulse interval ( 20 ms ) . Sets of 3 to 5 locations were selected along the major dendrites at different radial distances from the soma . The simulations were performed using the Neuron-C simulation language [26] , [30] . For most model runs , we saved the model's state parameters ( voltages , channel occupancies , etc ) after initial equilibrium , just before the first stimulus ( the sequence of “uncaging” in centripetal or centrifugal directions ) , and restored the model parameters after the first stimulus and just before the second . This obviated any interaction between the timing of the first stimulus and the second . When we removed the “restore” of the model's state before the second stimulus , we found that within an interval of 500 ms there was a noticeable effect of the first stimulus upon the second , consistent with the slow activation/deactivation of HCN . Because the data from the real cells was obtained with long intervals ( more than 1s ) between directions , we utilized the “save” , “restore” feature of the simulator for all other model runs . Directional summation ( DS ) was calculated for amplitude as ( Vpeak away – Vpeak toward ) / ( Vpeak toward – Vrest ) * 100 , and ( Charge away – Charge toward ) /Charge toward * 100 for charge .
We used local photolysis of caged glutamate to examine location and timing-dependent differences in summation of dendritic input . Whole-cell voltage and current clamp recordings were obtained from On ganglion cells in whole mounts of dark-adapted rat retina . A Fluoview 1000 equipped with a 405 diode laser was used to uncage glutamate over ganglion cell dendrites in whole mount retina ( see Methods ) . Postsynaptic response size was regulated by adjusting laser intensity to insure that the responses were subthreshold ( 1–4 mV ) and did not initiate action potentials . This approach elicited EPSPs with a time course that was similar to the light responses ( Figure 1A ) . Measured decay times were 98 . 0±14 . 9 ms ( light; N = 10 On cells ) and 109 . 0±14 . 4 ms ( uncaging; N = 9 On cells ) and the 10–90% rise time was 40 . 3±8 . 2 ms ( light; N = 10 On cells ) and 48 . 6±5 . 9 ( uncaging; N = 9 On cells ) . The differences in decay and rise time were not significant ( t-test; p>0 . 05 ) . This suggests that while the uncaging stimulus may not be identical to light stimuli , the EPSPs are in a physiologically relevant range . The size of the uncaging area was measured by uncaging at 2 µm intervals perpendicular to the dendrite ( Figure 1B ) . Current amplitude at each location was plotted against distance from the dendrite and fit with a Gaussian function . The average half-maximal width of that fit was then used as a measure of lateral ( XY ) resolution . On average , the half-maximal width of the Gaussian fit was 5 . 7±0 . 5 µm ( N = 7 cells; Figure 1C ) . As a result , individual uncaging locations were separated by a minimum of 20 µm , insuring no overlap between sites . To determine the timing rules underlying summation in On ganglion cells we measured the linearity of summation at an individual dendritic location in whole mount retina . To evaluate the timing rules governing summation , trains of three uncaging pulses were delivered at varied delays ( 1–125 ms ) , to the same location ( single pixel ) ( Figure 2A , B ) . Delays were presented in random order to avoid use-dependent artifacts . The amount of summation for each delay was then calculated by dividing the area ( charge ) or amplitude of the summed EPSP response by 3 times the charge or amplitude for a single EPSP response . As a result , a summed response with a total charge equal to 3X that of a single response would have a summation ratio of 1 , indicating linear summation . Ratios larger or smaller than 1 indicated supra or sub linear summation , respectively . Previous work in cortical neurons has suggested that summation is not uniform over the length of the dendrite [31] . To look for local differences in ganglion cell summation we compared summation at proximal locations ( <50 µm ) to summation from distal dendritic locations ( >50 µm; range 80–250 µm; Figure 2C , D ) . Surprisingly , we found no significant differences between the two groups ( paired t-test; p>0 . 05; N = 9 On cells ) , suggesting summation in ganglion cells is uniform within 250 µm along the length of the dendrites . Delays of 50 ms or less produced supralinear responses while longer delays resulted in near linear summation ( Figure 2E; N = 11 On cells ) . Summation ratios for short delays ( 1–50 ms ) were not significantly different from each other ( ANOVA; p>0 . 05; N = 11 On cells ) . However , delays of ≤50 ms resulted in significantly more summation than longer delay times ( >50 ms; ANOVA; p<0 . 0025 ) . Therefore , we used 50 ms delays for all subsequent summation experiments . Theoretical work has suggested that summation would be greater for inputs moving toward the soma than for those moving away from the soma [13] . In support of this hypothesis a recent study of cortical neurons demonstrated that summed EPSPs were larger in amplitude for input sequence directed toward the soma ( centripetal ) [14] . To test for a directional component to summation in ganglion cells , we activated multiple regions of the dendrite in both directions ( Figure 3A ) . We expected that somatic responses elicited from distal dendrites would be smaller and slower than those from proximal dendrites ( as measured at the soma ) . This is due to membrane leak , axial resistance and membrane capacitance , which increases with dendritic length . Surprisingly , we found the glutamate responses were relatively uniform along and between dendrites ( Figure 3B ) . Across all cells , depolarization amplitudes showed no significant location-dependent differences ( ANOVA; p>0 . 05; N = 15 cells ) . As ganglion cell dendrites are heavily branched it was possible that multiple dendrites could be activated by the uncaging beam , given that measured axial ( Z ) resolution was 40±4 . 7 µm ( N = 5 cells ) . While this could have been a potential interpretation problem , the uniform response amplitude argues against this possibility as activation of multiple dendrites would produce random and significant irregularity in EPSP magnitude . These results also suggest that ganglion cell dendrites employ mechanisms that compensate for electronic decay to insure input strength is accurately represented at the soma . This is consistent with predictions based on computational modeling which suggest that for a physiologic range of membrane resistance and moderate strength stimuli , inputs are equally effective at all points along the ganglion cell dendritic tree [9] . We found that ganglion cell summation recorded at the soma was directional , but unlike cortical neurons , it was larger for input sequences directed away from soma ( centrifugal; Figure 3C ) . To measure the linearity of summed EPSPs , individual EPSP amplitudes and the integrated charge of each waveform were arithmetically summed and amplitude and charge ratios were calculated for summed EPSPs ( summed EPSP/3x single EPSP; plots of raw amplitude and charge can be found in Figure S1 ) . Response amplitude ratios were significantly larger for inputs moving away from the soma ( Figure 3D ) . This was also true for the charge , which had a significantly greater supralinear ratio for away directed inputs ( Figure 3E ) . Summation experiments were repeated using older animals ( P22–35 ) to examine summation in mature retina . Again , the EPSP amplitude ratio was larger for sequences moving away from the soma ( 0 . 69±0 . 06 away , 0 . 6±0 . 06 toward; paired t-test p<0 . 01; N = 11 cells ) . The same was true for the charge ratio ( 1 . 32±0 . 37 away , 1 . 16±0 . 36 toward; paired t-test p<0 . 01; N = 9 cells ) . When summation for multiple dendrites on the same cell was examined we found similar ratios for all dendrites ( N = 6 cells ) . In contrast , we found that summation in hippocampus oriens lacunosum moleculare interneurons was larger when moving toward the soma ( Figure 4 . ) , similar to what was reported in cortical neurons [14] . These results suggest that retinal ganglion cells have unique integration properties setting them apart from cortical neurons , consistent with their specialized function . This radial directionality is similar to that exhibited by starburst amacrine cells where centrifugal stimulation produces increased dendritic calcium signals [21] , [22] . The directional summation ( DS ) we observe could be a network property rather than an intrinsic property of the ganglion cell . For example , neighboring amacrine cells could be activated by the free glutamate , changing the inhibitory input to ganglion cells . To test this possibility we repeated the experiment in Figure 3 by blocking inhibition . The addition of Picrotoxin ( 100 µM ) and Strychnine ( 1 µM ) to block GABA-A and Glycine receptors , respectively , resulted in unstable spontaneous activity , which rendered the data uninterpretable . Thus , we only included Picrotoxin in the bath solution to block inhibition , and found that peak amplitude and charge increased in both directions ( 28 . 2±5% ( away ) and 19 . 6±3% ( toward ) ) . However , picrotoxin had no effect on DS with an amplitude ratio of 1 . 27±0 . 08 ( away ) vs . 1 . 10±0 . 05 ( toward ) and charge ratios of 1 . 2±0 . 05 ( away ) vs . 1 . 12±0 . 04 ( toward; t-test; p<0 . 05; N = 7 cells ) , suggesting inhibitory inputs alone are not responsible for DS . As an additional test of network participation in DS we looked at summation in primary retina cultures . Whole-cell current clamp recordings were obtained from ganglion cells 8–21 days in culture and summation was measured in both directions . Figure 5A shows representative summed EPSPs confirming that DS was reconstituted in the cultures . Amplitude and charge ratios for culture experiments were calculated as away/toward . This was necessary because cultured neurons were difficult to hold for the lengthy time needed to acquire single and summed responses . For peak amplitude , summed EPSPs were larger moving away from the soma ( 1 . 18±0 . 06 vs . 1; paired t-test; p<0 . 05; N = 10 cells; Figure 5B ) . Charge was also higher moving away from the soma ( 1 . 29±0 . 14 vs . 1; paired t-test; p<0 . 05; N = 11 cells; Figure 5C ) . These results suggest that sequence-dependent summation is an intrinsic property of the ganglion cell rather than the network . Summation moving away from the soma was 26% larger than summation moving toward the soma in whole-mount retina . While this is a significant difference , the physiological importance of this increase is unclear . To determine whether this increase in summation translates to an increase in excitability we examined spike probability as a function of activation sequence . While we chose uncaging parameters intended to elicit subthreshold glutamate responses , a subset of experiments exhibited suprathreshold responses . For all cells exhibiting suprathreshold activity , we counted the number of suprathreshold trials for centripetal and centrifugal activation and calculated spike probability . Overall , spike probability was significantly higher when moving away from the soma ( paired t-test; p<0 . 05; N = 9 cells; Figure 6A , B ) . Spike probability for activation moving away from the soma was 0 . 28±0 . 05 and 0 . 1±0 . 04 when moving toward the soma ( Figure 6C ) . These results suggest that input sequence can have a significant impact on cell excitability and function for near threshold conditions . While the mechanisms responsible for creating DS are unclear , a likely possibility is the non-uniform expression of ion channels along dendrites . For example , the NMDA receptor has slow decay kinetics which can lengthen the integration time window and increase summation . If these channels were not uniformly distributed , location and direction-dependence of summation could result . In fact , when NMDA receptors were blocked with APV , EPSP summation was reduced and directionality eliminated in cortical neurons [14] . To determine the role of NMDA receptors in ganglion cell summation we compared summation before and after blockade of NMDA receptors with the antagonist APV ( 50 µM ) in whole mount retina . APV reduced mean charge and amplitude for individual EPSPs , however , this difference was not significant ( paired t-test; p>0 . 05; N = 9 cells ) . APV also reduced the summation ratio for charge from 1 . 51 ( away ) and 1 . 27 ( toward ) to 1 . 27 ( away ) and 1 . 05 ( toward ) . While modest ( 15 . 6 and 17 . 3% ) , this reduction was significant ( Figure 7A , B; paired t-test; p<0 . 025; N = 9 cells ) . Although APV reduced overall summation , it did not eliminate the directional component as summation remained significantly larger for activation sequences moving away from the soma ( Figure 7 ) . These results suggest that while NMDA receptors can enhance summation , they do not play a substantial role in establishing the direction-dependence of summation . We observed no difference in the effect of APV on proximal ( <50 µm ) vs . distal ( >50 µm; range 80–250 µm ) regions of dendrite , suggesting that NMDA receptor density was uniform . Hyperpolarization activated cyclic nucleotide-gated ( HCN ) channels are partially open at typical resting potentials where they depolarize the cell , decrease input resistance , and shorten the decay of synaptic signals , effectively reducing summation . Previous work has shown that these channels modulate EPSP summation and initiation of action potentials in a broad range of brain structures including hippocampus , brain stem , inferior colliculus and cortex [15]–[20] . In the hippocampus HCN current density is not uniformly distributed , increasing in density with distance from the soma [15] . Although mammalian retinal ganglion cells are thought to express these channels [26] , [32] , their distribution pattern has yet to be determined . If channel density is location-dependent , it could influence the observed directionality of summation in ganglion cells . To determine HCN channel density and their role in summation , we measured EPSP charge from whole mount retinal ganglion cells in the presence and absence of the HCN channel blocker ZD7288 ( 100 µM ) . Blockade of HCN channels increased single trial EPSP charge and amplitude ( Figure 8A , B ) . We hypothesized that under control conditions HCN should reduce summation by decreasing input resistance and decay time , reducing the integration time window . Regions of the dendritic arbor with higher HCN conductance should be more strongly affected than regions with reduced HCN conductance . Consistent with this possibility we found the effect of HCN blockade was largest in the distal dendrites ( >50 µm; range 75–275 µm; N = 8 cells ) . Following HCN blockade with ZD7288 , charge increased by 33 . 3±7 . 7% vs . 12 . 5±6 . 0% at proximal sites ( Figure 8B; <50 µm; paired t-test; p<0 . 025; N = 8 cells ) . Although we found that summation is uniform over the length of the dendrite under control conditions ( Figure 2 ) , we revisited this question to determine whether HCN channels contributed to this uniformity . As described for Figure 2 , three uncaging pulses were delivered to a single location with varying interstimulus intervals ( 1–125 ms ) . For proximal dendritic locations ( <50 µm ) the range of intervals over which supralinear summation occurred was increased from <50 ms to >75 ms as might be expected when summation is increased ( Figure 8C; N = 8 cells ) . However , this effect was much larger in the distal dendrites where all interstimulus intervals produced supralinear summation ( Figure 8C ) . To estimate the amount of HCN active at rest we applied voltage steps from −40 to −100 mV in the absence or presence of ZD7288 . IV curves were then created by subtracting the ZD7288 traces from controls and measuring tail current amplitude ( Figure 9A , B; N = 11 cells ) . We found that about 25% of HCN channels are open at −65 mV . These results suggest that HCN acts to compensate for dendritic filtering , imposing the uniformity in summation seen under control conditions . To determine which HCN channel subtype is expressed in the retina , we used fluorescent antibodies to label isoform 1 , 2 and 4 . We did not test for the presence HCN3 because it is thought to be restricted to cone pedicles [33] . To isolate signals from ganglion cells we immunolabeled HCN channels on cultured ganglion cells . Fluorescently labeled HCN1 channel protein was readily detected in the soma and dendrites of retinal ganglion cells . When overlaid with a dendritic marker antibody , microtubule-associated protein 2 ( MAP-2 ) , HCN1 immunoreactivity could be seen throughout the dendritic tree ( Figure 9C ) . Co-labeling for GluR2 subunits allowed us to identify ganglion cells [34] , [35] . HCN4 immunoreactivity was also present in the soma and dendrites ( Figure 9C ) . These results agree with earlier findings that both channel subtypes are found in retina [36] . We did not detect immunoreactivity for HCN2 subtype , consistent with previous reports [33] , [36] , [37] . To quantify expression levels of HCN1 for proximal and distal dendrites we measured pixel intensity in two bins , <50 µm and >100 µm from the soma . We found no clear difference in expression levels for these regions of dendrite ( Figure 9D; paired t-test; p>0 . 05; N = 115 dendrites; 50 cells ) . However , this type of assay is not highly sensitive and to reliably detect a difference in proximal and distal staining may require a substantial differential , perhaps as high as 50% . Blockade of HCN increased summed EPSP charge in both directions ( Figure 10A ) . However , this effect was larger for centripetal than for centrifugal summation , effectively eliminating the directional component of summation . Summation moving toward the soma was increased by 33 . 8±8 . 5% , while summation moving away from the soma was only increased by 16 . 5±5 . 2% ( Figure 10C; paired t-test; p<0 . 05; N = 8 ) . Under control conditions , the charge ratio was larger for input moving away from the soma ( 1 . 24±0 . 08 away vs . 1 . 03±0 . 07 toward; paired t-test , p<0 . 01; N = 8 ) . After application of ZD7288 the charge ratios were 1 . 21±0 . 04 ( away ) vs . 1 . 19±0 . 0 . 05 ( toward; paired t-test; p>0 . 05; N = 8; Figure 10B ) . ZD7288 application hyperpolarized resting membrane potential by an average change of −2 . 4±1 mV which was not significant ( paired t-test; p>0 . 05; N = 8; range 6 . 5–1 mV ) . However , it should be noted that small potential changes may be difficult to detect under current clamp conditions due to fluctuation in resting potential . As a result , we conclude that HCN plays an important role in establishing DS .
The unique integration properties of On ganglion cells differentiate them from cortical pyramidal and hippocampal neurons ( [14] and Figure 4 ) . Glutamate responses recorded at the soma are summed in a supralinear manner for centrifugal input arriving at frequencies greater than 20 Hz ( 1–50 ms delays ) . Lower frequency input ( 75–150 ms delays ) sums in a linear fashion . This DS translates to a 3-fold increase in spike probability for centrifugal input for the subthreshold stimuli used in our experiments , suggesting that the direction-dependent increase in summation has significant impact on cell excitability and function in retinal ganglion cells . Centrifugal DS could also facilitate back propagation of action potentials despite the relatively branched configuration of ganglion cell dendritic trees [41] . Active dendritic back propagation of action potentials is necessary in large ganglion cells to allow them to fire at low rates [7] , [8] . Here we examined dendritic integration in On ganglion cells . These cells were chosen based on large soma size and our sample is likely to contain multiple On cell subtypes . ∼85% of the cells tested showed centrifugal DS , suggesting that DS is a property of multiple On cell subtypes . In fact , we observed similar DS for Off ( N = 4 ) and On/Off cells ( N = 6 ) . These cells were also dependent on HCN for expression of DS . While our sample is insufficient to assess more subtle differences such as the magnitude of DS , we find that a broad range of ganglion cell subtypes use similar dendritic integration strategies . In our culture experiments it was not possible to determine cell subtype and we assume this sample also contains multiple subtypes . As a result , we find that DS is a general property of ganglion cells , used by a broad range of cell subtypes . Previous work in cortical neurons suggests that the directional component of summation was due to activation of NMDA receptors [14] . However , we found that while NMDA receptor blockade reduced summation , it had no effect on the centrifugal DS in ganglion cells ( Figure 7B ) . Although virtually all classes of ganglion cells express NMDA receptors , differences in subunit composition may preferentially increase summation in specific ganglion cell types . For example , NMDARs that contain the NR2B subunit decay more slowly than NR2A-containing NMDARs [42] , and there is evidence that ON ganglion cells preferentially express NR2B-containing NMDARs [43] , [44] . Accordingly , one might expect to find that blocking NMDARs will reduce temporal summation to a greater degree in On cells than Off cells . It will be particularly interesting to determine if temporal summation in the dendrites of On/Off ganglion cells ramifying in sublamina b is further reduced by NMDA block compared with dendrites in sublamina a . Differences in temporal summation in the On and Off pathway may contribute to asymmetries in the On and Off pathways [45] . Dendritic HCN channels lower input resistance , speed the decay of post synaptic potentials , and reduce EPSP summation . The higher HCN conductance we observe in the distal dendrites allows the cell to compensate for EPSP broadening due to dendritic filtering . Consistent with this observation , we find that EPSP responses are surprisingly uniform along and between dendrites of retinal ganglion cells ( Figure 2 ) , independent of input location [26] . Such a mechanism insures that input strength is accurately represented at the soma . As a result , EPSPs have similar kinetics independent of input location along the dendrite . The mechanism underlying higher HCN conductance in distal dendrites is unclear . One possibility is that there are more channels expressed on distal dendrites . While our immunohistochemistry quantification did not show such an expression gradient , this assay may not be sensitive enough to detect small differences in channel expression . However , it is also possible that channel expression is uniform but channel properties are differentially regulated by the availability of accessory subunits , non-uniform expression of additional channels , or regulation by ligand-gated channels . For example , TRIP8b interacts with the carboxyl-terminal region of HCN channels and regulates their cell-surface expression level and cyclic nucleotide dependence [46] . KV7 or KCNQ channels and CaV3 ( T-type ) Ca2+ channels also indirectly affect the activation of HCN channels through modulation of membrane potential and resistance [47]–[49] . The neurotransmitter dopamine has also been shown to modulate HCN channels via D1 receptors and cAMP [50] . This second mechanism is consistent with our modeling data which demonstrates that HCN by itself is not sufficient to create the centrifugal DS , additional channels are required . This may also explain why cortical pyramidal neurons , which also express HCN channels in a gradient [51] , demonstrate centripetal rather than centrifugal DS [31] . Perhaps these neurons do not express the appropriate channel partners found in ganglion cells . We find that HCN channel conductance is higher in distal dendrites ( Figure 7 ) and that HCN channels are necessary for expression of DS as blockade eliminates DS ( Figure 10 ) . Under control conditions the depolarization caused by a synaptic input spreads passively along the dendrites , closing HCN channels . While this is true for both the distal and proximal dendrites , proximal dendrites have a low HCN conductance . As a result , the spreading depolarization does very little to amplify the proximal EPSP . In the distal dendrites , HCN is high and there is a substantial increase in EPSP charge . A further source of amplification in the distal dendrites is suggested by our model which shows that Na and K channels are also required for DS . Larger distal EPSPs can in turn activate Na channels which could further increase distal EPSP amplification . In addition , the relatively small diameter of distal dendrites results in greater electrotonic isolation , increased input resistance , larger local EPSPs and increased activation of Na channels . We found that this effect is further enhanced when HCN channels are concentrated in the distal dendrites , preferentially depolarizing them . But the greater electrotonic isolation of very fine distal dendrites prevents transfer of their local DS to the soma . Thus , the model suggests that the DS effect measured at the soma depends on partial electrotonic isolation of distal dendrites [22] . However , this model depends on EPSPs of sufficient size to activate the channels and we used relatively small subthreshold EPSPs . To further assess the role of voltage gated Na channels in DS , we repeated our experiments in the presence of a low TTX concentration ( 0 . 1 µM ) . We used a low dose of TTX to avoid global blockade of all network Na channels which could result in complex outcomes for the cell that would be difficult to tease apart . TTX reduced single EPSP amplitude , confirming activation of Na channels under our baseline conditions . However , paradoxically , it had no effect on DS which remained intact ( N = 6 cells; data not shown ) . While these results might suggest voltage gated Na channels are not involved in DS , the experiment could be compromised by the presence of unblocked Na channels ( due to the low dose of TTX ) , and TTX-insensitive Na channels which have been demonstrated in retina . Further , we have only examined a limited number of channel types and there may be additional channels involved . Overall , we have demonstrated that DS can be achieved by a combination of voltage-dependent channels , independent of asymmetric presynaptic innervation . Over the course of evolution retinal ganglion cells acquired unique properties to perform complex functional roles such as sensing movement and direction of motion through natural environments . Perhaps the first step toward detecting motion was to bias retinal ganglion cells toward enhancing EPSP summation for responses to centrifugal motion . Mechanistically , this function can be accomplished by non-uniform dendritic distribution or activation of ion channels . Our simulations suggest that the centrifugal bias is a consequence of partial isolation of the ganglion cell's distal dendrites that contain voltage-gated Na , K , and HCN channels [22] , [26] . The Na channels , necessary to support active back-propagation of spikes into the distal dendrites for control of spike frequency [7] , [8] , also amplify subthreshold PSPs in the distal dendrites with the aid of depolarization from HCN channels . Thus , the voltage-gated channels also tend to amplify the larger distal PSPs evoked by centrifugal motion . This raises the question of whether such mechanisms , which might equalize responses to light across the ganglion cell's receptive field , or generate DS which might preferentially code looming objects , could be important for vision . Our results cannot answer these questions , but they highlight possible evolutionary routes for the development of direction selectivity . As more complex retinal networks formed later in time more specific features of movement were coded by layering additional mechanisms . For example , locust retina contains specialized ganglion cells that detect looming stimuli [52] . Voltage-dependent channels involved in spike accommodation are thought to underlie this ability [52] . One prediction of this model is that On/Off cells , which are better suited for detection of looming stimuli , will have a higher degree of supralinear summation than other ganglion cell types . The addition of asymmetrical inhibition [53] to these cells could then allow for detection of directional motion . Judicious combination of directional tuning mechanisms within a given cell could allow for a broad array of detectors assembled from basic ganglion cell building blocks . | Visual information is coded by the output of retinal ganglion cells . Through evolution retinal ganglion cells acquired unique properties that allowed them to transmit to the brain such signals as direction of movement . The quest for the cellular mechanism of the detection of movement by retinal ganglion cells has been the holy grail of research on direction selectivity . In this study we have found a mechanism that allows individual non-direction selective On retinal ganglion cells to code sequences of excitatory inputs moving either away or toward the soma . We observed that inputs moving away from the soma resulted in enhanced , supralinear EPSP summation . Evidence from computational modeling suggests that expression of a specific set of voltage-dependent channels in dendrites introduces nonlinearities that could give a ganglion cell the ability to code looming motion . We predict that in a retinal network , such a directional tuning mechanism , together with asymmetric presynaptic inhibition , could be the building block for the development of more complex detection of visual motion . | [
"Abstract",
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] | 2013 | Directional Summation in Non-direction Selective Retinal Ganglion Cells |
Grid cells in the rat entorhinal cortex display strikingly regular firing responses to the animal's position in 2-D space and have been hypothesized to form the neural substrate for dead-reckoning . However , errors accumulate rapidly when velocity inputs are integrated in existing models of grid cell activity . To produce grid-cell-like responses , these models would require frequent resets triggered by external sensory cues . Such inadequacies , shared by various models , cast doubt on the dead-reckoning potential of the grid cell system . Here we focus on the question of accurate path integration , specifically in continuous attractor models of grid cell activity . We show , in contrast to previous models , that continuous attractor models can generate regular triangular grid responses , based on inputs that encode only the rat's velocity and heading direction . We consider the role of the network boundary in the integration performance of the network and show that both periodic and aperiodic networks are capable of accurate path integration , despite important differences in their attractor manifolds . We quantify the rate at which errors in the velocity integration accumulate as a function of network size and intrinsic noise within the network . With a plausible range of parameters and the inclusion of spike variability , our model networks can accurately integrate velocity inputs over a maximum of ∼10–100 meters and ∼1–10 minutes . These findings form a proof-of-concept that continuous attractor dynamics may underlie velocity integration in the dorsolateral medial entorhinal cortex . The simulations also generate pertinent upper bounds on the accuracy of integration that may be achieved by continuous attractor dynamics in the grid cell network . We suggest experiments to test the continuous attractor model and differentiate it from models in which single cells establish their responses independently of each other .
Since the discovery of grid cells in the dorsolateral band of the medial entorhinal cortex ( dMEC ) [1] , several ideas have been put forth on how grid-cell activity might emerge [2]–[7] . The theoretical ideas suggested so far fall into two categories . In continuous attractor models ( see [8]–[15] and [2] , [4] , [7] for the grid cell system ) , which are the focus of this work , grid cell activity arises from the collective behavior of a neural network . The network's state is restricted to lie in a low-dimensional continuous manifold of steady states , and its particular location within this manifold is updated in response to the rat's velocity . In the second category of models [5] , [6] , [16] , [17] , grid-cell activity arises independently in single cells , as a result of interference between a global periodic signal and a cell-specific oscillation , whose frequency is modulated by the rat's velocity . These ideas differ radically from each other , but they share a common assumption about the nature of the input feeding into dMEC , namely , that the input conveys information primarily on the rat's velocity and heading . Within all these models , grid cell activity must then arise from precise integration of the rat's velocity . Grid cell firing exhibits remarkable accuracy: The periodic spatial tuning pattern remains sharp and stable over trajectories lasting 10's of minutes , with an accumulated length on the order of hundreds of meters [1] . Experiments performed in the dark show that grid cell tuning remains relatively accurate over ∼100 meters and ∼10 minutes even after a substantial reduction of external sensory inputs . However , in these experiments olfactory and tactile cues were not eliminated , and grid cell responses may have been informed by positional information from such cues . Therefore , the duration and length of paths over which coherent grid responses are maintained without any external sensory cues is not known . For position estimation on the behavioral level , we searched for but found no clear quantitative records of the full range over which rats are capable of accurate dead-reckoning . Behavioral studies [18]–[21] document that rats can compute the straight path home following random foraging trajectories that are 1–3 meters in length , in the absence of external sensory cues . How do theoretical models measure up , in estimating position from input velocity cues ? The theta-oscillation model of grid cells [5] , [6] , [16] , [17] , under idealized assumptions about internal connectivity , velocity inputs , and neural dynamics , is not able to produce accurate spatial grids over the known length- and time-scales of behavioral dead-reckoning if the participating theta oscillations deviate from pure sine waves . This is because the model is acutely vulnerable to subtle changes in the phase of the underlying oscillations . In reality , theta oscillations are not temporally coherent: cross-correlograms from in vitro intracellular recordings [17] , [22] , [23] and in vivo extracellular recordings [24] , [25] show that the phase of the theta oscillation in the entorhinal cortex typically decoheres or slips by half a cycle in less than 10 cycles or about 1 second , which corresponds to a distance of only 1 meter for a run velocity of 1 m/s . This means that the model grid cells will entirely lose track of the correct phase for the present rat position within that time . For continuous attractor models , we previously showed [3] that due to rotations and non-linear , anisotropic velocity responses , a detailed model [2] integrates velocity poorly , and does not produce a grid-cell firing pattern even with idealized connectivity and deterministic dynamics . Another model [7] generates grid responses in a small periodic network , but it includes no neural nonlinearities or variability in neural responses , and depends on real-time , continuous modulation of recurrent weights by the velocity inputs to the network . Conceptually , the existence of an integrating apparatus seems pointless if it is completely dependent on nearly continuous corrections coming from an external source that specifies absolute position . Thus , it seems reasonable to require that theoretical models of path integration in dMEC , if using faithful velocity inputs , have the ability to reproduce stable grid cell patterns for trajectories lasting a few minutes . Our aim , therefore , is to establish whether it is possible for model grid cells to accurately integrate velocity inputs . We restrict our analysis specifically to continuous attractor networks . As will become clear , the precision of velocity integration can strongly depend on various factors including network topology , network size , variability of neural firing , and variability in neural weights . Here we focus on three of these factors: boundary conditions in the wiring of the network ( periodic vs . aperiodic ) , network size , and stochasticity in neural activity We quantify path integration accuracy in both periodic and aperiodic recurrent network models of dMEC , and demonstrate that within a biologically plausible range of parameters explored , such networks have maximum attainable ranges of accurate path integration of 1–10 minutes and 10–100 meters . Larger , less noisy networks occupy the high end of the range , while smaller and more stochastic networks occupy the low end . We end with suggestions for experiments to quantify integration accuracy , falsify the continuous attractor hypothesis , and determine whether the grid cell response is a recurrent network phenomenon or whether it emerges from computations occurring within single cells .
We simulate dynamics in a network of neurons driven by velocity inputs obtained from recordings of a rat's trajectory ( see Methods ) . The network contains 1282 ( ∼104 ) neurons arranged in a square sheet . Neurons close to each edge of the sheet form connections with neurons on the opposite edge , such that the topology of the network is that of a torus . Figure 2A shows the population activity in the network at one instant of the run . A grid cell response , as reported in experimental papers , is obtained by summing the firing activity of a single neuron over a full trajectory . Unlike the population response , which is an instantaneous snapshot of full neural population , the single-neuron response is an integrated measure over time of the activity one cell . In the rest of this paper , SN response refers to the accumulated response of single neurons over a trajectory . In the periodic network , the SN response , accumulated over the ∼20 minute trajectory , and plotted as a function of the true rat position , shows coherent grid activity , Figure 2B . The network accurately integrates input velocity , as can verified directly by comparing the cumulative network pattern phase to the rat's true position , Figure 2C . The total error , accumulated over ∼260 m and 20 minutes , is <15 cm , compared to a grid period of about 48 cm . This corresponds to an average integration error of less than 0 . 1 cm per meter traveled and less than 0 . 01 cm per second traveled . The range of rat speeds represented in the input trajectory was 0–1 m/s , showing that this network is capable of accurate path integration over this range of speeds . A deterministic periodic network of only 402 ( ∼103 ) neurons also performs well enough to produce coherent SN grids over the same trajectory , Figure S1 . The presence of a clear spatial grid in the SN response to velocity inputs alone is a good indication of the accuracy of integration . If the rat's internal estimate of position were to drift by half a grid period , the neuron would fire in the middle of two existing vertices rather than on a vertex . As the rat traveled over its trajectory , the neuron would fire at various “wrong” locations , with the resulting SN response becoming progressively blurred until no grid would be discernible . This would happen even if the population pattern remained perfectly periodic throughout . Therefore , the following properties are equivalent: ( 1 ) Coherent grids in the SN responses , ( 2 ) Accurate path integration of the full trajectory over which the SN responses are visualized , with errors smaller than the grid period . An example of this equivalence is given in Figure 2A and 2C , which show sharp SN patterning and a very small integration error . Next , because the population pattern phase accumulates errors whenever the pattern slips relative to rat motion , another equivalent condition for accurate path integration is ( 3 ) Linear relationship between network flow velocity and input velocity over the input velocity range , independent of direction . These equivalent conditions for accurate integration apply to both periodic and aperiodic network models of grid cells ( discussed next ) . It is unclear whether a torus-like network topology , in which neurons along opposite edges of the network are connected to form periodic boundary conditions , exists in the rat's brain . Even if such connectivity exists , it may require , at an earlier stage of development , an initially aperiodic network ( see Discussion ) . Hence it is interesting to consider whether a network with non-periodic boundaries can produce grid-cell like SN activity . The difficulty here is that as the population pattern flows in response to velocity inputs , it must reform at the boundaries of the neural sheet . Newly forming activity blobs must be created at accurate positions , and the process must not interfere with the pattern's flow . A central result of the present work on aperiodic networks is that such networks can , in fact , accurately integrate velocity inputs . With an appropriate choice of architecture and inputs and with deterministic dynamics , an aperiodic network can produce SN responses that are as accurate as in the periodic case above . This is illustrated in the example of Figure 2D–F . At the aperiodic boundaries , the same dynamics that governed the initial pattern formation process also cause the pattern to continually regenerate as the pattern flows ( Figure 1C , bottom ) . The phases or locations of the renewing blobs at the boundary are consistent with the rest of the network pattern , in part because their placement is influenced by inhibition from the neighboring active neurons in the network interior . For the two types of networks from the previous section , the structure of the state-space is schematically illustrated in Figure 4 . The state-space illustration is instrumental in synthesizing the findings of the preceding section – in particular: Why does the pattern not rotate in the periodic network ? Why is the pattern pinned at low input velocities in the aperiodic network ? Why does network size matter more for aperiodic than for periodic networks ? We assume that the dynamics minimize an energy functional , whose local minima correspond a set of fixed points ( attractors ) ( This assumption is precisely correct in the absence of a velocity-driven shift mechanism , since the connectivity matrix is then symmetric [27] , [28] . ) Consider first the periodic network . Starting from a steady state of the dynamics , and rigidly translating the stable population pattern , produces an equivalent steady state with exactly the same energy . The set of all such states forms a continuous manifold of attractor states , related to each other by continuous translation . This manifold can be visualized as the trough of the energy surface , Figure 4A . Rotating a steady state pattern , on the other hand , produces states with higher energy . ( Rotation can be visualized as follows . Imagine first cutting open the toroidal periodic network along the edges of the sheet that were originally glued together to produce a periodic network . On the resulting sheet , rotate the pattern , and rejoin the cut edges . This procedure will produce discontinuities in the pattern along the rejoined edges . ) Hence the attractor manifold does not include continuous rotations . Inputs that induce pattern translation will stably move the network state along the trough , even if the inputs are small , and the integrated value of the input will be reflected in the updated network phase . On the other hand , inputs that attempt to induce rotations will not produce lasting changes in network state , because these states are unstable and will quickly ( over a few hundred milliseconds or less ) decay as the pattern relaxes to its preferred orientation . Similarly , distorting the pattern by stretching it , adding noise , or by removing blobs from the pattern will generate an unstable state , which will rapidly decay to a steady state within the attractor manifold . In the aperiodic network , translations of a steady state pattern are similar but not exactly equivalent , because the phase of the activity pattern relative to the boundary affects the energy of the state . Strictly speaking then , these states do not form a continuous attractor manifold , Figure 4B . Instead , the manifold is slightly rippled along the direction of translations . To drive translations , velocity inputs must be large enough to overcome the ripple barrier . This explains why below a critical velocity , the pattern is pinned in our simulations . The ripple amplitude depends on how much influence the boundary has on the network dynamics . If activity fades to zero sufficiently smoothly near the boundary the ripple can be small . Pattern translation then corresponds to motion along a nearly flat direction on the manifold , pinning is confined to a negligibly small range of velocities , and integration of inputs can be accurate . A reduction of pinning can be achieved also by increasing the network size , while keeping the boundary profile fixed , because boundary effects scale as the ratio of network periphery to network area . A stable population pattern state can be rotated around the center of a circular aperiodic neural sheet to obtain another stable state that is identical in energy to the original one . Hence , rotations correspond to a flat direction in the energy surface , Figure 4B . Any input that couples even slightly with the rotational mode can drive rotations in the network pattern . The velocity inputs to the network , though configured to drive translational pattern flow , can weakly drive rotations due to boundary effects that couple the translational drive to rotational modes . In spiking networks , discussed below , rotations can be driven also by noise . In the network models described here , the structure of the attractor manifold ( e . g . , Figure 4A or 4B ) is completely determined by the matrix of pairwise weights between neurons and the inputs received by each neuron . Once the weights between all pairs of neurons and the inputs to each neuron are specified , the matrix does not change if the locations of the neurons on the cortical sheet are shuffled , so long as the weights and inputs to each neuron are held fixed ( see Discussion ) . Thus , statements about the existence of a manifold of stable network states and stable SN grid responses , and the predictions that stem from them , do not depend on topography , even when stated here for expositional simplicity in terms of topographically arranged population-level patterns . So far we have considered errors in integration that occur in the absence of noise . Unlike in the noise-free case , neural noise can induce the population pattern to flow or rotate even when velocity inputs are absent . To assess how noise influences the precision of the network's response , we present results from spiking neural networks with the same connectivity as in the rate based models . Dynamics in these networks are noisy due to the stochasticity of discrete spiking events . For the same network parameters as in Figure 2 , and assuming that neural firing is an inhomogeneous Poisson process , we find that the periodic network continues to perform well enough to produce coherent SN responses over long trajectories ( Figure 5A and Figure S3 ) . In the aperiodic network , performance with Poisson spiking neurons is considerably worse than in the rate based model , enough to destroy the grid-like SN response over a ∼130 meter , 10-minute trajectory , in particular due to rotations ( Figure S3 ) . Network performance improves , however , if spiking in the network is more regular than implied by inhomogeneous Poisson statistics . To quantify this effect , we performed simulations with sub-Poisson statistics ( see Methods ) . The variance of neural firing is characterized , in our simulations , by the coefficient of variation ( CV ) of the inter-spike interval . With a sufficiently low CV , aperiodic network dynamics are precise enough to produce a coherent SN response over a trajectory lasting 10 minutes and ∼130 meters , Figure 5B and Figure S3 . Armed with the proof-of-concept results that a continuous attractor network model can integrate velocity inputs accurately enough to produce SN grids , we next seek to explore testable predictions of the continuous attractor hypothesis in the grid cell system and contrast them with the properties of models in which the grid responses emerge independently in each cell [5] , [6] , [16] . Unless explicitly specified , all proposed tests are intended for conditions in which external , spatially informative cues have been removed .
Accurate behavioral dead reckoning is a cascaded result of accurate velocity input ( relative to the rat's motion ) and accurate integration of that input . Our interest in this work was in assessing how well continuous attractor models of dMEC can integrate their inputs . Thus , we did not focus on potential inaccuracies ( noise or biases ) in the velocity inputs themselves . Even if the network were a perfect integrator , errors in the input would produce an incorrect position estimate . Such errors are likely to play a role in reducing the behavioral range over which rats display accurate dead-reckoning . A strength of attractor networks is that responses are self-averaging over the full network: if the velocity inputs are unbiased estimators of rat movements , but are noisy , or if the velocity inputs to the network are not perfectly balanced in number for all directions , the full network will average all its inputs , and the net pattern flow will only reflect this average . For accurate position estimation , however , it is important and therefore likely that inputs to the network are well tuned . Another factor that could degrade integration performance is inhomogeneity or stochasticity in the recurrent network weights . While stochasticity in neural activity causes the network state to drift along the attractor manifold , variability in network connectivity modifies the structure of the attractor manifold itself . If recurrent connectivity deviates significantly from the translation-invariant form needed to ensure that all translations of the pattern are accessible without crossing over energy barriers , the activity pattern can become pinned at particular phases [38] , reducing the fidelity of the network response to small velocity inputs . Because knowledge about synaptic strengths in the brain is exceedingly limited , it is unclear what level of variability should be expected in dMEC weights , and whether this amount is sufficient to cause significant pinning . A question for theory , not addressed in this work , is to estimate the amount of variability in the network weights that would be sufficient to reduce the accuracy of integration below that observed in dead reckoning behavioral experiments . For experiments , the difficult challenge is to obtain an estimate of variability in dMEC connectivity . The network size estimate in our continuous attractor model ( 103–104 neurons ) may be viewed as a wasteful proposed use of neurons , but it is broadly consistent with estimates for the total number of neurons in the entorhinal cortex [39]–[41] . By contrast , independent neuron models [5] , [6] , [17] , which do not require populations of neurons to produce grid cell responses , make far more parsimonious use of neurons . In such models , a natural question is to understand what function may be served by the large number of neurons in dMEC . Within dMEC , the breakdown of total neural allocation , between neurons per grid network versus the number of different grid networks , is unknown . dMEC might consist of a very large number of very small networks with different grid periods , which is optimal for representational capacity [42] . ( For a fixed neuron pool size , the addition of neurons per grid at the expense of the total number of different grids causes a large capacity loss [42] . ) But the dynamical considerations presented here suggest otherwise , because accurate path integration in each grid requires many neurons . In contradiction to optimal capacity considerations , therefore , continuous attractor models predict a large membership in each grid network , and correspondingly few different grids . A fascinating question is whether the discrete islands of cells observed in anatomical and imaging studies of cells in layer II of the human and primate entorhinal cortex [41] , [43]–[46] , as well as indications in rodents for modular structure in dMEC [46] , [47] correspond to separate attractor networks , in which case the number of different grid periods can be directly inferred . We have shown that both periodic and aperiodic networks can perform accurate integration . Which topology is dMEC likely to posses ? The models and results of this work are largely agnostic on this question . However , the aperiodic network requires fine-tuning of its parameters to perform nearly as well as an untuned periodic network . Even after fine-tuning , integration in the periodic network tends to be better , because unlike in the aperiodic case , the population pattern cannot rotate . Thus , from a functional perspective , periodic boundaries are preferable over aperiodic ones . Other constraints on network topology may stem from the developmental mechanism of the grid-cell network . Such developmental constraints could overrule potential functional preferences , in determining network topology . If neural locations in the cortical sheet are scrambled , while preserving the neural indices and the pairwise weights between neurons , the grid-like patterning in the cortical sheet will disappear , but there will be no change in the single neuron triangular lattice response or in any other dynamical property of the network . The underlying structure of the attractor manifold ( e . g . , whether or not it is continuous ) is a function of network connectivity , but does not depend on the layout of neurons on the cortical sheet . Thus , the lack of topography observed in experiments , in which neighboring neurons have different phases , is not a problem for the dynamics of continuous attractor models of grid cell activity . Instead , the problem is one of learning: how does a network wire up so that the intrinsic structure of the weight matrix resembles center-surround connectivity , but the neurons are themselves not arranged topographically in space ? A topographic , aperiodic model network would have relatively simple wiring rules ( if we ignore the directional neural labels and corresponding segregation of head-direction inputs and shifts in the outgoing weights required for the velocity-coupling mechanism ) : each neuron would simply have spatially restricted center-surround interactions with its neighbors . This has prompted the observation that such a topographic network could serve as a starting point for the development of a network with a less topographical layout and periodic boundaries [4] . For instance , the proposal by [4] for wiring an atopographic and periodic network is based on three assumptions: ( 1 ) that another area , the ‘teacher’ , contains an initial aperiodic , topographic network with population grid patterning and no velocity shift mechanism , ( 2 ) that the network pattern , when subject to intrinsic or extrinsic noise , tends to translate without rotation , ( 3 ) that the network projects through spatially random connectivity to the naive dMEC , and activity-dependent activity mechanisms within dMEC cause neurons that are coactivated by the teacher network , to wire together . However , results from the present work show that the fundamental features of aperiodic networks pose a problem for such a scheme . We showed that the population pattern in a deterministic aperiodic network fully equipped with a translational velocity shift mechanism and driven by purely translational velocity inputs , tends to rotate within a few minutes . This is the short end of the time-scales over which plasticity mechanisms for network development would act . If the network is entirely driven by noise and lacks a specific velocity shift mechanism ( as in [4] ) , the problem is far worse: undesirable rotations become as likely as translations , and the pattern orientation can decohere in seconds , invalidating assumption ( 2 ) . Thus , the precursor network pattern will not be able to entrain a periodic grid in the target network . The problem of pattern rotations over the time scale of learning is pertinent for any effort to produce a periodic network from an initially aperiodic one in the absence of anchoring sensory inputs and a velocity coupling mechanism . The concept of low-dimensional continuous attractors has influenced our understanding of neural systems and produced successful models of a number of neural integrators [8]–[10] , [13] , [14] , [48] , [49] . Yet proof of continuous attractor dynamics ( or some discrete approximation to continuous attractor dynamics ) in the brain has remained elusive: experiments in supposed continuous attractor systems have failed to unearth evidence to conclusively validate or falsify the continuous attractor hypothesis . The relative richness ( e . g . , size , dimensionality of the manifold ) of the grid cell response compared to other possible continuous attractor systems may provide a more structured and unambiguous testing ground for predictions stemming from the continuous attractor hypothesis . Testing of these predictions , many based on cell-cell correlations , is feasible with existing experimental technologies , and such tests may help to determine whether a low-dimensional continuous attractor is central to the dynamics of the grid cell system .
To simulate a Poisson process ( CV = 1 , where CV is the ratio of the inter-spike interval standard deviation with the mean ) , in each time-step neuron spikes with probability given by ( in our simulations , is always much less than , ensuring that ) . The synaptic activation is computed from neural spiking: it increments by 1 at time if neuron spiked at , and otherwise decays according to ( 6 ) The process for generating spike trains with ( for integer-valued ) is similar to that for generating a Poisson train . We first subdivide each interval into sub-intervals of length each , and simulate on this finer time resolution a fast Poisson spiking process with rate . We then decimate the fast Poisson process , retaining every m-th spike and discarding all the other spikes . This procedure generates a spike train with rate and . Aperiodic network: initially network activity is low; neurons receive external input with in addition to a small independent random drive , which leads to spontaneous pattern formation . Periodic network: we initialize an aperiodic network with otherwise identical parameters , and after pattern formation apply periodic boundary conditions . The parameters for the aperiodic network have to be chosen to be commensurate with the size of the network to avoid excess strain and the formation of defects when the boundaries are made periodic . We flow both the periodic and aperiodic network states with unidirectional velocity inputs , corresponding to a velocity of 0 . 8 m/s , in three different directions ( 0 , , ) for 250 ms each to heal any strain and defects in the formed pattern . After this healing period , we give as input to the network either real rat velocity ( data obtained by differentiating recorded rat trajectories – published in [1] – then linearly interpolating between the recording time-steps and the time-step in our simulations ) , or a sequence of velocity steps ( described next ) . The network is initialized to the exact same initial template state at the beginning of each step ( using a template pattern stored following one run of the initialization process described above ) . Each step consists of a constant velocity input , with one of four directions ( 0 , , , ) . The velocity is incremented in steps of 0 . 02 m/s . We use only the second half of the 5 s long steps to compute the network's velocity response . We track how far the pattern has flowed beyond a lattice period and beyond the scale of the network by continuously recording the velocity of the blob closest to the center , and integrating the obtained velocity . We track the orientation of the lattice by computing its Fourier transform and recording the angles of the three blobs closest to the origin in Fourier space . To assign units of centimeters to the accumulated network pattern flow and compare it to rat position ( Figure 2C , 2F , 3C , Figure S1 , and Figure S3 ) , we must obtain the scale factor relating the network pattern flow velocity to the velocity of the rat . The scale is determined by optimizing the match between network flow velocity and the derivative of the rat position throughout the simulation . The offset is set so that the network drift at time is zero . | Even in the absence of external sensory cues , foraging rodents maintain an estimate of their position , allowing them to return home in a roughly straight line . This computation is known as dead reckoning or path integration . A discovery made three years ago in rats focused attention on the dorsolateral medial entorhinal cortex ( dMEC ) as a location in the rat's brain where this computation might be performed . In this area , so-called grid cells fire whenever the rat is on any vertex of a triangular grid that tiles the plane . Here we propose a model that could generate grid-cell-like responses in a neural network . The inputs to the model network convey information about the rat's velocity and heading , consistent with known inputs projecting into the dMEC . The network effectively integrates these inputs to produce a response that depends on the rat's absolute position . We show that such a neural network can integrate position accurately and can reproduce grid-cell-like responses similar to those observed experimentally . We then suggest a set of experiments that could help identify whether our suggested mechanism is responsible for the emergence of grid cells and for path integration in the rat's brain . | [
"Abstract",
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] | [
"neuroscience",
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"neuroscience"
] | 2009 | Accurate Path Integration in Continuous Attractor Network Models of
Grid Cells |
Japanese encephalitis ( JE ) is very prevalent in China , but the incidence of JE among children has been greatly reduced by extensive promotion of vaccinations . The incidence of JE among adults , however , has increased in some parts of China . Data on JE in mainland China , in terms of incidence , gender , and age , were collected between 2004 and 2014 . We conducted spatial and temporal analyses on data from different age groups . Generally , children aged 0–15 years still represent the major population of JE cases in China , despite the gradual decrease in incidence over years . However , the incidence of JE among adults in several provinces is notably higher than the national average , especially during the epidemic waves in 2006 , 2009 , and 2013 . The JE cases in the 0–15-year-old group are distributed mainly in the area south of the Yangtze River , with peak incidence occurring from July to September . In the adult group , especially for those over 40 years old , the JE cases are concentrated mainly in the area north of the Yangtze River . JE incidence in the adult group in September and October is significantly greater compared to the other groups . Further analysis using Local Indicators of Spatial Association ( LISA ) reveals that the distribution of adult JE cases in the six provinces north of the Yangtze River , between north 30–35° latitude and east 110–130° longitude , is a hotspot for adult JE cases . The rate of JE case increase for adults is much greater than for children and has become a public health issue . Therefore , studies on the necessity and feasibility of vaccinating adults who live in JE-endemic areas , but have never been vaccinated for JE , should become a new focus of JE prevention in the future .
Japanese encephalitis ( JE ) is a viral disease transmitted by mosquitoes which can lead to severe viral encephalitis . The fatality rate among JE patients is approximately 30% , while 35% of the survivors show various degrees of neurological sequelae . JE is an infectious disease of the neuron system [1–3] and is prevalent in about 24 countries and regions in Asia and Oceania . According to the latest statistics from the World Health Organization ( WHO ) , approximately 3 billion people live in JE-endemic areas and are threatened by JE virus ( JEV ) infection . Nearly 67 , 900 JE cases occur each year , with a total incidence rate of 1 . 8/100 , 000 [2 , 4] . As a mosquito-borne disease , JEV can be carried by varied mosquito species , such as Culex , Anopheles , Aedes and so on . Among these species , however , Culex tritaeniorhynchus is the principle vector of JEV; other mosquitoes of the genus Culex like Culex annulirostris , Culex vishnui Theobald , Culex bitaeniorhynchus Giles , and Culex pipiens Linnaeus can also act as the vector . The larvae of Culex tritaeniorhynchus usually live in the paddy fields , while the adults are active in the residential areas of humans . Therefore , human beings , especially those living near the rice fields becomes more easily exposed to the mosquitoes , which greatly increases the risk of JEV infection . Pigs , both domestic and wild pigs , are the major reservoirs serving as the hosts of virus amplification during the enzootic cycle of JEV transmission . So , the distance between residents and pigsties is another important factor affecting the spread of JEV in local areas , especially in some parts of Asia where pigs are kept near homes . JE is highly prevalent in China , with several JE outbreaks occurring since 1950 [5 , 6] . More than 1 , 400 , 000 JE cases were reported in China between 1963 and 1975 , and the JE cases were distributed in 26 provinces , in addition to the provinces of Xinjiang , Xizang , and Qinghai [7] . The incidence of JE decreased significantly in China since the extensive introduction of JE vaccinations in 1980 . The incidence rate was 3 . 24/100 , 000 in 1990 and decreased to 0 . 16/100 , 000 in 2013 , which was approximately one one-hundredth the incidence rate during the period of 1960–1970 ( 8 . 32–20 . 92/100 , 000 ) . Steps taken to control JE by vaccination have been successful [7 , 8] . According to WHO data , 75% of the JE cases are children who are 0–14 years old [4] . However , a higher incidence of JE has emerged among adults in some parts of China [9] . For example , 407 JE cases were reported in 2013 in Shandong Province , along the east coast of China . Among the 407 JE cases , 109 were children 0–15 years old , while 298 cases ( 73% ) were more than 15 years old . More specifically , 200 ( 49 . 1% ) cases were more than 40 years old [10] . Therefore , the high incidence of adult JE cases has become a public health issue that should not be ignored . This study aims to elucidate the distribution of JE cases in China , from 2004 to 2014 , by analyzing JE incidence data spatially and temporally .
JE has been categorized as a notifiable infectious disease since 1950 [6] . Local JE cases are reported to the Chinese Center for Disease Control and Prevention ( China CDC ) by local health departments . The JE data used in this study were obtained from the China Information System for Diseases Control and Prevention ( CISDCP ) [11] . The study did not use patient’s medical records and all data were analyzed anonymously . These data included the reported numbers of JE cases and mortalities , and demographic information such as age and gender , from January 2004 to December 2014 in 31 provinces ( cities or autonomous regions ) . Among the 31 provinces , only Xinjiang , Xizang , and Qinghai are free of JE . Annual incidence and mortality , monthly incidence , and the cumulative cases from 2004 to 2014 , were presented in the form of line and bar charts to illustrate the trends and season patterns of JE cases . We used a series of thematic maps , based on JE incidence data from all the China provinces , between 2004 and 2014 , to analyze the spatial and temporal patterns of JE cases . To observe differences in the JE epidemiological features of different age groups , the JE cases were grouped into three categories , namely , 0–15 , 16–40 , and above 40 years of age . The Local Indicators of Spatial Association ( LISA ) was used to evaluate the spatial clusters of JE reported cases at the city level of six provinces north of the Yangtze River during three JE epidemic waves ( 2006 , 2009 and 2013 ) . By calculating the Local Moran's I coefficient , which typically ranges from –1 to 1 , the spatial correlations between the data on a local area unit basis , and the average of neighboring values in the surrounding units , are revealed on LISA cluster maps [12 , 13] . The Z-score is used to assess the significant of observed spatial correlations , as indicated by Local Moran's I . When the Z-score is greater than 1 . 96 or less than –1 . 96 , the spatial correlation of the local area units is significant ( α = 0 . 05 ) . A high positive Z-score indicates that the surrounding features have either similarly high values ( High-High ) or similarly low values ( Low-Low ) , while a low negative Z-score indicates a significant ( P < 0 . 05 ) spatial outlier ( High-Low or Low-High ) [14] . The spatial statistical analysis module of ArcGIS software ( version 9 . 3; ESRI , Redlands , CA ) was used to perform LISA analysis and identify the spatial clusters of JE cases in the JE-epidemic areas .
The results presented above indicate that the increase in adult JE cases , especially the adults more than 40 years old , gradually becomes the main contributor to the national increase in JE incidence from 2004 to 2014 . In addition , the fatality rate of JE cases in this group is very high . These factors contribute to the urgency to investigate the temporal and spatial variation of JE incidences of the above 40-year-old group .
The JE vaccine was included in the EPI by the China government in 2008 , and , thus , children 0–15 years old could get free JE vaccinations [6 , 8] . Since the rate of JE vaccinations of children increased , JE incidence in children decreased significantly after 2008 . For example , the JE incidence in children who were 0–15 years old in 2007 was 1 . 51/100 , 000 , while it fell to 0 . 56/100 , 000 in 2013 . The data suggest that the integration of the JE vaccine into the EPI significantly lowered the JE incidence in children , which was also the main reason for the recent decline of JE in China [6 , 8] . There is no doubt that the incidence and mortality rates for children under 15 years old would be reduced in response to the subsequent increase of JE vaccination . As previously mentioned , the decreased incidence of JE in China was not only attributed to the integration of JE vaccine into EPI , but the rapid development of Chinese economy in recent years also played an imperative role in reducing the JE incidence . Owe to the continually rapid economic development in past 30 years , people’s living standards have been greatly improved , which brought about a series of positive changes in hindering the spread of JEV . For example , people living in rural areas gradually moved from the simple thatched huts that cannot prevent the invasive of mosquitoes to modern buildings with screens , which notably reduced the exposure to JEV vectors than before as a result . In addition , the state invested heavily on the projects of improving water and sanitation in rural areas , and transformed the tatty latrines into clean and separate toilets in order to clear away mosquito breeding environments . Moreover , the relocation of pig farms from the residence area of the village to the places far away from the village was also conducted . These methods effectively prevented people from the exposure to mosquitoes and JEV infection and made positive contribution to reduce the JE incidence in China . Numerous adult JE cases are found in certain provinces ( Figs 5 , 7 and 8 ) . The phenomenon is identified using the nationwide infectious disease electronic reporting system , which became available in 2004 . JE cases throughout the country are reported to the network of the China CDC , followed by systematic analyses [11] . Mainland China is located in the southeastern part of Asia , between north latitudes 20° ( Guangdong province , the southern-most region ) to 55° ( Heilongjiang province , the northern-most region ) . Establishing 30° north latitude as a border , southern China is the region south of the Yangtze River , including some tropical regions [15] . The high average annual temperatures and heavy rainfall combined with rice-based agricultural production patterns make these areas favorable for mosquito breeding , especially for C . tritaeniorhynchus , which is the main vector for the JEV . Thus , southern China is regarded as a JE hot spot [5 , 6] . Northern China , which is in the region north of the Yangtze River , ( north of 30° North latitude ) , belongs to the north temperature zone . The four seasons are distinct in the northern part of China , with low average annual temperatures and rainfall , which are not suitable for mosquito breeding . Farmers in northern China plant drought-resistant crops such as wheat and corn , rather than rice . The density of mosquitoes is less than in southern China [6] . As a result , people living in northern China have fewer opportunities to receive the JEV passive boost immunization , compared with the people in southern China , due to climatic and geographical factors . As mentioned above , several JE outbreaks occurred in China during the last century , particularly in the region south of the Yangtze River . In response , China successfully developed the JE-inactivated ( P3 strain ) and JE-attenuated live ( SA14-14-2 strain ) vaccines in the late 1960s and 1980s , respectively [16] . However , due to difficult economic situations , low production capacity , and limited JE vaccine distribution capability , the need for JE vaccination of children in China could not be satisfied . JE vaccine produced each year was used for emergency vaccinations of children in high JE-endemic areas [8] . Emergency vaccinations in JE-prevalent areas 30–40 years ago contributed to the relatively high JEV antibody levels in adults older than 40 ( born before 1970s–1980s ) in the region south of the Yangtze River . In contrast , the area north of the Yangtze River is a low JE prevalence area , with fewer outbreaks historically , and the need for fewer emergency JE vaccinations . Laboratory tests found that , in Shanxi province , which is in northern China ( also one of the prevalent areas for the above 40-year-old group ) , the positive rate of neutralizing antibody to the JEV was 38% for the above 40-year-old group [17] . Thus , JEV antibody levels are relatively low for adults ( born before the 1970s–1980s ) in the region north of the Yangtze River , compared with the southern region , which is attributed to multiple climatic and historical factors . These factors contribute to the high frequency of adult JE cases in these areas . Global warming has led to increased average annual temperatures in the region north of the Yangtze River , where the density of mosquitoes has increased accordingly . The Culex and Armigeres mosquito genera carry JEV in the northern region [6] , so the opportunity for people to get infected by JEV has increased , especially for adults . Adults born during the previous century are more vulnerable to mosquitoes and JEV . They never received the JE vaccination and , thus , are easily infected by JEV . Therefore , it could be predicted that the number and proportion of JE cases in adults born during the 20th century , and not inoculated with the JE vaccine , would continue to increase . This phenomenon also occurred in other JE-epidemic areas in Asia . For example , JE vaccination of children was performed in Japan and Korea in the 1960s and 1970s , and no JE cases have been reported for children . However , adult cases in Japan and Korea continue to be reported every year , in the area north of 30° north latitude , which is similar to the spatial distribution of adult JE cases in northern China [18 , 19 , 20] . Humans are generally vulnerable to JEV . Both JE-inactivated and attenuated live vaccines are safe and effective for each age group [16 , 21] . The data in this study indicate that the proportion and number of adult JE cases , as well as the proportion of incident cases among adults , are increasing annually , which would cause both human suffering and an extra burden on the government and society in general . To reduce the number of adult JE cases and the associated disease burden on society , further study is needed to determine the necessity and feasibility of vaccinating adults who live in JE-endemic areas , but have never been vaccinated with the JE vaccine . The most closely related example is JE vaccination of people traveling to JE-endemic areas . Vaccinating adults in JE-endemic areas would significantly improve the JE antibody levels and reduce the incidence of JE in adults during the 21th century . | It is well known that children are the population most susceptible to Japanese encephalitis ( JE ) , and the incidence of JE among children in China has been greatly reduced by extensive promotion of vaccinations aiming at children . The incidence of JE among adults , however , has increased in some parts of China . Due to a dearth of studies on JE among adults , the spatio-temporal pattern of adult JE cases is poorly understood . Here , we explore and describe the spatial and temporal distribution of JE cases observed among different age-groups in China from 2004 to 2014 . The results indicate that the JE cases of 0–15-year-old group are distributed mainly in the area south of the Yangtze River; while , the adult cases , especially in the >40 age-group , are concentrated in the area north of the Yangtze River . Further cluster analysis reveals six provinces north of the Yangtze River are hotspots for adult JE cases . And the incidence of adult JE cases in these provinces is significantly higher than the national average . The increasing JE incidence among adults has become an imperative public health issue and should be attached sufficient attention . | [
"Abstract",
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"immu... | 2016 | The Spatio-temporal Distribution of Japanese Encephalitis Cases in Different Age Groups in Mainland China, 2004 – 2014 |
As the development of new classes of antibiotics slows , bacterial resistance to existing antibiotics is becoming an increasing problem . A potential solution is to develop treatment strategies with an alternative mode of action . We consider one such strategy: anti-adhesion therapy . Whereas antibiotics act directly upon bacteria , either killing them or inhibiting their growth , anti-adhesion therapy impedes the binding of bacteria to host cells . This prevents bacteria from deploying their arsenal of virulence mechanisms , while simultaneously rendering them more susceptible to natural and artificial clearance . In this paper , we consider a particular form of anti-adhesion therapy , involving biomimetic multivalent adhesion molecule 7 coupled polystyrene microbeads , which competitively inhibit the binding of bacteria to host cells . We develop a mathematical model , formulated as a system of ordinary differential equations , to describe inhibitor treatment of a Pseudomonas aeruginosa burn wound infection in the rat . Benchmarking our model against in vivo data from an ongoing experimental programme , we use the model to explain bacteria population dynamics and to predict the efficacy of a range of treatment strategies , with the aim of improving treatment outcome . The model consists of two physical compartments: the host cells and the exudate . It is found that , when effective in reducing the bacterial burden , inhibitor treatment operates both by preventing bacteria from binding to the host cells and by reducing the flux of daughter cells from the host cells into the exudate . Our model predicts that inhibitor treatment cannot eliminate the bacterial burden when used in isolation; however , when combined with regular or continuous debridement of the exudate , elimination is theoretically possible . Lastly , we present ways to improve therapeutic efficacy , as predicted by our mathematical model .
As we begin to lose the arms race against microbial infections , it is important that we develop new treatment strategies as a complement or alternative to antibiotics . In this paper , we use mathematical modelling to explain and predict the effects of a novel anti-adhesion therapy in the treatment of infected burn wounds , with the aim of improving treatment outcome . Each year , millions of lives are saved through the use of antibiotics to combat bacterial infections . However , sustained use of any given antibiotic leads to the clinical emergence of drug-resistant strains . Since the discovery of penicillin , many new classes of antibiotics have been identified , allowing clinicians to switch between antibiotics if resistance emerges either within an individual patient or within a patient population [1] . Over time , strains have emerged which exhibit resistance to multiple classes of antibiotics ( multi-drug resistance ) and reports of bacterial infections which are resistant to all known antibiotics ( pan-resistant ) are becoming increasingly common . At present , a reported 700 , 000 individuals worldwide die each year due to antimicrobial resistance and this figure is predicted to rise to 10 million per year by 2050 unless steps are taken to combat this threat [2] . While resistant strains continue to evolve , our ability to develop new classes of antibiotics is diminishing , the rate of antibiotic discovery having slowed significantly since its ‘Golden Era’ in the 1940s–1960s [1 , 3] . It is therefore vital that we develop alternative treatment strategies to replace or complement antibiotics [4 , 5] . One potential way forward is through the use of anti-virulence treatments . Whereas antibiotics either kill bacteria ( bactericidal ) or inhibit their growth ( bacteriostatic ) , anti-virulence treatments interfere with a pathogen’s ability to cause damage and disease in the host [6] . As such , they are likely to exert a smaller selective pressure upon a bacterial community , reducing the chances that resistance will develop ( though opinions vary over the extent to which they may be resistance-proof , see [7 , 8] ) . Anti-virulence treatments take a number of forms including those which target or inhibit toxin activity , adhesion , toxin secretion , virulence gene expression and inter-bacterial signalling [9–11] . In this paper , we consider a form of anti-adhesion treatment consisting of polystyrene microbeads coupled to a protein known as multivalent adhesion molecule ( MAM ) 7 ( see also [12] and other papers from their group for alternative anti-adhesion treatments that operate by blocking pilus assembly or function ) . MAM7 is anchored in the outer membrane of many Gram-negative bacteria , where it is responsible for initiating attachment of bacteria to host cells [13 , 14] . When applied to an infection site , MAM7-coated beads ( henceforth , inhibitors ) act as a bacteriomimetic , competitively inhibiting the infectious agent from binding to host cells . This prevents bacteria from deploying those virulence mechanisms for which cell-to-cell contact is required and renders them more susceptible to natural or artificial physical clearance . Given that inhibitors must bind to host cells before bacteria in order to block them from binding , it is unclear whether their application may ever be expanded from prevention ( prophylaxis ) to the treatment of established infections ( therapy ) . Bacterial infection is a major cause of mortality in patients with burn wounds , where it is responsible for up to 75% of deaths in cases where severe burns are sustained to more than 40% of the body surface area [15] . Burn wounds are commonly infected by Pseudomonas aeruginosa [15–18] , an opportunistic Gram-negative bacterium; the infection often being hospital-acquired ( nosocomial ) [15 , 19] . Current treatment of such infections involves use of topical and systemic antibiotics , and regular debridement ( mechanical wound cleaning ) . Debridement is either achieved through regular wound cleansing with a cloth , or through application of negative pressure devices ( negative pressure wound therapy , NPWT ) in which fluid is drawn out of the wound , either continuously or intermittently , using a pump , attached to a foam dressing covering the wound [20] . Some studies have shown NPWT to be effective in reducing the bacterial burden [21]; however , this result is not consistent across all studies [20 , 22] . In earlier work we have shown , using an experimental model for P . aeruginosa burn wound infections in the rat , that treatment with inhibitors can significantly reduce the bacterial burden in the wound without impeding wound closure [23] ( see Experimental set-up for more details ) . In vitro studies have also demonstrated the efficacy of inhibitor treatment in reducing cytotoxicity [9 , 24] and have shown that inhibitors do not interfere with host cell functions critical to wound healing [25] . A number of mathematical modelling studies have considered the use of anti-virulence treatments to combat bacterial infections . The majority of these studies focus upon anti-quorum sensing treatments ( see , for example , [26–33] ) . An exception to this rule; the model in [34] is of particular relevance to the present work . This ordinary differential equation ( ODE ) model considers a general anti-virulence treatment , which operates by enhancing innate immunity in bacterial clearance . The model predicts that , when used in isolation , anti-virulence treatment is unlikely to eliminate a bacterial infection . However , the model predicts that , when combined with antibiotics , anti-virulence treatments could eliminate bacteria , provided antibiotic and anti-virulence treatments are applied in staggered doses . Other modelling work has considered the bacterial invasion of burn wounds and the resultant tissue damage [30 , 35–37] , the influence of bacterial infection upon the healing of burn wounds [38] and the effects of ambient gas plasma treatment in this context [39] . Each of these models is formulated as a system of partial differential equations in one or two spatial dimensions . Models have also been developed to describe microbial adhesion to surfaces , for example , [40] developed an ODE model for the competitive colonisation of the gut wall by host and invader strains of Escherichia coli . Lastly , [41] have developed an individual-based model to describe the colonisation of a generic surface by phenotypically heterogeneous bacteria , in which bacteria may migrate between the surface and a liquid medium . In this paper , we construct a mathematical model , formulated as a system of ODEs , to describe the population dynamics and treatment of a bacterial infection within a burn wound . Basing our mathematical model upon Huebinger et al . ’s [23] experiments , we use it to explain the empirical results and to predict the effects of various treatment regimes , involving inhibitor dosing and debridement , with the aim of improving efficacy . A particular strength of this study is that we consider multiple parameter sets , twelve in total , each of which provides a good fit to the experimental data . Classifying these sets into four qualitatively different cases , we consider the long-term effects of each treatment strategy , predicting the conditions under which treatment will eliminate the bacterial burden across all four cases .
In this section we provide a simple description of the experimental set-up which forms the basis for our mathematical model . The experimental work was published previously in [23] , wherein a more detailed description can be found . We consider a burn wound infection model in the Sprague-Dawley rat ( see Fig 1 ) . Rats were anaesthetised and the portion of each rat which was to be burned was shaved . Rats were then immersed in 100°C water for 12s resulting in full-thickness cutaneous burns to 40% of the body surface area , in a region spanning the back and upper sides of the body . We label the time at which the burn is administered as day −2 . Rats were then resuscitated and given the appropriate pain control for the remainder of the experiment . On day 0 , two days after the burn was administered , a section of eschar ( dead ) tissue , approximately 4 × 4 cm in area , was surgically excised . Next , 5 × 106 CFU ( colony-forming units ) of multidrug-resistant P . aeruginosa were applied to the excised region , followed by suspensions containing either 3 × 108 inhibitor or control beads ( without a MAM7 coating ) in saline . Identical inhibitor and control treatments were repeated every 24 hours for days 1–5 post infection; however , since a scab ( i . e . a layer of solidified exudate ) forms over the excision by day 1 , treatments administered on or after day 1 are unlikely to enter the ( liquid ) exudate . Rats were euthanized after the experiment , on day 6 . A bioluminescent , multidrug-resistant P . aeruginosa isolate , Xen5 , was chosen , such that the bacterial burden and their spatial distribution across the wound could be detected . An IVIS Spectrum in vivo imaging system ( Perkin Elmer ) was used to record bacterial luminescence on days 1–6 post infection , from which the total flux ( photons sec−1 ) was calculated using MetaMorph software ( Molecular Devices ) to integrate over the pixels . The total number of bacteria in CFU was then calculated using the conversion factor 4 × 108 photons sec−1 ↔ 5 × 106 CFU , which was determined by measuring the luminescence of suspensions which contained an experimentally determined number of bacterial colony-forming units . 13 experiments were conducted using inhibitor and 11 using control beads . Two of the control bead experiments were discounted because the exposure setting used was too high to prevent the image from saturating . This may imply that the mean bacteria population size over time calculated for the control bead scenario slightly underestimates the true mean value . The experimental results are summarised in Fig 2 . These results raise two important questions: In what follows , we formulate a mathematical model of the burn wound infection experiment described above to help us answer these questions . The burn wound is assumed to consist of two physical compartments: the host cells , and a fluid compartment exuded by the host cells and ( hence ) known as the exudate . The host cells and the overlying exudate extend to the perimeter of the burn wound beneath the necrotic tissue , while the exudate is exposed to the air at the excision ( see Fig 3A ) . The area of the burn wound , Ar ( cm2 ) , remains essentially fixed during the experiment; however , the exudate height , h ( cm ) , and volume , V ( cm3 ) , are elevated for a short period following the application of bacteria and inhibitors to the excision at the beginning of day 0 ( see Fig 1A ) . This excess fluid is lost rapidly via run-off ( down the sides of the rat ) , evaporation and absorption ( into the host cells ) . Since the timescale over which the height and volume are elevated ( on the order of minutes ) is small compared to the timescale of the experiment ( on the order of days ) , we neglect this variation and assume a fixed height and volume throughout the course of the experiment . Both bacteria and inhibitors can exist in one of two states; either free in the exudate or bound to the host cells ( we note that inhibitors do not bind to bacteria ) . It is assumed that the system is well-mixed since any given bacteria ( or inhibitor ) has an equal chance of interacting with any given binding site and since all other processes ( growth , clearance , phagocytosis and unbinding ) are thought not to depend upon their spatial location . This allows us to forgo an explicit spatial component and so to construct an ODE model for the evolution of the free bacteria density , BF ( t ) ( cells cm−3 ) , bound bacteria density , BB ( t ) ( cells cm−2 ) , free inhibitor concentration , AF ( t ) ( inhibitors cm−3 ) , and bound inhibitor concentration , AB ( t ) ( inhibitors cm−2 ) , over time , t ( hr ) . It is assumed that the total binding site density on the host cells , consisting of both free and occupied sites , is conserved , such that the free binding site density E ( t ) = Einit − ϕBacBB ( t ) − ϕAAB ( t ) ( sites cm−2 ) , where Einit ( sites cm−2 ) is the initial density of free binding sites , and ϕBac ( sites cell−1 ) and ϕA ( sites inhibitor−1 ) are the number of binding sites occupied by a bacterium or an inhibitor respectively . The model is summarised in Fig 3B and described by the following governing equations d B F d t= r F B F ( 1 - B F K F ) ︸ logistic growth + ( 1 - η ( E ) ) H ( K B - B B ) r B h B B ( 1 - B B K B ) ︸ daughter cells freed from host cells upon division - α B a c A r B F E ︸ binding to host cells + β B a c h B B ︸ unbindingfromhostcells - ψ B a c ( t ) B F ︸ natural clearance , ( 1 ) d B B d t= ( 1 + ( η ( E ) - 1 ) H ( K B - B B ) ) r B B B ( 1 - B B K B ) ︸ logistic growth ( a proportion , η , remain attached ) + α B a c V B F E ︸ binding to host cells - β B a c B B ︸ unbindingfromhostcells - δ B B B ︸ phagocytosis , ( 2 ) d A F d t= - α A A r A F E ︸ bindingtohostcells + β A h A B ︸ unbindingfromhostcells - ψ A ( t ) A F ︸ naturalclearance , ( 3 ) d A B d t= α A V A F E ︸ bindingtohostcells - β A A B ︸ unbindingfromhostcells , ( 4 ) where parameter values are given in Tables 1 and 2 , and Table A in S2 Supporting Information . See Parameter fitting and S1 Supporting Information for details on how the parameters were obtained . Note that we consider multiple parameter sets , each of which provides a good fit to the data . Both free and bound bacteria are assumed to grow logistically with respective intrinsic growth rates rF ( hr−1 ) and rB ( hr−1 ) , and carrying capacities KF ( cells cm−3 ) and KB ( cells cm−2 ) . We interpret the carrying capacities to represent the maximum number of bacteria that can be sustained by available nutrients and the situation in which BF ( t ) = KF , or BB ( t ) = KB , to be one in which the rate of bacterial division is negligible ( see [42 , 43] ) . The burn wound exudate contains glucose and other nutrients and has been shown to be capable of supporting a proliferating population of P . aeruginosa [44 , 45] . We note that , in general , KB ≠ Einit/ϕBac , such that the number of bacteria that can be nourished on the host cells is not equal to the number that can bind to the host cells . For all of the parameter sets considered in this paper , KB < Einit/ϕBac ( see Tables 1 and 2 , and Table A in S2 Supporting Information ) . It is assumed that bacteria and inhibitors bind to and unbind from the host cells in accordance with the law of mass action , with respective binding rates αBac ( hr−1 sites−1 ) and αA ( hr−1 sites−1 ) , and unbinding rates βBac ( hr−1 ) and βA ( hr−1 ) . Examination of histological sections through the burn wound shows that neutrophils are present within and at the surface of the host cells , but not within the exudate [23] . Administration of a burn wound causes neutrophils to be fully activated such that no further neutrophils are recruited in response to the bacterial infection [23 , 46] ( in contrast to [34] ) . Therefore , the immune response can be captured by the exponential decay of bound bacteria with rate δB ( hr−1 ) , where δB accounts for the density of neutrophils . It is assumed that inhibitor degradation , if it occurs , is sufficiently gradual that it can be neglected . Several of the terms in Eqs 1–4 contain h , Ar or V as a factor in order to ensure dimensional consistency . These constants could have been combined with other parameters , but we retain them in the interests of clarity . A proportion of the daughter cells of bound bacteria , 0 ≤ η ( E ( t ) ) ≤ 1 ( dimensionless ) , remain bound to the surface , while the remaining fraction , 1 − η ( E ( t ) ) , enter the exudate . Daughter cells may not bind immediately either because the long axis of the parent cell is angled away from the host cell surface upon division or due to a lack of free binding sites on the neighbouring host cell surface . The proportion that remains bound , η ( E ( t ) ) , depends upon the density of free binding sites , E ( t ) , such that a larger fraction of the daughter cells remain bound when more binding sites are available . We capture this dependence using a Hill function with constant γ ( sites cm−2 ) and Hill coefficient n ( dimensionless ) as follows η ( E ) = η m a x E n γ n + E n , ( 5 ) where ηmax ( dimensionless ) is the maximum proportion of daughter cells which may remain bound to the surface . If the density of bound cells , BB ( t ) , exceeds the bound carrying capacity , KB , then the bound logistic growth term becomes a death term . In this case , the loss of bacteria is confined to the bound compartment and does not affect the free compartment . This is achieved through the use of a Heaviside step function , H ( KB − BB ( t ) ) , in Eqs 1 and 2 , where H ( x ) ≔ { 0 if x < 0 , 1 if x ≥ 0 . ( 6 ) The rates of clearance of bacteria and inhibitors , ψBac ( t ) ( hr−1 ) and ψA ( t ) ( hr−1 ) , vary with time , such that clearance occurs at a constant rate for the first 24 hours and then stops after this point due to the formation of a scab over the excision . Thus , clearance occurs at rates ψ B a c ( t ) = ψ ˜ B a c H ( 24 - t ) and ψ A ( t ) = ψ ˜ A H ( 24 - t ) , ( 7 ) where ψ ˜ B a c ( hr−1 ) and ψ ˜ A ( hr−1 ) are the constant rates of clearance in the first 24 hours , and H is a Heaviside step function , as defined in Eq 6 . We choose the time t = 0 ( hr ) to correspond to the point at which bacteria and inhibitors are applied to the burn wound following the excision . Bacteria and inhibitors are present only in the free compartment initially , not having had the opportunity to bind to the host cells , such that B F ( 0 ) = B F i n i t , B B ( 0 ) = 0 , A F ( 0 ) = A F i n i t , A B ( 0 ) = 0 , ( 8 ) where B F i n i t and A F i n i t are constants . See Tables 1 , 2 and Table A in S2 Supporting Information for parameter values . The parameters in Table 1 and Table A in S2 Supporting Information were fitted to the experimental data ( see Parameter fitting and S1 Supporting Information for details ) , while those in Table 2 were either measured , calculated or estimated . The area of each burn wound was determined from images , such as those in Fig 1 , using the MetaMorph software , while the height of the fluid layer was measured to be 1 mm . As described in Experimental set-up , the initial density of bacteria and the initial concentration of inhibitor are known . The volume of the exudate is calculated as the product of the wound area and the height of the exudate . We know that there are about 1 . 5 × 105 host cells per cm2 and that approximately 17 inhibitors may bind per host cell [9] . Taking the product of these two values gives us the initial density of free binding sites , Einit . We define a binding site to consist of the number of host cell binding receptors occupied by an inhibitor , such that an inhibitor occupies a single site and hence ϕA = 1 sites inhibitor−1 . Inhibitors have been designed to occupy the same number of host cell binding receptors as a bacterium . Therefore , we also have that ϕBac = 1 sites cell−1 . We note that while an inhibitor occupies the same number of binding sites as a bacterium , a rod-shaped P . aeruginosa cell ( which we have measured to be approximately 1μm×3μm ) covers up to three times the host cell surface area as a spherical ( 1 μm diameter [23] ) inhibitor without occupying any more sites . Simulations were found to be insensitive to the value of the Hill coefficient , n; therefore , we set it to unity for simplicity . We leave our equations in dimensional form so as to make them easier to interpret biologically and since non-dimensionalisation would not reduce the number of fitted parameters ( although it does reduce the total number of parameters ) . In addition to the untreated/control ( A F i n i t = 0 ) and single inhibitor dose ( A F i n i t > 0 ) scenarios based upon Huebinger et al . ’s [23] experiments ( see Experimental set-up ) , we consider a further six theoretical scenarios , five of which include either regular or continuous debridement ( see Table 3 ) . Since inhibitors operate by blocking bacteria from binding to the wound host cells , it is intuitive that this may result in the majority of bacteria occupying the free compartment . Thus , any treatment , such as debridement , which removes the exudate , could clear the free compartment of bacteria—and with them , inhibitors—significantly reducing the total population size of bacteria when combined with an inhibitor treatment . ( Bound bacteria and inhibitors are left mostly intact by debridement . ) Regular debridement consists of a series of discrete instantaneous debridement events , while continuous debridement consists of a sustained , high level of clearance ( ψBac = ψA = 1000 hr−1 ) and may be thought of as the limiting case of regular debridement in which the time between debridement events tends to zero . While it may not be possible to maintain such a high rate of clearance in practice , this clearance rate is chosen to determine the theoretical best-case-scenario were such a treatment to be applied . Following a discrete debridement event , it is assumed that the fluid compartment is restored on the timescale of a few minutes , such that the volume fluctuation can be neglected . For each of the treatment strategies , Eqs 1–8 were solved using the Matlab routine ode15s , a variable-step , variable-order solver based upon numerical differentiation formulas . The untreated and single inhibitor dose scenarios are those considered in the experiments and are described above and in Experimental set-up . The key difference between the numerical simulations ( see Numerical solutions ) and the experiments is that the simulations extend beyond the time frame of the experiments . In the regular inhibitor dose scenario ( and the regular inhibitor dose with regular debridement scenario ) , the repeat doses of inhibitors are identical to the initial dose , A F i n i t . The second dose is not applied until 48 hr for consistency with the treatments involving debridement ( see below ) . As noted in Experimental set-up , a scab forms over the wound after the first 24 hr , ending clearance and preventing further inhibitor doses from reaching the exudate . Thus , in practice , inhibitor doses could not be repeated without removing the scab and incurring further clearance at levels similar to those in the first 24 hr . However , since we are interested in the theoretical effect of repeated inhibitor doses independent of clearance , we neglect further clearance effects in this case . Were we to include additional clearance upon re-treatment with inhibitor , treatment efficacy would be improved . In the scenarios involving regular debridement , clearance is re-established for the first 24 hours after each debridement event , with rates given in Table 1 and Table A in S2 Supporting Information , to account for leakage due to the loss of the scab upon debridement . The first debridement event is chosen to occur at t = 48 hr , rather than some earlier time , so as to give the inhibitors time to bind to the host cells . We present two sets of sensitivity analyses . The first set ( presented in Case A–Case D ) shows the effect of a tenfold increase or decrease in each of the 13 fitted parameters , rF , rB , KF , KB , αBac , βBac , δB , ηmax , γ , ψ ˜ B a c , αA , βA and ψ ˜ A , on the total number of bacteria either at steady-state ( treatment scenarios 1 and 2 ) or at t = 90 days = 2160 hr ( treatment scenarios 3–8; Figs O–V in S2 Supporting Information ) . We truncate the simulations for the latter treatment scenarios at 90 days since simulating treatments with regular inhibitor doses or regular debridement is computationally expensive and those involving continuous debridement are close to steady-state by this time . We note that the total population size of bacteria oscillates in those treatments that involve regular debridement , undergoing a sharp drop upon each debridement event . We plot the value of BT ( t ) at the peak of the oscillation at t = 90 days ( directly prior to debridement ) , since we consider that it is by the maximum number of bacteria that the efficacy of a treatment should be judged . In the second set of sensitivity analyses ( presented in Inhibitor sensitivity analysis ) , we consider the effect of varying the binding and unbinding rates of inhibitors , αA and βA , in the space {10−12 , 10−11 , … , 1} × {10−12 , 10−11 , … , 1} upon the total number of bacteria at 4 weeks ( = 672 hr ) post infection in the 5 scenarios that involve inhibitor treatment ( Figs W , Y , AA , AC and AE in S2 Supporting Information ) . We also consider the effect of increasing all inhibitor doses by 10 fold from 6 . 12 × 107 inhibitors cm−3 ( the standard value ) to 6 . 12 × 108 inhibitors cm−3 ( Figs X , Z , AB , AD and AF in S2 Supporting Information ) . A combination of Markov Chain Monte Carlo ( MCMC ) and frequentist methods were used to fit the model given by Eqs 1–8 to the mean of the experimental data in the untreated and single inhibitor dose scenarios . Unfortunately , we have insufficient data to generate informative posterior distributions using the MCMC method; however , we are able to identify a number of good fits ( twelve parameters sets are presented here ) and to classify these into four general cases—A , B , C and D—based upon their qualitative behaviour ( see Results ) . By considering a range of valid parameter sets , rather than a single good fit , we are able to gain a more comprehensive understanding of the model behaviour . The fitting procedures used differ between parameter sets and are summarised in Table A in S1 Supporting Information . See S1 Supporting Information for more details . We confirmed these fits with a nonlinear mixed-effects model using the Matlab routine sbiofitmixed , with parameter sets 1–12 as initial guesses . Model fits are compared against the experimental data in Fig 4 and Figs A and B in S2 Supporting Information , where parameter sets 2 ( Case A ) , 3 ( Case B ) , 8 ( Case C ) and 12 ( Case D ) are presented in Fig 4 . Since the experimental data does not distinguish between free and bound bacteria , we compare it against the simulated total number of bacteria , BT ( t ) = VBF ( t ) + ArBB ( t ) . The model achieves a good fit to the experimental mean in all cases , remaining mostly within the shaded region denoting the standard error of the mean .
Steady-state analyses of Eqs 1–6 , in the absence of clearance , with and without a single dose of inhibitors were performed using Maple . Clearance was neglected since leakage of fluid from the wound only occurs in the first 24 hours . It was found that the system has two physically realistic steady-states in both the untreated and single inhibitor dose scenarios for all 12 parameter sets . In each case the first steady-state , at which bacteria are absent , is unstable , while the second steady-state , at which both free and bound bacteria are present , is stable ( see S3 Supporting Information for more details ) . By characterising the stability of the system in this way , we can be sure that we are not overlooking any potential stable steady-state solutions in the time-dependent simulations below . Having explored the behaviour of the system at steady-state , we consider the full time-dependent problem ( Eqs 1–8 ) . We begin by making a few general comments , before taking Cases A–D in turn . Further details can be found in S4 Supporting Information . In each case we present results to show the evolution in the total number of bacteria , BT ( t ) = VBF ( t ) + ArBB ( t ) ( Fig 5 , and Fig D in S2 Supporting Information ) , the total numbers of free and bound bacteria , B ^ F ( t ) = V B F ( t ) and B ^ B ( t ) = A r B B ( t ) , free and bound inhibitors , A ^ F ( t ) = V A F ( t ) and A ^ B ( t ) = A r A B ( t ) , and free binding sites , E ^ ( t ) = A r E ( t ) ( Fig 6 , and Figs E and F in S2 Supporting Information ) , and of the individual terms in Eqs 1–4 ( Figs G–L in S2 Supporting Information ) in the untreated and single inhibitor dose scenarios . We also present results to show the evolution in the total number of bacteria in the treatment scenarios involving regular inhibitor doses and regular or continuous debridement ( Fig 7 , and Figs M and N in S2 Supporting Information ) . Lastly , we present a sensitivity analysis showing the effects of a tenfold increase or decrease in each of the 13 fitted parameters ( Figs O–V in S2 Supporting Information , see Sensitivity analyses for details ) . In the remainder of this paper , we distinguish between rate constants , e . g . , αBac and δB , and the rate at which processes occur , e . g . , αBacArBFE and δBBB , the former being distinguished from the latter by the use of the word ‘constant’ . We also distinguish between the intrinsic growth rate , e . g . , rF , and the rate of logistic growth , e . g . , rFBF ( 1 − BF/KF ) . The time-dependent results are summarised in Table 4 . Treatments are most effective in Case A , some of them eliminating the bacterial burden completely . Most treatment scenarios are also effective in Case B . Surprisingly , treatment with inhibitors can actually increase the bacterial burden in Case C , although some treatments are still effective , while in Case D all treatments are ineffective , the bacterial burden settling to its untreated steady-state in all scenarios . When effective , treatment with inhibitors may reduce the total bacterial burden in two ways . Firstly , inhibitors may reduce the number of bound bacteria through competition for binding sites . The second way , which may follow as a consequence of the first , is by reducing the rate of production of daughter cells by bound bacteria . We note that the maximum proportion of bound daughter cells to enter the bound compartment , ηmax , ranges between O ( 10−10 ) and O ( 10−2 ) , across the 12 parameter sets considered ( see Table 1 , and Table A in S2 Supporting Information ) . Therefore , the majority of bound daughter cells enter the exudate in all cases ( though , once there , they will not continue to divide if the free carrying capacity is exceeded ) . This insight is not intuitively obvious , demonstrating the benefit of mathematical modelling .
As bacteria gain increasing resistance to antibiotics it is vital that we develop alternative treatment strategies . Anti-virulence treatments—specifically MAM7-coupled beads , which operate by competitively inhibiting the binding of bacteria to host cells—present a promising complement or alternative to antibiotics . Opinion as to the likely efficacy of such treatments is mixed , with some suggesting that their utility may be limited to preventing the initiation of a bacterial infection ( prophylaxis ) as opposed to treating a pre-existing infection ( therapy ) [6 , 10 , 47 , 48] . In this paper we have used mathematical models to help us interpret the results of an experimental model , involving the inhibitor treatment of a burn wound infected by P . aeruginosa in the rat ( see Experimental set-up ) . Our models allow us to predict the conditions under which treatment with inhibitors will be effective and to explore ways in which inhibitor dosing could be augmented to improve efficacy . Mathematical models were fitted to experimental data using a combination of MCMC and frequentist techniques ( Parameter fitting ) . A number of close fits were obtained ( 12 parameter sets are explored here ) and classified into four qualitatively different cases ( A–D ) . Given the significant qualitative , and not merely quantitative , differences in predicted treatment outcomes between Cases A–D , this work highlights the importance of considering a range of viable parameter sets . Had a single parameter set been chosen , our conclusions would have differed markedly from those in this more comprehensive study . It may be that different parameters sets could reflect inter-patient variability , as well as different bacterial species . Indeed , a variety of Gram negative and Gram positive bacteria have been found to infect burn wounds ( see , for example , [15–17 , 19] ) . Eight treatment scenarios were considered for each of Cases A–D ( see Treatment scenarios for details ) . The untreated and single inhibitor dose scenarios were considered in the experimental model , while the rest are theoretical treatments that remain to be tested experimentally . Continuous debridement , with clearance rates on the order of magnitude of those used in simulations , is probably not practically achievable; though it might be possible to maintain a high level of clearance using negative pressure wound therapy ( see Introduction ) . In any case , these simulations allow us to determine the theoretical best-case scenario where debridement is applied . Simulations for the 8 treatment scenarios in Cases A–D reveal a range of outcomes and provide insight into the bacterial population dynamics ( Case A–Case D ) . Before considering each case in turn , a few general observations can be made . Firstly , while the ratio between the number of bacteria at carrying capacity in the free and bound compartments , VKF and ArKB respectively , varies between parameter sets , the intrinsic growth rate in the bound compartment , rB , is consistently greater than that of the free compartment , rF ( see Table 1 and Table A in S2 Supporting Information ) . This suggests that the bound compartment is more favourable to the growth of bacteria than the free compartment , agreeing nicely with Huebinger et al . ’s [23] speculative explanation for the efficacy of inhibitor treatment . This might be because bound bacteria have access to additional nutrients derived from the host cells , not available to free bacteria . Our model predicts that in order for inhibitor treatment to be effective , inhibitors must bind rapidly and numerously . If they do not , then treatment may actually worsen a bacterial infection . If inhibitors bind in sufficient quantities to the host cells , then the number of bacteria in the bound compartment and hence their rate of logistic growth will remain small , reducing the flux of bound daughter cells into the free compartment . However , if inhibitors bind in lower numbers , then while the number of bound bacteria at steady-state may be reduced , their logistic growth rate increases ( see Fig 8 ) as does the flux of bound daughter cells into the free compartment , with the result that inhibitor treatment increases the total number of bacteria at steady-state above its untreated value . If debridement is applied too early , as in Set 7 , it may reduce treatment efficacy as inhibitors will have had insufficient time to bind before those in the free compartment are removed . If inhibitor binding rates could be determined experimentally then this would allow us to determine the optimum timing of debridement in relation to inhibitor dosing . All treatments are effective in significantly reducing the bacterial population size in Case A . In Case B , all treatments except regular debridement and a single inhibitor dose with regular debridement are consistently effective . Counterintuitively , a single inhibitor dose increases the bacterial burden in Case C , with treatments involving continuous debridement being the most effective . Lastly , in Case D , all of the treatment strategies result in a steady-state bacterial population size similar to that without treatment . The differences in behaviour between Cases A–D may be explained , to some extent , by the differences in the bacteria and inhibitor association constants and the ratio of these constants ( see Table 1 , and Table A in S2 Supporting Information ) . The bacteria association constant is lower in Case A than in Cases B–D , while the inhibitor association constant and the ratio of these constants are lowest in Case A , highest in Cases C–D and take intermediate values in Case B . Treatments are more effective where the inhibitor association constant is higher and the bacterial association constant is lower , and hence where the ratio of association constants is lower . The qualitative difference between Cases C and D is less significant than between the other cases , thus it is not surprising that the order of magnitude of their association constants and their ratio are not distinct . Given the correlation between bacteria and inhibitor association constants and treatment efficacy , a natural way to seek to improve treatment would be to decrease the bacterial association constant or to increase the inhibitor association constant , shifting the system behaviour towards that in Case A . The bacterial association constant could be reduced by treating with a molecule that binds to and blocks bacterial adhesins used for binding to the host cells e . g . mannosides , which bind FimH [12] . In order for these molecules not to interfere with the inhibitor treatment they would need to either bind to an adhesin other than MAM7 or be applied to a wound before the inhibitor dose , giving the molecules time to bind to the bacteria . The inhibitor association constant could be increased by coupling more MAM7 molecules to each bead . Sensitivity analysis of the inhibitor binding rate constant , unbinding rate constant and dose concentration predicts conditions under which treatments involving inhibitor would eliminate the bacterial burden within 28 days ( Inhibitor sensitivity analysis , Fig 9 , and Figs W–AF in S2 Supporting Information ) . Neither a single nor regular doses of inhibitor on their own were predicted to be capable of eliminating the bacterial population , though they may reduce it by several orders of magnitude . This is to be expected since , on its own , inhibitor can only prevent bacteria from entering the bound compartment , and is unable to remove bacteria from the free compartment . Encouragingly , when combined with regular or continuous debridement , inhibitor treatment is predicted to have the potential to eliminate the bacterial population . A single inhibitor dose with continuous debridement is predicted to be the most effective treatment , followed by regular inhibitor doses with regular debridement , with a single inhibitor dose with regular debridement being the least effective of the three . Treatment is predicted to be more effective for higher binding rate constants , lower unbinding rate constants and higher concentration inhibitor doses , where treatment efficacy is more sensitive to the binding rate constant than to the unbinding rate constant . In future work we will proceed on two fronts: experimental and theoretical . We will conduct further experiments to test our model predictions and to improve model parametrisation . Experiments like those described in Experimental set-up could be performed over a longer time-span and separate measurements made for the numbers of free and bound bacteria and inhibitors . This would enable us to refine the range of possible parameter sets . This would also be aided by more regular wound imaging than the current daily set-up . Further experiments to test the model predictions concerning the other treatment strategies suggested here would also be valuable . We will develop our mathematical modelling in at least two directions . Firstly , we will incorporate treatment with antibiotics , seeking to determine the optimum treatment regime when combined with inhibitor dosing and regular or continuous debridement . Secondly , we will consider a discrete-stochastic cellular automata model to capture some of the mechanisms in the system in more detail , including the spatial spread of infection , which has been shown experimentally to be limited upon treatment with inhibitor [23] . Our models could also be extended to incorporate quorum sensing and biofilm formation . In conclusion , our mathematical models suggest that inhibitor treatment could be effective in eliminating or significantly reducing the bacterial burden in a burn wound when combined with regular or continuous debridement . Where inhibitor treatment is effective , it operates both by preventing bacteria from occupying the host cells ( where growth rates are predicted to be higher ) and , consequently , by reducing the flux of bound daughter cells into the exudate . Our model predicts that inhibitor treatments , in particular those involving regular or continuous debridement , could be effective when used both prophylactically and therapeutically . Our models further predict that treatment efficacy can be improved by optimising inhibitor design and dosing schedules . | Humankind is engaged in an arms race; one we are in danger of losing . Since the development and application of the first antibiotics , resistant strains of bacteria have steadily emerged . As the rate of discovery of new antibiotics slows , the threat increases . At present , 700 , 000 individuals globally die each year due to antimicrobial resistance and this number is predicted to rise to 10 million per year by 2050 unless fresh action is taken . It is important , therefore , that we explore alternative treatment strategies to replace or complement traditional antimicrobials . Here we use mathematical models to explain and predict the effects of a novel anti-adhesion therapy applied to infected burn wounds . This theoretically resistance-proof therapy operates by impeding bacteria from binding to host cells by blocking the host cell binding sites . This prevents bacteria from accessing nutrients and renders them susceptible to artificial clearance . Fitting our model to experimental data , we identify a number of valid parameter sets , and predict the conditions under which treatment will be effective for each set . These predictions are experimentally testable , and could be used to guide the development and application of anti-adhesion treatments in a clinical setting . | [
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"r... | 2018 | Predictive modelling of a novel anti-adhesion therapy to combat bacterial colonisation of burn wounds |
Correct chromosome segregation is essential in order to prevent aneuploidy . To segregate sister chromatids equally to daughter cells , the sisters must attach to microtubules emanating from opposite spindle poles . This so-called biorientation manifests itself by increased tension and conformational changes across kinetochores and pericentric chromatin . Tensionless attachments are dissolved by the activity of the conserved mitotic kinase Aurora B/Ipl1 , thereby promoting the formation of correctly attached chromosomes . Recruitment of the conserved centromeric protein shugoshin is essential for biorientation , but its exact role has been enigmatic . Here , we identify a novel function of shugoshin ( Sgo1 in budding yeast ) that together with the protein phosphatase PP2A-Rts1 ensures localization of condensin to the centromeric chromatin in yeast Saccharomyces cerevisiae . Failure to recruit condensin results in an abnormal conformation of the pericentric region and impairs the correction of tensionless chromosome attachments . Moreover , we found that shugoshin is required for maintaining Aurora B/Ipl1 localization on kinetochores during metaphase . Thus , shugoshin has a dual function in promoting biorientation in budding yeast: first , by its ability to facilitate condensin recruitment it modulates the conformation of the pericentric chromatin . Second , shugoshin contributes to the maintenance of Aurora B/Ipl1 at the kinetochore during gradual establishment of bipolarity in budding yeast mitosis . Our findings identify shugoshin as a versatile molecular adaptor that governs chromosome biorientation .
Accurate chromosome segregation into daughter cells requires the formation of correct chromosome attachments to the mitotic spindle . Each of the sister kinetochores has to attach to microtubules ( MTs ) emanating from opposite spindle poles in order to achieve biorientation of sister chromatids . Correctly attached and bioriented kinetochores ( KTs ) generate tension caused by attached MTs , which is , in turn , opposed by sister chromatid cohesion [1] . Consequently , biorientation is achieved by stabilization of correct bipolar attachments that generate tension and continuous dissolution of incorrect , tensionless attachments [2] , [3] . The spindle assembly checkpoint ( SAC ) recognizes KTs lacking attachments and halts the progression from metaphase to anaphase until all pairs of sister chromatids become bioriented . How exactly cells recognize erroneous tensionless attachments is not well understood . The chromosome passenger complex ( CPC ) consisting of the conserved kinase Aurora B/Ipl1 , Incenp/Sli15 , Survivin/Bir1 , and Borealin/Nbl1 plays a crucial role in dissolving faulty connections by phosphorylating multiple substrates at the MT-KT interface , thereby creating unattached KTs leading , in turn , to SAC activation ( reviewed in [1] ) . Other proteins , most prominently the conserved shugoshin/MEI-S322 family of proteins , have been proposed to facilitate the establishment of biorientation by promoting the correction of tensionless attachments [4]–[7] . Shugoshins recruit the heterotrimeric protein phosphatase PP2A-B56/Rts1 to the centromere via their conserved N-terminal coiled-coil domain to ensure the protection of cohesion during meiosis and in many organisms also in mitosis [8]–[11] . Besides this canonical function , shugoshin also affects Aurora B/Ipl1 kinase in mitosis by facilitating its centromeric localization in fission yeast [5] , [12] . Moreover , Aurora B kinase activity in Xenopus egg extracts is impaired upon depletion of Sgo2 [13] . However , the exact function of shugoshin and PP2A in tension sensing and the establishment of biorientation remains unclear . Kinetochores , large proteinaceous complexes assembled on centromeric DNA , are crucial interaction hubs for events necessary for accurate chromosome segregation . KTs are flexible and dynamic structures that become stretched when MTs attach and bipolarity is achieved [14] , [15] . Furthermore , centromeric and pericentric chromatin creates a flexible spring-like filament that is responsive to the tension exerted by MT-mediated pulling forces of the spindle [16] , [17] . This elasticity is ensured by the concerted activity of several multi-protein complexes including cohesin and condensin that bind and organize the pericentric chromatin into inter- and intramolecular loops ( reviewed in [18] ) . Interestingly , mutants that are not able to maintain the functional organization of the pericentric region fail to create bioriented MT-KT attachments during mitosis [19]–[21] . Cohesin has been shown to control KT geometry [22] and condensin in C . elegans is required for centromere resolution [23] . Moreover , depletion of condensin I suppresses KT stretching in HeLa cells , thereby causing a SAC-mediated mitotic delay [15] . Taken together , cohesin and condensin contribute to the architecture and elastic properties of pericentric chromatin . However , whether the pericentric architecture modulates CPC activity or localization in response to lack of tension on sister KTs and how it affects the turnover of erroneous MT-KT attachments has not been clarified so far . Here , we show that Sgo1 function is essential for the recruitment of condensin to the pericentric region . Moreover , Sgo1 localizes the PP2A subunit Rts1 to the same region , which facilitates the binding of condensin to centromeric chromatin . The failure to load functional condensin onto pericentric chromatin impairs the cellular response to tensionless attachments . Additionally , Sgo1 is required for the maintenance of the Ipl1 kinase on centromeres , which , in turn , allows for correction of erroneous MT-KT attachments . We propose that Sgo1 is a scaffold protein that via spatio-temporal modulation of kinase and phosphatase activity on KTs ensures a dual function - organization of the pericentric chromatin that mediates tension sensing and the maintenance of centromeric Ipl1 that facilitates correction of tensionless attachments .
Shugoshin proteins interact with PP2A-B56/Rts1 via their conserved N-terminal coiled-coil domains [8]–[11] . Earlier studies in higher eukaryotes and budding yeast have shown that substitution of a highly conserved asparagine residue within the N-terminus of Sgo1 abolishes the interaction with PP2A while retaining other functions of the protein [11] . We introduced this substitution ( N51I ) into budding yeast Sgo1 in order to study the role of the Sgo1-PP2A interaction during mitosis . As expected , the N51I mutation abrogated the interaction between the recombinantly purified N-terminus of Sgo1 and PP2A-Rts1 complexes purified from yeast mitotic lysates ( Figure S1A ) , but did not affect the proper localization of the mutant protein to the centromeric region during mitosis ( Figure 1A ) . PP2A is a ubiquitously expressed serine/threonine phosphatase with a broad substrate specificity [24] . It consists of a dimeric catalytic core that interacts with various regulatory subunits to promote diverse cellular functions . We found that the binding to Sgo1 is specific for PP2A complexes containing the Rts1 regulatory subunit , as we did not observe any specific interaction between Sgo1 and PP2A containing the regulatory subunit B55/Cdc55 ( Figure S1B ) . Rts1 localizes diffusely to the cytoplasm and nucleus , but it becomes enriched specifically at the centromeric region in pre-anaphase cells ( Figure 1B , [25] ) . Replacement of wild type SGO1 with the sgo1-N51I allele resulted in the reduction of the Rts1-GFP signal intensity between the spindle pole bodies ( SPBs ) ( Figure 1B ) . Chromatin immunoprecipitation ( ChIP ) of Rts1-FLAG confirmed that , in comparison to a control locus , Rts1 is enriched at centromeres and this enrichment depends on the presence of Sgo1 in mitotic cells ( Figure S1C ) , similarly as previously shown in budding yeast meiosis [26] . To further elucidate the interplay between Sgo1 and the two forms of PP2A , we analyzed genetic interactions among respective mutants . We found that the sgo1Δ rts1Δ double mutant shows a comparable growth defect as sgo1Δ alone and the sgo1-N51I rts1Δ mutant has a milder phenotype resembling rts1Δ . In contrast , we observed an additive growth defect of the sgo1Δ cdc55Δ double deletion that was only slightly improved by introduction of the sgo1-N51I allele ( Figure S1D ) . Thus , protein interaction and localization analyses as well as genetic evidence suggest that Sgo1 interacts with PP2A-Rts1 ( and not with PP2A-Cdc55 ) via its N-terminal coiled-coil domain . Loss of Sgo1 or introduction of the sgo1-N51I mutation causes sensitivity to the microtubule-destabilizing drug benomyl , indicating that the recruitment of PP2A-Rts1 is crucial for Sgo1's functions in mitosis ( Figure 1E , S2A , B ) . Importantly , this sensitivity was alleviated by artificially tethering Rts1 to the KT via fusion to the inner kinetochore protein Mtw1 ( Figure 1C ) . Remarkably , artificial tethering of the second PP2A regulatory subunit Cdc55 partially rescued the benomyl sensitivity of sgo1Δ cells as well ( Figure 1C ) , whereas the control Mtw1-GFP fusion protein did not affect the benomyl sensitivity of sgo1Δ cells ( Figure S2C , D ) . Also , a fusion protein of mutant Sgo1-N51I and Cdc55 suppressed the sensitivity of sgo1Δ cells ( Figure S2E ) . The observed rescuing effects cannot be explained by increased levels of PP2A regulatory subunits , because massive overexpression of Rts1 or Cdc55 by itself did not restore the growth of sgo1Δ cells on plates containing benomyl ( Figure S2F , G ) . These observations suggest that the localization of PP2A complexes at the centromere is required for correct chromosome segregation in the presence of microtubule poisons . By visualizing TetO arrays integrated 1 kb away from the centromere on chromosome 4 [27] , we determined that loss of the centromeric Rts1 pool increases the frequency of segregation errors ( Figure S3A ) . We found that 86% of wild type cells segregated chromosome 4 correctly into the daughter cells within 75 min after release from nocodazole arrest which elevates the rates of syntelic , tensionless MT-KT attachments ( Figure S3A , B ) . In contrast , only about 50% of cells expressing the Sgo1-N51I mutant correctly segregated chromosome 4 after release from nocodazole-induced cell cycle arrest ( Figure S3A , B ) . Similar results were obtained with cells lacking Rts1 ( Figure S3A , B ) . The elevated frequency of chromosome missegregation in rts1Δ strains was rescued to the wild type level upon genetic complementation with a vector carrying RTS1 under the control of its endogenous promoter ( Figure S3B ) . This finding supports the notion that Sgo1-dependent Rts1 localization to centromeres is important for the correction of erroneous MT-KT attachments . The lack of Sgo1 or Rts1 leads to a failure to halt cell cycle progression upon loss of functional sister chromatid cohesion resulting in tensionless MT-KT attachments ( Figure 1D , [4] , [11] ) . In contrast , the SAC response in the presence of the microtubule depolymerizing drug nocodazole that creates unattached KTs is fully preserved ( Figure S3C , [4] ) . To further elaborate the role of Sgo1/PP2A-Rts1 in the repair of tensionless MT-KT attachments , we took advantage of a recently developed genetic tool that promotes the formation of syntelic attachments at high frequencies by overexpression of the coiled-coil domain ( Cik1-cc; amino acids 81-360 ) of the kinesin co-factor Cik1 [28] . Importantly , overexpression of Cik1-cc does not impair spindle geometry or localization of kinetochore proteins [28] . Galactose-induced overexpression of Cik1-cc is lethal in cells lacking Sgo1 ( Figure 1E , [28] ) . The presence of Rts1 on centromeres also contributes to correct chromosome segregation , because cells lacking the Sgo1-Rts1 interaction or Rts1 alone cannot proliferate in conditions in which syntelic attachments are created at high frequencies ( Figure 1E ) . Similarly , both rts1Δ and the sgo1-N51I mutant showed increased sensitivity to microtubule depolymerizing drugs ( Figure 1E , S2A ) . Taken together , our results show that both Sgo1 and PP2A activities at the centromere are important for accurate chromosome segregation and play a critical role in inducing a cell cycle delay upon formation of tensionless , syntelic attachments . Yet , the finding that the sgo1Δ strain shows a stronger phenotype than rts1Δ and sgo1-N51I shows that Sgo1 performs both Rts1-dependent and Rts1-independent functions during chromosome segregation . The response to tension across sister KTs is mediated by the structural integrity of pericentric chromatin that is maintained by cohesin and condensin complexes [16] , [29] , [30] . Therefore , we speculated that the defect observed in sgo1Δ and rts1Δ cells might result from an altered centromeric architecture . Since localization of cohesin and sister chromatid cohesion are not affected by deletion of SGO1 in budding yeast mitosis ( Figure S4A , B , C , [31] , [32] ) , we hypothesized that Sgo1 might affect the centromeric localization of condensin . Indeed , we found that whereas the non-SMC condensin subunit Ycg1-GFP was enriched between SPBs in mitotic wild type cells , cells lacking Sgo1 or Rts1 failed to enrich condensin ( Figure 2A , B ) . In contrast , condensin localization was not impaired by deletion of the second PP2A regulatory subunit Cdc55 ( Figure 2A , B ) . Similarly , the centromeric localization of another condensin subunit , Smc2-GFP , was severely decreased in the absence of Sgo1 or Rts1 ( Figure S4D , E ) . Condensin is also highly enriched at rDNA repeats , which can be observed as a characteristic crescent-shaped sub-nuclear signal by imaging of GFP-labelled condensin subunits [33] . This localization was not affected in any of the analyzed mutants ( Figure 2A , S4D ) . ChIP analysis confirmed that the lack of Sgo1 did not impair the enrichment of condensin on the rDNA locus , but the centromeric and pericentric pools of FLAG-tagged Smc2 were reduced in nocodazole-arrested cells lacking Sgo1 to 30% of the wild type level ( Figure 2C ) . The reduced levels of Smc2 at centromeric regions were also observed in rts1Δ cells by ChIP-qPCR experiments , although to a lesser degree in comparison to sgo1Δ ( 58% of the wild type level; Figure 2D ) . Condensin co-localizes on the rDNA with Lrs4 and Csm1 and its recruitment to rDNA strictly depends on these proteins [34] . Whereas we observed Csm1-GFP to localize to rDNA throughout the cell cycle as previously reported [34] , no enrichment was detected between SPBs in pre-anaphase cells ( Figure S5A ) . Our results show that condensin is localized to the pericentric region via a Sgo1/PP2A-Rts1-dependent pathway and that this localization is independent of condensin's association with rDNA . Previously , it was shown that the lack of condensin reduces the ability to withstand the outward-directed forces of the mitotic spindle and leads to extensive centromeric stretching [16] . The finding that Sgo1 and Rts1 recruit condensin to the centromere predicts that the conformation of centromeres might be altered in sgo1Δ and rts1Δ cells . We therefore analyzed the compaction state of centromeric chromatin of chromosome 4 . In wild type cells , most of the centromeres appear as a single spot that slightly separates during the process of biorientation ( also called “kinetochore breathing” ) and only 10% of cells contained stretched centromeres ( Figure 2E , [16] , [27] ) . In contrast , cells lacking SGO1 or RTS1 or carrying the sgo1-N51I allele contained up to 40% of stretched centromeres ( Figure 2E ) . Since the absence of CDC55 did not affect centromeric stretching ( Figure 2E ) , we conclude that the centromeric localization of condensin is facilitated by Sgo1 acting in collaboration with Rts1 . Thus , the lack of Sgo1 or Rts1 affects centromeric conformation similarly as the lack of condensin , further strengthening our findings that Sgo1 and to a lesser degree also PP2A-Rts1 are needed for the maintenance of functional condensin on centromeric chromatin . Since both sgo1Δ and rts1Δ cells are impaired in their response to lack of tension on sister KTs , we asked whether condensin mutations alone show a similar phenotype . The induction of syntelic attachments by Cik1-cc overexpression impaired the growth of cells carrying the temperature-sensitive condensin alleles smc2-8 or ycg1-10 even at a non-restrictive temperature , thus demonstrating that these mutants fail to recognize or repair syntelic attachments ( Figure 2F ) . To test whether the pre-anaphase centromeric functions of condensin can be separated from its anaphase functions , we used the yeast strain D1225 that expresses non-posphorylatable mutants of the condensin subunits Ycg1 , Brn1 , and Ycs4 , which interfere specifically with condensin's function in anaphase [35] . Importantly , the presence of syntelic attachments did not affect the proliferation of the D1225 mutant strain ( Figure 2F ) . Additionally , deletion of the Lrs4 and Csm1 proteins that localize condensin to rDNA , but not to the centromere , did not impair the ability of cells to repair syntelic attachments induced by overexpression of Cik1-cc ( Figure S5B ) . Thus , Sgo1/PP2A-Rts1-dependent recruitment of condensin to the pericentric region is essential for the pre-anaphase chromatin conformation and for the correction of faulty MT-KT attachments . This function of condensin can be clearly separated from its anaphase-specific function in chromosome compaction as well as from its function at repetitive rDNA sequences . To elucidate the mechanism of condensin localization to centromeres , we analyzed whether Sgo1 interacts with condensin in mitotic cells . To this end we precipitated Sgo1-TAP from yeast protein lysates . Indeed , we found that Sgo1 specifically pulls down the condensin subunit Smc2 . This interaction is abolished in cells lacking Rts1 , further strengthening the notion that Rts1 aids Sgo1 in condensin recruitment to the centromere ( Figure 3A ) . The finding that Rts1 is important for the pulldown of Smc2 with Sgo1-TAP prompted us to ask whether the phosphatase activity of PP2A-Rts1 plays a role in the recruitment of condensin to centromeres . Therefore , we treated cells with okadaic acid ( OKA ) , a potent inhibitor of the phosphatase activity of PP2A and analyzed its influence on condensin localization to centromeres in wild type cells . We found that the centromeric recruitment of the condensin subunit Ycg1-GFP is insensitive to treatment with OKA ( Figure 3B , C ) . To exclude that this result is due to the low efficacy of the inhibitor in vivo , we analyzed the anaphase localization of Kin4 , a well characterized target of PP2A-Rts1 [36] , [37] . These experiments showed that the same concentration of OKA that did not affect Ycg1 localization efficiently delocalized Kin4 from the mother SPB within 30 min ( Figure S6A , B ) . Similar observations were previously reported for Xenopus egg extracts and HeLa cells , where PP2A physically interacts with condensin II and recruits it to chromosomes independently of its phosphatase activity [38] . Taken together , our results suggest that Sgo1 and PP2A-Rts1 mediate condensin enrichment at the pericentric region . The recruitment of condensin does not depend on the phosphatase activity of PP2A , but rather relies on a physical interaction with Sgo1 supported by Rts1 . Recent high-throughput analysis revealed that condensin is required for the correct localization of the conserved kinase Aurora B/Ipl1 during metaphase and anaphase [39] . Since Sgo1 and PP2A-Rts1 are essential for condensin localization , we would expect a defective Aurora B/Ipl1 localization in sgo1Δ and rts1Δ mutants as well . Visualization of Ipl1-GFP revealed that the initial recruitment of the kinase to the KTs ( before separation of the SPBs ) was comparable in both wild type and sgo1Δ mutant cells ( Figure S7A ) . In contrast , we observed a marked difference in Ipl1 localization at pre-anaphase spindles . Ipl1 localizes exclusively to centromeres and a diffuse nuclear signal was only rarely observed in wild type pre-anaphase cells ( Figure 4A , B ) , which is in agreement with previous observations ( e . g . [40] [41] ) . Careful analysis revealed that whereas cells with very short pre-anaphase spindles localize Ipl1-GFP between the SPBs , the cells with a diffuse nuclear localization of Ipl1-GFP had on average longer spindles ( Figure S7B ) . In the absence of Sgo1 , 57 . 8% of cells exhibited a diffuse nuclear signal of Ipl1-GFP , pointing to a defect in maintaining Ipl1 at the centromere ( Figure 4A , B ) . Moreover , the correlation between spindle length and Ipl1-GFP localization was lost in sgo1Δ cells ( Figure S7B ) . Ipl1 localization was also altered in cells lacking Rts1 or carrying the sgo1-N51I allele , although the phenotype was milder than in the cells lacking Sgo1 ( 25 . 0% and 31 . 2% of cells with diffuse Ipl1 signal , respectively; Figure 4A , B ) . No changes were observed in cells lacking Cdc55 ( Figure 4A , B ) . ChIP analysis in cells arrested by nocodazole treatment showed that the levels of centromeric and pericentric Ipl1-FLAG were diminished in sgo1Δ cells to 52% of the wild type levels ( Figure 4C ) . In contrast , the lack of RTS1 did not considerably impair Ipl1-FLAG recruitment to centromeres ( Figure 4D ) . This indicates that Sgo1 might contribute to the maintenance of centromeric Ipl1 independently of Rts1 recruitment , or that , for technical reasons , the effect of PP2A-Rts1 is difficult to detect in nocodazole-arrested cells . Based on these observations we propose that Sgo1 is dispensable for the initial recruitment of Ipl1 , but becomes essential for maintaining the centromeric localization of Ipl1 during the establishment of biorientation in budding yeast . Next , we asked whether impairing the centromeric condensin pool leads to a more general defect in mitotic cells affecting the protein occupancy of the centromeric region , perhaps due to an altered DNA conformation . To test this possibility , we analysed strains carrying the temperature-sensitive condensin allele smc2-8 . This mutation leads to a dramatic delocalization of Ycg1-GFP from centromeres at the non-permissive temperature ( Figure S8A ) . However , we observed that Sgo1 and Rts1 localization was only marginally affected by the smc2-8 mutation ( Figure S8B , C ) . Markedly , almost 40% of pre-anaphase spindles showed a diffuse Ipl1-GFP localization ( Figure S8D ) . This suggests that condensin may contribute to the maintenance of Ipl1 on centromeres . The data presented above suggest a linear pathway where Sgo1 , PP2A and condensin cooperate to ensure the maintenance of Ipl1 activity at the centromere . At the same time , we observed one marked difference: whereas condensin localization requires both Sgo1 and to a lesser degree also Rts1 function , the localization of Ipl1 depends only on Sgo1 . To further explore the functional interactions , we exploited the finding that the CPC members Bir1 and Sli15 are high-copy-number suppressors of the benomyl sensitivity and ploidy-specific lethality of sgo1Δ mutants [41] , [42] . We asked whether the overexpression of CPC subunits would restore the reduced levels of Ipl1 on the centromere in cells lacking centromeric Rts1 . Indeed , Ipl1-GFP localization between the SPBs at pre-anaphase spindles was completely rescued by overexpression of Sli15 or Bir1 ( Figure 5A , B ) . We next tested whether the restored localization of Ipl1 eliminates the defect in correction of tensionless MT-KT attachments in sgo1Δ and rts1Δ cells . As previously observed , overexpression of Sli15 partially rescued the growth of sgo1Δ in the presence of microtubule-depolymerizing drugs , but we found that the growth defect of rts1Δ cells was not improved ( Figure 5C , [41] ) . We suggest that this difference reflects the different roles of Sgo1 and Rts1 in the maintenance of Ipl1 on centromeric DNA . Additionally , we found that the overexpression of Sli15 cannot rescue the exquisite sensitivity of sgo1Δ and rts1Δ mutants to syntelic attachments induced by Cik1-cc overexpression ( Figure 5C ) . Thus , although the increased abundance of Sli15 or Bir1 increases the pool of Ipl1 localized between the SPBs , this is not sufficient to repair syntelic attachments when they are generated at high frequencies . To determine why cells that localize Ipl1 properly , but lack centromeric Sgo1 , cannot correct syntelic attachments , we analyzed the localization of the GFP-tagged condensin subunit Ycg1 in sgo1Δ cells overexpressing either Sli15 or Bir1 . Importantly , we observed that the overexpression of CPC subunits did not restore centromeric condensin localization in sgo1Δ mutants ( Figure 5D , E ) . From these results we conclude that the two functions of Sgo1 at the centromere – maintenance of the Ipl1 kinase at the MT-KT interface and providing flexibility to the pericentric region by condensin recruitment – are at least partially independent ( Figure 6 ) . Our findings illustrate that Sgo1 coordinates two essential functions at the centromere in order to facilitate chromosome biorientation during mitosis .
Here , we clarify the function of the Sgo1/PP2A-Rts1 interaction during the establishment of bioriented MT-KT attachments in budding yeast mitosis . We show that the Sgo1-dependent recruitment of PP2A-Rts1 is required for efficient localization of condensin to the centromere and that Sgo1 ensures the maintenance of centromeric Aurora B/Ipl1 . Moreover , Sgo1 pulls down condensin in the presence of Rts1 . Importantly , centromeric enrichment of both condensin and Aurora B/Ipl1 are essential for correct chromosome segregation . Our results are in agreement with the recent finding that shugoshin facilitates chromosome biorientation via condensin recruitment [43] and together suggest that Sgo1 serves as a hub protein that coordinates the molecular activities required for the biorientation of sister chromatids . Budding yeast Sgo1 , as all other members of the shugoshin protein family analyzed so far , interacts with the B56 regulatory subunit of protein phosphatase PP2A ( Rts1 in budding yeast ) via its N-terminal coiled-coil domain [8]–[11] . This interaction is crucial for shugoshin-mediated protection of centromeric cohesin from cleavage by separase during meiosis I and from phosphorylation-mediated removal during mitosis in vertebrates ( reviewed in [44] ) . Yet , several lines of evidence suggest that in budding yeast , Sgo1 together with PP2A facilitates the establishment of biorientation , but by a mechanism independent of cohesin regulation . First , although lack of Sgo1 does not affect cohesion in budding yeast mitosis , the cells fail to recognize and correct improperly attached sister chromatids ( Figure 1D , E , S4A , B , C , [4] , [7] , [41] ) . Moreover , cells carrying a mutation that impairs the Sgo1-Rts1 interaction fail to arrest in the presence of tensionless attachments induced by depletion of cohesin ( Figure 1D , [11] ) . Additionally , cells lacking RTS1 or carrying mutations that impair the Sgo1-PP2A interaction show a marked sensitivity to microtubule depolymerizing drugs ( Figure 1E , S2 , [11] ) . We also observed that lack of Rts1 leads to defects in chromosome segregation ( Figure S3A , B ) . Very recently , it was reported that Rts1 is not required for establishment of chromosome biorientation in cells arrested in metaphase by depletion of the anaphase regulator Cdc20 , although a Sgo1 mutant that has lost the ability to interact with Rts1 showed a strong phenotype [43] . This result differs from our findings that Rts1 and the Sgo1-Rts1 interaction are required for correct chromosome segregation . Thus , future experiments should focus on dissecting the functions of Sgo1 and PP2A-Rts1 during the establishment of biorientation by identification of additional separation-of-function alleles . The correct conformation of pericentric chromatin is essential for bioriented MT-KT attachments because it provides the rigidity and at the same time elasticity necessary for chromatin to withstand the opposing forces exerted by spindle MTs [17] . The inactivation of either cohesin or condensin results in the loss of tension across KTs and in defective turnover of syntelic attachments [30] , [45] . We speculated that Sgo1 together with PP2A-Rts1 might affect the conformation of pericentric chromatin during metaphase , possibly by affecting loading of condensin onto this region . An increasing body of evidence supports this idea . First , condensin , similarly to cohesin , is a member of the SMC ( structural maintenance of chromosomes ) protein family and is , among others , required for the structure and organization of pericentric regions [18] . Second , condensin has been implicated in tension sensing and lack of condensin abolishes the conformational changes upon tension [16] , [19] , [20] . Finally , the ability of the inner KT to undergo conformational changes in response to altered tension is impaired in yeast cells lacking either Sgo1 , or Bub1 , a mitotic kinase essential for Sgo1 localization [21] . Here we demonstrate that Sgo1 and to a lesser degree also Rts1 are required for the localization of condensin specifically to centromeric and pericentric regions , but not to rDNA ( Figure 2A , B , C , D , S4D , E ) . Importantly , cells with defective condensin cannot proliferate in the presence of microtubule poisons or when syntelic attachments are formed at high frequencies due to Cik1-cc overexpression and this condensin function can be separated from its anaphase functions in chromosome hypercondensation ( Figure 2E ) . We hypothesized that PP2A might inhibit the phosphorylation of condensin to prevent its premature removal , similarly as was observed for cohesin molecules [44] . However , the localization of condensin is resistant to treatment with okadaic acid , a potent inhibitor of PP2A , suggesting that phosphatase activity is not required ( Figure 3B , C ) . This is in agreement with studies in Xenopus egg extracts and HeLa cells , where recruitment of condensin II to chromosomes relies on the presence of PP2A independently of its phosphatase activity [38] . Yet , unlike yeast condensin , the localization of condensin II in higher eukaryotes is affected by the presence of okadaic acid; only the use of a catalytically inactive but correctly localized PP2A mutant revealed that chromosomal association of PP2A , but not its phosphatase activity , is essential for the targeting of condensin II to chromatin in Xenopus [38] . The difference might be explained by the fact that centromeric PP2A-Rts1 is recruited through an interaction with Sgo1 , whereas Takomoto and colleagues analyzed the recruitment of condensin II to chromatin along entire chromosomes . Future research should clarify the nature of the interaction between Sgo1 and condensin as well as the role of Rts1 in condensin recruitment . What is the function of condensin on centromeres ? One possibility is that condensin maintains the conformation of centromeric regions , thereby facilitating the intrinsic bias of budding yeast KTs to biorient on the mitotic spindle [6] . This model would predict that cells lacking centromeric condensin should create monooriented attachments more often than wild type cells . However , cells lacking Sgo1 ( and hence centromeric condensin ) do not show any defect in the intrinsic bias of sister kinetochores to biorient [6] . Therefore , we suggest that the inability to localize centromeric condensin impairs error sensing and correction rather than contributing to the generation of more erroneous attachements . We considered two possible roles for condensin in the correction of tensionless attachments . First , condensin might be required to facilitate the centromeric localization of Aurora B/Ipl1 . Indeed , recently it has been shown that metaphase centromeres and KTs become deformed and Aurora B is mislocalized upon depletion of either condensin I or II in vertebrate cells [20] . Similarly , budding yeast lacking condensin fail to localize Ipl1 properly to the centromere during metaphase as well as to the spindle during anaphase [39] . This finding is in agreement with our observation that the absence of Sgo1 ( and hence the absence of centromeric condensin ) alters the localization of Ipl1 on centromeres during the establishment of biorientation . These results might imply a linear pathway where Sgo1 recruits PP2A , which , in turn , by recruiting condensin facilitates localization of Ipl1 to the centromere . Several important observations cannot be fully reconciled with this hypothesis . First , whereas full centromeric localization of condensin requires the presence of Rts1 , localization of Ipl1 likely does not ( compare Figure 2 and Figure 4 ) . Second , although the overexpression of the CPC subunits Bir1 or Sli15 partially rescues the segregation defects and benomyl sensitivity of sgo1Δ cells and centromeric localization of Ipl1 in rts1Δ cells ( Figure 5A , B , C , [41] ) , it does not affect the localization of condensin , nor the sensitivity of sgo1Δ and rts1Δ strains to Cik1-cc overexpression ( Figure 5C , D , E ) . Additionally , deletion of SGO1 and mutations in condensin subunits are synthetically lethal , suggesting that they have non-overlapping functions [30] . Thus , although the functions of condensin and Ipl1 at the KT are closely linked , they are not arranged in a single linear pathway ( Figure 6 ) . We favor a model in which condensin is mainly required to establish the centromeric conformation that allows the generation of tension across bioriented KTs . Recent results suggest that condensin contributes to tension sensing by maintaining stiff , but flexible chromatin structure , which further supports our model [16] . In the wild type scenario , the majority of cells with very short spindles show an enrichment of Ipl1 between the SPBs , but with increased spindle length more cells with diffuse nuclear Ipl1 can be observed . One possible interpretation of this observation is that once tension is established , Ipl1 becomes more dynamic and is eventually released from the bioriented KTs . This is in agreement with the observation that Ipl1-GFP is often delocalized from centromeres in cells arrested in metaphase by Cdc20 depletion [40] , [46] . The dynamics of Ipl1/Aurora B localization on centromeres is likely regulated by phosphorylation . A previous report demonstrated that shugoshin proteins from S . pombe and human cells interact with CPC subunits and this interaction depends on phosphorylation by Cdk1 [47] . Moreover , Ipl1 co-precipitates during mitosis with Sgo1 in budding yeast . Interestingly , this co-precipitation as well as centromeric Ipl1 localization was impaired in a strain carrying the mutant allele sgo1-3A which interferes with the Sgo1-Rts1 interaction [43] . Finally , Ipl1 is phosphorylated by the cyclin-dependent kinase Cdc28 upon anaphase onset; this phosphorylation prompts the binding of Ipl1 to Bim1 , a microtubule plus-end tracking protein [46] . In the future , it should be determined whether Sgo1 affects the Cdc28-dependent phosphorylation of Aurora B/Ipl1 or other CPC subunits , thereby regulating their dynamics on the centromere during mitosis . Additionally , the role of protein phosphatases in this process remains to be characterized . The centromeric localization and activity of Ipl1 in pre-anaphase cells is regulated by several factors . First , defective condensin impairs the localization of Ipl1 ( but not vice versa [39] ) and it has been shown that condensin subunits interact with Bir1 , a member of the CPC [39] . Second , the overexpression of Bir1 or Sli15 localizes Ipl1 to the region between the SPBs in the absence of Rts1 ( Figure 5A , B ) . Although we do not understand why Ipl1 localization is restored and whether under these conditions Ipl1 localizes exactly as in wild type cells , it has recently been postulated that Aurora B is tethered to the centromeric chromatin within the inner KT by Incenp/Sli15 [48] . The lack of Sgo1 also impairs the maintenance of Ipl1 localization during metaphase ( Figure 4 ) . This is in line with previous data that shugoshin proteins from human cells , fission and budding yeasts interact with the CPC , thereby contributing to the localization of Aurora B/Ipl1 at the pericentromere [43] , [47] . Taken together , these results suggest the intriguing possibility that Sgo1 maintains centromeric localization of the CPC via a direct interaction ( Figure 6 ) . Lastly , the initial recruitment of Ipl1 to the centromere is independent of Sgo1 ( Figure S7A , [41] , [43] ) , indicating the existence of another recruiting mechanism , likely via the interaction of the CPC protein Bir1 with the KT protein Ndc10 [49] , [50] . The fact that Aurora B/Ipl1 localization is regulated by several different mechanisms in both yeast and higher eukaryotes indicates that the CPC needs to be positioned correctly and in a timely manner in order to perform its functions . Yet , recent data suggest that even mislocalized Ipl1 can efficiently correct erroneous MT-KT attachments [51] . Further research should elucidate the exact spatio-temporal coordination of the molecular events during the establishment of biorientation . Our data show that Sgo1 is an important player during this event , serving as a hub modulating centromeric conformation and integrating phosphatase and kinase activities at the metaphase spindle .
All yeast strains used in this study are listed in Supporting Table S1 and derived from the genetic background of W303 ( leu2-3 , 112 trp1-1 can1-100 ura3-1 ade2-1 his3-11 , 15 ) or BY4741 ( his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 ) . Gene deletions and epitope-tagging were performed using standard PCR-based methods . Cells were grown in either full ( YP; 1% yeast extract , 2% Bacto-Peptone ) or synthetic complete ( SC; 1 . 34% yeast nitrogen base , 0 . 04% complete synthetic mix ) medium supplemented with 2% glucose ( YPD ) , 2% raffinose ( YPR ) or 2% galactose ( YPG ) . Cells were arrested in G1 using 10 µM α-factor ( Core Facility , Max Planck Institute of Biochemistry , Martinsried , Germany ) or in mitosis using 30 µg/ml nocodazole ( Santa Cruz Biotechnology ) . Mutants were grown at 25 °C to reduce chromosome missegregation . For viability assays cells were grown overnight in full medium , diluted to an OD600 of 0 . 3 and tenfold serial dilutions were spotted on YPD/YPG plates or on SC plates containing indicated concentrations of benomyl and nocodazole , respectively . The phosphatase inhibitor okadaic acid was used at concentrations of 10 or 20 µM as stated for the individual experiments . All plasmids used in this study are listed in Supporting Table S2 . Construction of plasmids was performed as described previously using standard cloning procedures . A fragment encoding amino acids 81–360 of Cik1 was cloned into the multiple cloning site ( MCS ) of pRS406 containing the inducible GAL1 promoter with a C-terminal TAP- or GFP-tag . SGO1 was cloned with its endogenous promoter into pRS405 with a C-terminal TAP-tag . Plasmids encoding the Mtw1-fusion proteins were constructed by cloning MTW1 with its endogenous promoter adjacent to the corresponding gene into the MCS of pRS405 . Chromosomal integration of plasmids encoding Sgo1- or Mtw1-fusion proteins was targeted into the endogenous locus using restriction enzymes cutting in the respective promoter region . Point mutations were introduced using the PCR-based Quick Change ( Stratagene ) site-directed mutagenesis approach . Protein extracts of S . cerevisiae were prepared either by glass bead lysis or alkaline lysis followed by TCA precipitation . Proteins were separated by SDS-PAGE , transferred to PVDF or nitrocellulose membranes and detected using antibodies according to standard protocols . Commercially available antibodies were used to detect individual proteins: myc ( 9E10 , Santa Cruz Biotechnology ) , HA ( Y-11 , Santa Cruz Biotechnology ) , PAP ( Sigma-Aldrich ) , Clb2 ( y-180 , Santa Cruz Biotechnology ) , Pgk1 ( Invitrogen ) . Protein extracts from S . cerevisiae were prepared by glass bead lysis in a lysis buffer ( 10 mM Hepes , 200 mM KCl , 1 mM MgCl2 , 1 mM DTT , 1 mM PMSF , 0 . 5% Triton X-100 , supplemented with Roche Protease Inhibitor Mix ) followed by centrifugation ( 100000 g , 45 min , 4°C ) . Sgo1-TAP was purified by incubation of approx . 3 . 5 mg protein extract with pre-washed calmodulin-coated beads ( GE Healthcare ) . Beads were washed using lysis buffer containing increasing salt concentrations ( up to 600 mM KCl ) and finally eluted in the presence of 5 mM EGTA . Proteins were precipitated with TCA and co-purified proteins were analyzed by SDS-PAGE followed by immunoblotting . Heterotrimeric PP2A complexes were purified from mitotic lysates of S . cerevisiae via Rts1- or Cdc55-TAP fusion proteins using calmodulin-coupled agarose as described above . Equal amounts of PP2A complexes were incubated with recombinant Ni-NTA agarose-bound His-Sgo1 fragments containing the conserved coiled-coil domain ( amino acids 1–340 ) in a pulldown buffer ( 50 mM Tris , 300 mM NaCl , 10 mM imidazole , 1 mM PMSF supplemented with Roche protease inhibitor mix ) for 2 hours . Proteins were eluted by boiling in SDS sample buffer and subjected to SDS-PAGE and immunoblotting to detect the bound PP2A subunits . Images of cells were obtained using a fully automated Zeiss inverted microscope ( AxioObserver Z1 ) equipped with a MS-2000 stage ( Applied Scientific Instrumentation , USA ) , a CSU-X1 spinning disk confocal head ( Yokogawa , Herrsching ) , LaserStack Launch with selectable laser lines ( Intelligent Imaging Innovations , USA ) and an X-CITE Fluorescent Illumination System . Images were captured using a CoolSnap HQ camera ( Roper Scientific , Canada ) under the control of the Slidebook software ( Intelligent Imaging Innovations , USA ) . All fluorescence signals were imaged with a 63x oil objective . A total of 10 z-stacks were collected and each optical section was 0 . 4 µm thick . Projected images were used for display . Only cells with spindles shorter than 2 µm and fully localized in the mother cell were scored to evaluate the localization in pre-anaphase . For Ipl1-GFP microscopy we defined three types of Ipl1-GFP localization: “centromeric” – fluorescence signal only in the area between the SPBs , “partially diffuse” – diffuse nuclear fluorescence signal and increased fluorescence intensity between the SPBs and “diffuse” – only diffuse nuclear fluorescence signal . Chromatin immunoprecipitation ( ChIP ) was performed as previously described [52] . In brief , 100 ml of exponentially growing yeast cultures were arrested with 20 µg/ml nocodazole for 3 h at room temperature and subsequently cross-linked with formaldehyde at a final concentration of 1% . The cross-linking reaction was stopped with glycine; the cells were harvested and lysed using Silica beads . Chromatin was sheared to 300–500 bp fragments by water-bath sonification ( Bioruptor UCD-200 , Diagenode ) . FLAG-tagged proteins were immunoprecipitated using the monoclonal ANTI-FLAG antibody coupled to superparamagnetic beads ( Sigma-Aldrich , M8823 ) . DNA was recovered by phenol/chloroform extraction followed by ethanol precipitation . Quantitative RT-PCR was performed using the Light Cycler LC480 system ( Roche ) to evaluate the enrichment of analyzed proteins . The ratio of DNA-IP to DNA-Input was calculated for centromeric/pericentromeric regions ( 0 . 1 kb away from CEN1 , 1 . 1 kb away from CEN4 and 5 kb away from CEN12 ) as well as for the rDNA locus ( NTS1-2 ) . The relative enrichment was calculated by normalization to the IP/Input ratio for a control locus on the arm of chromosome 10 . Three independent immunoprecipitation experiments from metaphase-arrested cells were performed for FLAG-tagged Smc2 and Ipl1; Rts1 and Mcd1 ChIP experiments were performed twice . | Accurate chromosome segregation is required for the equal distribution of genetic information to progeny . Failure to equally segregate chromosomes leads to aneuploidy , cell death or cancer . Proteins of the conserved shugoshin family contribute to accurate chromosome segregation in both meiosis and mitosis . The role of shugoshin in protection of centromeric cohesion during meiosis is well understood , but only little is known about shugoshin's function during mitosis . We show that Sgo1 mediates localization of the heterotrimeric phosphatase PP2A-Rts1 to the centromere and that this is in turn important for the efficient recruitment of condensin to the centromere . The failure to load centromeric condensin results in a defect during correction of improper microtubule-kinetochore attachments . Moreover , Sgo1 facilitates the maintenance of a centromeric pool of Aurora B/Ipl1 , a conserved mitotic kinase essential for the correction of faulty microtubule-kinetochore attachments . Our results show that Sgo1 operates as a multifunctional hub that coordinates two centromeric functions essential for correct chromosome segregation . | [
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] | 2014 | Sgo1 Regulates Both Condensin and Ipl1/Aurora B to Promote Chromosome Biorientation |
Bat echolocation is an ability consisting of many subtasks such as navigation , prey detection and object recognition . Understanding the echolocation capabilities of bats comes down to isolating the minimal set of acoustic cues needed to complete each task . For some tasks , the minimal cues have already been identified . However , while a number of possible cues have been suggested , little is known about the minimal cues supporting obstacle avoidance in echolocating bats . In this paper , we propose that the Interaural Intensity Difference ( IID ) and travel time of the first millisecond of the echo train are sufficient cues for obstacle avoidance . We describe a simple control algorithm based on the use of these cues in combination with alternating ear positions modeled after the constant frequency bat Rhinolophus rouxii . Using spatial simulations ( 2D and 3D ) , we show that simple phonotaxis can steer a bat clear from obstacles without performing a reconstruction of the 3D layout of the scene . As such , this paper presents the first computationally explicit explanation for obstacle avoidance validated in complex simulated environments . Based on additional simulations modelling the FM bat Phyllostomus discolor , we conjecture that the proposed cues can be exploited by constant frequency ( CF ) bats and frequency modulated ( FM ) bats alike . We hypothesize that using a low level yet robust cue for obstacle avoidance allows bats to comply with the hard real-time constraints of this basic behaviour .
Rhinolophidae are echolocating bats specialized in hunting for airborne prey among vegetation using echolocation . To cope with clutter echoes returning from vegetation they employ a unique sensorial strategy for detecting prey . They emit long narrow-band pulses and listen for frequency and amplitude shifts , so called glints , in the echoes caused by fluttering prey [1] . Echoes from stationary obstacles do not contain these glints and do not interfere with the detection and localization of prey [2] . While the sensorial adaptations of Rhinolophidae for prey detection have been extensively researched ( see [1] for a review ) , the cues supporting the ability of these bats to navigate and orient in cluttered environments have received much less attention . Nevertheless , their ability to navigate small spaces [3–6] and their well-studied echolocation apparatus [1 , 7] makes them an interesting taxon to study how echolocating bats avoid obstacles in natural environments . Indeed , as argued in the discussion , understanding the cues Rhinolophidae use to negotiate space is potentially informative about how other bats using frequency modulated pulses could avoid obstacles as well . It would seem that Rhinolophidae , using long narrowband signals , lack both the bandwidth and the temporal resolution available to bats using short broadband signals . Indeed , bats using broadband signals typically shorten their calls ( typically 1–3 ms [8] ) and increase the bandwidth when moving into cluttered spaces [8] . Rhinolophidae , in contrast , negotiate cluttered space using much longer ( about 10–50 ms ) and narrowband signals that seem not particularly well suited for obstacle avoidance . Indeed , while Rhinolophidae also shorten their calls and increase the bandwidth when moving into cluttered space [9 , 10] , their calls remain longer and more bandwidth limited than those of FM bats under the same conditions . The characteristic cyclical pinna movements shown by Rhinolophidae [11 , 12] have been suggested to compensate for the lack of spatial cues available to bats relying on broadband calls . Mogdans et al . [3] performed behavioural experiments to test specifically the role of these ear movements for obstacle avoidance based on Interaural Intensity Differences ( IIDs ) . The hypothesis [3 , 10] that the moving ears generate changing IIDs encoding the reflector position in both the horizontal and the vertical plane was found by these authors to be in agreement with the results from their wire-avoidance experiments and put forward as a possible explanation for the bats’ obstacle avoidance ability . Since then , simulation studies and robotic experiments have corroborated that these ear movements do indeed provide various localization cues that would allow localizing individual reflectors , such as prey items [13–15] . However , natural environments encountered by bats are typically made up of objects that consist of many stochastic reflectors returning many overlapping echoes [16] . Therefore , for 3D localization of reflectors , e . g . based on typical ear movement induced IID patterns , to be considered a plausible mechanism underlying the obstacle avoidance abilities of bats , it has to be proven first that such a localization capability is robust in the presence of multiple overlapping echoes . Hence , while it has been shown that pinnae movements play a significant role in obstacle avoidance [3] , it is still not clear what information Rhinolophidae extract from such pinna movements to allow them to avoid natural ( and complex ) obstacles . To complement behavioural experiments , we use the synthetic methodology , i . e . understanding natural systems by building artefacts [17–19] , computer simulations , in this case , to study bat obstacle avoidance behaviour . In particular , we propose a sensorimotor system that does not rely on the bat reconstructing the 3D spatial layout of reflectors from the echoes , but instead relies on the dynamics of the bat-obstacle interaction to result in obstacle avoidance behaviour . A similar approach is taken in ref . [20] for prey-catching behaviour in echolocating bats assuming that only a single reflecting target is present giving rise to a unique isolated echo . This assumption is warranted in the case of prey-catching behaviour as the bat can choose to hunt away from clutter [8] or take active measures to separate the echoes from the foreground prey item from the clutter background ones ( e . g . [21 , 22] ) . In contrast , realistic obstacles , e . g . foliage and/or man-made structures , will always give rise to multiple overlapping echoes [16] . The sensorimotor system we propose is intentionally kept as simple as possible . It uses IID and time delay of the first echo onset in combination with alternating pinna movements to guide the bat . In particular , it processes only the first millisecond of the echo train . Furthermore , it does not need the right and left ear echo signals to be segmented into contributions from individual reflectors , as would be required by any approach that reconstructs the spatial layout of the bat’s surroundings . While approaches that attempt to reconstruct the spatial layout of the environment first as a prerequisite for obstacle avoidance [23 , 24] , when successful , are clearly sufficient to explain such behaviour , we aim to show with the proposed sensorimotor system that such a reconstruction capability is not a necessary condition . The main advantage of the proposed obstacle avoidance mechanism is that because of its simplicity as well as its reliance on the first millisecond of the echo train only it can react very rapidly to the relevant information contained in an otherwise very complex echo signal consisting of many overlapping echoes . This allows the system to respond appropriately under hard real-time conditions independent of the complexity of the environment . In this paper , we first present the environments used to simulate the echoes received by a bat moving through realistic , cluttered spaces . Next , we propose a sensorimotor system that results in obstacle avoidance behaviour by extracting echo delay and IID information from the onset of the first echo in combination with alternating pinna movements . Finally , we test the performance of the sensorimotor system in simulated 2D and 3D environments showing that despite its simplicity the system can avoid obstacles in a complex environment without the need to reconstruct the 3D spatial layout of the reflectors present .
We tested the proposed sensorimotor system both in environments that were artificially generated and in environments derived from 3D laser scans of real bat habitats . Below we discuss the construction of both types of test environments . The intensity of the echo returning from each point reflector i was calculated for each call . The intensity gi ( in dB ) of the echo received from reflector i is given by the sonar equation [31] , g i = g b a t + 40 · log 10 0 . 1 r i + 2 · ( r i - 0 . 1 ) · a f + d ϕ i , p + s i + c ϕ i ( 1 ) In Eq ( 1 ) , gbat is the intensity of the call at 10 cm from the mouth , in this paper taken to be 120 dBspl [9] . The parameters ri , af , dϕi , p , si give the range to reflector i , the atmospheric absorption at frequency f [35] , the directional sensitivity dϕi , p of the sonar apparatus of the bat for angle ϕi and pinnae position p ( see below ) , and the echo strength si of the reflector respectively . Simon et al . [36] ensonified leaves for a range of aspect angles and found reflector strength to vary from −30 dB to −6 dB . Therefore , variations in aspect dependent reflector strength si were modelled by choosing the reflector strength randomly from a uniform distribution over this interval for each call . As stated above , for the regularly spaced artificial environments mimicking the wire avoidance tests of Mogdans et al , [3] the reflector strength si was fixed at −66 dB corresponding to the target strength of a wire with a diameter of 0 . 16 mm [29] . In the torus environment , the reflector strength was chosen randomly from the interval −46 to −34 dB . This corresponds to −40 dB , the approximate target strength of a sphere with diameter 5 cm [31] , plus and minus 6 dB . In Eq ( 1 ) , cϕi denotes an additional attenuation reflecting changes in cochlear sensitivity for different frequencies . The cochlea of Rhinolophidae is highly tuned to the species-specific constant frequency component of the call ( Reviewed in [1] ) . While flying , these bats compensate the Doppler shift of the returning echoes by lowering the emission frequency . In doing this , they effectively ensure that echoes return with a frequency very close to the frequency their cochlea is tuned to , i . e . the reference frequency . However , the Doppler shift Δfϕi of an echo depends on the heading direction ϕi of reflector i as follows , Δ f ϕ i = f e m i s s i o n · 2 · v b a t v s o u n d · cos ϕ i ( 2 ) We were unable to find flight speed data for R . rouxii . However , bats weighing about 10 grams were reported to commute with a speed of 6 ms−1 [37 , 38] . Therefore , we modelled the maximum speed of R . rouxii as vbat = 6 ms−1 . R . ferrumequinum is capable of drastically reducing its flight speed when near an obstacle . Aldridge [4] reports a flight speed of about 0 . 3 ms−1 at the maximum turning rate for R . ferrumequinum . Moreover , this bat starts reducing its speed from about 5 meters before landing [9] . Hence , we model the flight speed of R . rouxii as 0 . 3 ms−1 and 6 ms−1 at 0 and 5 meter ( and more ) from the nearest obstacle respectively ( See Fig 1a ) . We interpolate linearly between these points . Notice that this implies that the simulated flight speed in the regularly spaced artificial environments ( see below ) where obstacles are spaced 15 cm apart is maximally about 0 . 47 ms−1 . The details of how Rhinolophidae lower their emission frequency when faced with multiple reflectors with different Doppler shifts remain unknown . Experiments using masking tones [39] suggest the bats lower their emission frequency such that the frequency of the maximally Doppler shifted echo is close to the reference frequency ( i . e . the frequency they are maximally sensitive to ) . However , the compensation exhibited depends also on the intensity and delay of the echoes as well as the time constant of the feedback loop [39 , 40] . As a first order approximation , we assumed that the synthetic bat lowers its emission frequency by about 2 . 6 kHz to compensate the Doppler shift for reflectors with heading ϕ = 0 ( at vbat = 6 m/s and femission = 75 kHz ) . Lower flight speeds result in reduced Doppler shifts . This implies that we assume that reflectors i with ϕi > 0 return echoes with frequencies between 0 and about 2600 Hz below the reference frequency . Hence , in our simulations , we attenuate echoes for ϕi > 0 as bats are less sensitive to frequencies below the preferred frequency . The attenuation cϕi for each echo as a function of the heading angle ϕi was determined based on data reported by Neuweiler [7] ( See Fig 1b ) . It should be noted that this simple implementation of the Doppler compensation mechanism overestimates the loss in sensitivity due to Doppler shifts . Indeed , we assume the maximum Doppler shift experienced ( and , hence the decrease in emission frequency ) is always equal to the hypothetical Doppler shift for an object with heading zero degrees—even if these echoes have large delays or low amplitudes . In reality , bats lower their frequency to a lesser extent when echoes have low intensity and/or long delays [39 , 40] . In the current simulations , we modeled the bat Rhinolophus rouxii which uses constant frequency calls in the range 73–79 kHz [5] . We choose to approximate the call frequency using 75 kHz . The atmospheric absorption af at 75 kHz was set to 2 . 4 dB/m [35] . The directional sensitivity dϕi , p of the synthetic bat’s hearing and emission for 75 kHz was taken from previous simulation studies [13 , 14 , 41] . The maximum gain of the head related transfer function was set to 4 . 5 dB at 75 kHz [42] . As pointed out above , experimental results confirm that the typical ear movements of Rhinolophidae support obstacle avoidance [3] . The continuous movement of the pinnae is approximated by modeling the directional sensitivity of the two extreme positions p of the ears . This is warranted by the fact that the controller proposed in this paper ( detailed in the next section ) only processes the onset of the echoes , i . e . the first millisecond . The available evidence [11 , 12 , 43] suggest that the pinnae are in the most extreme position at the onset of the echo and sweep to the inverse orientation while receiving the echo ( es ) . Pinna movements are simulated by rigidly rotating the hearing spatial sensitivity pattern before combining it with the emission directivity to obtain the complete directional sensitivity ( see [13 , 14] for details ) . Measurements have shown that the ears of Rhinolophidae do not undergo rigid rotations but instead deform while rotating [44] . However , current evidence leaves open the question whether the effects of this deformation on the hearing spatial sensitivity pattern is functionally relevant or not . Ref . [45] discusses the validity of modeling the ear movements as rigid rotations . The modeled head related transfer functions for the two pinna positions p are depicted in Fig 2 . At the heart of the sensorimotor system responsible for obstacle avoidance behaviour we propose a biologically feasible controller that does not rely on explicit reconstruction of the 3D layout of individual reflectors to explore the possibility that Rhinolophidae can avoid obstacles without making use of a 3D model of the world . The controller is illustrated in Fig 3 . We assume that the flight parameters are updated after every call based only on the echoes of the last call . Hence , the proposed controller constructs no internal model of the world and does not explicitly exploit changes in echo characteristics across calls . Assuming otherwise would require us to specify a segmentation and grouping mechanism by which individual echoes from subsequent calls are assigned to so-called echo-streams corresponding one-to-one with particular objects . The use of such echo-streams has been hypothesized [46] as a means for a bat’s perceptual system to organize acoustic information from complex environments . However , no explicit computational mechanism capable of the required segmentation and grouping of complex echo signals has been put forward so far . Also , while neurophysiological evidence [47 , 48] for an echo stream based representation for single reflector stimuli has been found , no multiple reflector stimuli have been experimented with yet . Hence , until the possible use of an echo stream based representation in obstacle avoidance behaviour is further clarified we propose our reactive controller as a simpler and computationally explicit hypothesis . The main advantage of a reactive approach is that it considers the world as its own best model [49 , 50] which is always exactly up to date and always contains every detail there is to be known [51] . By avoiding the delay due to the reconstruction of a 3D model of the environment and/or planning a path , a reactive approach results in a highly responsive and robust controller [50] . However , it should be noted that relying only on the echoes from the last call to determine the controller’s response does not make the proposed sensorimotor system memoryless . Indeed , the dynamics of the interaction between the controller and its environment introduce an implicit memory of information extracted from previous call-echo pairs . Put differently , the state of the controller , i . e . position and velocity , and latest call-echo pair jointly determine the bat’s next move , thereby ensuring that the perceptual history , i . e . previous call-echo pairs , and not just the last call-echo pair determine the controller’s response . In the simulations , we assume the speed of the synthetic bat vbat to be a function of the time of flight of the first echo , i . e . the distance to the nearest object . The range of speeds goes from 6 ms−1 to 0 . 3 ms−1 ( see above ) . In addition to the speed , the flight direction also needs to be updated based on the echoes from each call . For an obstacle avoidance algorithm based on sonar , desirable flight directions are characterized by low amplitude echoes . Indeed , for the same reflector strength , weaker echoes imply obstacles that are further away or located more to the periphery . A heuristic leading to weaker echoes is to turn towards the direction of the ear which receives the weakest echoes , e . g . turning right if the right ear receives the weakest echoes . With stationary ears , moving in the direction of the ear receiving the weakest echo would only allow for updating the horizontal flight direction . However , the ear movements of Rhinolophidae result in the main sensitivity axis of each ear to alternately point up and down . The available evidence suggests that Rhinolophidae move one ear up and the other ear down while receiving echoes [11 , 12 , 43] . The ears move in the other direction while receiving the next echo . In this paper , we simplified the continuous movement of the pinnae by modeling only the two extreme positions of the ears ( see below and Fig 2 ) . Considering the extreme positions of each ear results in the sonar system sampling four directions during each pair of successive calls . Therefore , we propose our controller to turn left or right depending on which ear receives the weakest echo . In addition , the controller steers up or down depending on whether the ear receiving the weakest echo is currently pointing up or down . Echoes arriving earlier are reflected by more proximate obstacles . Hence , the initial part of the echo signal is of greater importance to an obstacle avoidance sensorimotor system . Therefore , we chose to take only the first millisecond of the echo into account ( i . e . the controller only uses the onset of the echo train ) . We do not claim that the remainder of the echo has no function in obstacle avoidance , but we propose , as indicated by the results , that the onset of the echoes already contains sufficient information . Rhinolophidae have their ears at extreme positions in between calls and move them into the opposing configuration while receiving echoes [12] . Hence , by focussing on the onset of the echoes , we can further simplify the model and use only the extreme ear positions for each call . Apart from the resulting simplifications to our model we argue that focussing on the onset of the echoes has advantages for bats as well . Any mechanism that makes use of specific characteristics of the modulation pattern of the echo introduced by the complete pinna movement instead ( e . g . [13 , 14] ) , needs to control and/or to measure the ear movement in greater detail requiring a more complex and less robust system . In our simulations , the echoes received at each ear t during the first millisecond after the arrival of the first echo are summed with randomized phase shifts . The intensity gt , in decibels , of the summed echoes i received at ear t is given by , g t = 20 · log ( | ∑ i 10 g i 20 e j · φ i , t | ) ( 3 ) In Eq ( 3 ) , ϕi , t is a random phase angle ( between −π and π ) modeling the interference between narrowband echoes . Note that this phase angle is randomized independently for each reflector i and ear t . The hearing threshold was assumed to be 0 dBspl . Therefore , echo amplitudes gi lower than 0 dBspl were set to 0 and did not contribute to intensity gt . We propose the bat rotates in the direction of the ear receiving the weakest echoes , given by gt ( Fig 3 , box 5 ) . If gl < gr , the bat turns left . Conversely , if the right ear receives the weakest echoes ( gl > gr ) , the bat turns to the right . Moreover , if gl < gr and the left ear is pointing up ( down ) the bat turns up ( down ) . In addition to the direction of the turn , the controller also needs to specify the magnitude of the turn ( Fig 3 , box 6 ) . In the proposed controller , the magnitude of the turn depends on the flight speed ( which in turn depends on the distance to the closest obstacle , Fig 3 , box 7 ) . Jones and Rayner [6] report on the speed and angular rotation of Myotis daubentonii ( See Fig 1c ) . We fitted a linear function to this data to obtain the following expression for angular rotation R in degrees per second as a function of flight speed , R = 665 − 116 × Vbat . Values of R smaller than zero were set to zero resulting in the curve depicted in Fig 1c . Incidentally , the turning rates thus obtained correspond largely to those reported by Holderied [38] . Note that for low flight speeds the turning rate could be greatly increased . For example , Aldridge [4] reports that R ferrumequinum is capable of turning with a curvature of up to 115 m−1 ( turning radius < 1 cm , angular rotation speed ∼ 1900 deg/s ) when suddenly faced with a barrier . Nevertheless , as we did not aim at modeling such last minute avoidance manoeuvres , we opted for fixing the maximum turning rate to the conservative value of 665 degrees per second at Vbat = 0 . In summary , the controller turns left or right depending on whether the left or the right ear received the loudest echoes . In addition , it turns up or down depending whether the ear receiving the loudest echoes is currently pointed up or down . The speed of the bat is determined by the closest ( detected ) obstacle ( Fig 1a ) . In turn , the rotation speed is determined by the speed of the bat ( Fig 1c ) . See Algorithm 1 for a listing of the computations and Fig 3 for a graphical depiction of the complete controller . We give the synthetic bat the same aerodynamic freedom in the horizontal ( left and right ) and vertical plane ( up and down ) . This is; it can turn at the same rate without taking gravity into account ( but see below for a version of the controller taking into account the gravity vector ) . Indeed , if the synthetic bat turns upwards/downwards for long enough , it might eventually fly upside down with respect to its initial orientation . There are two reasons for modeling the vertical rotation in this way . First , while it is well known that bats are very agile , to the best of our knowledge very little information is available about the aerodynamic constraints on climbing and ascending flight of the bat . Second , and more importantly , by introducing the same constraints on both horizontal and vertical rotations , we can compare the sensorial performance of the algorithm in both the horizontal and vertical plane in the absence of differences in motor constraints . Nevertheless , we are aware that having the same constraints for both turning rates is artificial . Hence , we also test a variant of the controller that introduces a constraint on the maximum vertical rotation ( see ‘constrained’ controller below ) . Both R . rouxii and R . ferrumequinum emit a pulse every 80 to 90 ms on average [5 , 52] . For computational ease and to simulate a lower bound update rate , the synthetic bat was simulated to emit a pulse every 100 ms . On approaching a landing site , the pulse rate of R . ferrumequinum was found to increase to about 80 Hz ( i . e . about 12 ms interval ) [9 , 53] . However , the informational update of 80 Hz might not translate into an ability of the bat to update its direction 80 times a second . Rhinolophidae flap their wings at about 12 Hz ( i . e . about 80 ms interval ) irrespective of their air speed [54] . Considering a wing beat as the minimal unit that allows changing the direction of the flight , would allow for an update rate of at most 12 Hz . In the proposed controller , each pulse corresponds to a single update in the flight direction . Hence , as 12 Hz is very close to the modeled pulse rate of 10 Hz , the interpulse interval was fixed at 100 ms . In the simulations , we account not only for the time needed for the echoes to arrive but also for the time required by a bat to process the echoes and produce a motor response . Übernickel et al . [55] found a reaction time of about 50 ms to transient targets in the trawling bat Noctilio leporinus in accordance with a similar range of reaction times 47–63 ms found in ref . [56] . Hence , we allowed for 50 ms to process the echoes . In the interval between the emission of the call and the start of the turn , the current direction and speed of flight is maintained . The interval between call and start of turn is given by ( 1 ) the time for the first echo to arrive , ( 2 ) 1 ms over which the echoes are summed and ( 3 ) 50 ms of processing time . Note that as the duration of the turn is given by the fixed call period ( 100 ms ) minus the interval between call and start of turn , both the rotation speed R and the duration of the bat’s turn depend on the distance to the closest object . As the time for the first echo to arrive gets shorter , the turn duration gets longer . This increases the rotational gain of the controller even more for nearby obstacles . The controller was tested in 2D and 3D environments . The performance of the controller described above , referred to as the default controller from now on , was compared to that of five related controllers: Fixed ears: This controller models a bat with static ears . A single directional sensitivity is used for each ear . As such , the directional sensitivity does not change from call to call . The directionality used is depicted in the top row of Fig 2b . The azimuthal rotation is updated as in the default controller . However , the sign ( up/down ) of the elevation rotation was selected at random . Off axis pinnae: This controller is identical to the default controller . However , this controller consistently has the left ear pointing downwards and the right ear pointing upwards . If the left ( right ) ear receives the weakest echoes , the bat turns downwards ( upwards ) . The azimuthal rotation is updated as before . Random A: The controller differs from the default controller by randomly turning left or right at each call . While the direction of rotation is chosen randomly , the magnitude is calculated as in the default controller . Random B: The same controller as the Random A variant with the addition that the rotation speed of the bat is also chosen randomly from the interval 0 to 350 degrees per second . Constrained: This controller is identical to the default controller , but it constrains the angle of the bat in the vertical plane . The bat can maximally attain a climbing or descending angle of ±60 degrees . The controller with fixed ears allows us to test the contribution of ear movement to obstacle avoidance . The controller with the pinnae fixated in an off-axis position allows us to test whether the cues necessary for obstacle avoidance are still present if the ears are fixed but are not aligned with the horizontal plane . Random A and Random B are included in the tests as baseline conditions against which to compare the other controllers . Similarly , in behavioural obstacle avoidance experiments , the performance of the bat is typically compared to the number of collisions expected from following a random path through space , e . g . [3 , 25 , 26] . Finally , the constrained controller adds more realistic constraints to the vertical rotation of the bats .
We tested the controller and its four variants in environments populated with reflectors on hexagonal grids spaced 15 cm apart ( See Fig 4c and 4f for examples ) . In these environments , collisions are counted as the number of time steps ( calls ) the controller was closer than 2 . 5 cm to any obstacle . Hence , we modeled the synthetic bat as having a body width of 5 cm , in agreement with Mogdans et al . [3] . In most results reported below , the various controller variants differed in the resulting average distance kept from reflectors and , therefore , in their average speed and distance travelled . To compensate for this , we normalized the number of collisions for all controllers to the number of collisions per 100 m travelled . The results show that the default controller successfully avoided both the vertical wires and the horizontal wires ( Fig 4 ) . Indeed , in these 2D tasks the number of registered collisions was much lower than in both random A and B . The controller with the fixed ears performed equally well in avoiding the vertical wires . However , avoidance of the horizontal wires was reduced to chance level by fixing the ears in the horizontal plane . In contrast , fixating the pinnae off axis , restored the obstacle avoidance performance for the horizontal wires ( and did not reduce performance for the vertical wires ) . The controller that was constrained in its vertical rotation performed much worse than the default controller in avoiding horizontal wires ( Fig 4d ) . This indicates that while the moving ears supplied the necessary information to avoid obstacles , the imposed aerodynamic constraint is too restrictive to allow for successful obstacle avoidance in our grid of simulated wires . Overall the performance results in the regularly spaced grids match the finding of Mogdans et al . [3] that obstructing the pinnae movements only interferes with the avoidance of horizontal wires , i . e . only obstacle avoidance in the vertical dimension is affected . Fixating the ears did not have an effect on the avoidance of the vertical wires . In addition , our simulations suggest that pinnae fixated in an off-axis position provide sufficient cues for obstacle avoidance in both azimuth and elevation . Fig 5a and 5d show the number of collisions registered for the bat in 100 replications with reflectors scattered in either the horizontal or the vertical plane for the four variants of the controller . In these runs , collisions are defined as the number of time steps ( per 100 m travelled ) the controller was closer than 15 cm to the nearest obstacle , i . e . approximately half the wingspan of R . rouxii . The results indicate that the default controller is capable of avoiding obstacles in both the vertical and the horizontal plane . Fixing the pinnae has no effect on obstacle avoidance in the horizontal dimension . However , obstacle avoidance in the vertical dimension is reduced to chance level ( i . e . similar number of collisions than controller Random A ) . In the horizontal plane , the constrained controller has the same degrees of freedom as the default controller and , therefore , has the same performance . Constraining the elevation angle of the bat clearly limits its freedom . Hence , the number of collisions does increase compared to the horizontal plane . However , the number of collisions is still less than in both random baselines . The reduction in performance for the constrained controller is less dramatic than for the regularly spaced obstacles discussed above as can be seen from comparing the performance of the constrained controller in Figs 4d and 5d . We provide two movies illustrating the behaviour of the controllers in the 2D environments of Fig 5 as supplementary material . The default controller , as well as the four derived controllers , were also tested for obstacle avoidance in 3D point clouds ( Fig 6 , also provided as MATLAB figure in the supplementary material ( S3 Fig ) . The default algorithm performs best . Fixing the ears does not result in an increase in the number of collisions . However , it results in flying somewhat closer to obstacles . The number of collisions does not increase by fixing the ears as the controller is still able to avoid obstacles in the horizontal plane . This implies the controller with the fixed ears solves the 3D obstacle avoidance problem as a sequence of 2D problems . Indeed , the 3D point clouds do not require the controller to perform obstacle avoidance in both horizontal and vertical plane simultaneously , it can avoid collisions by avoiding obstacles in a single plane . The two random controllers performed worse than the default controller with a drastic increase in the number of collisions . The constrained controller performed at the same level as the default controller with respect to the number of collisions . Hence , the reduced freedom in elevation rotation does not seem to hamper this controller in this environment . The tilted torus environment explicitly tests whether the controller ( s ) can follow a corridor in both azimuth and elevation . The results depicted in Fig 7 show that the random controllers result in more collisions ( Fig 7a ) and flying closer to reflectors ( Fig 7b , ( also supplied as a MATLAB figure in the supplementary material ( S4 Fig ) than the other controllers . The number of collisions follows a similar pattern as the number of collisions in the 3D environment depicted in Fig 6 . However , more importantly , only the controllers with moving ears ( i . e . the Default and Constrained controllers ) succeed in following the torus . The random controllers often exit the torus quickly , explaining the low number of collisions for the controller Random B . The controller with fixed ears stays in the torus without colliding but is unable to complete a circular path inside the torus . It is confined to a subsection of the torus . Fig 8 shows the results of 50 replications of the experiment using the 3D scanning data from the fir forest . Likewise Fig 9 shows the results of 50 experimental runs using the 3D scan of the forest corridor . As real bats show nearly 2D flight behaviour in similar real environments ( as found e . g . in Holderied [57] ) , we ignored the elevation commands of the controller resulting in 2D flight paths in these simulations . In both environments , Random A and B performed substantially worse than any other variant . Note that , in these experiments , while the bat’s flight path is restricted to a plane the echo signals the controller derives its decisions from are calculated based on the full 3D environment .
The results presented in this paper can be readily extended to bats using frequency-modulated ( FM ) calls . For obstacle avoidance in the horizontal plane , this extension follows directly from our results . Indeed , in our simulations the controller avoids obstacles in the horizontal plane by using first echo delay and IID extracted from a single narrow frequency band . Moreover , the bat only processes the onset of the echo ( i . e . the first millisecond ) . This type of transient information is also available to bats using FM signals . The main difference between FM and CF bats in this respect is that FM bats have access to IIDs across multiple frequency bands . Bats navigating along hedgerows [57] or among the trunks of trees could make use of this horizontal obstacle avoidance mechanism . To demonstrate that the proposed mechanism indeed extends to FM bats avoiding obstacles , we modeled an FM bat flying in heterogeneous artificial environments ( identical to those used in Fig 5 ) . The controller was adapted to use the head related transfer function [81] and emission directivity [82] of the FM bat Phyllostomus discolor at 60 kHz ( atmospheric attenuation: 2 dB/m [35] ) . P . discolor uses frequency modulated calls which include frequencies between 40 and 90 kHz [83 , 84] . However , in the current simulations , we simulated only one of the frequency channels available to this bat . i . e . we modelled a single frequency channel at 60 kHz . No cyclic ear movements were simulated . In addition , as FM bats do not compensate for Doppler shifts this behaviour was omitted . Apart from these changes , the controller was not altered . In flight , the calls of P . discolor have been reported to reach a peak intensity of 124 dB ( cited in [85] ) . Hence , we used 120 dB as emission strength ( gbat , Eq ( 1 ) ) as before . The maximum gain of the HRTF was set to 6 dB [86] . To the best of our knowledge , ear movements of FM bats in flight have only been studied in the final approach during prey capture , e . g . [22 , 87] . Hence , it is unknown whether FM bats exhibit ear motions while avoiding obstacles . However , as indicated above , ear movements are not necessary for successful obstacle avoidance in the vertical plane . Indeed , the controller with pinnae fixed in an off-axis position performed nearly as well as the default controller ( see Fig 4 ) . Therefore , we hypothesize that FM bats might be able to avoid obstacles in azimuth as well as elevation by turning their pinnae off-axis . We tested this by combining the controller with an HRTF obtained by rotating the left ear down by 15 degrees and the right ear up by 15 degrees ( see Fig 12 ) . This is the same rotation of the pinnae as used for the simulations of R . rouxii . We used the same configuration of the simulated ears for the Constrained controller . The default controller , on the other hand , has both ears co-located in the horizontal plane ( see Fig 12 ) . Fig 13a-13c shows that , as expected , the controller using the P . discolor directionality can avoid obstacles in the horizontal plane . The only controller variants that were unable to avoid obstacles were Random A and Random B . Pointing the ears off axis did not have an ( adverse ) effect on the obstacle avoidance behaviour . The results in Fig 13d-13f show that equipping the FM bat controller with ears pointing off-axis results in increased obstacle avoidance performance . In contrast , having the ears co-located in the horizontal plane ( i . e . the Default controller ) leads to numerous collisions . Rotating their ears into an off-axis position is only one way in which FM bats could compensate for the absence of cyclic ear movements during flight . They could also change the orientation of their heads and/or bodies between calls . In fact , this behaviour has been observed in CF bats when being prevented from rotating their pinnae . Mogdans et al . [3] reported that in their experiment , the CF bats with immobilized pinnae showed more vigorous head movements than before surgery and compared to the controls while hanging in the flight room . They also reported , as referred to above , that flight records of intact bats revealed they sometimes passed vertical wires with the head tilted off the horizontal plane . Evidence for changes in head orientation in FM bats has been reported for Eptesicus fuscus which has been shown to be able to shift its beam from call to call , e . g . [88] . Likewise , pipistrelle bats were found to exhibit extensive scanning behaviour in azimuth and elevation while flying through natural habitats [89] . This behaviour in combination with the mechanism proposed above , i . e . rotate towards the ear receiving the weakest echo , would support obstacle avoidance based on the same cues used by our controller . The sensorimotor strategy proposed in this paper can be readily incorporated into a behavior-based control architecture . This type of controller , originally proposed for robots by Brooks [90] and inspired by neuroscience [50] , decomposes complex behavior into a number of independent sensorimotor loops ( reviewed in refs . [50 , 91 , 92] ) . Each sensorimotor loop controls a single behaviour such as obstacle avoidance , approaching targets or corridor following . All sensor data is fed into each loop . However , loops only extract the information necessary for the behaviour they control . An action selection mechanism ( e . g . mutual inhibition of behaviours [90] ) ensures that only a single sensorimotor loop drives the actuators [93] at each point in time . Brooks proposed the behaviour-based control architecture as an alternative to so-called deliberate control architectures . These controllers process the sensor data to derive a general representation of the world first . Once a general and complete representation has been derived , planning and reasoning algorithms are employed next to determine the most suitable action sequence [49] . However , deriving a representation that supports all required actions has proven to be the most challenging aspect of deliberate controllers . Indeed , experience in robotics has learned this is only possible for highly simplified environments . Today , no autonomous robot operating in realistic environments is operated by an entirely deliberate control architecture [91] . In contrast , behaviour based controllers avoid having to compute explicitly an internal representation of the world . Indeed , in the words of Brooks , in a controller consisting of multiple sensorimotor loops We argue that the fact that behaviour-based control does not depend on the extraction of a general representation of the environment makes it an appealing candidate as a control strategy in echolocating bats . Indeed , the sparseness and unreliability of localization cues makes deriving a general representation of the world very difficult , if not impossible under many real world conditions . A behaviour-based control architecture would circumvent this issue by only relying on extracting ( and possibly storing [94] ) those cues necessary for a particular sensorimotor loop . Furthermore , behaviour-based control architectures readily allow for redundancy . Each behaviour ( e . g . obstacle avoidance [90] ) can be controlled by multiple , independent sensorimotor loops each exploiting different cues . For example , in the current paper we have proposed a sensorimotor loop for obstacle avoidance based on IID and time of flight cues derived from the onset of the first echo . However , we acknowledge that bats may use many more echo cues than the ones we have exploited in this paper . Also , they are likely to integrate more echo information across calls , i . e . base their decisions on echo-stream information . In particular , CF bats might be using the complete echo for extracting IID , use Doppler shifts or use the FM parts of the echoes . FM bats , on the other hand , are very likely to use spectral cues whenever available . Each of these cues could be extracted , evaluated , stored and mapped to motor commands by a set of dedicated sensorimotor loops taking precedence through an adequate action selection mechanism . This would lead to a high level of robustness as , in case a particular sensorimotor loop fails to extract the relevant cues , other loops will take over motor control . In summary , we tentatively propose that many aspects of bat echolocation—including prey capture , obstacle avoidance and navigation—could be modeled by a behaviour-based control architecture consisting of a set of sensorimotor loops each extracting and exploiting a subset of cues from the echoes . Indeed , other sensorimotor loops proposed in the past fit readily in this framework , e . g . the prey capture strategies proposed by Kuc [20] and Walker et . al . [15] or the models of target approach proposed by Lee et . al . [95] and Bar et al . [96] . In the case of obstacle avoidance , we consider the proposed obstacle avoidance behaviour to be a robust sensorimotor loop to which both FM and CF bats can fall back on in case less reliable cues are unavailable . We maintain that a behaviour-based controller would result in a robust echolocator capable of exploiting a wide range of cues whilst keeping computational demands limited by avoiding the need to reconstruct a general representation of the environment from noisy and complex echoes . Proposing a behaviour-based architecture as a model for echolocation based control in bats implies that future research should not only focus on identifying sensorimotor loops underlying different behaviours but also on how these loops interact and how context-dependent action selection is achieved . Indeed , a behaviour-based controller offers a framework in which to analyze the bats’ flexibility in exploiting a variety of ( multimodal ) cues under changing circumstances , e . g . [74] . In conclusion , we propose that Interaural Intensity Differences calculated on the onset of the first echo , in combination with first echo delay , constitute a sufficient set of stable and robust cues for avoiding obstacles in a 3D world—without the need to reconstruct the 3D layout of the reflectors from complex and noisy echo signals . Our simulations suggest that exploiting these cues would allow both FM and CF bats to perform this basic echolocation subtask with a limited computational load and minimal latency providing a hard real-time response capability . | Echolocating bats can fly through complex environments in complete darkness . Swift and apparently effortless obstacle avoidance is the most fundamental function supported by biosonar . Despite this , we still do not know which acoustic cues , from among the many possible cues , bats actually exploit while avoiding obstacles . In this paper , we show using spatial simulations ( 2D and 3D ) that the Interaural Intensity Difference ( IID ) and travel time of the first millisecond of the echo train in combination with alternating ear positions provide robust and reliable cues for obstacle avoidance . Simulating the echoes received by a flying bat , we show that simple phonotaxis can steer a bat clear from obstacles without performing 3D reconstruction of the layout of the scene . As such , this paper presents the first computationally explicit explanation for obstacle avoidance in realistic and complex 3D environments . We hypothesize that using low level yet robust cues for obstacle avoidance allows bats to comply with the hard real-time constraints of this basic behaviour . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Sensorimotor Model of Obstacle Avoidance in Echolocating Bats |
Because coevolution takes place across a broad scale of time and space , it is virtually impossible to understand its dynamics and trajectories by studying a single pair of interacting populations at one time . Comparing populations across a range of an interaction , especially for long-lived species , can provide insight into these features of coevolution by sampling across a diverse set of conditions and histories . We used measures of prey traits ( tetrodotoxin toxicity in newts ) and predator traits ( tetrodotoxin resistance of snakes ) to assess the degree of phenotypic mismatch across the range of their coevolutionary interaction . Geographic patterns of phenotypic exaggeration were similar in prey and predators , with most phenotypically elevated localities occurring along the central Oregon coast and central California . Contrary to expectations , however , these areas of elevated traits did not coincide with the most intense coevolutionary selection . Measures of functional trait mismatch revealed that over one-third of sampled localities were so mismatched that reciprocal selection could not occur given current trait distributions . Estimates of current locality-specific interaction selection gradients confirmed this interpretation . In every case of mismatch , predators were “ahead” of prey in the arms race; the converse escape of prey was never observed . The emergent pattern suggests a dynamic in which interacting species experience reciprocal selection that drives arms-race escalation of both prey and predator phenotypes at a subset of localities across the interaction . This coadaptation proceeds until the evolution of extreme phenotypes by predators , through genes of large effect , allows snakes to , at least temporarily , escape the arms race .
The phenotypic interface of the predator–prey interaction between garter snakes and newts of the genus Taricha revolves around tetrodotoxin ( TTX ) . TTX is one of the most potent neurotoxins known , binding to the outer pore of voltage-gated sodium channels in nerve and muscle tissue , thereby blocking the propagation of action potentials [46 , 47] . Taricha have high levels of TTX in the skin and are lethal to a variety of potential predators [28 , 48–52]; individuals from some populations have up to 14 mg of toxin , which is enough TTX to kill thousands of mice or up to 10–20 humans . A growing body of evidence suggests that newts produce their own TTX , but the genetics and biosynthesis of this process are poorly understood [50 , 53–57] . Some garter snakes of the genus Thamnophis have evolved resistance to this prey toxin through modifications of the sodium channel structure in skeletal muscle [58 , 59] and are capable of ingesting whole adult newts without permanent adverse effects [60 , 61] . Resistance in snakes is heritable [62 , 63] and is associated with a cost of reduced locomotor performance [64] . The functional interactions and relationships between individual newt toxicity and effects on individual snakes have been worked out in detail [27 , 58–63 , 65 , 66] . Both toxicity of newts and resistance of snakes vary geographically [28 , 60 , 62 , 63] . Where newts are absent or nontoxic , T . sirtalis are not resistant to TTX [28 , 62] . Elevated TTX resistance in western T . sirtalis is clearly derived , reaching levels 10–1 , 000 times that of other members of the genus in some populations [65] . Population differences in resistance are correlated with functionally important differences in amino acid sequences of skeletal muscle sodium channels [59] . Although considerable effort has been devoted to understanding the evolution of geographic and genetic patterns of TTX resistance in snakes , similar information regarding variation in newt toxicity is lacking . Data from only a few localities in the Pacific Northwest of North America suggest tight matching between prey and predator phenotypes [60] , but a comprehensive survey of newt toxicity has not been previously conducted .
Mean total skin TTX levels of newts ranged from no detectable TTX to 4 . 69 mg/newt and differed among populations ( Figure 1; ANOVA: F28 , 382 = 20 . 38 , p < 0 . 0001 ) . Across this phenotypic range , newt toxin levels were closely correlated with the resistance of sympatric snakes ( Spearman ranked correlation , ρ = 0 . 71 , p < 0 . 0001 ) . Geographically , regions of highest newt toxicity also corresponded to regions of highest snake resistance , with extreme values of both traits found in the Willamette Valley of Oregon and the San Francisco Bay Area of California ( Figure 1 ) . Isocline maps of newt toxicity and snake resistance show generally similar spatial patterns of phenotypic variation ( Figure 2A and 2B ) . Despite the overall spatial concordance of predator and prey phenotypes , an analysis of functional interaction reveals that over one-third of the localities sampled may qualify as ecological mismatches ( Figure 3 , Table 1 , and Figure S1 ) . The isocline map of the degree of mismatch , d , indicates that most of the geographic range of the newt–snake interaction is best characterized as mismatched and that regions of close ecological match are small and spatially restricted ( Figure 2C , yellow to red areas ) . Localities where phenotypes are closely matched do not uniformly coincide with areas of elevated predator and prey phenotypes , but instead , include both ends of the phenotypic distributions of newts and snakes ( Figures 1 and 2 and Table 1 ) . The observed levels of phenotypic mismatch ranged from near zero to d > 2 . 5 ( Table 1 ) . Values of d > 0 . 6 indicate populations that lay outside the 15% and 85% lines in Figure 3 and values of d < 0 . 6 indicate populations that fell between the 15% and 85% lines . Estimates of 0 < |d| < 0 . 6 indicate that localities lie within the zone of potentially experiencing reciprocal selection . In every case of mismatch ( ten localities ) , predator resistance was much greater than the effective level of toxicity of local prey ( Figure 3 , gray zone , Table 1 , and Figure S1A ) . Mismatches included six populations of newts ( Parsnip Lake , Oregon; Bear Ridge , California; Inland Lake , British Columbia; Crescent City , California; Latah , Idaho; and Scott Lake , Oregon ) with little or no TTX ( Figure 3 , purple symbols in gray zone , Table 1 , and Figure S1A ) . Snake populations at the same localities all fall in the lowest level of TTX resistance for garter snakes , wherein ingestion of ≈0 . 1–0 . 5 mg of TTX would reduce performance to 50% . This level of resistance is equivalent to the ancestral level of TTX resistance for the genus Thamnophis ( 50% dose ≈ 0 . 11 mg ) , including mostly species that have never coevolved with tetrodotoxic newts [65] . Four additional localities not explicitly recognized as mismatched ( Skagit River , Washington; Orick , California; Priest Lake , British Columbia; and Vandenburg , California ) , also exhibited predator resistance greater than toxicity of local prey as well as reduced levels of resistance and toxicity ( Figure 3 , blue symbols in gray zone and one blue symbol in the nonshaded zone , Table 1 , and Figure S1B ) . These nearly mismatched localities had phenotypic distributions of newt toxicity wherein a small reduction in the performance of local snakes could only result from ingestion of the most toxic newts present . We did not observe a single case where prey levels were greater than comparable predator abilities . The other four mismatches include four populations of moderately to highly toxic newts ( San Mateo , California; East Bay , California; Willow Creek , California; and Omo , California ) that co-occur with the most resistant snake populations known ( Figure 3 , light green and yellow symbols in gray zone , Table 1 , and Figure S1A ) . All of these localities occur within two geographic regions ( the Bay Area of California and central Sierra Nevada ) and include three species of Taricha ( Ta . torosa and Ta . granulosa in the Bay Area and Ta . sierrae in the central Sierra Nevada ) and likely two lineages of T . sirtalis [67] . As with the other mismatched populations , snakes in these localities can ingest sympatric newts with no reduction in performance or fitness consequence ( Figure 4 and Figure S1A ) , however both newt and snake phenotypes are highly elevated , compared with ancestral conditions and conspecific populations ( Figures 1 , 2A , 2B , and 3 , and Table 1 ) . Population-specific interaction gradients confirm the interpretation of mismatch across the interaction ( Figure 4 , Figure S1 , and Table S1 ) . Regressions of the predicted performance of snakes after ingestion of co-occurring newts indicate that TTX levels observed in newts at mismatched localities do not have variable effects ( Figure 4 , Figure S1A , and Table S1 ) . Resulting interaction gradients in these mismatched populations have an average slope that is substantially lower that that seen in matched populations , and no differences in expected fitness are associated with variation in either TTX levels and TTX resistance at these localities ( Figure 4 , Figure S1 , and Table S1 ) . Interaction gradient slopes ( β ) of the four nearly mismatched localities were an order of magnitude greater than in mismatched populations , but much less than in matched localities , suggesting that these nearly matched localities experience reduced potential for selection relative to other matched populations , but greater than our mismatched localities ( Figure S1 and Table S1 ) .
Our results suggest , contrary to previous analyses [60] , that extreme trait mismatches are not uncommon in this predator–prey system . However , the absence of localities in which newt toxicity was high enough to kill or disable any sympatric snake suggests that it is possible for the predator , but not the prey , to evolutionarily escape the reciprocal selection of the arms race . This directional asymmetry appears to contradict theoretical predictions arising from equilibrium theory [68] , as well as the so-called “Life-Dinner Principle” ( i . e . , that prey experience stronger selection than predators in an arms race ) [69] , which predict that arms-race coevolution should favor defensive adaptations in prey over offensive adaptations in predators . This pattern may reflect a reversal in selective inequity as predicted for systems with deadly prey [70] , or it may be particular to the unique biology of the newt–garter snake interaction . The adaptive changes in resistance and toxicity are mediated not only by the strength of selection , but also by the genetic architecture of the traits at the interface . There is reason to expect adaptive changes in mechanisms of TTX resistance to proceed in a less-than-gradual fashion . If phenotypic changes in resistance are due to one or a few genes , then fixation of such genes in snake populations could be rapid and lead to phenotypic mismatches in one ( or few ) evolutionary step ( s ) . Much of the variation in TTX resistance in T . sirtalis results from the expression of TTX-resistant voltage-gated sodium channels in skeletal muscle [58 , 59] . Resistance in these sodium channels is conferred by a small number of nucleotide substitutions in the TTX binding site [59] . The extreme resistance in at least one of these mismatched snake populations ( Willow Creek , California ) results from the substitution of a single amino acid [59] . Rapid fixation of such a simple mutation could explain how some populations of predators have escaped the arms race with prey . On the prey side , little is known about the basis of differences in TTX toxicity in newts , but some [52 , 71 , 72] have suggested that constraints on toxicity due to limited exogenous factors ( e . g . , environmentally derived precursors of TTX ) may be one factor allowing predators to outpace prey in the arms race . However , the extreme levels of toxicity found in some newt populations demonstrate that elevated levels of the trait are possible within Taricha . Our results indicate that geographic regions of phenotypic escalation are not necessarily congruous with coevolutionary hot spots . Coevolutionary hot spots ( where reciprocal selection is intense ) and cold spots ( where selection is absent ) are defined on the nature of the interaction rather than the level of the phenotype [1 , 2 , 4 , 27 , 60] . Our data reveal that current cold spots exist at localities with upper and lower extremes of phenotypes in both predator and prey ( Figures 1–3 ) . These results contradict earlier assessments of the geographic mosaic of coevolutionary hot spots in this system , which assumed that elevated predator phenotypes coincided with intense coevolution [60] . Similarly , reciprocal selection is possible at localities previously identified as cold spots ( e . g . , Vancouver Island , British Columbia ) where phenotype distributions overlap and appear well matched despite the low levels of prey toxicity and predator resistance ( Figure 1 ) . Despite the fact that both predator and prey phenotypes show similar geographic patterns of escalation , the distribution of phenotype mismatch ( i . e . , cold spots ) is not concordant ( Figure 2 ) . Coevolutionary hot spots may not be unequivocally assessed on phenotypic data alone , but at least the potential for reciprocal selection is observed across the range of phenotypic values in both taxa . The observed pattern of trait mismatches among localities suggests a general arms-race dynamic for the process of predator–prey coevolution between Thamnophis and Taricha . The majority of localities occupy a broad band of phenotypic values within which potential reciprocal selection might occur ( Figure 3 ) . This zone of possible matching includes linearly increasing values of both newt toxicity and snake resistance that range from ancestral levels and increase several orders of magnitude , consistent with a counter-escalating arms-race dynamic in which pairs of populations experience reciprocal selection and evolve ever-increasing trait values [1 , 2 , 37 , 69] . This phenotypic zone includes multiple lineages of Taricha as well as at least two lineages of snakes that have evolved extreme levels of TTX resistance , suggesting that escalating dynamics have occurred multiple times during this evolutionary interaction [60 , 67 , 73 , 74] . Because many factors might ameliorate reciprocal selection at these localities , it is not possible to be certain that each of these localities represents a currently coevolving pair of populations . One clear and testable prediction from this interpretation is that older interactions should represent the populations with elevated phenotypes if average realized selection and the genetic architecture of toxicity and resistance are stable across localities . Mismatched localities fall into two distinct groups that likely have different explanations and implications . At one end of the phenotypic distribution are the four populations of moderately to highly toxic newts that co-occur with the most resistant snake populations known ( Figures 1 and 3 , light green and yellow symbols in gray zone , and Table 1 ) . As with other mismatched populations , snakes in these localities can ingest sympatric newts with no or little reduction in performance or fitness consequence , however both newt and snake phenotypes are highly elevated compared to ancestral conditions and conspecific populations . This pattern suggests that these localities have undergone arms-race coevolution , but that predators have escaped the arms race through the rapid evolution of extreme TTX resistance ( see above ) . The extreme levels of TTX present at other localities ( e . g . Benton , Oregon ) suggest that there does not appear to be a physiological limit to toxicity that explains these mismatches . These localities occur in nearby geographic regions ( the Bay Area of California and the central Sierra Nevada; Figure 1 ) and involve different species of Taricha ( Ta . granulosa and Ta . torosa in the Bay Area , and Ta . sierrae in the Sierra Nevada ) [75] . Phylogeographic evidence suggests these represent two related groups of snake populations [67] , indicating that escape from the arms race has occurred once or possibly twice in this fashion . At the opposite end of the phenotypic distribution , we see mismatched localities from multiple lineages that appear never to have engaged in the arms race ( Figures 1 and 3 , purple symbols in gray zone ) . Population-specific interaction gradients ( Table S1 and Figure S1A ) and values of d ( Figure 3 and Table 1 ) confirm that the opportunity for reciprocal selection in these localities is negligible . Both prey and predator traits at these localities appear to be close to estimated ancestral levels , wherein snakes have the slight ecological advantage of some predisposition to TTX resistance [60] . Average TTX levels in these newt populations range from 0 ( or below our measurable lower limit of 0 . 0001 mg ) to a high of around 0 . 002 mg ( Parsnip , Oregon ) . This level of TTX is at or below the concentrations detected in related salamandrid species . In Notophthalmus , the sister genus to Taricha , reported levels of TTX range from 0 to a high of ≈0 . 06 mg per animal ( estimated from [76] ) . In Cynops pyrrohgaster , an Asian TTX-bearing newt , typical whole-animal TTX levels range from 0–0 . 2 mg , with most population means ≈0 . 002 mg ( estimated from [77] ) . The highest reported TTX level in the European newt genus Triturus ( sensu lato ) is 0 . 017 mg TTX ( Tr . cristatus ) , with TTX levels in other species of Triturus an order of magnitude lower [78] . These comparative data suggest that mismatched localities at the low end of the phenotypic distribution have not engaged in counter-escalating coevolution . Alternatively , these populations may have been coevolving in the past , but once reciprocal selection was alleviated , costs of toxicity and resistance drove levels of both traits back to reduced levels . The four nearly mismatched localities ( Figure 3 , blue symbols in gray zone and one blue symbol in nonshaded zone ) suggest that populations in this lower range can move from disengaged to engaged or that the process may be cyclical . Multiple snake and newt lineages are represented at the localities with unelevated phenotypes , suggesting that the phenomenon is not merely a phylogenetic artifact [67 , 73–75] . The apparent dynamic of arms-race coevolution in the newt–snake system , then , includes three or more stages . First , we see localities with low levels of traits at the phenotypic interface . Some of these localities , for reasons not yet clear , do not experience reciprocal selection and appear never to have engaged in the arms race . All of these localities involve predators able to subdue toxic prey without ill effect , suggesting that if newt toxicity rose in these populations , reciprocal selection would follow . As populations of newts gain toxicity ( through mutation , migration from more toxic locales , or some exogenous influences ) , counter-escalation ensues and can lead to up to three–order-of-magnitude increases in traits . Initial increases in toxicity might be promoted by selection from interactions with other species as in other systems [3 , 17 , 22] , including predators on early life stages [50] . Some localities ( e . g . , Benton , Oregon ) seem to persist in this escalation zone , while others ( e . g . , Omo , California ) escape from the arms race due to rapid evolution of extreme resistance through simple genetic mechanisms . Such adaptive changes suspend reciprocal selection , and no counter escalation follows . The next step for these populations is unclear . If costs to either resistance or toxicity are high enough , we might expect to see such escaped populations eventually lose phenotypic value and return to the lower left of Figure 3 , resulting in de-escalation and a long-term cyclical dynamic . This scenario is plausible and has been suggested as an important dynamic in the chemically mediated coevolution between parsnip webworms and their host plant [79] . However , de-escalation is not supported by the observed patterns in the newt–snake interaction , which do not reveal mismatched localities with intermediate levels of resistance or toxicity .
We sampled a total of 383 newts from 28 localities co-occurring with populations of garter snakes for which TTX resistance has been described [60] . This sampling regime included most of the geographic range of this interaction and included localities from the central coast of British Columbia to the central coast region of California ( Figure 1 ) . The number of individuals sampled for each locality ranged from two to a maximum of 57 ( Table 1 ) . Only sexually mature animals were assayed in order to minimize variation in toxin levels associated with ontogeny . We included both males and females in our analysis; sex ratios of specimens varied among localities . Although earlier work suggested that there might be minor gender differences in toxicity of Ta . granulosa [55] , we detected no such differences in our data set ( ANOVA: F1 , 370 = 3 . 26 , P = 0 . 0719 ) . We sampled populations of Ta . granulosa , Ta . torosa , and Ta . sierrae . Because average toxicities of Ta . torosa and Ta . sierrae populations were completely within the range of Ta . granulosa populations , we included all three species in a single analysis for this study ( Table 1 ) . We predicted and evaluated the distribution of expected performance outcomes for each population of snakes interacting with sympatric newts over the range of toxicity observed in newts from each given population . This model of the chemical ecology and physiology of the interaction is based on an extensive understanding of the functional interaction between newt toxicity and snake resistance [27 , 28 , 49 , 53 , 55 , 56 , 60–63 , 66] . For each locality , we estimated the toxicity ( in mg of TTX ) of newts , the doses of TTX ( in mg ) required to reduce performance of co-occurring snakes to 15% , 50% , and 85% of their baseline performance , and the degree of match or mismatch between newt and snake phenotypes ( see below for details ) . Newt toxicity estimates and quantification of skin TTX levels . The amount of TTX present in dorsal skin of individual newts was quantified with high-performance liquid chromatography–fluorescence detection , and estimates of total skin TTX ( in mg ) per animal were generated following previously published methods [28 , 49 , 50 , 53 , 55] . This methodology has been shown to be a highly repeatable and accurate method for measuring dorsal skin TTX levels [28 , 49 , 53 , 55] as well as for estimating the total skin TTX of individual animals [49] . Quantification of TTX-resistance in Th . sirtalis . Whole-animal resistance data ( in mass-adjusted mouse units or MAMU ) were taken from Brodie et al . [60] , in which TTX-resistance was measured with a bioassay based on a reduction in organismal performance after an interperitoneal ( IP ) injection of TTX [60 , 62 , 63] . This bioassay provides a highly repeatable estimate of individual and population level differences in susceptibility to TTX that expresses resistance as a percentage of baseline locomotor performance . A measure of 50% resistance means that an individual ( or population on average ) could crawl at 50% of its baseline speed after an injection of a given amount of TTX . TTX-resistance estimates used here are based on data from a total of 2 , 449 snakes from 269 families from 28 populations . We used these published dose-response curves to interpolate the average 15% , 50% , and 85% IP resistance doses ( in MAMU ) for each locality . Comparing TTX toxicity in newts with TTX resistance in Th . sirtalis . Because absolute levels of TTX resistance in snakes ( i . e . , doses in mg rather than in MAMU ) are related to size [60 , 61 , 66] , we adjusted population average TTX resistance with respect to post-partum female mass for each population . Adult females are the largest size class in a given population and therefore are the most likely to prey on newts . Additionally , because of asymptotic growth curves in snakes , adult females represent the best size class to compare across populations . The 15% , 50% , and 85% IP doses ( in mg ) of TTX for adult post-partum female snakes at each population of Th . sirtalis were thus estimated using the average mass of this demographic group at each locality ( Table 1 ) . In the case of one locality , East Bay , size data were unavailable and we used an estimate of the average female mass based on its nearest geographic neighbors . Because 1 MAMU = 0 . 01429 μg TTX per gram of snake [60 , 62 , 63 , 66 , 80] , the IP dose of TTX ( in mg ) required to reduce performance to a given amount ( e . g . , 50% ) for an adult female snake at any given locality is estimated as: where θ is the performance reduction dose of interest ( e . g . , 15% , 50% , or 85% in MAMU ) and snake mass is the mean post-partum weight of female snakes from a given population . We modeled the effect of oral consumption of newts by snakes by converting the above IP doses to oral dose . The relationship between oral and IP doses of TTX is linear for Th . sirtalis ( as well as other vertebrates; e . g . , mice ) . At all levels of resistance and doses of TTX , the oral dose required to achieve the same effect as an IP dose is 40× [61] . We converted the IP 15% , 50% , and 85% resistance doses ( in mg ) to oral doses ( in mg ) by multiplying each dose by 40 . Modeling mismatch . We defined a functional mismatch if ecological interactions between individuals of sympatric species do not result in variable fitness consequences for either taxa ( i . e . , all predators are able to subdue all prey without impairment , or all prey able to repel or kill all predators ) . We defined a given locality as “matched” if a sympatric interaction could potentially result in variable fitness outcomes for one or both taxa . This outcome was conservatively judged to occur if the average performance reduction of a local snake ingesting any sympatric newt fell between 15% and 85% of normal crawl speed . The phenotypic space referred to as “matched” is more properly the region wherein potential reciprocal selection could occur between TTX toxicity and resistance . At performance levels <15% , snakes that ingest newts are fully immobilized or killed and newts escape [81] , whereas at performance levels >85% snakes are unaffected and all captured newts die . We visualized match and mismatch at individual populations by plotting total skin TTX of newts against the size adjusted , oral 50% dose of snakes at each locality along with 15% and 85% dose model lines ( see below ) on a log scale ( Figure 3 ) . The actual range of newt phenotypes at each locality was used to illustrate the distribution of prey phenotypes . Because predator phenotypes are based on an estimated asymptotic function , it was not possible to plot them as range and we used the 95% confidence interval around each localities 50% as an estimate of phenotypic range . Our data included populations of newts that had no measurable TTX; as a result we transformed all values ( TTX in newts and 50% doses in snakes ) by adding 0 . 0001 mg to each value . This adjustment maintained the overall relationship between newt phenotypes and snakes phenotypes but allowed zero values to be plotted . The 15% , 50% , and 85% model lines were plotted using the absolute ( i . e . , in mg ) estimates of the 15% , 50% , and 85% resistance doses ( see above for details ) for each locality . Quantification of phenotypic mismatch . We calculated ( d ) as the orthogonal distance from the joint mean of each locality to the predicted 50% performance line ( Figure 3 ) . This estimate of distance d from the best match provides a quantification of the degree of mismatch at a given locality . An analogous approach has been used to evaluate arms-races between the sexes within species [37] . Although the choice of 50% to express this mismatch metric is somewhat arbitrary , the model of performance was robust and returned similar results for a range of ( 40% to 60% ) of hypothetical matches . Because of the extreme range and nonlinearity of snake 50% doses and the presence of newt populations that had TTX levels below our detectable levels , we used log-transformed values of the following— ( newt total skin TTX + 0 . 0001 ) and ( snake 50% dose + 0 . 0001 ) —to calculate d ( see above ) . This method uses the equation for estimating the shortest distance from a point to a line: where A and B are the respective components of the slope and C is the intercept of the line . Our model assumes that the best functional match of newt and snake phenotypes at a given locality is one in which ingestion of an average newt by an average adult female snake will result in a reduction of that snake's crawl speed to 50% of baseline . This assumption results in the prediction that the model line describing perfect match is: Thus the line describing perfect phenotypic match has a slope and intercept of 1 and 0 respectively , and A = 1 , B = −1 , and C = 0 , and our estimate of d simplifies to: where xi = log ( 50 % dose + 0 . 0001 ) of snakes from a given locality , and yi = log ( average total skin TTX + 0 . 0001 ) of co-occurring newts . Population-specific interaction gradients . Interaction gradients were generated for each locality by estimating the performance reduction experienced by an average snake after ingesting any of the observed sympatric newts . Thus the gradients reflect the observed distributions of whole newt toxicity for each locality . Interaction gradients are estimated with simple linear regression ( SNAKE PERFORMANCE = ( NEWT TTX ) * β + ERROR ) . to reveal the average slope of the fitness consequence analogous to directional selection gradients , regardless of the form of regression that best fits the data [82] . Snake performance values are calculated from population-specific dose-response curves ( see above ) . For the purposes of plotting , we normalized newt TTX levels to range from 0–1 , with the most toxic newts scaled to 1 for each locality . Phenotypic distributions and functional matching . We used the quantitative estimate of mismatch d to visualize geographic patterns of mismatch . Isocline maps that included all sampled localities seen in Figure 1 were generated using inverse distance-weighted interpolation based on observed values ( i . e . , TTX levels , snake resistance , and d ) and the latitude and longitude coordinates for each population . Because of nonlinearity in resistance values ( see also [60] ) oral 50% doses of >5 mg were entered as 5 mg . The function's power was set at two and the neighborhood at 500 km . Analyses were performed in ArcView GIS 3 . 3 with Spatial Analyst 2 . 0 . Analysis of geographic patterns of TTX-resistance and justification for phenotype classes in snakes ( Figure 2B ) was performed as per [60] . We used multiple post-hoc comparisons to estimate phenotype classes for Figure 2A ( newt total skin TTX ) . Populations with values of d > 0 . 6 ( i . e . , those that lie outside the range of the 15% and 85% dose lines and were considered mismatched ) are colored in blue and purple ( Figure 2C ) . Populations with values of d < 0 . 6 fell between the 15% and 85% lines and are colored in red , orange , yellow , and green ( Figure 2C ) . | Arms races between natural enemies can lead to the rapid evolution of extreme traits , high degrees of specialization , and the formation of new species . They also serve as the ecological model for the evolution of drug resistance by diseases and for host–pathogen interactions in general . Revealing who wins these arms races and how they do so is critical to our understanding of these processes . Capitalizing on the geographic mosaic of species interactions , we examined the dynamics of the arms race between snakes and their toxic newt prey . Garter snakes in some populations have evolved dramatic resistance to the tetrodotoxin defense of the their local prey . By evaluating the pattern of mismatches between toxicity and resistance , we discovered that predators sometimes escape the arms race through the evolution of extreme resistance , but that prey never come out ahead . The reason for this one-sided outcome appears to depend on the molecular genetic basis of resistance in snakes , wherein changes to a single amino acid residue can confer huge differences in resistance . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"evolutionary",
"biology",
"ecology"
] | 2008 | Phenotypic Mismatches Reveal Escape from Arms-Race Coevolution |
Primary open angle glaucoma ( POAG ) is a leading cause of blindness worldwide , with elevated intraocular pressure as an important risk factor . Increased resistance to outflow of aqueous humor through the trabecular meshwork causes elevated intraocular pressure , but the specific mechanisms are unknown . In this study , we used genome-wide SNP arrays to map the disease gene in a colony of Beagle dogs with inherited POAG to within a single 4 Mb locus on canine chromosome 20 . The Beagle POAG locus is syntenic to a previously mapped human quantitative trait locus for intraocular pressure on human chromosome 19 . Sequence capture and next-generation sequencing of the entire canine POAG locus revealed a total of 2 , 692 SNPs segregating with disease . Of the disease-segregating SNPs , 54 were within exons , 8 of which result in amino acid substitutions . The strongest candidate variant causes a glycine to arginine substitution in a highly conserved region of the metalloproteinase ADAMTS10 . Western blotting revealed ADAMTS10 protein is preferentially expressed in the trabecular meshwork , supporting an effect of the variant specific to aqueous humor outflow . The Gly661Arg variant in ADAMTS10 found in the POAG Beagles suggests that altered processing of extracellular matrix and/or defects in microfibril structure or function may be involved in raising intraocular pressure , offering specific biochemical targets for future research and treatment strategies .
Elevated intraocular pressure is a strong risk factor for glaucoma development and progression [1] . In POAG , increased resistance to outflow of aqueous humor through the trabecular meshwork is the cause of elevated intraocular pressure [2] . Currently , the only proven treatments for POAG patients involve reduction of intraocular pressure by inhibiting aqueous humor production , or bypassing the diseased trabecular meshwork . The mechanisms of increased resistance to aqueous humor outflow are not well-understood [2] , but may involve changes in extracellular matrix composition of the trabecular meshwork [3] . Linkage studies have identified a number of POAG loci [4] . So far only three genes have been shown to be associated with POAG [4] , but they account for only a small fraction of POAG cases , and none have shed much light on the disease process . Although genome-wide association studies could be a powerful tool to establish more POAG loci , this requires recruitment of a large number of patients . Moreover , causal association between sequence variants and disease can be difficult to establish in human studies . In this study , we have used a canine model to identify a candidate POAG gene , which has the advantage of availability of tissues from normal and affected dogs as well as future gene rescue experiments to investigate the pathogenic mechanisms of the gene variant . A colony of Beagle dogs established in 1972 [5] , which is a well-characterized and naturally occurring animal model of POAG , was used for this study . For POAG-affected dogs in this colony , increases in intraocular pressure begin at 8 to 16 months of age , due to increased resistance to outflow of aqueous humor [6] , despite normal appearing open iridocorneal angles . As with POAG in humans , optic nerve cupping , loss of optic nerve axons [7] and vision loss occur in affected Beagles following slowly progressing and sustained elevations of intraocular pressure , if left untreated . Multigenerational breeding experiments have shown that POAG in the Beagle colony is inherited as an autosomal recessive trait [8] . Domestication of the dog from wolves and recent breed creations have resulted in extensive linkage disequilibrium and large haplotype blocks , which makes mapping Mendelian traits possible with far fewer markers and fewer individuals as needed for human studies [9] , [10] . The dog genome has been sequenced and microarrays for whole-genome high-density SNP genotyping have been established and used to map traits in dogs [10]–[12] . The aim of this study was to map the disease locus and then to identify candidate disease genes by high-throughput sequencing of the entire disease locus .
To map the POAG locus , we genotyped 19 affected and 10 carrier dogs from the POAG Beagle colony using version 2 of the Affymetrix Canine Genome SNP array . Since the colony has been maintained primarily by affected to affected breeding , with periodic introduction of unrelated normal Beagles ( Figure 1 ) , we hypothesized that the disease allele would be contained within an extensive haplotype block homozygous for affected and heterozygous for carrier dogs . Therefore , we identified SNPs that fulfilled the zygosity criterion , defined as being both homozygous for all affected dogs and heterozygous for all carriers . Regions of homozygosity for all affected dogs were common for all chromosomes , as expected for the highly inbred pedigree . However , only Chromosome 20 contained SNPs heterozygous for all carriers ( Figure 2A ) , consisting of 41 consecutive SNPs covering 4 . 7 Mb . Of those 41 SNPs , 27 consecutive SNPs were also homozygous for all affected dogs , satisfying the zygosity criterion ( Figure 2B and Figure S1 ) . Haplotype analysis of the region revealed informative recombination events within the pedigree that defined a 4 Mb locus in which all carriers were heterozygous and all affected dogs homozygous for the affected haplotype ( Figure 2C ) . In addition to applying the zygosity criterion , two-point and multipoint parametric linkage analyses of the pedigree genotype data were performed . Initial power calculations predicted that with the available pedigree , a single locus could be identified with a LOD score of 2 . 67 . With genome-wide two-point analysis , regions with LOD score >2 were found on chromosomes 5 , 15 and 20 ( Figure 3A ) . Follow-up multipoint linkage analysis reduced the LOD score of the chromosome 5 region to below 0 . 5 , excluding this as a candidate locus ( Figure 3B ) . For chromosome 15 , multipoint analysis did not reduce the LOD score ( Figure 3C ) . However , haplotype analysis revealed a pattern of inheritance discordant with phenotype ( Figure 4 ) , excluding chromosome 15 . The distal end of chromosome 20 had a two-point LOD score of 2 . 42 and a multipoint LOD score of 2 . 70 ( Figure 3D ) , consistent with initial power calculations . This region identified by linkage analysis coincided with the 4 Mb locus identified by the zygosity criterion . Therefore , the results of genome-wide linkage analysis independently verified that the disease locus in the POAG Beagles maps to the same 4 Mb region of chromosome 20 identified using the zygosity criterion . Comparison of the 4 Mb POAG locus in dog with the human genome revealed shared synteny within a segment of human chromosome 19 , previously identified as a quantitative trait locus for intraocular pressure in humans [13] ( Figure 5A ) . The order and number of genes within the POAG locus on the canine chromosome are highly conserved in the human syntenic region ( Figure 5B ) . Since increased intraocular pressure is the initial manifestation of disease in the POAG Beagles , synteny with the human intraocular pressure locus offers compelling biological support that the 4 Mb region contains the disease-causing genetic variant . To identify the disease gene , the entire 4 Mb POAG locus in an affected and a carrier dog , as well as a normal dog from the colony ( dogs 3 , 9 and 11 , Figure 1 ) was isolated by microarray-based sequence capture and then sequenced with the Illumina Genome Analyzer . Alignment of the sequences to the reference canine genome revealed 2 , 692 sequence variants segregating with disease ( homozygous for the affected dog and heterozygous for the carrier dog , with the additional criterion that the normal dog is not homozygous for the same allele as the affected dog ) . Of the segregating variants , 54 were located within coding regions of canine genes identified by the human protein alignment track of the UCSC genome browser ( http://genome . ucsc . edu ) . Of the 54 variants within coding regions , 8 resulted in non-synonymous amino acid substitutions in 7 genes . Among those 8 variants , based on BLOSUM62 score for amino acid substitution and mammalian conservation score from the vertebrate multiz alignment and conservation track of the UCSC human genome browser , the best candidate variant was at position 56 , 097 , 365 of chromosome 20 ( canFam build 2 ) from a G in the reference sequence , to an A in the affected dog . This variant was confirmed by conventional Sanger sequencing of affected , carrier and normal dogs from the POAG colony ( Figure 6A ) . To determine the frequency of the disease allele ( 56097365 A ) in the normal Beagle population , 48 Beagles not affected by glaucoma and not related to the colony were sequenced . Only one of the unaffected dogs was found heterozygous for the disease allele , the rest were homozygous for the normal allele , suggesting a disease allele frequency of ∼1% in Beagles . The 56097365 G->A variant is within exon 17 of ADAMTS10 , a member of the disintegrin and metalloproteinase with thrombospondin motifs family of secreted proteases involved in formation of the extracellular matrix [14]–[16] . The variant results in a Gly->Arg substitution at position 661 within the protein sequence ( NCBI accession XP_854320 ) . The glycine at position 661 is completely conserved in 38 vertebrate species ( 7 representative species shown , Figure 6B ) . The Gly661Arg substitution was predicted to have a deleterious effect on protein function by the prediction programs SIFT [17] and SNPs3D [18] and occurs within the cysteine-rich domain ( Figure 6C ) , which may be involved in regulation of protease activity [19] . Western blot analysis of protein extracts from tissues dissected from normal dog eyes showed high expression of ADAMTS10 protein in the trabecular meshwork , relative to other eye tissues examined ( Figure 7 ) . ADAMTS10 was also expressed in the cornea , and to a much less extent in the iris , ciliary body and optic nerve ( Figure 7 ) . Structural modeling was performed using crystal structure of ADAMTS13 [20] to predict the structures of normal and Gly661Arg ADAMTS10 proteins . In the predicted fold of ADAMTS10 , Gly661 is located within a tight turn ( Figure 8A ) , suggesting a glycine may be required at this position for proper folding . Gly661 is predicted to be buried in the structure within the interface between the CA and T1 domains ( Figure 8B ) . Substitution of arginine for glycine at position 661 would be sterically unfavorable , with the longer charged side chain of arginine extending into the T1 domain ( Figure 8C ) , suggesting that the Gly661Arg change would likely disrupt normal ADAMTS10 structure . To investigate possible effects of the Gly661Arg substitution on ADAMTS10 protein stability , the protein half-lives for normal and mutated ADAMTS10 were determined . Since ADAMTS10 produced by trabecular meshwork cells would be secreted into aqueous humor , half-lives were determined in the presence of aqueous humor . In vitro transcribed normal and mutated ADAMTS10 protein labeled with biotinylated lysine was incubated in aqueous humor for various time periods and the amount of ADAMTS10 remaining at each time point was determined by Western blotting with fluorescently labeled streptavidin . The Gly661Arg mutant appeared to decay more rapidly than did normal ADAMTS10 ( Figure 9 ) . The Log2 of the band intensities were plotted vs . time to determine the protein half life , which is equal to the negative inverse of the slope of the best fit line . In each of four independent experiments , mutated ADAMTS10 decayed more rapidly than did normal ADAMTS10 ( 261+/−29 . 5 vs . 601+/−219 . 7 min . , mean +/− SD , half-lives for mutated and normal , respectively , significantly different , p<0 . 05 ) . The slopes of the lines fit to data from all four experiments , combined by normalizing band intensities to the initial time point , were significantly different ( p<0 . 001 ) and correspond to half lives of 255 . 8 min . for mutated and 636 . 9 min . for normal ADAMTS10 ( Figure 9C ) . These results suggest that mutated ADAMTS10 decays more rapidly , with a protein half-life ∼40% that of normal .
By application of the zygosity criterion , linkage and haplotype analyses , we were able to map the Beagle POAG locus to a single 4 Mb region on chromosome 20 . This canine POAG locus is syntenic with a region on human chromosome 19 within a quantitative trait locus for regulation of intraocular pressure identified by a genome-wide scan of 486 families [13] . Since ocular hypertension occurs early in the disease process in the Beagles , synteny with the intraocular pressure locus gives biological support to the genetic identification of the POAG locus and suggests that the disease gene directly participates in intraocular pressure regulation . Furthermore , synteny with the human locus suggests that the disease gene found in the Beagles may also be disrupted in human glaucoma patients . Using affected and unaffected dogs of other breeds to fine map the disease locus has proven to be an effective approach in other canine genetic studies [10] . However , because clinical identification of POAG cases in dogs is rare , this approach would be challenging and further would require the assumption that the POAG locus in Beagles is shared with affected dogs of other breeds . Alternatively , refining the locus by further breeding within the colony to allow for informative recombinations would be time consuming and costly since definitive diagnosis cannot be made until two years of age . To overcome these limitations , we obtained high quality sequence information for the entire 4 Mb locus by sequence capture and next-generation sequencing . Using this approach , 2 , 692 single nucleotide variants that segregated with disease were identified , 54 of which were within exons , 8 of which were nonsynonomous . Since POAG in the POAG Beagle colony is autosomal recessive with 100% penetrance , we focused on nonsynonomous changes because these are likely to have strong functional effects . However , synonomous changes in coding regions or variants outside coding regions could have pathogenic effects and cannot be ruled out . In addition , our sequence capture and sequence analysis rely on the quality of the reference canine genome and therefore our approach could miss variants due to errors in the reference genome assembly or annotation . Among the 8 nonsynonomous variants segregating with disease , the strongest candidate identified was a single base pair change in the affected dogs that results in a non-conservative amino acid substitution in a region of ADAMTS10 that is highly conserved in vertebrate species . In POAG-affected dogs , an arginine is substituted for a glycine at amino acid position 661 which is an invariant amino acid in ADAMTS10 in 38 species , from lamprey to human . Consistent with a highly penetrant rare disease allele , the frequency of the variant in ADAMTS10 estimated from genotyping 48 unrelated normal Beagles was 1% . ADAMTS10 is a member of a family of secreted metalloproteinases [14] , [16] . All ADAMTS family members share a common structural organization including a metalloproteinase domain followed by a disintegrin-like module , a thrombospondin repeat unit , a cysteine-rich domain and a spacer region ( see Figure 6C ) . Diversity within the ADAMTS family largely arises from structural differences in ancillary domains of the carboxy-terminal half of the proteins . The Gly661Arg variant found in this study is within the cysteine-rich domain of ADAMTS10 and is predicted to disrupt protein function by the amino acid substitution prediction programs SIFT [17] and SNPs3D [18] . Consistent with this , our homology modeling of the ADAMTS10 structure suggests that the Gly661 residue is located within a tight turn and is buried within the interface between the cysteine rich and thrombospondin repeat domains . The long polar side chain of arginine substituted at this position is predicted to disrupt the normal protein fold . Consistent with disruption of normal protein folding , we found that the Gly661Arg form of ADAMTS10 is less stable , with a protein half-life ∼40% that of normal ADAMTS10 . Although we cannot be certain if the reticulocyte lysate-based in vitro transcription and translation system produced normally folded protein , this system has been used to produce functional secreted proteins such as metalloproteinases [21] , neutrophil elastase [22] and myocilin [23] . Any effects of the in vitro system on folding would be experienced by both the normal and mutated proteins in our assays . Our data show that the mutated form of ADAMTS10 has a shortened half-life , consistent with our homology modeling which suggested that the Gly661Arg substitution would disrupt interactions at the interface of two domains . Clinical evidence for the importance of the cysteine rich domain in ADAMTS function comes from patients with thrombotic thrombocytopenic purpuria ( OMIM #274150 ) who have autoantibodies recognizing the cysteine-rich domain of ADAMTS13 , causing reduced proteolytic activity of ADAMTS13 in vivo and in vitro [24] , [25] . Structural studies and deletion analysis have established that the cysteine-rich domain plays a vital role in regulation of protease activity or substrate recognition for ADAMTS family proteins [20] , [25] . In addition , alignment of the cysteine-rich domains of all 19 human ADAMTS family members and 5 related ADAMTSL proteins by Akiyama et al . [20] , revealed that Gly661 of ADAMTS10 is an invariant amino acid . Such stringent evolutionary conservation of this glycine residue , across 38 vertebrate species and within 24 protein superfamily members , supports the hypothesis that the arginine substitution would have a detrimental effect on ADAMTS10 function . Unlike the three POAG genes identified thus far in humans ( MYOC , WDR36 and OPTN ) [4] , the ADAMTS10 variant identified in this study has obvious functional implications , supporting ADAMTS10 as a strong candidate gene . A role for metalloproteinases in ocular hypertension has long been suggested by numerous in vitro studies [26] . Changes in the amount or composition of extracellular matrix within the trabecular meshwork have been hypothesized to contribute to ocular hypertension by increasing resistance to outflow of aqueous humor through the trabecular meshwork [3] . Although the specific substrate for ADAMTS10 is unknown , other ADAMTS family members are known to participate in collagen processing and proteoglycan degradation . ADAMTS10 is likely to function in some capacity in regulation of extracellular matrix and therefore disruption of its function could lead to POAG by increasing resistance to aqueous humor outflow through the trabecular meshwork . Several ADAMTS family members have been investigated as candidates for regulating outflow resistance , and it has been shown that perfusion of anterior segment organ cultures with ADAMTS4 increases outflow facility [27] . The faster decay of the Gly661Arg ADAMTS10 would likely reduce the amount of ADAMTS10 available , which could possibly result in increased resistance to aqueous humor outflow . Future studies with anterior segment organ cultures perfused with normal and mutated ADAMTS10 could test this hypothesis . The Beagles of the POAG colony are phenotypically normal with no systemic abnormalities other than POAG in the affected dogs . Our Western blotting results showed that ADAMTS10 is expressed at high levels within the trabecular meshwork as compared to other eye tissues , which would be consistent with an effect of the Gly661Arg ADAMTS10 variant specific to aqueous humor outflow . Mutations in ADAMTS10 have been identified in human patients with autosomal recessive Weill-Marchesani syndrome ( WMS ) [28] , [29] , a rare disease with systemic features including short stature and stubby hands and feet ( OMIM #277600 ) . A mutation in type I fibrillin has also been found in autosomal dominant WMS [30] , which is clinically indistinguishable from the autosomal recessive form [31] , suggesting a functional link between ADAMTS10 and type I fibrillin . WMS belongs to a group of rare connective tissue disorders , including Marfan syndrome ( OMIM #154700 ) , for which causative mutations in type I fibrillin , a major constituent of microfibrils [32] , have been found . While glaucoma is common in WMS patients [31] , the mechanism is not well-studied , due to the extremely small patient population . The prevalence of glaucoma in Marfan syndrome patients is higher than in the general population [33] . Clinically , glaucoma in Marfan syndrome most often presents as POAG , with elevated intraocular pressure and open iridocorneal angles [33] . As type I fibrillin is involved in microfibril formation and function , presentation of POAG in patients with Marfan syndrome caused by type I fibrillin mutations suggests that microfibril defects may be involved in POAG pathogenesis . This notion is supported by another common ocular manifestation in WMS and Marfan syndrome , ectopia lentis ( dislocated or malpositioned lens ) . Consistent with a defect in microfibril structure or function , ectopia lentis is caused by defects in the zonule fibers that hold the lens in place and are composed of fibrillin-containing microfibrils [34] . Recently , mutations were found in other members of the ADAMTS superfamily , ADAMTS17 in autosomal recessive WMS [29] and ADAMTSL4 in isolated ectopia lentis [35] , supporting a role for ADAMTS family members in microfibril structure and function . Ultrastructural studies of human trabecular meshwork have shown changes with age that are more pronounced in POAG patients , including a thickening of sheaths that surround elastin fibers and are composed of extracellular matrix , including fibrillin and fine fibrils , as well as an accumulation of sheath-derived plagues in the aqueous humor outflow pathway [36] . We have previously described similar changes in the trabecular meshwork of POAG-affected Beagles [37] , which could be explained by microfibril defects caused by the Gly661Arg variant in the ADAMTS10 gene . Additionally , microfibrils play an important regulatory role in the homeostasis of extracellular matrix by controlling the activation and localization of TGFβ [32] , which is elevated in the aqueous humor of glaucomatous eyes [38] , [39] . Involvement of microfibril defects in glaucoma is further suggested by recent findings in primary congenital glaucoma of a null mutation in LTBP2 , which shares homology with fibrillins and is a structural and functional component of microfibrils [40] . Identification of the Gly661Arg variant of ADAMTS10 in the POAG Beagles in this study provides genetic evidence that microfibril abnormalities may be involved in increased resistance to outflow of aqueous humor through the trabecular meshwork in POAG . The precise mechanisms of increased resistance to outflow of aqueous humor have remained a long-standing puzzle in glaucoma research . Current treatments for POAG patients involve reduction of intraocular pressure by inhibiting production of aqueous humor , or bypassing the diseased outflow pathway , but do not address the root of the problem . The robust expression of ADAMTS10 in the trabecular meshwork suggests that any defect in ADAMTS10 function caused by the Gly661Arg variant could have particularly pronounced effects on the functioning of the trabecular meshwork , specifically affecting aqueous humor outflow . Identification of ADAMTS10 as a candidate gene in the POAG Beagles suggests that altered processing of extracellular matrix and/or defects in microfibril structure or function may be involved in raising intraocular pressure , offering specific biochemical targets for future research and treatment strategies .
Blood samples from dogs were obtained by licensed veterinarians or veterinary technicians by standard venipuncture , in accordance with the Institutional Animal Care and Use Committees of Vanderbilt University and the University of Florida . A total of 48 canine DNA samples , including 30 dogs from the POAG Beagle colony , 7 unrelated normal Beagles and 11 unrelated mixed-breed dogs , were genotyped using version 2 of the Affymetrix genome-wide SNP genotyping array ( http://www . broadinstitute . org/mammals/dog/caninearrayfaq . html ) . For combined genotype data of all dogs , 40 , 600 informative SNPs had call rates >90% , and were heterozygous for <50% of dogs . For duplicate samples , 99 . 6% of SNPs received identical calls . The disease status of dogs was determined by clinical eye exams by veterinary ophthalmologists . The minimal age of the dogs at final diagnosis was 2 . 2 years . Initial power calculations were performed using the SIMLINK V 4 . 12 program ( http://csg . sph . umich . edu/boehnke/simlink . php ) . Two-point and multipoint linkage analyses of the genome-wide SNP data were performed using SuperLink Online [41] assuming an autosomal recessive model with complete penetrance . Dogs 1 through 31 , except dog 6 , were included in the analysis ( Figure 1 ) . SNPs uninformative for Beagles were removed from analysis . Mendelian error checking was performed and inconsistent SNPs removed for all individuals . Minor allele frequencies were calculated using SNP data from 8 unaffected Beagles whose unrelatedness was confirmed using the Graphical Representation of Relationship ( GRR ) software package [42] . Enrichment for genomic sequence within the 4 Mb locus was carried out using capture microarrays designed and manufactured by Roche NimbleGen , using build 2 of the canine genome . The capture arrays consisted of 385 , 000 capture probes >60 bp in length , designed to capture all non-repetitive sequence from base position 55 , 800 , 000 to 59 , 850 , 000 on canine chromosome 20 . Sequence capture was carried out on 3 dogs from the POAG Beagle colony ( dogs 3 , 9 , and 11 , Figure 1 ) , essentially as described in Albert et al . [43] and Okou et al . [44] , with modifications to optimize for the Illumina Genome Analyzer II sequencing platform . Hybridization of the captured DNA fragments to the flow cell and amplification to form clusters was performed using the Illumina cluster station , following the standard Illumina protocol . The captured DNA fragments were used at a final concentration of 5 pM during hybridization/cluster generation to achieve cluster density of ∼160 , 000 clusters/tile . Paired end , 38 base pair read sequencing was carried out with the Illumina Genome Analyzer II . Fluorescent images were converted into base pair calls using the Illumina Pipeline software . Paired end alignments to the canine genome build 2 were carried out using Bowtie [45] . For the 3 samples , 53 . 7% of the reads aligned to the 4 . 05 Mb target region representing 0 . 17% of the genome , yielding a 316-fold enrichment of the target sequence . The percentage of the capture region covered >8-fold ranged from 91 . 3% to 92 . 1% . The average coverage of genes , represented by the human protein alignment track in the UCSC genome browser ( http://genome . ucsc . edu/ ) ranged from 93 . 1% to 94 . 4% . For the 29 SNPs in the capture region represented on the SNP genotyping array , complete concordance in genotype calls was found between the Illumina sequencing and SNP array data . Bases different from the reference canine sequence ( variant SNPs ) were identified using SAMtools [46] ( http://samtools . sourceforge . net ) . SNPs segregating with disease were defined as being a homozygous variant in the affected dog and heterozygous for the carrier dog , with the additional criterion that the normal dog could not be homozygous for the variant found in the affected dog . Segregation of the 56097365 G->A variant with disease status was confirmed by Sanger sequencing of affected , carrier and normal dogs from the POAG Beagle colony . To determine the disease allele frequency in Beagles , 48 normal Beagles examined by licensed veterinarians and found to be not affected by glaucoma were also sequenced . The normal Beagles were unrelated to the POAG colony and did not share common grandparents . Postmortem eyes from dogs were obtained by veterinary technicians in accordance with the Institutional Animal Care and Use Committee of Vanderbilt University . Eyes were removed from normal dogs free of eye disease within 30 min after sacrifice . Cornea , trabecular meshwork , iris , ciliary body and optic nerve were isolated by dissection under a stereo microscope . Protein was extracted by homogenization in 150 mM LiCl , 50 mM Tris/pH 7 . 5 , 1 mM dithiothrietol , protease inhibitors and 1% lithium dodecyl sulfate . Protein concentration was determined using a fluorescence-based protein assay ( Nano-Orange Protein Assay , Invitrogen ) . Lysates of HEK293 cells transiently transfected with either empty vector or vector containing an epitope-tagged , full length human ADAMTS10 construct ( Origene ) were used as controls . For SDS-PAGE under reducing conditions , 10 µg of total protein from eye tissues or 5 µg from cell lysates were loaded into wells of 10% pre-cast polyacrylamide gels ( Criterion , Bio-Rad ) . After SDS-PAGE , proteins were transferred to PVDF membrane ( Bio-Rad ) . Standard Western blotting was performed using 1 µg/ml goat anti-human ADAMTS10 antibody ( Santa Cruz ) or 3 . 3 µg/ml mouse anti-human glyceraldehyde-3-phophate dehydrogenase ( GAPDH ) antibody ( clone 6C5 , Millipore ) . Blots were imaged and molecular weights determined using an Odyssey infrared imaging system ( Li-Cor Biosciences ) . A single immunoreactive band for ADAMTS10 ran at an apparent mw of 130 kDa , the same as previously reported for the intact ADAMTS10 zymogen [15] . The homology model of ADAMTS10 was calculated using the program I-TASSER [47] and is based on the structure of ADAMTS13 ( PDB entry 3GHM; [20] ) . Superposition of the calculated model with ADAMTS13 in the program O [48] resulted in a RMS deviation of 1 . 0 Å for 347 Cα atoms . Figure 8 was made using MOLSCRIPT [49] and RASTER3D [50] . The domain nomenclature and color coding follow those of Akiyama et al . [20] . Canine aqueous humor was obtained from laboratory-quality dogs using protocols approved by the Institutional Animal Care and Use Committee of Vanderbilt University and placed immediately in sealed sterile tubes and stored at −80°C . An expression vector with a T7 promoter upstream of a cDNA insert encoding full-length human ADAMTS10 corresponding to NCBI accession number NM_030957 with a c-terminal Myc-DDK tag was obtained from Origene . The ADAMTS10 insert was verified by Sanger DNA sequencing on both strands . A PCR-based mutagenesis kit ( Quick Change II , Stratagene ) was used to introduce the G to A mutation found in the POAG-affected Beagles , resulting in a glycine to arginine substitution at amino acid 661 into the expression construct . Mutagenesis was confirmed by Sanger sequencing of the entire construct . A rabbit reticulocyte-based in vitro coupled transcription/translation kit ( TNT Quick , Promega ) was used to express normal and mutated ADAMTS10 protein from the expression vector constructs following the manufacturer's protocol . Modified lysine-specific tRNA was included in the reaction ( Transcend tRNA , Promega ) to produce ADAMTS10 protein with biotinylated lysines . To measure protein half-life , samples were made with 4 µl of in vitro reaction mixed with 26 µl aqueous humor in sterile O-ring-sealed tubes and placed in a 37°C water bath . At various times , samples were removed from the water bath and placed in −80°C . The aqueous humor used in the experiments was pooled from 3 individual dogs and included 50 µg/ml cycloheximide ( Sigma ) to prevent protein synthesis during incubation . Samples were separated by SDS-PAGE using 7 . 5% pre-cast polyacrylamide gels ( Criterion , Bio-Rad ) . After SDS-PAGE , proteins were transferred to PVDF membrane ( Bio-Rad ) . The membrane was blocked 1 h in PBS/1% casein and then probed with streptavidin conjugated to IRDye 680 ( Li-Cor Biosciences ) . Membranes were imaged and molecular weights and background subtracted band intensities determined using an Odyssey infrared imaging system ( Li-Cor Biosciences ) . A single band at the expected molecular weight of ∼130 kDa was detected , similar to that reported previously [15] and found in eye tissue in this study . Protein decay was assumed to follow the equation:where A ( t ) is the amount of protein at time t , A ( t = 0 ) is the amount of protein at time t = 0 and h is the half life . The decay equation can be rearranged to: By plotting the Log2 of the band intensity versus time of incubation , the half-life of the protein was determined as the negative inverse of the slope of the linear fit to the data . Four independent experiments were performed . | Primary open angle glaucoma ( POAG ) is a leading cause of vision loss and blindness affecting tens of millions of people . Ocular hypertension is a strong risk factor for the disease and the only effective target of treatment . Ocular hypertension results from increased resistance to outflow of aqueous humor through the trabecular meshwork , a specialized filtration tissue consisting of alternating layers of cells and connective tissue , but the specific reasons for the increased resistance are not known . The animal model for human POAG used in this study was a colony of Beagle dogs that carry an inherited form of the disease in which ocular hypertension is the primary manifestation . We have found a variant in ADAMTS10 that belongs to a family of genes that contribute to formation of extracellular matrix and may itself be involved in formation of elastic microfiber structures . We found that the ADAMTS10 protein is expressed at particularly high levels in the trabecular meshwork . The candidate variant in ADAMTS10 found in the POAG–affected Beagles suggests that altered processing of connective tissue and/or elastic microfiber defects may be involved in raising eye pressure , offering specific biochemical targets for future research and treatment strategies . | [
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... | 2011 | Mapping of the Disease Locus and Identification of ADAMTS10 As a Candidate Gene in a Canine Model of Primary Open Angle Glaucoma |
Three closely related thermally dimorphic pathogens are causal agents of major fungal diseases affecting humans in the Americas: blastomycosis , histoplasmosis and paracoccidioidomycosis . Here we report the genome sequence and analysis of four strains of the etiological agent of blastomycosis , Blastomyces , and two species of the related genus Emmonsia , typically pathogens of small mammals . Compared to related species , Blastomyces genomes are highly expanded , with long , often sharply demarcated tracts of low GC-content sequence . These GC-poor isochore-like regions are enriched for gypsy elements , are variable in total size between isolates , and are least expanded in the avirulent B . dermatitidis strain ER-3 as compared with the virulent B . gilchristii strain SLH14081 . The lack of similar regions in related species suggests these isochore-like regions originated recently in the ancestor of the Blastomyces lineage . While gene content is highly conserved between Blastomyces and related fungi , we identified changes in copy number of genes potentially involved in host interaction , including proteases and characterized antigens . In addition , we studied gene expression changes of B . dermatitidis during the interaction of the infectious yeast form with macrophages and in a mouse model . Both experiments highlight a strong antioxidant defense response in Blastomyces , and upregulation of dioxygenases in vivo suggests that dioxide produced by antioxidants may be further utilized for amino acid metabolism . We identify a number of functional categories upregulated exclusively in vivo , such as secreted proteins , zinc acquisition proteins , and cysteine and tryptophan metabolism , which may include critical virulence factors missed before in in vitro studies . Across the dimorphic fungi , loss of certain zinc acquisition genes and differences in amino acid metabolism suggest unique adaptations of Blastomyces to its host environment . These results reveal the dynamics of genome evolution and of factors contributing to virulence in Blastomyces .
Blastomyces is a genus of a thermally dimorphic fungal pathogen , which is the etiological agent of blastomycosis , a lung infection that can become a systemic mycosis . In North America , Blastomyces is endemic in the Ohio and Mississippi river valleys , the Great Lakes region , and the St . Lawrence River [1] . Within Blastomyces , two lineages of B . dermatitidis have been recognized [2] , with recent work providing evidence that one lineage is a distinct species , B . gilchristii [3] . Both species can infect humans , and vary in morphology , virulence and immune responses by the host . The primary mode of infection is inhalation of conidia and the subsequent conversion of these conidia into parasitic yeast [4 , 5] . Clinical manifestations range from asymptomatic infection to symptomatic disease and include pneumonia , acute respiratory distress syndrome , and a rapidly progressive dissemination involving multiple organ systems that is often fatal [5 , 6] . Diagnosis is often complicated by the similarity of symptoms to those of viral or bacterial respiratory infection and by the aforementioned variety of manifestations [7] . As a thermally dimorphic fungus , Blastomyces has the remarkable ability to switch between two different morphologies in response to external stimuli , predominantly temperature [5] . At 22–25°C , Blastomyces grows as septate hyphae that produce infectious conidia and at 37°C it grows as a budding yeast [8] . Blastomyces is part of a larger group of dimorphic fungal pathogens , including Histoplasma , Paracoccidioides , and Coccidioides , all belonging to the order Onygenales . The dimorphic fungi collectively are the most common cause of invasive fungal disease worldwide and account for several million infections each year [8] . Unlike opportunistic fungi , such as Candida albicans , Cryptococcus neoformans , or Aspergillus fumigatus , the dimorphic fungi can infect immunocompetent and immunocompromised hosts [6 , 9–11] . Previous work has shown that in Blastomyces , the temperature-dependent switch from hyphae to yeast along with upregulation of yeast-phase specific genes is critical for virulence [12–14] . The dimorphism-regulating kinase-1 ( DRK1 ) promotes the temperature-dependent conversion from mold to yeast , and its deletion renders Blastomyces avirulent during experimental murine pulmonary infection [12] . The upregulation of yeast-phase specific genes , such as the Blastomyces yeast-phase specific gene 1 ( BYS1 ) [15] and the Blastomyces adhesion-1 gene ( BAD1 ) [13 , 14] , is also important for the adaptive response of the yeast cells in the host environment . BAD1 is considered an essential virulence factor in Blastomyces , since it binds yeast cells to host tissue and impairs host immune defenses by inhibiting the production of tumor necrosis factor-α and blocking CD4+ T lymphocyte activation [13] . Within the Onygenales , Blastomyces , Histoplasma and Paracoccidioides belong to the family Ajellomycetaceae . Also within Ajellomycetaceae is the genus Emmonsia , which includes E . crescens and E . parva , the etiological agents of adiaspiromycosis , a pulmonary disease of small mammals and occasionally of humans [16] . Recently , a cluster of systemic infections of HIV-positive patients in South Africa were shown to be caused by Emmonsia isolates [17] . While E . crescens and E . parva also undergo a dimorphic shift at high temperature , they transform into large , thick-walled adiaspores rather than yeast cells [18] ( S1 Table ) . Two phylogenetic studies using 18S ribosomal DNA sequences found that E . parva was the sister species to Blastomyces [19 , 20] . The positioning of E . crescens was less clear; in one analysis it was a sister group to Paracoccidioides [19] while in the other analysis it was grouped with Blastomyces and E . parva [20] . In neither phylogeny was the alternative positioning of E . crescens strongly supported . To further investigate the genomic basis of differences observed among the Ajellomycetaceae in terms of pathogenicity , morphology , and the infection process , we sequenced six genomes of Blastomyces and Emmonsia , as well as sequencing the B . dermatitidis transcriptome during macrophage co-cultivation and in vivo pulmonary infection . The newly sequenced genomes included three representative strains of B . dermatitidis ( ER-3 , ATCC18188 , and ATCC26199 ) , and one strain of each of B . gilchristii ( SLH14081 ) , E . parva ( UAMH139 ) , and E . crescens ( UAMH3008 ) . Blastomyces dermatitidis ER-3 was isolated from a woodpile located in a highly endemic region of Wisconsin and is hypovirulent in mice [21 , 22] . The ATCC18188 strain is the only current example of the 'a' mating type ( MAT1-1 locus ) available for B . dermatitidis [23] . ATCC26199 is a clinical isolate from South Carolina that is commonly used for in vitro and in vivo laboratory assays [14] . Blastomyces gilchristii SLH14081 is a human clinical isolate that is highly virulent in a murine model of blastomycosis [22 , 24] . Both Emmonsia strains were isolated from small mammals , E . parva from a weasel in Ravelli County , Montana , and E . crescens from lungs of a rodent ( Arvicola terrestris ) in Norway . Utilizing this genomic data , we find that the Blastomyces genomes are much larger than those of their close relatives , and are characterized by large , isochore-like GC-poor regions overrun by repetitive elements . Our whole-genome analyses provide further evidence for the phylogenetic relationships between Blastomyces and Emmonsia and other Onygenales . Finally , we identify novel sets of candidate virulence factors through comparison of the Blastomyces transcription during in vivo pulmonary infection to growth in co-culture with macrophages or in different media or temperature . This combination of genomic and transcriptomic analysis provides a foundation and new candidate genes to further characterize the underlying molecular differences that determine the infectious potency of Blastomyces strains and give rise to the clinical profiles attributable to blastomycosis .
We sequenced and assembled the genomes of three Blastomyces dermatitidis strains and one B . gilchristii strain , and representatives of two Emmonsia species . The Blastomyces strains were sequenced using either Sanger technology or a hybrid of Sanger and 454 technologies . The Emmonsia strains were sequenced using Illumina technology , and de novo assemblies were generated for each strain ( Methods ) . Comparison of the genomes of four Blastomyces strains , SLH14081 , ER-3 , ATCC18188 and ATCC26199 , revealed they were over twice the size of all other Onygenales . The Blastomyces assemblies range in size from 66 . 6 Mb for B . dermatitidis strain ER-3 to 75 . 4 Mb B . gilchristii strain SLH14081 ( Table 1 ) . These assemblies were over twice as large as those of other dimorphic pathogens in the order Onygenales including the Emmonsia species ( 30 . 4 Mb ) , although the use of only short reads from a single library for the two Emmonsia may under-represent repetitive sequence ( Fig 1 ) . The assemblies of two Blastomyces strains , SLH14081 and ER-3 , were sequenced to a higher depth than the other two strains , and as a result contain nearly all of the assembled sequence in a relatively small number of scaffolds , 100 and 25 scaffolds respectively . As an independent assessment of genome size and structure , we generated an optical map of the SLH14081 strain ( S1 Fig ) . Consistent with our assembly of this strain , the map had an estimated size of 79 . 6 Mb , arranged in eighteen linkage groups . In addition , a total of 65 . 9 Mb of the 75 . 4 Mb of the SLH14081 assembly was anchored to the optical map ( S2 Table ) . The total number of predicted genes in Blastomyces , Emmonsia , and other related fungi was similar despite the large difference in genome size . In Blastomyces , the number of predicted genes varied between 9 , 180 in ATCC26199 to 10 , 187 in ATCC18188; for E . parva and E . crescens the counts were similar , 8 , 563 and 9 , 444 , respectively ( Table 1 ) , as were those of other sequenced Onygenales ( Fig 1 ) . High representation of core eukaryotic genes in each genome provides evidence that their gene sets are nearly complete; E . parva includes 88% of core eukaryotic genes , while the E . crescens and Blastomyces gene sets include 96–98% ( S2 Fig ) . To compare gene content and conservation , we identified orthologous gene clusters in the six genomes sequenced here , 10 additional Onygenales genomes , including three other pathogenic species ( Histoplasma , Paracoccidioides , and Coccidioides ) , and three Aspergillus genomes . Using 2 , 062 single copy core genes present in all strains , we estimated a phylogeny of these organisms using RAxML ( [25]; Fig 1 ) . This analysis strongly supports the clustering of Blastomyces with E . parva ( 100% of bootstrap replicates and 100% Gene Support Frequency ( GSF ) [26] ) as previously reported [19 , 20] . In contrast to prior work , Histoplasma is strongly supported as sister group to Blastomyces and E . parva ( 100% of bootstrap replicates and 90% GSF ) , with E . crescens strongly supported as a sister group to that clade ( 100% of bootstrap replicates and 100% GSF ) , and with Paracoccidioides in a basal position ( Fig 1 ) . The polyphyletic nature of Emmonsia suggests that the Ajellomycetaceae have undergone multiple evolutionary transitions allowing the infection of humans and other mammals . Within Blastomyces , we found support for strain SLH14081 as an outgroup relative to the other three strains ( S3 Fig ) . This is consistent with the placement of strain SLH14081 within the newly described species B . gilchristii [3]; the other three strains sequenced here are still classified as B . dermatitidis . A bimodal distribution of GC-content observed in all Blastomyces sequenced , which was less pronounced in E . parva and E . crescens and absent in other Ajellomycetaceae , suggests that these genomes are organized in large isochore-like regions of high and low GC-content . This finding for nuclear DNA explains the GC-poor fraction of the Blastomyces genome initially identified using CsCl gradient analytical ultracentrifugation [27] , which the authors hypothesized was due to a large proportion of GC-poor mitochondrial DNA in Blastomyces cells . Examining the genome wide GC content revealed a bimodal distribution for all strains of Blastomyces including ER-3 and SLH14081 , the smallest and largest assembly , respectively ( Fig 2 ) , and was observed for all window sizes ranging from 2 kb to 256 kb ( S4 Fig ) . The detection of a bimodal signal in larger windows supports the organization of the genomes in large isochore-like regions , with average GC content of 29 . 6% and 31 . 0% in GC-poor regions and 45 . 9% and 46 . 6% for the rest of the genome in B . gilchristii strain SLH14081 and B . dermatitidis strain ER-3 , respectively ( Table 2 ) . Analysis of the related pathogens H . capsulatum , P . lutzii , and C . immitis showed no evidence for bimodality of GC content , while both E . parva and E . crescens revealed small peaks of low GC sequence . Read-based analysis and using smaller window sizes ( e . g . 128 bp ) supported these findings , suggesting they are not due to differences in assembly completeness ( S5 Fig ) . To further examine the organization of GC-content across the genome , we next defined the boundaries of low GC content regions in Blastomyces . In the smallest assembly , of the ER-3 strain , we identified 221 GC-poor tracts with an average size of 186 . 0 kb , encompassing a total size of 41 . 1 Mb ( Tables 2 and S3 ) . In the largest assembly , of the SLH14081 strain , we identified 350 GC-poor tracts with an average size of 140 . 2 kb , encompassing a total size 49 . 1 Mb ( Tables 2 and S3 ) . The 8 Mb difference between the total size of GC-tracts in the genomes of B . dermatitidis ER-3 and B . gilchristii SLH14081 accounts for nearly all of the 8 . 8 Mb difference in assembly size . Notably , GC-poor tracts in Blastomyces can be quite long , and reach maximal lengths of 1 . 3 Mb . In the assemblies of E . parva , E . crescens and other Ajellomycetaceae , long GC-poor tracts were rarely observed ( e . g . , a total of only 4 GC-poor regions larger than 10 kb in E . parva were found adjacent to a long GC-rich region in the same scaffold , and just 1 in E . crescens ) , corresponding to the less pronounced bimodal GC distribution of the genome assembly . However , more contiguous assemblies would be needed to reveal the overall extent of long GC-poor tracts . The only other fungal genome noted to have an isochore-like structure , Leptosphaeria maculans [28] , contains a smaller expansion of GC-poor regions ( Fig 2 ) ; individual tracts were on average half the size ( 70 . 4 kb ) of those in Blastomyces , and encompassed a smaller fraction ( 36% ) of the L . maculans genome [28] . This difference is consistent with the lower fraction of long AT blocks we observe by comparing windows of different sizes in Blastomyces and L . maculans ( S4 Fig ) . The GC-poor regions include nearly all the repetitive elements in the genome and consequently have a lower density of predicted genes ( e . g . , see Fig 3 ) . In ER-3 , 93 . 7% of repetitive sequence is found in GC-poor regions ( Table 2 ) . The gypsy elements that dominate repetitive sequence in the Blastomyces genomes have low GC-content; on average those in ER-3 and SLH14081 have respective GC-content of 31 . 0% and 29 . 9% , matching the overall GC level of the GC-poor regions ( Table 2 ) . GC-poor tracts of Blastomyces contain only approximately one fifth of the predicted protein-coding gene set , including some notable genes such as 1 , 3-beta-glucan synthase component ( FKS1 ) , Blastomyces yeast phase-specific gene ( BYS1 ) , and one of two BYS1-like proteins we identified ( S6 Fig and S4 Table ) . By contrast , BAD1 , which encodes an essential virulence factor involved in host cell interaction and immune evasion [13] , is found within a GC-rich region . Intergenic regions are also larger here than for other genes in the genome; the average intergenic region for ER-3 is 18 . 5 kb in GC-poor regions , a 3-fold expansion compared to the 6 . 0 kb genome-wide average ( Table 2 and Figs 3 and S6 ) . The GC-poor regions also show lower synteny between the Blastomyces genomes compared to other regions with more typical GC content ( e . g . , see Fig 3 ) . Overall , B . dermatitidis strain ER-3 and B . gilchristii strain SLH14081 shared 125 syntenic blocks including 93 . 8% and 94 . 5% of genes , encompassing only 69 . 5% and 69 . 3% of each assembly . These percentages are much smaller than those observed among strains of related species ( such as 95% and 93% synteny between strains of P . brasiliensis [29] ) . The lower synteny among Blastomyces strains is largely explained by the proportion of genes found in repeat-rich , GC-poor regions ( Table 2 and Fig 3 ) . Nearly all ( 99% ) of genes in GC-rich regions are highly syntenic across Blastomyces strains , even between B . dermatitidis strain ER-3 and B . gilchristii strain SLH14081 . However , the GC-poor regions have more limited synteny even within strains of Blastomyces encompassing 74 to 76% of genes in those regions ( Table 2 and Fig 3 ) . Overall , the function , expression , and selective pressure of genes in GC-poor regions appear similar to those genes found elsewhere in the genome . Despite the lower synteny , GC-poor regions are not significantly enriched for Blastomyces-specific genes , nor did they show much functional enrichment ( S1 Text , S5 Table ) . Comparing selection pressure on the 7 , 228 single copy orthologs present in all four Blastomyces genomes also did not find a significant difference in the number of genes with high omega values ( omega > 1 ) ( Methods ) . These analyses suggest that despite the dynamic reorganization due to invading gypsy elements , the GC-poor regions do not appear to be fast evolving by these measures . Furthermore , there is no large-scale difference in the expression levels of genes in GC-poor regions . Comparing transcript levels for genes in GC-poor and GC-rich regions , we found that genes in both GC classes show similar expression levels ( S7 Fig ) , again supporting the general similarity of genes found in these two genomic neighborhoods . The 2-fold larger size of the Blastomyces genomes compared to other dimorphic fungi is due largely to an expansion of repetitive sequence . The proportions of the Blastomyces genome assemblies that were covered by repeats ranged from 56 . 6% ( 41 . 6 Mb ) for B . dermatitidis ATCC18188 to 63 . 0% ( 47 . 5 Mb ) for B . gilchristii SLH14081 . SLH14081 had the highest repeat content and the largest assembly size . The E . parva and E . crescens assemblies both had a lower repeat content , 9 . 9% ( 3 . 0 Mb ) and 5 . 4% ( 1 . 6 Mb ) , respectively ( Table 1 ) . In all genomes , a small number of transposable element classes as well as AT-rich simple sequence regions were highly represented ( Fig 4A ) . More specifically , the genome expansion in Blastomyces strains has resulted from a proliferation of gypsy LTR retrotransposons , including both ancestral and lineage-specific proliferation . In the Blastomyces genomes , Gypsy elements account for almost all repetitive DNA , with a lower frequency of sequences similar to the non-LTR Tad1 and copia LTR retroelements ( Figs 4A and S8 ) . In all Blastomyces and Emmonsia genomes the most frequent Gypsy element subtype matches the “ACa” ( Ajellomyces or Histoplasma capsulatum ) Gypsy element from Repbase [30] ( Fig 4A and 4B ) . Further phylogenetic characterization of 2 , 331 Gypsy elements identified four subtypes that appear specific to Blastomyces ( S1 Text and Figs 4 and S9 ) . Some subtypes had diversities that were primarily the result of ancestral duplication , resulting in large numbers of orthologous elements between strains ( e . g . , Fig 4B ) , while other subtypes appeared to predominantly contain strain-specific paralogous expansions , consistent with the cryptic speciation in the Blastomyces genus ( e . g . , Fig 4C ) . Gypsy elements were also detected in the Emmonsia and Histoplasma assemblies , but in far fewer copies ( Figs 3 and 4 ) , consistent with the recent expansion in Blastomyces . Gypsy elements are frequently observed in fungal genomes [31] , including Coccidioides [32] and Paracoccidioides [29] but in far fewer copies . To identify gene content that could play a role in the evolution of the dimorphism and pathogenesis within the family Ajellomycetaceae , we searched for expansions or contractions in functionally classified genes compared to the other fungi from the order Onygenales ( S6 Table ) . We identified PFAM domains , KEGG pathways , Gene Ontology ( GO ) terms , or kinase families that were significantly enriched or depleted . Domains associated with polyketide synthases were depleted in the Ajellomycetaceae , and an independent prediction of secondary metabolite enzymes confirmed that Blastomyces and other fungi from the Ajellomycetaceae contain fewer PKS gene clusters than other Onygenales ( S7 Table , S1 Text ) . Other differences between the Ajellomycetaceae and other Onygenales include fewer copies of multiple classes of peptidases ( M36 , M43 , S8 ) as well as an associated inhibitor ( I9 , inhibitor of S8 protease ) , variable copy number of LysM-domain proteins potentially involved in chitin binding , which are most expanded in dermatophytes but at next highest levels among the human pathogens in Blastomyces , and a higher number of protein kinases ( S6 Table and S10 Fig ) , including an expansion of the FunK1 family similar to that previously noted in Paracoccidioides [29] . We next identified 140 gene clusters conserved in Blastomyces , Emmonsia , Histoplasma , and Paracoccidioides , but absent from other Onygenales and Aspergillus ( S8 Table ) . These gene clusters include a predicted heme oxygenase ( BDBG_02689 ) , which could produce free iron from host heme . In addition to the 140 gene clusters , we also identified conserved genes in subsets of the Ajellomycetaceae including homologs of two previously typed antigens; a gene similar to the 27 kDa antigen of Paracoccidioides [33] is present in Blastomyces and one Histoplasma genome , and a gene cluster specific to Blastomyces and Paracoccidioides shares similarity with the antigenic Aspergillus cell wall mannoprotein [34] . To identify potential genetic features of the Ajellomycetaceae pathogenic to immunocompetent humans ( Blastomyces , Histoplasma , and Paracoccidioides ) relative to E . parva and E . crescens , we conducted a second enrichment analysis as described above ( S9 Table ) . The primary pathogens showed enrichment of only two PFAM domains , a phosphorylase and endonuclease ( S9 Table ) . The phosphorylase domain over-represented in Blastomyces is associated with nucleoside phosphorylases; many of these proteins also contain Ankyrin repeats and NACHT domains . Phosphorylases are involved in nucleotide and amino acid salvage , and could allow pathogens greater metabolic versatility when certain building blocks are unavailable . The absence of any larger pattern of gain or loss of functional classes suggests that smaller changes in gene content , independent gain and loss between the species , or expression differences may account for differences in pathogenesis . We then identified specific orthologs present in all four strains of Blastomyces but absent from both non-pathogenic Emmonsia species . Comparing the ortholog set of Blastomyces to E . parva and E . crescens , we found a total of 552 ortholog clusters that were present in all Blastomyces strains but absent in both Emmonsia genomes ( S10 Table ) . Most of these ( 393 clusters ) were present only in Blastomyces , and while most of these proteins ( 92% in B . gilchristii strain SLH14081 ) had no PFAM domain assignment , the set did include the Blastomyces yeast phase-specific protein 1 ( BYS1 ) . This gene is a marker of the phase transition to and from the yeast phase [15] , although it has recently been shown not to be required for virulence in studied strains [24] . While both E . parva and E . crescens are not reported to be primary human pathogens , phylogenetic analysis suggests that the transition to this lifestyle may have been independent , resulting in differential gene loss . One of the genes absent only in E . crescens is the siderophore iron transporter mirB ( MIRB ) . Many pathogenic microorganisms have evolved high affinity iron acquisition mechanisms , which include the production and uptake of siderophores . In B . dermatitidis , the expression of genes involved in the biosynthesis of siderophores and uptake of siderophores , including two iron transporters ( MIRB and MIRC ) , are induced under iron-poor conditions [35] . While MIRB appears to be absent in E . crescens , siderophore uptake may be still enabled by the second transporter , MIRC , which is conserved in this species . To better understand which Blastomyces genes play roles in pathogenicity and virulence , we carried out RNA-Seq of B . dermatitidis strain ATCC26199 to profile expression under varying temperature , nutrient availability , and infection status . Combining this data allowed us to disambiguate expression variability due solely to differences in temperature and media-specific nutrient availability from those specific to macrophage interactions in vitro or host infection in vivo . Five conditions were sampled: 37°C with macrophages in RPMI media , 37°C in RPMI media , 37°C in HMM media , 22°C in HMM media , and in vivo pulmonary infection with yeast in a mouse model ( Fig 5A ) . For each condition , two biological replicates were performed , and the read counts per transcript were highly correlated between replicates ( R> 0 . 99 , S11 Fig ) . Gene expression levels and mapping statistics are presented in S11 and S12 Tables , respectively . When B . dermatitidis yeast cells were co-cultured with human bone marrow derived macrophages , the majority of yeast cells ( 59% ) were internalized by macrophages . Comparison of yeast co-cultured with and without macrophages identified 140 genes differentially expressed between these two conditions , 112 of which were upregulated in the presence of macrophages ( S13 Table ) . This upregulation suggested a small , specific response to macrophages in this experiment . Examination of this set of genes revealed numerous genes that have the potential to facilitate adaptation to the host environment . The 20 most significantly upregulated genes ( Table 3 ) include a predicted secreted endo-1 , 3 ( 4 ) -β-glucanase ( BDFG_03060 ) involved in cell separation after cytokinesis in C . albicans [36] , transporters , including an ABC transporter ( BDFG_05060 ) homologous to Aspergillus fumigatus MDR1 and a zinc transporter ( BDFG_02462 ) similar to the vacuolar zinc transporter ZRT3 in S . cerevisiae , dehydrogenases involved in amino acid catabolism , and antioxidants peroxisomal catalase ( CATP , BDFG_02965 ) and superoxide dismutases ( SOD3 , BDFG_01204; SOD2 , BDFG_07895 ) , which may protect against reactive oxygen species ( ROS ) . The induction of endo-1 , 3 ( 4 ) -β-glucanase and CATP in the presence of macrophages was also confirmed by qRT-PCR ( S12 Fig and Methods ) . We also identified gene expression changes specific to in vivo murine pulmonary infection from our transcriptomic data of B . dermatitidis strain ATCC26199 . By k-means clustering of expression values , we identified a set of 72 genes that are upregulated in vivo during mouse infection relative to all other conditions , regardless of temperature , media , and in vitro macrophage interactions ( Fig 5B and S14 Table ) . Using all conditions for this comparison helped eliminate from consideration differences observed , for example , between the yeast samples cultured in different media . Genes in this set with greater than 2-fold upregulation in vivo are highlighted in Table 4 , and primarily fell into five functional categories: 1 ) secreted proteins , 2 ) zinc acquisition , 3 ) antioxidants and oxygenases , 4 ) amino acid metabolism , and 5 ) transporters . The most highly expressed gene in vivo was BAD1 ( BDFG_03370; S11 Table ) , which encodes a yeast-phase specific virulence factor that facilitates adhesion to host cells and evasion of host immune defenses [13] . BAD1 also had the highest transcript abundance for yeast co-cultured with macrophages and yeast without macrophages at 37°C ( S13 Table ) . Thus , BAD1 was not identified among the set of 72 differentially expressed genes because the transcription of BAD1 is influenced by temperature [37] . The effect of temperature during the mold to yeast transition on the transcriptome of dimorphic fungal pathogens has been the topic of previous studies [38–41] and was therefore not further evaluated here . A total of nine secreted proteins were identified in this set of 72 , including five of the ten most highly upregulated genes by fold change , potentially playing roles in host-pathogen interactions . Another highly up-regulated secreted protein ( BDFG_00717 ) contains a predicted CFEM domain as well as a GPI-anchor; these features , as well as small size ( 236 amino acids ) , are shared with member of the haemoglobin-receptor gene family in C . albicans [42] . The most highly upregulated gene , BDFG_05357 , encodes a HRXXH domain-containing secreted protein that may function as a zinc scavenging protein ( Tables 4 and S14 ) . This gene is present in the genomes of Blastomyces and Coccidioides , but absent from those of Emmonsia , Histoplasma and Paracoccidioides . BDFG_05357 is a homolog of C . albicans PRA1 ( pH-regulated antigen-1 ) [43] and S . cerevisiae ZPS1 ( zinc-pH-regulated protein ) . In C . albicans , the transcription of PRA1 and ZPS1 is induced under alkaline pH and zinc-deplete conditions [44 , 45] , and PRA1 is co-regulated with its upstream gene , ZRT1 , which encodes a high-affinity zinc transporter that interacts with zinc-bound PRA1 [45] . Similarly , the B . dermatitidis homolog of ZRT1 , BDFG_09159 , is highly expressed in vivo; the induced expression of both PRA1 and ZRT1 was confirmed by qRT-PCR ( S12 Fig ) . However unlike in C . albicans , ZRT1 is not adjacent to PRA1 in the B . dermatitidis genome . While PRA1 is conserved in all four Blastomyces genomes , there is no copy of this gene in Histoplasma as previously noted [45] , nor in Emmonsia or Paracoccidioides , suggesting differences in how zinc is acquired within the Ajellomycetaceae . In addition to PRA1/ZPS1 and ZRT1 , a larger module of genes that regulate zinc acquisition is co-regulated in Blastomyces . The transcript abundance of BDFG_07269 , which encodes a low-affinity zinc transporter ( ZRT2 ) , is also significantly upregulated in the mouse model . In S . cerevisiae , the zinc-responsive transcription factor ZAP1 regulates expression of ZRT1 and ZRT2 , along with ZPS1 . We identified the ortholog of ZAP1 in strain ATCC26199 as BDFG_07048 , which was also significantly upregulated in vivo relative to all other conditions ( S14 Table ) despite not being identified by k-means clustering . These results suggest that zinc acquisition and homeostasis may play a critical role for survival of B . dermatitidis in vivo . Genes that convert reactive oxygen species to dioxygen and dioxygen to metabolites were also highly upregulated in vivo . These include two superoxide dismutases ( SOD3: BDFG_01204 and SOD2: BDFG_07895 ) , which were even more upregulated in vivo than in macrophages . Four dioxygenases ( BDFG_04184 , BDFG_04185 , BDFG_08059 , BDFG_06504 ) were also upregulated in vivo , representing almost half of the dioxygenases found in the genome , which utilize dioxygen to drive amino acid catabolism . This set includes 4-hydroxyphenylpyruvate dioxygenase , ( 4-HPPD; BDFG_04184 ) and homogentisate 1 , 2-dioxygenase ( BDFG_04185 ) , which are involved with tyrosine catabolism [46] . Other upregulated oxygenases include indoleamine 2 , 3-dioxygenase ( BDFG_06504 ) and squalene monooxygenase ( ERG1—BDFG_07857 ) , which are involved with tryptophan catabolism and ergosterol biosynthesis respectively . ERG1 is a target of current antifungal drugs , including terbinafine . High in vivo expression of this gene may suggest that drugs targeting it may be more effective in vivo than in vitro . Genes involved in cysteine biosynthesis and catabolism were highly upregulated during infection including cysteine synthase A ( BDFG_02039 ) and cysteine dioxygenase ( BDFG_08059 ) . Cysteine synthase A may provide a large pool of synthesized cysteine for B . dermatitidis metabolism; the induced expression during infection was confirmed by qRT-PCR ( S12 Fig ) . Based on orthology analysis , cysteine synthase A is absent from the genome of H . capsulatum , and previous studies have shown that Histoplasma yeast are auxotrophic for cysteine [47 , 48] . Cysteine dioxygenase catabolizes cysteine to L-cysteinesulfinic acid , an intermediate that can be used for taurine biosynthesis or metabolized to sulfite and pyruvate . A homolog of C . albicans SSU1 ( BDFG_06814 ) , which encodes a sulfite efflux pump and is co-regulated with cysteine dioxygenase in C . albicans [49] , was also upregulated in B . dermatitidis . Transporters in fungi have been associated with an enhanced ability to remove harmful products as well as to survive on diverse nutrient sources , both of which could contribute to virulence and pathogenicity . Of the 72 genes upregulated in vivo during mouse infection , 11 are predicted transporters . These included the major facilitator type ( MFS; BDFG_06068 –unknown function , BDFG_06042 –glycose transport , BDFG_02038 –unknown function ) , amino acid transporters ( BDFG_02310 , BDFG_07447 ) and metal transporters ( zinc/iron transporters discussed above , BDFG_09159 , BDFG_07269 , and NIC1 nickel transporter , BDFG_02449; S14 Table ) . This upregulation potentially reflects differences in metabolite and cofactor availability in the host relative to in vitro conditions .
Our whole-genome based phylogenetic analysis recovered a sister-group relationship between Blastomyces spp . and Emmonsia parva , as previously reported from ribosomal DNA sequences [19 , 20] . However , our study placed Histoplasma as the next most basal species , and uniquely placed E . crescens between Histoplasma and the basal Paracoccidioides with strong bootstrap support . This more external position of Paracoccidioides compared to Histoplasma agrees with an earlier rDNA tree without Emmonsia [50] . Furthermore , gene support frequencies ( GSF ) were relatively high , and increased when we subsampled only well-supported genes , providing additional support for the topology presented here . The polyphyletic nature of the non-human pathogen Emmonsia suggests substantial plasticity in regard to human pathogenesis in this group . Ancestral variation in the ability of these species to infect other mammals could then be associated with exaptation to human hosts . Additional diversity of Emmonsia , including the third described species , E . pasteuriana [51 , 52] and other closely related isolates [17] suggests that the full breadth of the Emmonsia genus may not be captured by the two isolates sequenced here . Interestingly , both E . pasteuriana and related isolates produce yeast cells at high temperature , rather than the adiaspores produced by E . parva and E . crescens . Further sequencing of Emmonsia species and other related strains may reveal additional patterns and trends in the evolution of the dimorphic fungi . The mosaicism observed here in the genome of Blastomyces differs substantially from that observed in other fungi and larger eukaryote genomes . While isochore-like GC-poor regions are unprecedented at this scale in fungal genomes described to date , there are parallels to the organization of L . maculans , though GC-poor regions occupy a smaller fraction of that genome [28] . Longer GC-poor isochores of more than 300 kb are commonly found in mammals and other vertebrates [53–55] . GC-poor isochores in vertebrates are often more stable over long evolutionary times [55 , 56] and have other properties such as lower gene expression [55] that do not appear to be shared by the GC-poor tracts of B . dermatitidis and B . gilchristii ( S1 Text ) . Characterization of repetitive sequence in GC-poor regions suggests these originated with a dramatic expansion of elements of the LTR/Gypsy category . Phylogenetic analysis suggests these elements swept through a lineage leading to the present-day B . dermatitidis and B . gilchristii , and to a lesser extent Emmonsia parva , and have further expanded during the diversification of Blastomyces . While H . capsulatum does not have such an expanded genome , or a sizable GC-poor component , and so appears less affected by gypsy expansion , Histoplasma may alternatively have more robust defense against repetitive elements or be less able to accommodate large amounts of repeats in its genome . While GC-poor tracts have been particularly dynamic areas due to Gypsy element insertions during the recent evolution of Blastomyces , these regions appear typical in gene content and expression . Perhaps due to their recent origin , the GC-poor regions do not appear to have sequestered particular classes of genes such as secreted proteins or have other hallmarks of rapidly evolving gene content . The long GC-poor regions also include some well characterized genes involved in phase transitions and pathogenesis , including the Blastomyces yeast-specific gene BYS1 , a marker of the phase transition to and from the yeast phase [15 , 24] . Reduced levels of synteny in the GC poor regions between B . dermatitidis and B . gilchristii could prevent effective meiotic recombination between the two lineages , further supporting their designation as separate species . Despite the large increase in genome size in Blastomyces , the total number of protein coding genes is only modestly expanded . Blastomyces and other sequenced species from the Ajellomycetaceae family , including the human primary pathogens Histoplasma and Paracoccidioides , have similar gene content with only a few gene loss or gain events that map to common functional classes . This stability suggests that more modest differences in gene content and sequence , as well as potential divergence of gene regulation , contribute to phenotypic differences between the species . Larger differences exist between the Ajellomycetaceae and other more divergent members of the Onygenales . There is no expansion of degradative proteases as previously noted for Coccidioides [57]; in fact , three peptidase families ( M36 , M43 , and S8 ) are present at lower copy number in Blastomyces and the other Ajellomycetaceae . While Blastomyces contains a higher number of LysM proteins than the dimorphic Onygenales , the number is small relative to that found in Dermatophytes [58] . This analysis also identified candidate genes involved in host interaction , including proteins homologous to antigens in related fungi and a heme oxygenase that could release iron from host heme . For yeast co-cultured with macrophages and yeast in vivo , some aspects of the transcriptional response were shared including response to oxidative stress and amino acid catabolism . Yeast co-cultured with macrophages showed upregulation of numerous genes involved in oxidative reduction , which may play a major role in protecting Blastomyces from ROS . The macrophage phagosome is poor in glucose and amino acids , but rich in ROS [59 , 60] . Blastomyces is relatively resistant to ROS and virulence correlates with the ability to minimize ROS generation in innate immune cells [61 , 62] . The upregulation of superoxide dismutases ( SOD3 , SOD2 ) and catalase P may protect B . dermatitidis yeast against oxidative stress . In H . capsulatum , extracellular SOD3 and intracellular catalase P , contribute to survival within macrophages [63 , 64] . Moreover , SOD3 promotes H . capsulatum virulence in a murine model of pulmonary infection [63] . The upregulation of 4-HPPD , which is involved with pyomelanin biosynthesis , contributes to antioxidant defense and intracellular survival of Penicillium marneffei [65] . Inhibition of 4-HPPD in P . brasiliensis and P . marneffei blocks the phase transition to yeast at 37°C [65 , 66] . Furthermore , in vivo numerous dioxygenases were upregulated , suggesting that dioxide produced in response to ROS may be utilized for amino acid metabolism . Changes in amino acid metabolism were prevalent in both the macrophage co-cultured and in vivo Blastomyces , suggesting the recycling of amino acids as an energy source ( Results , S1 Text ) . In particular , the very specific increase in cysteine catabolism ( cysteine dioxygenase ) and biosynthesis ( cysteine synthase A ) during in vivo infection suggests that cysteine may be critical to virulence . In mice , deletion of cysteine dioxygenase ( CDG1 ) in C . albicans results in attenuated virulence [49] . Furthermore , upregulation of sulfite efflux pump ( SSU1 ) , which is co-regulated with CDG1 in C . albicans , could play a role in B . dermatitidis virulence during in vivo infection . Exported sulfite can destabilize host proteins by reducing disulfide bonds and facilitates the growth of dermatophytes on keratinized tissue [67] . How breakdown of tryptophan by indoleamine 2 , 3-dioxygenase ( IDO ) , which can supply de novo nicotinamide adenine dinucleotide ( NAD+ ) , alters the fungal-host interaction is unknown . In cancer , tumor cells with increased expression of IDO may facilitate tryptophan depletion in the microenvironment to suppress the host immune response [68] . Infection with H . capsulatum , P . brasiliensis , and C . albicans upregulates host IDO activity , reduces fungal growth , impairs Th17 T-cell differentiation , and blunts excessive tissue inflammation [69–71] . The specific in vivo upregulation of genes that encode secreted proteins as well as those involved with transmembrane transport ( e . g . , amino acids , glucose ) , amino acid metabolism ( e . g . , cysteine ) , and metal acquisition ( e . g . , zinc , nickel ) highlights virulence factors potentially being missed by in vitro studies and the importance of understanding nutrient and co-factor availability in any study system . Uptake of zinc and nickel , which serve as enzyme co-factors , contribute to virulence in C . albicans and Cryptococcus neoformans respectively [45 , 72] . PRA1 encodes a secreted “zincophore” under alkaline and zinc-poor conditions that acts in concert with ZRT1 to promote zinc acquisition and facilitate endothelial cell damage by C . albicans [45] . NIC1-mediated nickel uptake is critical for urease activity , which contributes to C . neoformans invasion of the central nervous system [72] . In C . posadasii , urease induces host tissue damage [73] . While genes involved with the acquisition of zinc ( e . g . , ZRT1 , ZRT2 , ZAP1 homologs ) and nickel ( e . g . , NIC1 homolog ) are largely conserved with other fungi , the absence of PRA1 in Histoplasma , Paracoccidioides , and Emmonsia highlights recent evolutionary changes in zinc acquisition mechanisms in the family Ajellomycetaceae . This , in conjunction with differences in cysteine metabolism between Blastomyces and Histoplasma , suggest that despite the many common elements of dimorphism and pathogenesis , each genus of dimorphic fungi likely has unique nutritional requirements .
Four strains of Blastomyces were sequenced: SLH14081 representing the new species B . gilchristii , and ER-3 , ATCC18188 and ATCC26199 representing B . dermatitidis . The SLH14081 strain is a highly virulent , clinical isolate that can cause disease in immunocompetent persons , while ER-3 was isolated from a woodpile and is hypovirulent in mice [21 , 22] . The remaining two strains are strain ATCC18188 , a representative MAT 'alpha' isolate , and ATCC26199 , a commonly used laboratory isolate . Two species that are closely related to Blastomyces , that can cause pulmonary disease in rodents ( adiaspiromycosis ) , were also sequenced: Emmonsia parva UAMH139 and Emmonsia crescens UAMH3008 . These isolates were chosen for comparison as these species are not typically human pathogens , yet they are closely related to the three pathogenic dimorphic genera Blastomyces , Histoplasma and Paracoccidioides , with which they form a clade that is nested within the order Onygenales and represents the Ajellomycetaceae family [20] . Genomic DNA for sequencing was prepared from mycelial or yeast culture , using phenol/chloroform extraction . For the Blastomyces SLH14081 and ER-3 strains , whole genome shotgun sequence was obtained using Sanger technology on an ABI 3730 from a Fosmid ( epiFOS ) and two plasmid ( pJAN and pOT ) libraries . For B . dermatitidis ATCC18188 , whole genome shotgun sequence was obtained from two small insert libraries ( fragment and 1 . 5 kb ) using Roche 454 technology and from a Fosmid library using Sanger technology . For B . dermatitidis ATCC26199 20X of sequence was generated using 454 technology from a small insert fragment library . In addition , a plasmid ( pOT ) and Fosmid ( epiFOS ) library were constructed and sequenced using Sanger technology by the Washington University Genome Center , generating a total of roughly 3 . 6X coverage . For each Emmonsia species , a single library was used to generate 101 bp paired-end reads using Illumina technology on a Genome Analyzer II generating a total of 116X coverage for E . parva UAMH139 and 163X coverage for E . crescens UAMH3009 . Libraries of average insert size of 639 bp for E . parva and of 686 bp for E . crescens were chosen based on the electropherograms obtained from Bioanalyzer . Sequencing of both Emmonsia genomes was performed at the Genomic Sequencing Laboratory , University of California , Berkeley . Blastomyces strains SLH14081 and ER-3 were assembled with Arachne [74] ( Assemblez Build 20080911 ) . For B . dermatitidis ATCC18188 , a hybrid assembly was generated with Newbler version 2 . 3 . For B . dermatitidis ATCC26199 , a hybrid assembly of the Sanger and 454 data was generated with Newbler version "MapAsmResearch-03/15/2010" with options-rip and -scaffold . For the Emmonsia genomes , assemblies were generated using multiple programs , including the SOAPdenovo / GapCloser package [75] , ABYSS [76] and Velvet [77] . SOAPdenovo assemblies were selected based on quality metrics . While assemblies with high k values increased the fraction of GC-poor regions represented in the assembly , optimal assembly of gene sequences were achieved using lower k values , based on comparing each assembly to gene sets of Blastomyces and other related dimorphic fungi using TBlastN . The assemblies for the Emmonsia genomes ( k = 27 for E . parva and k = 29 for E . crescens ) were processed using the program GAEMR ( http://www . broadinstitute . org/software/gaemr/ ) , where overall assembly metrics were used to select the best assembly considering both continuity and completeness , though these measures were lower than for genomes assembled from multiple libraries . To validate the assembly of strain SLH14081 and anchor it onto chromosomes , we constructed an optical map , a single-molecule based ordered restriction map . The map of B . gilchristii strain SLH14081 was constructed by OpGen using the restriction enzyme BsiWI ( C^GTACG ) . The optical map consists of 16 linkage groups , with size ranging from 9 . 728 Mb to 730 kb . The total size of the map was estimated as 79 . 64 Mb in size , slightly larger than the 75 . 35 Mb genome assembly , likely due to repetitive element sequence missing from the assembly . A total of 36 assembly scaffolds covering 68 . 9 Mb were mapped based on shared restriction sites to the optical linkage groups ( S2 Table ) . To enable more accurate gene prediction and analyze gene expression , RNA was prepared and deeply sequenced from five conditions ( yeast with or without macrophages in RPMI media , in vivo during murine pulmonary infection , and in vitro yeast and mold in Histoplasma macrophage media ( HMM ) ) with two biological replicates per condition . ATCC26199 yeast cells were co-cultured with bone marrow derived murine macrophages from C57BL/6 mice in RPMI media with 10% heat inactivated FBS and supplemented with penicillin ( 100 U ) and streptomycin ( 100 ug ) or incubated in this media alone . Yeast and macrophages were co-cultured using a ratio of one yeast for every two macrophages ( MOI 0 . 5 ) . The use of alveolar macrophages was precluded due to the large numbers of mice that would be needed to harvest these cells . Following inoculation of cell culture flasks with B . dermatitidis yeast , the co-cultures were incubated at 37°C for 24 hrs . The majority of the yeast were either single cells or cells with one bud ( average 89% ) . The extent of macrophage internalization of yeast was measured using Uvitex staining to differentiate between extracellular and intracellular yeast . A total of 1 , 976 cells were counted across seven individual fields of view , with an average of 59% Uvitex negative ( intracellular ) and 41% Uvitex positive ( extracellular ) . The majority of B . dermatitidis cells exhibited yeast morphology ( > 96% ) ; pseudohyphal growth occurred in 2 . 4% of co-cultured yeast and 3 . 7% of yeast grown in RPMI media without macrophages . Harvested yeast cells were flash frozen in liquid nitrogen , ground with a mortar and pestle into a fine powder , and RNA extracted using the phenol-guanidium thiocyanate-1-bromo-3-chloropropane extraction method [78] . For in vivo transcriptional profiling , C57BL/6 mice were infected with 2 x 103 B . dermatitidis ATCC26199 yeast cells intratracheally and monitored for signs and symptoms of infection [79] . Mice with euthanized by carbon dioxide at 17 days post infection and yeast were isolated from murine lung tissue using the technique developed by Marty et al . [80] . Briefly , excised lungs were homogenized in pre-chilled ( 4°C ) double-distilled , sterile water ( ddH2O ) supplemented with DNase I 10 μg/ml ( Roche ) using an Omni TH tissue homogenizer ( Omni International , Kennesaw , GA ) , passed through a 40 μm cell strainer ( ThermoFisher Scientific , Waltham , MA ) , and centrifuged at 770g for 5 minutes at 4°C . The supernatant and interface were removed using a serologic pipette and yeast pellet was washed with ice-cold ddH2O and rapidly frozen in liquid nitrogen for RNA extraction . Time ex vivo was less than 30 minutes and samples were near-freezing ( 4°C ) during all isolation steps . Quality control analyses using qRT-PCR demonstrated that the short ex vivo time ( < 30 minutes ) at 4°C minimized changes in transcript abundance that would have occurred if the samples were processed at higher temperatures or for a longer duration [80] . Total RNA isolated from B . dermatitidis yeast during pulmonary infection was divided into 2 pools of 5 mice each ( pool #1 and pool #2 ) . In vitro yeast were incubated in liquid Histoplasma macrophage media ( HMM ) at 37°C while shaking [81] . The majority of cells had yeast morphology; less than 3 . 25% of cells grew as pseudohyphae . To generate mycelia , yeast cells were incubated in liquid HMM for 14 days at 22°C while shaking . Harvested yeast and mycelial cells were flash frozen in liquid nitrogen , ground with a mortar and pestle into a fine powder , and RNA extracted using the phenol-guanidium thiocyanate-1-bromo-3-chloropropane extraction method [78] . Total B . dermatitidis RNA from all samples ( in vivo , in vitro , co-cultures ) was treated with TurboDNase ( Bio-Rad , Hurcules , CA ) and cleaned using an RNeasy column ( Qiagen ) . RNA integrity and quality was assessed using Nanodrop spectrophotometry , 0 . 8% agarose gel electrophoresis , and an Agilent Bioanalyzer ( Agilent Technologies , Santa Clara , CA ) . RNA integrity numbers ( RIN ) for in vivo samples were > 7 . 5 ( 7 . 6 for pool #1 , 7 . 8 for pool #2 ) . RIN values for in vitro and co-cultures ( including yeast only RPMI ) were ≥ 8 . 7 . For RNA-Seq , poly-A mRNA was purified for each total RNA sample and strand-specific libraries prepared as previously described [82 , 83]; each library was sequenced using Illumina Technology , generating an average of 65 , 174 , 908 101 bp reads per sample . RNA-Seq was incorporated into gene prediction and used to detect differentially expressed transcripts as described below . For initial gene sets , a total of 38 , 405 ESTs generated from yeast and mycelial samples of ATCC26199 ( Washington University ) and from a normalized cDNA library of SLH14081 ( Broad Institute ) were used for gene prediction . To achieve better transcript coverage , strand-specific RNA-Seq data from 10 samples representing the above yeast , mold , and infection stages was assembled with the Inchworm component of Trinity [84] and processed with PASA [85] to generate a set of transcripts for gene prediction . Gene sets were generated by using EvidenceModeler ( EVM ) [85] to select the best gene call for a given locus from the gene prediction programs SNAP , Augustus , Geneid , and Genewise and from PASA RNA-Seq transcripts as previously described [85 , 86] . Project numbers and locus tag prefixes assigned to gene sets are as follows: B . gilchristii SLH14081 ( PRJNA41099 , locus tag prefix BDBG ) , B . dermatitidis ER-3 ( PRJNA29171 , prefix BDCG ) , ATCC18188 ( PRJNA39265 , prefix BDDG ) , and ATCC26199 ( PRJNA39263 , prefix BDFG ) ; the E . parva UAMH139 ( PRJNA178178 , prefix EMPG ) and E . crescens UAMH3008 ( PRJNA178252 , EMCG ) . RNA-Seq reads were aligned to the transcript sequences of B . dermatitidis strain ATCC26199 using Bowtie [87] . Transcript abundance was estimated using RSEM [88] , TMM-normalized FPKM for each transcript were calculated , and differentially expressed transcripts were identified using edgeR [89] , all as implemented in the Trinity package version r2013-2-25 [90] . To identify GO term enrichment of differentially expressed genes , we classified transcripts using Blast2GO [91] and then performed comparisons with Fisher’s exact test . A 2-fold difference in FPKM values and a false discovery rate below 0 . 05 were used as a criteria for significant differential expression . To identify possible functions of the gene products of significantly differentially expressed parasitic-phase genes , protein homologs were assigned based on BLAST , Gene Ontology ( GO ) terms and protein family domains ( PFAM , TIGRFAM ) . Total RNA was extracted from B . dermatitidis yeast as described above . One microgram of DNase-treated total RNA was converted to cDNA using iScript cDNA synthesis kit ( Bio-Rad ) . qRT-PCR was performed with SsOFast EvaGreen Supermix ( Bio-Rad ) using a MyiQ real-time PCR detection system ( Bio-Rad ) . Reactions were performed in triplicate using the following conditions: 1 cycle 95°C x 30 sec , followed by 40 cycles at 95°C for 5 sec , 60°C for 10 sec . Transcript abundance for genes of interest were normalized relative to the transcript abundance of GAPDH . Relative expression ( RE ) was calculated as RE = 2-ΔCt , ΔCt = Ctgene of interest−CtGAPDH [92] . Primer sequences used were as follows: AATCCTTTGACAGTGAAAC ( forward ) and CCATAAATCTGCTACAACAG ( reverse ) for BDFG_03060 , ACTGTCGGTGGAGAGAAG ( forward ) and ACTGGGGTGTTGTTGAAG ( reverse ) for BDFG 02965 , GACTATCCCATCCACAAC ( forward ) and TACAGAGCGGAATCTTTG ( reverse ) for BDFG 05357 , TTTGGCACTGGAGTTATG ( forward ) and TGCTTCGTAGTCTAAAGTC ( reverse ) for BDFG 09159 , GTGCTACAACGGAGATAC ( forward ) and GATAACCACCACGAACAC ( reverse ) for BDFG 02039 , ACCCCCGCTCCTCCATCTTC ( forward ) and GAGTAGCCCCACTCGTTGTCATACC ( reverse ) for BDBG_07959 ( GAPDH ) . We used the IsoFinder GC segmentation program ( http://bioinfo2 . ugr . es/oliver/isofinder; [93] ) to segment all ER-3 and SLH14081 scaffolds into long homogeneous genomic regions ( LHGRs ) . The option p2 ( parametric/student t-test with different variances ) , a window size of 5 kb and a p value cutoff of 0 . 01 ( P parameter 0 . 99 ) were chosen after evaluating P cutoffs between 0 . 95 and 0 . 99 , and window sizes ranging between 3 and 5 kb . The final settings were chosen as they accommodated gene synteny between ER-3 and SLH14081 in the GC-poor segments , obviating the need to manually remove narrow GC peaks caused by short genic regions . To identify the coordinates of the longer GC-poor and GC-rich tracts on the assemblies of Blastomyces strains ER-3 and SLH14081 , we set the boundary between GC-poor and GC-rich at 38% GC , a value that is in the deep valley between the two components of these genomes’ bimodal GC distribution . The deep valley is robust and persists over a wide range of window/segment sizes ranging up to > 60 kb ( S4 Fig ) . We then grouped adjacent segments located between successive transitions ( regime switches ) across the 38% GC border . Islands of N’s in the interior of the GC-poor tracts were retained , but those at the tract fringes ( i . e . , next to a jump across the 38% GC threshold ) were not . This procedure yields a large-scale segmentation of all scaffolds into strictly alternating “GC-poor” and “GC-rich” tracts . The GC-poor tracts and genes in those regions are listed in S3 and S4 Tables , respectively; GC-rich tracts form the remainder of the assemblies . We performed MySQL joins to identify the genes and repeats ( GFF files produced by RepeatMasker of elements from RepeatModeler ) located entirely or partly in the GC-poor tracts . DAGchainer [94] was used to identify syntenic blocks with a minimum of 6 genes , which were required to be in the same order and orientations in the compared genomes . Synteny plots were generated using a custom perl script , using the GDgraph library; code is available at https://github . com/gustavo11/syntenia . Geneious Pro was used to visualize smaller-scale syntenies within and among genome assemblies . De novo repetitive sequence in each assembly was identified using RepeatModeler version open-1 . 0 . 7 ( www . repeatmasker . org/RepeatModeler . html ) . Copies of de novo repeats and fungal sequences from RepBase [95] were mapped in each assembly using RepeatMasker version open-3 . 2 . 8 ( www . repeatmasker . org/ ) . For phylogenetic analysis of gypsy elements , reverse transcriptase domains were identified from each element; matches to the PFAM RVT_1 domain were identified with HMMER ( version 3 . 1b1 ) [96] for 6-frame translations of each element generated by EMBOSS transeq ( version 6 . 5 . 7 with parameters-frame 6-clean Y ) [97] . The best domain match for each element was selected , requiring 50% alignment coverage and c-Evalue < 1e-5 . The domains identified in Blastomyces SLH14081 ( 991 total ) and ER-3 ( 1 , 296 total ) , E . parva ( 40 total ) , and similar Repbase gypsy elements ( 4 total ) were aligned with MAFFT ( version 6 . 717 ) [98] , and a phylogeny estimated using FastTreeDP ( version 2 . 1 . 8 ) [99] . Four large subgroups were identified and visualized using iTOL [100] . A total of 16 genomes from the Onygenales order and three Aspergillus genomes were chosen for comparative analyses ( S15 Table ) . These include the four Blastomyces ( SLH14081 , ATCC26199 , ATCC18188 , ER-3 ) and two Emmonsia species ( UAMH139 , UAMH3008 ) as well as the following: Histoplasma capsulatum WU24 ( AAJI01000000 ) , H . capsulatum G186AR ( ABBS01000000 ) , Paracoccidioides lutzii Pb01 ( ABKH02000000 ) , P . brasiliensis Pb03 ( ABHV02000000 ) , and P . brasiliensis Pb18 ( ABKI02000000 ) , Coccidioides immitis RS ( AAEC00000000 ) , C . posadasii C735 delta SOWgp ( ACFW00000000 ) , Uncinocarpus reesii 1704 ( AAIW00000000 ) , Microsporum gypseum CBS118893 ( ABQE00000000 ) , Trichophyton rubrum CBS118892 ( ACPH01000000 ) , Aspergillus nidulans FGSC A4 ( AACD00000000 ) , A . flavus NRRL3357 ( AAIH00000000 ) , A . fumigatus Af293 ( AAHF01000000 ) . OrthoMCL was used to cluster the protein-coding genes of the 19 chosen genomes by similarity . To estimate the species phylogeny , a total of 2 , 062 orthologs present in a single copy in all of the 19 genomes were identified . Protein sequences of the 2 , 062 genes were aligned using MUSCLE , and a phylogeny was estimated from the concatenated alignments using RAxML v7 . 7 . 8 with model PROTCATWAG . To more closely examine the relationship of the Blastomyces isolates , single copy orthologs were identified in all four strains of Blastomyces and E . parva; the protein sequences of a total of 6 , 605 single copy orthologs were aligned using MUSCLE , and the resulting sequences replaced with the corresponding codons . A phylogeny was estimated from this nucleotide alignment using RAxML v7 . 3 . 3 with model GTRCAT . A total of 1 , 000 bootstrap replicates were used for each analysis . The level of support for the best RAxML tree was also evaluated using individual gene trees , by calculating the gene support frequency ( GSF , [26] ) . A phylogeny was estimated and bootstrapped using the same parameters as for the concatenated sequence matrix , and gene trees with high bootstrap support at all nodes were then selected . A total of 162 gene trees were supported by at least 70% of bootstrap replicates at all nodes; the percent of gene trees supporting the RAxML best tree was calculated using RAxML and is shown in Fig 1 . We also evaluated larger subsets of trees including those with 60% bootstrap support at all nodes , 50% bootstrap support , or all trees regardless of support , and found lower support respectively in each subset for our best tree . To examine selective pressure on genes in GC-poor regions , we identified 7228 genes that were single copy in the four Blastomyces genomes from the OrthoMCL run . dN/dS values for each gene were computed on codon-based nucleotide alignments with the codeml module of PAML [101] , using the one-ratio ( M0 ) model . Genes were functionally annotated by assigning PFAM domains , GO terms , and KEGG classification . HMMER3 [96] was used to identify PFAM domains using release 27 . GO terms were assigned using Blast2GO [91] , with a minimum e-value of 1x10-10 . Protein kinases were identified using Kinannote [102] and divergent FunK1 kinases were further identified using HMMER3 . Secondary metabolite gene clusters were predicted with antiSMASH version 2 . 0 . 2 [103] . Genes were clustered using OrthoMCL [104] with a Markov inflation index of 1 . 5 and a maximum e-value of 1x10-5 . To identify functional enrichments in Blastomyces and other subsets of the 19 compared genomes , we used four gene classifications: OrthoMCL similarity clusters ( see above ) , PFAM domains , KEGG pathways , and Gene Ontology ( GO ) , including different hierarchy levels . A gene was considered to be a member of a given gene class when , respectively , the gene ( a ) belonged to the given OrthoMCL cluster , ( b ) contained at least one instance of the given PFAM domain in the encoded protein , ( c ) belonged to the given KEGG pathway , or ( d ) was tagged by the given GO label . Using a matrix of gene class counts for each classification type , we identified enrichment comparing two subsets of queried genomes using Fisher’s exact test . Fisher’s exact test was used to detect enrichment of PFAM , KEGG , or GO terms between groups of interest , and p-values were corrected for multiple comparisons [105] . Significant ( corrected p-value < 0 . 05 ) PFAM and GO terms expansion or depletion was examined for three comparisons: Ajellomycetaceae compared to other Onygenales ( S6 Table ) , pathogenic compared to non-pathogenic from Ajellomycetaceae ( S9 Table ) , and Blastomyces compared to other Ajellomycetaceae; the only terms found to be expanded only in Blastomyces included nucleosome and zinc ion binding . No significant enrichment in KEGG terms was detected for these comparisons . | Dimorphic fungal pathogens including Blastomyces are the cause of major fungal diseases in North and South America . The genus Emmonsia includes species infecting small mammals as well as a newly emerging pathogenic species recently reported in HIV-positive patients in South Africa . Here , we synthesize both genome sequencing of four isolates of Blastomyces and two species of Emmonsia as well as deep sequencing of Blastomyces RNA to draw major new insights into the evolution of this group and the pathogen response to infection . We investigate the trajectory of genome evolution of this group , characterizing the phylogenetic relationships of these species , a remarkable genome expansion that formed large isochore-like regions of low GC content in Blastomyces , and variation of gene content , related to host interaction , among the dimorphic fungal pathogens . Using RNA-Seq , we profile the response of Blastomyces to macrophage and mouse pulmonary infection , identifying key pathways and novel virulence factors . The identification of key fungal genes involved in adaptation to the host suggests targets for further study and therapeutic intervention in Blastomyces and related dimorphic fungal pathogens . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2015 | The Dynamic Genome and Transcriptome of the Human Fungal Pathogen Blastomyces and Close Relative Emmonsia |
Recent discoveries on the origins of modern humans from multiple archaic hominin populations and the diversity of human papillomaviruses ( HPVs ) suggest a complex scenario of virus-host evolution . To evaluate the origin of HPV pathogenesis , we estimated the phylogeny , timing , and dispersal of HPV16 variants using a Bayesian Markov Chain Monte Carlo framework . To increase precision , we identified and characterized non-human primate papillomaviruses from New and Old World monkeys to set molecular clock models . We demonstrate specific host niche adaptation of primate papillomaviruses with subsequent coevolution with their primate hosts for at least 40 million years . Analyses of 212 HPV16 complete genomes and 3582 partial sequences estimated ancient divergence of HPV16 variants ( between A and BCD lineages ) from their most recent common ancestors around half a million years ago , roughly coinciding with the timing of the split between archaic Neanderthals and modern Homo sapiens , and nearly three times longer than divergence times of modern Homo sapiens . HPV16 A lineage variants were significantly underrepresented in present African populations , whereas the A sublineages were highly prevalent in European ( A1-3 ) and Asian ( A4 ) populations , indicative of viral sexual transmission from Neanderthals to modern non-African humans through multiple interbreeding events in the past 80 thousand years . Remarkably , the human leukocyte antigen B*07:02 and C*07:02 alleles associated with increased risk in cervix cancer represent introgressed regions from Neanderthals in present-day Eurasians . The archaic hominin-host-switch model was also supported by other HPV variants . Niche adaptation and virus-host codivergence appear to influence the pathogenesis of papillomaviruses .
Papillomaviruses ( PVs ) are ubiquitous , non-enveloped , small double-stranded circular DNA viruses that cause proliferation of epithelial cells in a wide range of vertebrate host species , from reptiles to mammals [1 , 2] . Currently , over 200 PVs infecting primate hosts ( human and non-human ) have been characterized and shown to group predominantly within 3 highly divergent genera—Alphapapillomavirus , Betapapillomavirus , and Gammapapillomavirus [3] . All oncogenic PVs associated with the development of cervical carcinoma , including human PV ( HPV ) types 16 , 18 , 31 , 33 , 35 , 39 , 45 , 51 , 52 , 56 , 58 , and 59 and Macaca fascicularis PV type 3 ( MfPV3 ) , share a common ancestor within the Alphapapillomavirus [4–7] . Among these oncogenic types , which are sexually transmitted primarily through intercourse [8 , 9] , HPV16 is globally the most prevalent HPV type detected , suggesting an increased fitness [10–12] . Moreover , HPV16 is also the most common HPV type detected in cervical cancer , which is the fourth most common cancer among women worldwide [13] . Nevertheless , most exposures to HPV types are transient , and many PVs appear to be more commensal than pathogenic [14] . Strict coevolution of a host and its pathogen is more likely if the pathogen is transmitted vertically and there is little or no cross-species acquisition . Persistent infection by pathogens generally indicates that they are well adapted to their host and that extinction will be rare so long as the host survives . Hence , in scenarios of coevolution , the evolutionary history of a pathogen should mirror that of its host , both in divergence times and phylogenic history ( Fahrenholz’s rule ) [15 , 16] . These criteria have been shown to hold for feline PVs within the genus Lambdapapillomavirus isolated from oral lesions [17] . On the other hand , horizontal transmission of pathogens through host switching without restricted species specificity will produce a very different evolutionary history between host and pathogen . In hosts harboring many different types of PVs ( e . g . , bovines , humans , and macaques ) , the selection pressure exerted by PVs on their hosts appears negligible in comparison with what the hosts exert on the PV pathogens . Within human populations , for example , the ancient dispersal of HPV variants ( e . g . , HPV16 and HPV58 ) challenges a simple evolutionary pattern of viruses migrating with modern Homo sapiens [18] , and instead indicates codivergence of viruses with archaic hominins and transmission to modern humans [19 , 20] . The genetic heterogeneity of PVs implies a complex evolutionary history with many interacting factors , including but not limited to virus-host codivergence , tissue tropism , lineage sorting , transmission , recombination , and natural selection [21 , 22] . Understanding the capacity for , and history of , viral adaptation to host ecological environments is essential for understanding the genetic basis of HPV carcinogenicity [23] . However , the origin and evolution of oncogenic PVs remains poorly understood . In this report , we estimate the divergence times of HPV16 and other oncogenic HPV types using a well-established Bayesian molecular clock model with newly characterized primate PV genomes that validate the divergence times of primate HPVs within niche-specific clades . Our analyses of the evolutionary dynamics of primate PVs , including specific focus on HPV16 variants , provide novel insights into the complex phylodynamic interactions between viruses and hosts and their pathologic outcomes .
In an effort to study the diversity of non-human primate PVs ( NHP-PVs ) to better understand the evolution of oncogenic HPVs , we screened cervicovaginal specimens from 10 adult female squirrel monkeys ( Saimiri sciureus ) , and the paired oral , perianal , and genital samples from 8 adult rhesus monkeys ( Macaca mulatta ) ( 4 females and 4 males ) . Three novel Saimiri sciureus PV types ( SscPV1 , 2 and 3 ) and three novel Macaca mulatta PV types ( MmPV2 , 3 and 4 ) were isolated and characterized and had genomes ranging in size from 7424 bp to 8051 bp ( S1 Table ) . All genomes contained five early genes ( E6 , E7 , E1 , E2 , and E4 ) , two late genes ( L2 and L1 ) , and an upstream regulatory region ( URR ) between L1 and E6 genes . Phylogenetic trees based on the nucleotide sequence alignment of the concatenated four open reading frames ( ORFs ) ( E1 , E2 , L2 , and L1 ) ( Fig 1 and S1 Fig ) or individual genes , e . g . , E1 or L1 ORFs ( S2 Fig , S3 Fig and S4 Fig ) support a monophyletic clade grouping SscPV1/2/3 and howler monkey Alouatta guariba PV 1 ( AgPV1 , KP861980 ) [24] within the genus Dyoomikronpapillomavirus . MmPV2 and MmPV3 cluster into the genus Alphapapillomavirus , with the closest HPVs being HPV54 ( within the species Alpha-13 ) and HPV117 ( within the species Alpha-2 ) , respectively . MmPV4 shares <70% of L1 ORF similarity with members of the species Gamma-10 ( e . g . , HPV121 and HPV130 ) and may represent a novel species within the genus Gammapapillomavirus . We focused on HPV16 because it is the most prevalent and potent carcinogen among the oncogenic HPVs [5] . To interrogate HPV16 evolution using a molecular clock , we utilized HPVs and NHP-PVs characterized in our labs and by others where the host species separation times have been well established [25 , 26] . This step is essential in order to validate a vertical mutation rate model suitable for HPV variants . This model estimates the mutation rate for infectious PVs over long periods of time and might differ from horizontal mutation rates not measured in this study . Papillomaviruses have been identified in a wide range of NHP species , including Old World monkeys and apes ( e . g . , macaque , chimpanzee ) and New World monkeys ( e . g . , squirrel monkey , brown howler ) [24 , 27–33] . Using a maximal likelihood algorithm and a nucleotide sequence alignment of the concatenated E1-E2-L2-L1 ORFs for 141 PV types representing each species or unique host ( S2 Table ) , we found that the majority of primate PVs phylogenetically clustered into Alphapapillomavirus , Betapapillomavirus , or Gammapapillomavirus genera , corresponding predominantly to the anatomical sites where the viruses were originally isolated ( e . g . , mucosal or cutaneous epithelium ) , which was independent of the host species ( Fig 1 , S1 Fig and S2 Table ) . For example , MmPV1 is a rhesus macaque PV type ( within the species Alpha-12 ) isolated from cervicovaginal cells that shares a most recent common ancestor ( MRCA ) with oncogenic mucosal HPV16 ( within the species Alpha-9 ) but is distantly related to MmPV4 ( within the genus Gammapapillomavirus ) , which was also isolated from a rhesus macaque . Since topological incongruence has been noted in the phylogenies of HPVs when trees are constructed with either late or early regions of the viral genomes [22 , 34] , we also examined the topologies of such trees . Although there was some incongruence , the majority of the primate PVs maintained their topological positions ( see S2 Fig , S3 Fig and S4 Fig ) . Fahrenholz’s proposal for strict codivergence of host and parasites states that the “parasite phylogeny mirrors that of its host , ” indicating that specific pathogens isolated from an individual host species should be monophyletic to the exclusion of viruses from other host species ( reviewed in de Vienne et al . ) [35] ( Fig 2 ) . In the case of primate PVs , however , viruses infecting a given host species do not always cluster together , implying an ancient viral divergence model in which viral ancestors may have first split into separated viral clades corresponding to niche adaptation to specific host ecosystems ( i . e . , tissue tropism ) . Following host ancestor speciation , distinct but homophyletic viruses were transmitted to similar ecosystems ( e . g . , mucosal or cutaneous sites ) between closely related host animals , resulting in the radiation observed in the extant primate PV tree where viruses sort by tissue tropism and not host species . This prediction was evaluated with a permutational multivariate analysis of variance ( PERMANOVA ) test [36] using primate PV nucleotide sequence pairwise distances , which revealed that tissue tropism ( here defined by different genera ) contributed to more of the variability of viral divergence ( accounting for 26% of the total variance , p<0 . 001 ) than that of the host ( 6% , p<0 . 001 ) ( Table 1 ) . To estimate the divergence times of primate PVs from their MRCAs , we used a Bayesian statistical framework employing previously established PV evolution rates [17] . Infectious PVs have been shown to have a slow mutation rate based on the observations that these double-stranded DNA viruses use the host cell DNA replication machinery , characterized by high fidelity , proofreading capacity , and post-replication repair mechanisms [37] . Since primate PVs , taken together , do not follow strict viral-host codivergence , each genus was evaluated separately to estimate divergence times . A combination of relaxed lognormal molecular clock and coalescent constant population models provided the best performance using the phylogenetic tree as shown in Fig 3A . The Alphapapillomavirus–Dyoomikronpapillomavirus split from a MRCA around 39 . 9 million years ago ( mya ) ( 95% highest posterior density ( HPD ) , 36 . 4–43 . 7 mya ) ( Fig 3A , S2 Fig and Table 2 ) is consistent with the time frame of the split between New World and Old World primate ancestors [26] . Similar virus-host codivergence events were observed between Old World monkey PVs and their closest HPV relatives , and were estimated to approximately 14–31 mya ( Fig 3 , S5 Fig , S6 Fig and S7 Fig ) . For example , the species Alpha-12 ( PVs mainly isolated from genital lesions of macaques ) split from a MRCA with the species Alpha-9 ( represented by oncogenic genital HPV16 ) around 27 mya coincided with the time span of the speciation between macaques and apes/humans that occurred approximately 25 mya [38 , 39] . An enigmatic observation in these data is the clustering of macaque PVs ( e . g . , MfPV3 ) and baboon PV ( Papio hamadryas PV 1 , PhPV1 ) within the species Alpha-12 group , suggesting either a recent viral transmission between macaque and baboon monkeys , or a more complex phylogeny of the sub-family Cercopithecinae . The majority of distinct human PV types arose during the end of the Miocene and/or the beginning of the Pliocene epoch coincident with the divergence of humans and chimpanzees occurring around 6–8 mya ( Fig 3 ) [40] . The divergence times and tree topologies support a model of intrahost divergence of primate PVs in which ancient viruses diverged and adapted to specific host ecosystems ( e . g . , tissue tropism or different types of epithelial cells ) within an ancestral host animal lineage ( e . g . , the MRCA of primate animals ) ( Fig 4 ) . Following periods of host speciation , continuing intrahost viral divergence events occurred as distinct but phylogenetically related viral types were transmitted to similar host ecosystems by the closely related host animals . This pattern of ancient viral divergence coupled to niche adaptation may explain , for example , the differences in the prevalence of HPV16 and HPV18 between squamous cell carcinomas and adenocarcinomas of the cervix [41] . This difference might represent the emergence of further viral adaptation to different ecological niches within the cervix , one dominated by stratified squamous epithelium the other by columnar epithelium , respectively [42] . The fact that we do not observe similar or parallel diversity of NHP-PVs compared to HPVs ( broken lines in right panel of Fig 4B ) could be due , in part , to reduced sampling effort , limited population size of NHPs , bottlenecks of viral transmission , and/or restricted host migration . Next , we constructed a phylogenetic tree of HPV16 variants based on 212 complete genomes to classify variant lineages and sublineages ( S3 Table ) . The tree topology shows two deeply separated clades corresponding to the previously classified Eurasian and African lineages ( S8 Fig ) , with a mean nucleotide sequence difference of 1 . 72% ± 0 . 09% ( S4 Table ) . The African lineage variants were more than twice as diverse ( intragroup mean difference of 0 . 77% ± 0 . 04% ) as the Eurasian variants ( 0 . 32% ± 0 . 02% ) . Since geographic nomenclature systems suffer from sampling biases and preconceived notions about virus ancestry , we utilized an agnostic alphanumeric nomenclature based on HPV16 phylogeny and complete genome nucleotide differences to assign HPV16 variants into four lineages designated A , B , C , and D . Each lineage could be divided into four sublineages ( A1-4 , B1-4 , C1-4 , and D1-4 ) , based on previously described criteria ( S9 Fig ) [43] . The previously named Asian ( As ) and North American 1 ( NA1 ) variants are designated sublineages A4 and D1 , respectively [44] . The maximum pairwise difference between the most diverse isolates , from sublineages A1 and D3 , was 2 . 23% . Based on single-nucleotide polymorphism ( SNP ) patterns and phylogenetic tree topologies , we assigned 3256 HPV16 partial sequences from 22 countries/studies into variant lineages and sublineages using maximum likelihood methods ( Table 3 ) . As shown in the summarized charts of HPV16 phylogeography ( Fig 5A ) , isolates from Asians and Caucasians ( Australians/Europeans , and North Americans ) were predominantly represented by A variants , with abundances of 92% and 83% , respectively . The majority of A4 variants ( 352/357 , 99% ) were from Asian individuals . Within the African population , 90% of HPV16 infections were B and C lineages . HPV16 variants in South/Central Americans were equally assigned as A1-3 ( 50% ) and D ( 48% ) . Using a weighted UniFrac algorithm , variants were well clustered into groups ( African , Eurasian , and South/Central American ) corresponding to the geographic origin of the isolates ( Fig 5B ) . Globally , A1-3 sublineages were the most widespread; whereas , the D lineages were detectable at low prevalences in many populations outside of South/Central Americans , such as in Caucasian ( 11% ) , African ( 7% ) , and Asian ( 6% ) individuals ( Fig 5C ) . In contrast , A4 and B/C lineages were rarely found outside of Asian and African populations , respectively . The molecular clock models used to estimate the divergence times of primate PVs support a scenario of virus-host codivergence after the virus has adapted to a specific host ecosystem . Using a similar Bayesian Markov chain Monte Carlo ( MCMC ) framework , we initially applied six combinations of clock models to estimate the divergence of HPV16 variants from their MRCA , without any prior assumption of virus-host codivergence ( Table 4 , no calibration ) . Interestingly , a combination of the relaxed lognormal molecular clock and coalescent Bayesian skyline models indicated that HPV16 A and BCD had divided around 618 . 5 thousand years ago ( kya ) ( 95% HPD: 331 . 5–996 . 1 ) . This estimation is within the time span of the separation between Homo sapiens and archaic hominins ( e . g . , Neanderthal/Denisova ) but around two-five times longer than the estimated modern Homo sapiens divergence time ( ca . 150–200 kya ) [45] indicative of an ancient divergence of HPV16 variants prior to the emergence of modern human ancestors . Based on the geographic distribution of HPV16 variants above , we then used an archaic hominin-host-switch ( HHS ) scenario to calibrate the divergence time between HPV16 A and non-A variants ( 500 kya , 95% HPD: 400–600 ) , and a modern-out-of-Africa ( MOA ) scenario between BC and D variants ( 90 kya , 95% HPD: 60–120 ) . When time calibrations were introduced into the phylogenetic tree , the HHS scenario showed the strongest support for time inference and estimated an initial divergence of HPV16 variants at approximately 489 kya ( 95% HPD: 394–581 ) , predating the out-of-Africa migration of modern humans ( ca . 60–120 kya ) ( Fig 6 and S10 Fig ) [46 , 47] . In addition , the demographic model of the Bayesian skyline plot for the population function through time showed a recent exponential expansion of the effective population size of present-day HPV16 occurring in the last 25 kya , lagging behind the growth of modern human populations ( starting from the last 40–50 kya ) ( see the top panel of Fig 6 ) . This plot most likely reflects the concurring increase and mobility of modern human populations and present-day virus populations in the last epoch . We observed a similar divergence timeframe for other HPV variants , splitting from their MRCAs approximately 300–600 kya and showing a strong correlation between evolution times and genomic diversities ( Fig 7 , Table 5 ) . In all cases , the deep separation between HPV16 variant lineages A and BCD ( and the deepest lineage separations of other HPV variants ) suggests an ancient virus-host codivergence , coinciding with the split between archaic Neanderthal/Denisova and modern human ancestors from their MRCA ( Fig 8 ) . Neanderthals spread out over Eurasia with at least two populations splitting approximately 77–114 kya from each other based on analysis of archaic genomes from Vindija , Mezmaiskaya ( Caucasus ) , and Denisova ( Siberia ) [48] . This time period corresponds to the diversion of HPV16 A sublineages and in particular the split of A4 from A1/2/3 and the emergence of HPV16 A4 in Asia , likely representing independent transmission of A4 from archaic hominins to modern humans in the east .
In this work , we used a Bayesian MCMC framework to estimate the divergence times of primate PVs and propose an early ancient intrahost viral divergence model ( i . e . , niche adaptation ) followed by viral-host coevolution . This form of viral evolution has been documented for polyomaviruses [49] , herpesviruses [50] , and some retrovirus genera [51] . With the assumption of host niche adaptation as a fundamental process , the estimation of primate PV divergence times within niche-specific clades mirrors that of the primate host evolutionary history ( Fig 4 ) . It is clear that the evolutionary history of these well adapted , slowly evolving PVs may be significantly more complex than previously appreciated [37] . The implication of host niche adaptation of primate PVs preceding virus-host codivergence suggests a critical role for viral genetic heterogeneity and natural selection . The origin of viral genetic determinants of cervical niche adaptation further supports the hypothesis that a group of well-evolved viral genotypes also contain the determinants for cervical cancer , since this phenotype cannot exert selective pressure , as it does not support the production of infectious virus . It may also explain why a large set of cervicovaginal macaque PVs ( within the species Alpha-12 ) associated with cervical neoplasia shares a common origin with the high-risk clade of human PVs ( e . g . , Alpha-9 ) ( Fig 3A ) [6 , 27] . Our findings provide a framework for studying the past evolution of primate PVs infecting the genital tract niche and support a molecular clock based on phylogeny , since the generation time of PVs can only be extrapolated from empiric data based on coevolution models [17 , 52] . We used this well-supported molecular clock model to estimate the divergence times of HPV16 variants . HPV16 is the most common oncogenic HPV type and shows diversity in persistence and carcinogenicity [53–55] , suggesting further biological differences between variant lineages . We observed specific geographic/ethnic dispersals of HPV16 variants , such as A4 predominance in Asian populations and BC predominance in African populations . The estimated divergence times between HPV16 A and BCD variants largely predated that of the out-of-Africa migration of modern human populations , consistent with a previously reported archaic hominin-host-switch scenario [19 , 20] . One interpretation of the data implies that the present-day Eurasian HPV16 A variants were probably the products of multiple interactions between Neanderthals/Denisovans and modern Homo sapiens established during sexual contact after a long period of separation ( e . g . , 400–600 kya ) . This notion of viral sexual transmission between groups is reflected in the recent genetic admixture ( e . g . , 80 kya ) between groups [48 , 56–59] , with evidence of 2–4% of nuclear DNA in Eurasians that can be traced to Neanderthals [48 , 58] . This assumption is likely ubiquitous in a number of Alpha-HPV variants ( Fig 7 , Table 5 ) , although their pathogenesis , evolution , and epidemiology warrant further study . Recent evidence indicates that Neanderthals spread out over the Eurasian continent and also admixed with ancestors of the present-day East Asian population [60 , 61] . Since HPV16 A4 lineage is exclusively found in East Asians ( approximately 40% of HPV16 ) and presents a higher risk of cervix cancers in Asian populations [62 , 63] , we speculate that a subset of Neanderthals heading east into Asia over more than 100 thousand years of existence in Eurasia could have interbred with East Asian modern humans and transmitted the HPV16 A4 sublineage and introgressed specific gene alleles that provided a selective advantage to the HPV variants coevolving with them [59 , 64] . Overall , HPV16 BCD variants have higher genomic diversity than A isolates ( see S4 Table ) , which may imply a potential population bottleneck of horizontal transmission reducing the diversity of current day A lineage isolates . In contrast , BCD variants have accumulated more genetic mutations , consistent with the observations that African populations and their pathogens have deeper origins reflected in greater diversity [65] . This idea supports one theory that both HPV16 BCD and modern humans arose in Africa ( Fig 8 ) . Following a relatively recent out-of-Africa migration , the modern humans acquired the A variant from sex with archaic hominins and possibly carried D variants into Eurasia under conditions of a small population size . The ancestors of East Asian people crossed the Bering Strait and were early populators of the Americas ( based on historical records and genetic relatedness ) [66] . Surprisingly , the D lineage is phylogenetically rooted in the African clade , but we did not find a major reservoir of the D lineage in the present-day African populations . This interesting observation suggests either an advantage of niche colonization and expansion of HPV16 D variants in Native Americans or a bottleneck of HPV16 variants present in people populating the Americans . Alternatively , the lack of A4 and the high proportion of D lineages in the Americans could be the result of an early colonization of the Americas by an unknown group from Africa . More data is needed to sort out the evolutionary history of the HPV16 D lineage and might provide clues to new features of the populating of the Americas . Sexual interactions between archaic hominins and modern human ancestors likely occurred over multiple time- and space-scales . For example , viral transmission might have also occurred from modern humans to Neanderthals/Denisovans , based on the evidence of ancient gene flow from early modern humans into Eastern Neanderthals [57] . Since PVs usually establish infections at the basal layer of epithelial cells , it will be impossible to detect viruses from fossil bones of archaic hominins and document the presence of HPVs in archaic hominin populations [20] . The evolutionary histories and origins of modern H . sapiens are undergoing dramatic revisions with the introduction of advanced sequencing techniques and methods to analyze genomic samples from archaic hominin specimens [67–69] . Since the reproductive success per copulation between H . sapiens and archaic hominins is predicted to have lower viability than that of modern human reproductive events , high levels of sexual interaction were likely present facilitating HPV transmission , in addition to genetic introgression observed in modern non-African populations [70] . For example , the human leukocyte antigen ( HLA ) B*07:02 and C*07:02 alleles associated with increased risk in cervix cancers appear to be introgressed regions in present-day Eurasians and Melanesians from Neanderthals or Denisovans [71–73] . This also suggests that adaptive introgression of modern humans from archaic hominins influences the pathogenic outcome of these infections by as yet unknown mechanisms [70 , 74] . However , it can be speculated that introgressed genes providing some selective advantage to hybrid human-archaic hominin offsprings could also make them more susceptible to HPV variants adapted to archaic hominins over hundreds of thousands of years of coevolution . The introgressed genes are most likely related to immunity against infections , whatever the pathogens might be and HPV was along for the ride , since HPV is not known to affect reproductive fitness of the host . This study has its strengths and limitations . We expand the current understanding of HPV16 evolution beyond the recent description of HPV transmission between archaic and modern humans that used existing data [20] in important ways . We have expanded the understanding of HPV16 in the context of human and non-human primate PV evolution by characterizing additional New World and Old World monkey PVs and using the known divergence times of specific primate species to establish a valid molecular clock . This approach was used to establish the times of Neanderthal divergences [48] . We demonstrate that niche adaption had to proceed viral-host coevolution , and suggest that subsequent niche adaptation might underlie the difference in prevalence of HPV16 and HPV18 in cervical squamous and glandular lesions . We have identified and characterized additional HPV16 variants enabling us to establish the HPV16 variant taxonomy that includes subvariants that have unique biological characteristics [53] . Moreover , we propose that evolution of HPV16 A in Neanderthals over time led to allopatric emergence of the HPV16 A4 lineage as Neanderthals moved east and interbred with modern humans in Asia . We have also expanded the number of HPV16 isolates from around the world to establish the global distribution of HPV16 variants . Lastly , we provide new interpretations and questions on the HPV16 D lineage that is part of the African clade , but is highly prevalent in South/Central America . Nevertheless , there are also limitations to the current study and interpretations . The understanding of human evolution is constantly being challenged with new data and it is possible the models of human evolution used in this study will change [75] . We have not sampled every population and it is possible that additional HPV16 isolate data could change our interpretations . The data obtained on the geographic locations of the HPV partial sequences could be incorrect resulting in underestimating the true associations between variants and historic origins . Lastly , it is possible that very low population sizes of humans migrating out of Africa carried HPV16 A lineage variants leaving no traces in Africa , but expanding throughout Eurasia . This unlikely possibility would influence the interpretations of both our work and that of previous studies analyzing the evolution of HPV16 [20] . In conclusion , the biology and natural life cycle of oncogenic HPVs that results in infectious viral particles ( i . e . , vegetative virus life cycle ) is highly adapted to the differentiation program of epithelial cells [76] . Poorly differentiated precancerous and cancerous cells in the cervix do not support the HPV vegetative life cycle , and thus viral-associated transformation does not contribute to the fitness of HPVs . Viral phenotypes that serve to adapt to a specific ecological niche , evade host immune mechanisms , and support persistent viral production , however , should contribute to viral fitness . Therefore , further investigations of viral-host interactions and the underlying mechanisms of viral oncogenicity , should continue to focus on features of viral evolution and niche adaptation that contribute to fitness , since the oncogenic outcome of HPV infections appear to be “collateral damage” affecting host morbidity and mortality . The current data provides a framework to unravel the mysteries of oncogenic HPV genomes as we expand our understanding of viral-host evolution .
The studies providing human cellular samples have been approved by the Institution Review Board of the Albert Einstein College of Medicine , Bronx , NY , and the Joint Chinese University of Hong Kong-New Territories East Cluster Clinical Research Ethics Committee . All human subjects were older than 18 years of age and samples were anonymized without individual identifying information . Written informed consent was obtained from each participant . The animal use protocol was reviewed and approved by the Institutional Animal Care and Use Committee ( IACUC ) of Albert Einstein College of Medicine ( protocol number 20060908 ) . All procedures involving animals were conducted in compliance with applicable state and federal laws , guidelines established by the Animal Care and Use Committees of the respective institutions , and standards of the U . S . Department of Health and Human Services , including the National Institutes of Health Guide for the Care and Use of Laboratory Animals . The programs for animal care and welfare at Albert Einstein College of Medicine has been fully accredited by the Association for Assessment and Accreditation of Laboratory Animal Care ( AAALAC ) . The Animal Welfare Assurance ( A3312-01 ) is on file with the Office for Laboratory Animal Welfare . The Saimiri sciureus PV DNA was isolated from exfoliated cervical cells of two adult female squirrel monkeys screened using polymerase chain reaction ( PCR ) -based MY09/11 and FAP59/64 primer systems [77 , 78] . Sequences from the PCR products were compared with a PV database maintained in the Burk lab using a Blastn search and shown to have < 90% similarities to previously characterized PV types . The whole genomes were PCR-amplified as two overlapping fragments using degenerate primer sets designed on available L1 gene sequences and consensus E1 alignments , and subsequently Sanger sequenced using primer walking in the Einstein Sequencing Facility , New York [33] . Geneious R9 . 1 . 7 was used to assemble segmented sequences into the complete genome sequences and identify ORFs [79] . The Macaca mulatta PV DNA was purified from exfoliated cervical cells of one adult female rhesus monkey and swabs from the penis surface of one adult male rhesus monkey . The viral DNA was initially detected using multiplexed next-generation sequencing ( NGS ) assays targeting two small fragments ( 136 bp and 83 bp , respectively ) within the L1 ORF [80 , 81] . Sequences of a Blastn search against a PV database showed < 90% similarities to characterized PV types . The total DNA underwent a metagenomic sequencing on an Illumina HiSeq4000 at Weill Cornell Medicine Genomics Resources Core Facility , New York , using paired-end 100 bp reads . The short reads were filtered for host genome contamination and assembled de novo using Megahit v1 . 0 . 6 to build contigs with long length [82] . The whole genomes of novel Macaca mulatta PVs were validated using type-specific PCR in three overlapping fragments and Sanger sequencing using a primer walking strategy . The complete genome sequences of SscPV1/2/3 and MmPV2/3/4 have been submitted to NCBI/GenBank database , with access numbers of JF304765 to JF304767 and MG837557 to MG837559 , respectively . In our previous work , we sequenced the complete genomes of 78 HPV16 isolates ( see HPV16 list in S3 Table ) [83 , 84] . In the current study , 122 cervicovaginal samples containing HPV16 DNA were randomly chosen from the Kaiser Permanente Northern California ( KPNC ) -NCI HPV Persistence and Progression ( PaP ) cohort study [85] and a population-based HPV prevalence survey coordinated by the International Agency for Research on Cancer ( IARC ) [63] . The complete genomes were characterized using nested overlapping PCR and Sanger sequencing as previously reported [86] . The PaP study samples were also sequenced using Ion PGM platform [87] . In addition , 12 HPV16 complete genomes sequenced by others were included in this study [88–92] . To evaluate the phylogenetic relationships of PVs , the concatenated nucleotide sequences of four open reading frames ( ORFs ) of the E1 , E2 , L2 , and L1 genes of 141 PV types representing 132 species and unique hosts were used ( see PV list in S2 Table , column labelled “Selected type” marked yes ) . Because all known PVs contain these four core ORFs , the concatenated partitions provide a comprehensive evaluation of the evolutionary history of Papillomaviridae . In addition , the highly conserved E1 early gene and L1 late gene were used to characterize phylogenetic incongruence . The nucleotide sequences of each coding region were aligned based on the corresponding amino acid sequences previously aligned using MUSCLE v3 . 8 . 31 [93] in Geneious R9 . 1 . 7 . For HPV16 lineage/sublineage classification and phylogenetic analyses , all 212 complete genome nucleotide sequences ( see HPV16 list in S3 Table ) were linearized at the ATG of the E1 ORF and aligned using MAFFT v7 . 221 [94] . Maximum likelihood ( ML ) trees were constructed using RAxML MPI v8 . 2 . 3 [95] and PhyML MPI v3 . 1 [96] with optimized parameters based on the aligned complete genome nucleotide sequences . Data were bootstrap resampled 1 , 000 times in RAxML and PhyML . MrBayes v3 . 1 . 2 [97] with 10 , 000 , 000 cycles for the Markov chain Monte Carlo ( MCMC ) algorithm was used to generate Bayesian trees . A 10% discarded burn-in was set to eliminate iterations at the beginning of the MCMC run . The average standard deviation of split frequencies was checked to confirm the independent analyses approach stationarity when the convergence diagnostic approached <0 . 001 as runs converge . For Bayesian tree construction , the computer program ModelTest v3 . 7 [98] was used to identify the best evolutionary model; the identified General Time Reversible ( GTR ) model was set for among-site rate variation and allowed substitution rates of aligned sequences to be different . The CIPRES Science Gateway [99] was accessed to facilitate RAxML and MrBayes high-performance computation . Permutational multivariate analysis of variance was performed using the adonis function in R’s package ‘vegan’ and the pairwise distance based on 220 primate papillomavirus E1-E2-L2-L1 nucleotide sequences ( S2 Table ) . A dataset of 3256 partial sequences spanning variable genes/regions of HPV16 was obtained from GenBank that included the geographic source of the sequences mainly from indigenous ethnicities and/or local communities including 22 countries/regions throughout the world . These included , in Africa: Burkina Faso [100] , Nigeria [101] , Rwanda [102] , Uganda [103] , and Zambia [104]; in Asia: China [105–107] , India [108 , 109] , Japan [110] , Korea [111] , and Thailand [112 , 113]; in Europe: Germany [114] , Italy [115–118] , Netherland [119 , 120] , Portugal [121] , Russian [122] , Spain [123] , and United Kingdom [124]; in North America: Canada ( GenBank , see details in Table 3 ) , Costa Rica [9]; in South/Central America: Bazile [125–127] and Mexico [128–131]; and Australia [132] ( see Table 3 ) . We used a maximum phylogenetic likelihood algorithm in pplacer v1 . 1 . alpha17 [133] to place partial sequences on a reference tree inferred from an alignment composed of the 212 HPV16 variant complete genomes described in this study . A cutoff value of maximum likelihood ≥ 0 . 8 was set as confident assignment of HPV16 isolates into lineages and sublineages . The abundance of each lineage from the same country was combined and normalized using a percentage . According to the geographic patterns of HPV16 variants [44] , four ethnical groups , namely African , Asian , Caucasian , and South/Central American , were summarized; for each HPV16 ( sub ) lineage , its frequency in each group was calculated based on the summary of individual percent abundance divided by the summary of total percent abundance . We used a weighted UniFrac method in R’s package ‘GUniFrac’ [134] to calculate the pairwise distances between geographic locations , based on which a principle component analysis ( PCoA ) was performed to visualize the clustering of geographic groups of HPV16 variants using the betadisper function in R’s package ‘vegan’ . We used a Bayesian Markov Chain Monte Carlo ( MCMC ) method implemented by BEAST v2 . 4 . 5 [135] and the previously published PV evolutionary rates [17] to estimate the divergence times of primate PVs from their most recent common ancestors ( MRCAs ) . Times were calculated separately for Alphapapillomavirus ( n = 85 ) , Betapapillomavirus ( n = 54 ) , and Gammapapillomavirus ( n = 81 ) ( S2 Table ) , given that primate PVs , taken together , do not follow strict virus-host codivergence . Three tree priors were estimated using the following demographic models: ( 1 ) coalescent constant population , ( 2 ) Yule model , and ( 3 ) coalescent Bayesian skyline , with assumptions that ( 1 ) the PV genome has a strict mutation rate or ( 2 ) there is an uncorrelated lognormal distribution ( UCLD ) molecular clock model of rate variation among branches , resulting in six combinations of models . In addition , we chose the GTR sequence revolution model with the gamma-distributed rate heterogeneity among sites and a proportion of invariant sites ( GTR + G + I ) determined by the best-fit model approach of Modeltest v3 . 7 [98] . The concatenated nucleotide sequence partitions of six ORFs ( E6 , E7 , E1 , E2 , L2 , and L1 ) with variable rates of substitution over time were used: 2 . 39 × 10−8 ( 95% confidence interval 1 . 70–3 . 26 × 10−8 ) substitutions per site per year for the E6 gene , 1 . 44 × 10−8 ( 0 . 97–2 . 00 × 10−8 ) for the E7 gene , 1 . 76 × 10−8 ( 95% CI: 1 . 20–2 . 31 × 10−8 ) for the E1 gene , 2 . 11 × 10−8 ( 95% CI: 1 . 52–2 . 81 × 10−8 ) for the E2 gene , 2 . 13 × 10−8 ( 95% CI: 1 . 46–2 . 76 × 10−8 ) for the L2 gene , and 1 . 84 × 10−8 ( 95% CI: 1 . 27–2 . 35 × 10−8 ) for the L1 gene , as previously described [17] . In order to calibrate the divergence times , we introduced three time points inside and at the root of the Alphapapillomavirus tree , with assumptions of codivergence histories between primate PVs and their hosts: ( 1 ) the node between HPV13 and chimpanzee PpPV1 ( Pan paniscus PV 1 ) at 7 mya ( 95% CI , 6–8 mya ) matching the split between hominin and chimpanzee ancestors; ( 2 ) the node between the species Alpha-12 ( represented by Macaca mulatta PV 1 ) and Alpha-9/11 ( represented by HPV16 ) at 28 mya ( 25–31 mya ) matching the speciation between hominin and macaque ancestors; and ( 3 ) the node between Alphapapillomavirus and Dyoomikronpapillomavirus ( represented by Saimiri sciureus PV 1 ) at 49 mya ( 41–58 mya ) matching the divergence between Old World and New World monkey ancestors [26] . For Betapapillomavirus and Gammapapillomavirus trees , the calibration time point ( s ) was set between macaque PVs and their closet HPV relatives . To estimate divergence times of HPV16 complete genome variants , a Hominin-host-switch ( HHS ) model assuming there was an ancestral viral transmission between archaic and modern human populations [20] was applied by setting two evolutionary time points to calibrate the HPV16 variant phylogenetic tree: ( 1 ) the archaic divergence of modern humans and Neanderthals/Denisovans around 500 thousand years ago ( kya ) ( 95% CI , 400–600 kya ) [136] matching the split between HPV16 Eurasian ( A ) and African variants ( BCD ) , and ( 2 ) the modern human out-of-Africa migration at 90 kya ( 95% CI , 60–120 kya ) [45 , 137] , locating the era when HPV16 D variants diverged from their most recent common ancestor ( MRCA ) . A HPV16 variant substitution rate was used for validation of a uniform prior rate: 1 . 84 x 10−8 ( 95% CI , 1 . 43–2 . 21 x 10−8 ) [20] , with combinations of three tree priors and two clock models as described above . Due to the lack of geographic/ethnic dispersal information of other HPV type variants , we estimated the youngest divergence events splitting from their MRCA using complete genome alignments and HPV16 variant substitution rate without time point calibration . To compare the population dynamics of HPV16 variants and the modern human host , Bayesian skyline plots were created using BEAST . A total of 311 globally sampled present-day human mitochondrial DNA ( mtDNA ) sequences , excluding the 1120 bp non-coding D-loop ( that evolves at a higher rate ) to give an alignment of 15 , 471 bp in length [138] , were analyzed using a strict clock model and a coalescent Bayesian skyline , with an estimated rate of 2 . 47 x 10−8 ( 95% CI , 2 . 16–3 . 16 x 10−8 ) substitutions per site per year [139] , as these sequences have been shown to evolve in a roughly clock-like manner [140 , 141] . Two evolutionary time points were used to calibrate the modern human mtDNA tree: ( 1 ) the age of the MRCA between the maximum distanced modern humans , estimated to be 171 , 500 ± 50 , 000 years ago , and ( 2 ) the age of the MRCA of the youngest clade that contains both African and non-African lineages , approximately 52 , 000 ± 27 , 500 years ago [140] . The MCMC analysis was run for 100 , 000 , 000 steps , with subsampling every 10 , 000 generations . A discarded burn-in of the first 10% steps was set to refine trees and log-files for further analysis . Effective sample sizes ( ESS ) of all parameters are >300 ( Alphapapillomavirus tree ) and >2000 ( HPV variant trees of each type ) , indicating that all Bayesian chains were well sampled and have converged . Best model estimates were selected using a posterior simulation-based analogue of Akaike's Information Criterion for MCMC samples ( AICM ) [142] , as implemented in Tracer v . 1 . 6 . The lower AICM values indicated a better model fit . A consensus tree was inferred using TreeAnnotater v . 2 . 4 . 5 and visualized using scripts developed in-house in R . The linear model ( lm ) function in R was used to estimate the correlation between sequence diversity and divergence time of HPV types and variants . | Epidemiologic studies have demonstrated that persistent infection of select oncogenic human papillomaviruses ( HPVs ) is the main cause of cervix precancer and cancer . Nevertheless , our knowledge of the underlying evolutionary mechanisms driving the divergence and emergence of viral oncogenicity in specific types of HPVs is incomplete . To better understand the molecular evolution of oncogenic HPVs , we isolated viruses from non-human primates , evaluated papillomavirus molecular clock models , and estimated the divergence times of HPV16 and other HPV type variants from their most recent common ancestors . Primate PV-host tissue tropisms indicated niche adaptation of viruses to host ecosystems as the first stage of the evolution of oncogenic HPVs . The data also provided evidence of ancient codivergence of HPV variants with archaic hominins and recent viral transmission from Neanderthals to modern non-African humans through sexual intercourse . Understanding the evolution of papillomaviruses should provide important biological insights and suggest mechanisms underlying HPV-induced cervical cancer , since niche adaptation rather than oncogenicity drives viral fitness . | [
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"ph... | 2018 | Niche adaptation and viral transmission of human papillomaviruses from archaic hominins to modern humans |
During its developmental cycle within the sand fly vector , Leishmania must survive an early proteolytic attack , escape the peritrophic matrix , and then adhere to the midgut epithelia in order to prevent excretion with remnants of the blood meal . These three steps are critical for the establishment of an infection within the vector and are linked to interactions controlling species-specific vector competence . PpChit1 is a midgut-specific chitinase from Phlebotomus papatasi presumably involved in maturation and degradation of the peritrophic matrix . Sand fly midgut chitinases , such as PpChit1 , whether acting independently or in a synergistic manner with Leishmania-secreted chitinase , possibly play a role in the Leishmania escape from the endoperitrophic space . Thus , we predicted that silencing of sand fly chitinase will lead to reduction or elimination of Leishmania within the gut of the sand fly vector . We used injection of dsRNA to induce knock down of PpChit1 transcripts ( dsPpChit1 ) and assessed the effect on protein levels post blood meal ( PBM ) and on Leishmania major development within P . papatasi . Injection of dsPpChit1 led to a significant reduction of PpChit1 transcripts from 24 hours to 96 hours PBM . More importantly , dsPpChit1 led to a significant reduction in protein levels and in the number of Le . major present in the midgut of infected P . papatasi following a infective blood meal . Our data supports targeting PpChit1 as a potential transmission blocking vaccine candidate against leishmaniasis .
Emerging and reemerging vector-borne diseases pose significant threats to human and animal health [1] . The emergence of insecticide resistance as well as the lack of other efficient insecticidal tools to control disease vectors imply that new methodologies need to be developed in order to reduce vector-borne disease transmission [1] . For this , the study of vector-pathogen interaction pinpointing factors underlying vector competence can reveal new molecular targets to be disrupted , preventing pathogen transmission [2] , [3] . In sand flies , midgut molecules are known or believed to be involved in defining a species ability to transmit Leishmania in nature . For a successful development within the midgut of the sand fly vector , Leishmania must overcome several barriers that include an early proteolytic attack [4] , [5] , [6] , [7] , [8] , the need to escape the peritrophic matrix ( PM ) [8] , [9] , [10] , [11] , [12] , and attachment to the midgut epithelia to prevent excretion with the remnants of the blood meal [13] , [14] , [15] , [16] . Attachment to midgut epithelia has long been associated with the type of lipophosphoglycan ( LPG ) present on the surface of Leishmania , and is associated with defining sand fly-Leishmania pairs in nature [15] , [16] , [17] . For Leishmania major V1 strain , with LPG displaying highly decorated side chains with prominent galactose residues , we demonstrated that PpGalec , a P . papatasi galactose-binding protein , is the docking site for Le . major on the midgut epithelium of Phlebotomus papatasi [14] . Recently , LPG-independent midgut binding has been associated with the degree of glycosylation detected on proteins expressed by midgut epithelial cells [18] . For events leading up to the midgut binding , such as early parasite survival during the proteolytic attack and escape of the endoperitrophic space , some investigators suggested that midgut proteases , such as trypsins and chymotrypsins , also are responsible for defining vector-Leishmania specificity [4] , [5] , [6] , [7] . Such proteases were shown to be specially harmful to transitional stages amastigotes [8] . A role of the PM on sand fly vector competence was suggested through comparisons of Leishmania development in different sand fly species displaying different PM degradation rates [9] , [10] , [11] . Studies later revealed a dual role for the sand fly PM in parasite development; protecting Leishmania from digestive enzymes in the beginning of blood digestion , yet becoming a barrier to parasite escape when mature [8] . Recent data also indicate that an anterior PM plug located at the junction between the anterior and posterior midgut acts as a barrier to Leishmania migration towards the stomodeal valve [12] . Regarding Leishmania escape from the PM , it was firstly proposed to be solely accomplished by a parasite chitinase [19] . Further work demonstrated that a Le . mexicana chitinase-overexpressing strain had an accelerated escape from the PM in Lutzomyia longipalpis [20] . However , since the characterization of a blood induced chitinolytic system in the sand fly midgut [21] , it became apparent that the parasite must take advantage of the sand fly peak chitinolytic activity within midgut , approximately 40–48 hours after a blood meal , for their escape [12] , [21] . PpChit1 is presumably involved in PM maturation/degradation in P . papatasi [8] . Based on the fact that Leishmania must escape the PM , and that this escape may be aided by the vector's own chitinase , we predicted that PpChit1 knock down ( via RNAi ) would interfere with Le . major development . Our data indicates that dsRNA-mediated silencing of PpChit1 transcripts leads to a reduction in the parasite load within the midgut of P . papatasi , pointing to the role of this molecule in P . papatasi vector competence and its potential for the development of a transmission-blocking vaccine .
The use of animals during this study was reviewed and approved by the Kansas State University Institutional Animal Care and Use Committee ( KSU-IACUC ) . P . papatasi ( Israeli strain -PPIS ) was reared in the Department of Entomology , Kansas State University , according to conditions described [21] . For all experiments , three-to-five day old female sand flies were used . Blood feeding was performed through a chicken skin membrane attached to a feeding device . Prior to sand fly feeding , fresh mouse blood was heat inactivated for 30min at 56°C and supplemented with 50 µl/ml of Pen/Strep solution ( MP Biomedicals , Solon , OH , USA ) as well as 1 mM ATP ( MP Biomedicals ) . Sixteen to twenty four hours after blood feeding , fully engorged females were separated from partially engorged and non-blood fed by anesthetizing flies with CO2 and examining the midgut distension under a stereoscope microscope . Only fully fed individuals were maintained for further analyses . Fully engorged sand fly midguts were individually dissected on RNAse free ( cleaned with ELIMINase , Fisher Scientific , Pittsburgh , PA , USA ) glass slides , transferred to 50 µl of 1× PBS buffer ( RNase free , pH 7 . 4; Fisher Scientific ) , and thoroughly homogenized using a hand held tissue homogenizer and RNAse-free pestle . Half the homogenate volume ( 25 µl ) was transferred to 350 µl of RLT buffer ( supplemented with 1% β-mercaptoethanol ) provided by the RNA extraction kit ( RNAeasy mini kit , Qiagen , Valencia , CA , USA ) and stored at −80°C for quantitative real-time PCR assays . The remaining 25 µl of midgut homogenate was used in Western blot assays , as described below . Infections of sand flies with Le . major amastigotes V1 strain were done by addition of 5×106 parasites/ml into the blood meal . Le . major amastigotes were harvested from BALB/c mouse footpads lesions formed roughly 30 days after inoculation with 5×105 parasites from late phase culture according to [22] . dsRNA for PpChit1 were synthesized using the primers PpChit1/T7i_2–F ( 5′–TAATACGACTCACTATAGGGAGAATGAAGATATCATTGTGTGC-3′ ) and PpChit1/T7i_2–R ( 5′– TAATACGACTCACTTAGGGAGATCAGCATTGGACCAGGAAGG-3′ ) , which contain the complete T7 promoter and amplify the full length sequence encoding the mature PpChit1 . PCR reactions were performed with 0 . 5pmoles of each primer along with 1 µl of cDNA ( synthesized from midgut dissected at 72 h post-blood meal , PBM ) , and 10 µl of GoTaq PCR master mix ( Promega , Madison , WI , USA ) . The 20 µl PCR reactions were carried according to the conditions: 10 cycles of 95°C for 1 min , 55°C for 1 min , and 72°C for 1 min and 15 sec , followed by 35 cycles 95°C for 1 min , 65°C for 1 min , and 72°C for 1 min and 15 sec . The reaction products were purified and concentrated using the YM-100 filters ( Millipore , Billerica , MA , USA ) , and 1 µg DNA was used for dsRNA synthesis using the Megascript RNAi kit ( Ambion , Austin , TX , USA ) . dsRNA synthesis reactions were performed for four hours at 37°C , and the products were further purified following manufacturer's recommendations . Thereafter , dsRNAs were suspended in ultra-pure water and further purified and concentrated to approximately 3 . 5 mg/ml or 4 . 5 mg/ml using the YM-100 filters ( Millipore ) . The positive control provided by the Megascript RNAi kit ( Ambion; used in Real-Time PCR and Western blot assays ) or a dsRNA specific to a green fluorescence protein gene ( dsGFP [23]; for parasite counting assays ) was used as controls for dsRNA injection assays . For dsRNA injections , individual females were anesthetized with CO2 , kept on a cold dish , and injected intra-thoraxically with either 23 nl ( 3 . 5 mg/ml , 80 . 5 ng ) or 32 nl ( 4 . 5 mg/ml , 144 ng ) of dsRNA using Nanoject II microinjector ( Broomall , PA , USA ) . Immediately following injection , flies were transferred to a 500 ml plastic container , provided with 30% sugar embedded cotton , and maintained inside a high humidity chamber ( 85–95% humidity at 25°C ) . Flies were allowed to recover for 48 hours and blood fed on an uninfected blood meal through a chicken membrane , as described above . Total RNA was isolated from individual midguts dissected as described above . RNA extraction was carried out using the RNAeasy mini kit ( Qiagen ) following manufacturer's instructions . Following extraction , the Turbo DNA-free kit ( Ambion ) was used to eliminate DNA contamination . After quantification , 25 ng total RNA was used for cDNA synthesis using 200 units of SuperScript III Reverse Transcriptase ( 200 u/µl ) , 2 . 5 µM Oligo ( dT ) 20 primer , and 0 . 5 µM dNTPs ( 10 mM ) . These reagents were incubated at 65°C for 5 minutes ( min ) and kept in ice for at least 1 min . This step was followed by addition of a mix containing 4 µl 5× SuperScript III Reverse Transcriptase First-Strand Buffer , 5 mM DTT ( 0 . 1 M ) , 20 Units of RNaseOUT to the reaction . The mixture was incubated for one hour at 50°C and stored at −20°C . All the reagents for cDNA synthesis were purchase from Invitrogen ( Carlsbad , CA , USA ) . Real-Time PCR reactions were performed using BioRad SYBR green and BioRad iCycler ( BioRad , Hercules , CA , USA ) . The reactions were carried out in duplicate using 0 . 5 µl cDNA , 6pmoles of each primer ( 10 µM ) , 10 µl of 2× SYBR green , and 8 . 3 µl of Ultra Pure DNase/RNase-Free Water ( Invitrogen ) . The primers used for chitinase amplification were PpChit_137F ( 5′ - ATGATCTGCATGGTTCTTGG - 3′ ) and PpChit_137R ( 5′ - GGAGCTCCATTTCGAATCC - 3′ ) while the S3 primers ( Pp40S_S3_136F: 5′ - GGACAGAAATCATCATCATG – 3′ and Pp40S_S3_136R: 5′ – CCTTTTCAGCGTACAGCTC – 3′ ) were used for amplifications of the housekeeping control gene ( encoding the protein S3 of ribosomal subunit 40S ) . The reaction cycle of 94°C for 1 min , 57°C for 1 min , and 72°C for 30 sec was repeated 40 times , and the amplification profiles were assessed using the BioRad iCycler software ( BioRad ) . Polyclonal anti-PpChit1 sera were obtained by injecting three month old female BALB/c mice subcutaneously into the ears . Mice were injected three times in two weeks intervals with approximately 10 µg of purified VR2001 plasmid [24] encoding the mature chitinase protein [21] per mouse ear . Blood was collected from the submandibular vein ( “cheek bleed” ) of injected animals and antibody levels accessed via Easy-Titer IgG Assay Kit ( Pierce , Rockford , IL , USA ) . Sera were maintained at −20°C until used . For Western blots , seven midgut extracts from flies injected with dsPpChit1 and dsControl were pooled together in RNasefree microcentrifuge tubes containing 1 µl of complete protease inhibitor ( Thermo Scientific , Rockford , IL , USA ) and concentrated using the YM-10 filters ( Millipore ) . Total protein concentration in midgut extracts was quantified using BCA Protein Assay Kit ( Thermo Scientific ) . Similar proteins amounts ( 5 µg per lane ) from midguts of dsPpChit1 and dsControl injected sand flies were fractionated on 10% Bis-Tris NuPAGE gels ( Invitrogen ) . Proteins were transferred to a nitrocellulose filter ( Whatman , Dassel , Germany ) , incubated with PpChit1 antisera ( 1:100 dilution ) overnight at 4°C , washed three times in TBS-T ( 1× TBS buffer with 0 . 1% tween-20 ) for 15 minutes each time . Blot was incubated with anti-mouse conjugated to alkaline phosphatase ( 1:10 , 000 in TBS-T ) antibodies ( Promega ) for one hour at room temperature and washed in TBS-T as indicated above . The protein bands ( 56 kDa , [21] ) were visualized using the Western Blue substrate for Alkaline Phosphatase ( Promega ) . Alternatively , Western blot was incubated with anti-mouse-Horseradish Peroxidase secondary antibody ( 1:10 , 000 ) and detected with SuperSignal West Pico Chemiluminescence Substrate ( Thermo Scientific ) in chemiluminescence assays . Densitometry analysis was performed using the TotalLab TL100 software ( Nonlinear Dynamics , Durham , NC , USA ) . In order to assess the PpChit1 knockdown effects on Le . major development , 80 . 5 ng of dsRNA was injected intra-thoraxically into P . papatasi , and flies were fed on an infected blood meal as described above . Midguts from fully engorged-only flies were dissected at 48 h and 120 h after the infective blood meal and homogenized in 30 µl of PBS buffer ( pH 7 . 4 ) . Parasites were counted with a hemocytometer . Two independent experiments were carried out for each time point . Mann-Whitney U test was performed to compare expression profiles as well as parasite numbers between sand flies injected with either dsRNA targeting PpChit1 transcripts ( dsPpChit1 ) or the dsRNA control ( dsControl ) injected flies . D'Agostino & Pearson omnibus normality test was performed to assess whether parasite numbers followed a normal distribution . The Chi-square test ( or Fisher's exact test ) was performed in order to assess whether dsPpChit1-injected flies exhibit altered Le . major load compared to the dsControl-injected flies . Parasite infection load in flies dissected at 48 h post infection was scored according to parasite numbers in the sand fly midgut as no parasite , light infection ( 1–1 , 000 parasites ) , moderate infection ( 1 , 001–10 , 000 ) , or heavy infection ( >10 , 000 ) , in accordance to [25] . For flies dissected at 120 h PBM parasite loads were categorized in two groups: zero or light infections ( 0–1 , 000 parasites ) was arranged as one group , and moderate infection ( >1 , 000 parasites ) as another . Differences were considered statistically significant at p<0 . 05 , and tests were carried out using GraphPad Prism v . 5 . 01 software ( GraphPad Software , Inc ) .
Injection of 80 . 5 ng of dsRNA into the sand fly thorax targeting the midgut expressed PpChit1 gene led to a significant decrease in PpChit1 mRNA levels in comparison with the control dsRNA-injected flies ( Figure 1 ) . Reduction of PpChit1 expression after a blood meal varied over time . Twenty four hours after blood meal ( and 72 h after injection of dsPpChit1 ) , a 27% reduction of PpChit1 transcripts was detected ( Figure 1A ) . At 48 h PBM ( previously shown to be the maximum activity for PpChit1 [21] ) and at 72 h PBM , reductions of 58% and 53% on average of the PpChit1 expression were observed ( Figure 1A ) . Finally , at 96 h PBM ( 120 h after dsRNA injection ) , when no chitinolytic activity was detected [21] , the reduction in PpChit1 expression was 72% . On the other hand , injection of 144 ng of dsPpChit1 into P . papatasi thorax displayed a weaker reduction in PpChit1 expression levels than injection of 80 . 5 ng ( Figure 1B ) . Although similar expression reduction at 24 h PBM was exhibited ( 26% , Figure 1B ) , expression differences between dsPpChit1 and dsControl injected flies at 48 h and 72 h PBM were lower ( 13% and 43% , respectively ) than detected at the same time points when 80 . 5 ng of dsRNA was injected ( Figure 1B ) . These differences could be occurring due to a still obscure feedback loop for transcription activation upon knock down , as proposed elsewhere [26] . Silencing of the PpChit1 message RNA produced a concomitant reduction in the amount of PpChit1 protein as determined by Western blots ( Figure 2 ) . Similar to the Real-Time PCR data , reduction in PpChit1 protein levels in dsPpChit1 injected flies was detected at 48 h and 72 h PBM ( Figures 2A–C ) when either 80 . 5 ng or 144 ng of dsRNA was injected . No PpChit1 expression was detected at 24 h PBM ( Figure 2B ) . Likewise , densitometry analysis of blot developed using a chemiluminescence method displayed 95% reduction in PpChit1 protein levels at 48 h PBM when 144 ng dsPpChit1 ( Figure 2C and D ) . Interestingly , the corresponding time point only led to 13% reduction of PpChit1 mRNA levels , as shown in Figure 1B . As injection of either 80 . 5 ng or 144 ng of dsRNA targeting PpChit1 transcripts are capable of significantly reducing PpChit1 expression levels in the midgut of P . papatasi ( Figure 1 and 2 ) , we assessed the effects of injecting 80 . 5 ng of the dsRNA on Le . major development within the injected flies . Following the injection of the PpChit1 dsRNA , flies were provided an infective blood meal , and dissected at different time points after feeding . Our results demonstrate that dsPpChit1-targeted knock-down resulted in significant reductions in parasite load within the sand fly midgut as the numbers of Le . major were reduced by 46% ( or 1 . 85 fold ) at 48 h post infection ( Figure 3A ) and by 63% ( or 2 . 70 fold ) at 120 h PBM post infection ( Figure 3B ) . The injection of dsPpChit1 also affected the range of parasite loads . An analysis of the range of parasite load at 48 h and 120 h post infection points to a normal distribution of parasite numbers in the dsControl-injected flies ( 48 h PBM , p = 0 . 51 , and 120 h PBM , p = 0 . 26 , D'Agostino & Pearson omnibus normality test ) , whereas for dsPpChit1-injected flies this distribution was significantly affected ( 48 h PBM , p = 0 . 004 , and 120 h PBM , p<0 . 0001 , D'Agostino & Pearson omnibus normality test ) . Changes in P . papatasi infection levels following silencing of PpChit1 were further confirmed by comparing infection prevalence . For instance , injection of dsPpChit1 reduced the prevalence of heavy infection from 47% ( dsControl-injected ) to 19% , and of light infection from 19% ( dsControl-injected ) to 6% at 48 h post blood feeding ( Figure 4A ) . Likewise , moderate infections levels were reduced from 57% ( dsControl-injected ) to 14% at 120 h post infection ( Figure 4B ) .
After a blood meal , sand flies synthesize a PM type 1 that is fully developed at approximately 36–40 h PBM [27] . In addition to compartmentalizing the blood meal and protecting the epithelia , the sand fly PM serves an additional dual role regarding Leishmania infection: as a barrier to these parasites but also as protection against proteolytic attack on transitional-stage amastigotes [8] , [20] , [28] , [29] , [30] . In order to successfully complete its cycle within the sand fly , Leishmania nectomonads must escape from endoperitrophic space , through the PM , to prevent being passed together with remnants of the digested blood meal [8] . We have previously characterized a functional , blood-induced chitinolytic system , in the midgut of P . papatasi and L . longipalpis sand flies [21] , [31] . We also demonstrated that polyclonal antibodies to PpChit1 inhibit the midgut chitinolytic activity in vitro , and this effect also was shown across different sand fly species [21] . PpChit1 is presumably involved in the maturation and degradation of P . papatasi PM ( as is its ortholog in L . longipalpis , LlChit1 ) [21] , [31] , and addition of allosamidin , a chitinase inhibitor to the infective blood meal of this sand fly led to entrapment of Le . major within the peritrophic space [8] . Although allosamidin may have also inhibited chitinase secreted by Leishmania , taken together , these data suggested that PpChit1 also can be involved with Leishmania escape from the endoperitrophic space . To address whether silencing of PpChit1 transcripts via RNAi-induced pathway would affect Le . major development within its natural vector , P . papatasi , we synthesized a dsRNA specifically targeting PpChit1 . Injection of dsRNA targeting specific transcripts has now been widely applied in disease vectors and proven an invaluable tool for the understanding of underlying events in pathogen-vector relationships [32] , [33] , [34] . In sand flies , gene silencing with dsRNA was first applied to L . longipalpis cell culture [35] , inducing a non-specific antiviral response . Recently , dsRNA injection of adult sand flies led to a specific reduction of Xanthine dehydrogenase expression [36] , and to an effect on Le . mexicana development when a midgut trypsin produced by L . longipalpis was silenced [30] . The midgut chitinase PpChit1 is only expressed following a blood meal [21] . Thus , following injection of dsPpChit1 double-stranded RNA , sand flies were blood fed and midguts dissected at different intervals after feeding . Specific silencing of PpChit1 transcripts was detected by quantitative real-time PCR analyses ( Figure 1 ) , with concomitant knock down of PpChit1 protein levels assessed by Western blots ( Figure 2 ) . Based on the presumptive role of PpChit1 in the maturation and degradation of the PM1 , we expected that silencing of this gene would lead to entrapment of Leishmania within the endoperitrophic space . Our results are consistent with this hypothesis , as Le . major load was reduced 120 h PBM in midguts of dsPpChit1 injected P . papatasi ( Figures 3 and 4 ) suggesting that PpChit1 is indeed involved in PM1 degradation . Moreover , reduction of the Le . major load at 48 h PBM in dsChit1 compared to control-injected flies might have been caused by at least two scenarios: 1 ) a reduction in nutrient availability in the endoperitrophic space as the PM may be less permeable to proteolytic enzymes , or in the contrary , 2 ) to inability of parasites to escape leading to longer exposure to digestive enzymes inside the peritrophic space . Regardless of the mechanism , it still remains to be determined . Future studies will assess whether this is a feasible approach in preventing transmission from an infected animal to a naïve host . Moreover , the results support the targeting of PpChit1 as a mean to interfere with Leishmania development within the sand fly – a candidate transmission-blocking vaccine . | For a successful development within the midgut of the sand fly vector , Leishmania must overcome several barriers which are imposed by the vector . The ability to overcome these barriers has been associated with species specificity , and interference with the sand fly vector-parasite balance can change the outcome of the infection in the vector . Recently , our group has carried out a transcriptome assessment of the sand fly Phlebotomus papatasi midgut , uncovering many transcripts possibly associated with the barrier to Leishmania development . In order to validate the role of such genes , we have developed a dedicated RNA interference ( RNAi ) platform to assess whether RNAi targeting such genes can reduce Leishmania major development . PpChit1 is a midgut-specific chitinase presumably involved in the maturation/degradation of the peritrophic matrix in the gut of the sand fly after a blood meal . Our results show that knockdown of PpChit1 via RNAi led to a significant reduction of Le . major within the gut , supporting the potential use of PpChit1 as a target for transmission blocking strategies against sand fly-transmitted leishmaniasis . | [
"Abstract",
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] | 2010 | Targeting the Midgut Secreted PpChit1 Reduces Leishmania major Development in Its Natural Vector, the Sand Fly Phlebotomus papatasi |
Genome-wide somatic mutation profiles of tumours can now be assessed efficiently and promise to move precision medicine forward . Statistical analysis of mutation profiles is however challenging due to the low frequency of most mutations , the varying mutation rates across tumours , and the presence of a majority of passenger events that hide the contribution of driver events . Here we propose a method , NetNorM , to represent whole-exome somatic mutation data in a form that enhances cancer-relevant information using a gene network as background knowledge . We evaluate its relevance for two tasks: survival prediction and unsupervised patient stratification . Using data from 8 cancer types from The Cancer Genome Atlas ( TCGA ) , we show that it improves over the raw binary mutation data and network diffusion for these two tasks . In doing so , we also provide a thorough assessment of somatic mutations prognostic power which has been overlooked by previous studies because of the sparse and binary nature of mutations .
Tumourigenesis and cancer growth involve somatic mutations which appear and accumulate during cancer progression . These mutations impair the normal behaviour of various cancer genes , and give cancer cells an often devastating advantage to proliferate over normal cells [1–3] . Systematically assessing and monitoring somatic mutations in cancer therefore offers the opportunity not only to better understand the biological processes involved in the disease , but also to help rationalise patient treatment in a clinical setting . Rationalising treatment involves finely characterising the genomic abnormalities of each given patient to discover which may be treatable by a targeted therapeutic agent , as well as improving prognosis using molecular information [4–6] . The development of fast and cost-effective technologies for high-throughput sequencing in the last decade has triggered the launch of numerous data collection projects such as The Cancer Genome Atlas ( TCGA ) [7] or the International Cancer Genome Consortium ( ICGC ) [8] , aiming at characterising at the molecular level , including genome-wide or exome-wide somatic mutations , thousands of cancer samples of multiple origins . By systematically comparing the molecular portraits of the resulting cohorts , one might expect to be able to detect frequently mutated genes or groups of genes , and find associations between particular mutations and cancer phenotypes , response to treatment , or survival [9–12] . The analysis of somatic mutation profiles is however challenging for multiple reasons . First , most somatic mutations detected by systematic sequencing are likely to be irrelevant for biological or clinical applications . This is due to the fact that only a few driver mutations are required to confer a growth advantage to the cancer cell , and therefore most somatic mutations are likely to be passenger mutations which do not contribute to the cancer phenotype [3 , 13] . Second , sequencing efforts have shown that while a few genes are frequently mutated , the vast majority of genes are mutated in only a handful of patients [14 , 15] . As a result , the mutation profiles of two tumours often only share a few if any genes in common . Third , even if originating from the same tissue , tumours may exhibit widely varying mutation rates . The overall mutational burden of a tumour constitute a strong and informative signal [16–18] but can however complicate the retrieval of more subtle signals . Combined with the inherent high dimensionality of somatic mutation datasets , this makes any statistical analysis of cohorts of whole-exome somatic mutation profiles extremely challenging . In order to make somatic mutation profiles more amenable to statistical analysis , several studies have used gene networks as prior knowledge [19 , 20] . Considering genes in the context of networks instead of analysing them independently allows sharing mutation information among neighbouring genes and identifying disruptions at the level of pathways or protein complexes instead of single genes . A popular method to leverage this prior knowledge consists in using a diffusion process on the gene network . This technique first appeared for the analysis of gene expression and GWAS data [21–25] , and has more recently been used for mutation profiles [26–31] . Network diffusion processes allow smoothing binary vectors of somatic gene mutations into non-negative real-valued vectors of mutational statuses , where the mutational status of a gene increases when it is close to mutated genes in the network . This approach led to state-of-the-art methods for the discovery of driver pathways or complexes [30] and for the stratification of patients into clinically relevant subtypes [31] using whole-exome mutation profiles . In this work we propose NetNorM , a new method to enhance mutation data with gene networks . NetNorM transforms a patient’s binary mutation profile by either removing mutations or creating “proxy” mutations based on the gene network topology , until all patients reach a consensus number of mutations . The resulting mutation matrix is binary like the initial one , nonetheless we establish that it encodes new information reflecting both local network neighbourhood mutational burdens and the overall tumour mutational burden . We evaluate the relevance of NetNorM on two tasks: survival prediction and patient stratification from exome somatic mutation profiles . In doing so , we also provide a thorough assessment of somatic mutations prognostic power which has been overlooked by previous studies because of the sparse and binary nature of mutations [32] . We show that NetNorM produces state-of-the-art results for these two tasks compared to the raw binary mutation data and to network diffusion-based methods . By comparing results obtained with real versus randomised networks , we further show that the increase in relevance is actually partly driven by the gene’s network prior knowledge . However , we observe that considering interactions between mutated genes and their network neighbours only is enough do achieve state-of-the-art results , thereby shedding light on which are the network features that are the most informative .
NetNorM takes as input an undirected gene network and raw exome somatic mutation profiles and outputs a new representation of mutation profiles which allows better survival prediction and patient stratification from mutations ( Fig 1 ) . Here and in what follows , the “raw” mutation profiles refer to the binary patients times genes matrix where 1s indicate non-silent somatic point mutations or indels in a patient-gene pair and 0s indicate the absence of such mutations . The new representation of mutation profiles computed with NetNorM also takes the form of a binary patients times genes mutation matrix , yet with new properties . While different tumours usually harbour different number of mutations , with NetNorM all patient mutation profiles are normalised to the same number k of genes marked as mutated . The final number of mutations k is the only parameter of NetNorM , which can be adjusted by various heuristics , such as the median number of mutations in the original profiles , or optimised by cross-validation for a given task such as survival prediction . In order to represent each tumour by k mutations , NetNorM adds “missing” mutations to samples with less than k mutations , and removes “non-essential” mutations from samples with more than k mutations . The “missing” mutations added to a sample with few mutations are the non-mutated genes with the largest number of mutated neighbours in the gene network , while the “non-essential” mutations removed from samples with many mutations are the ones with the smallest degree in the gene network . These choices rely on the simple ideas that , on the one hand , genes with a lot of interacting neighbours mutated might be unable to fulfil their functions and , on the other hand , mutations in genes with a small number of interacting neighbours might have a minor impact compared to mutations in more connected genes . In this study , we compare NetNorM-processed profiles with the raw mutation data and with profiles processed with network smoothing ( NS ) [33] ( also called network diffusion , or network propagation ) followed by quantile normalisation ( QN ) as implemented in [31] . We refer to this method as NSQN below . Mutation profiles , either raw or processed with NetNorM or NSQN , are restricted to the genes present in the network used . While both NetNorM and NSQN leverage gene network prior knowledge to enhance mutation data , the two methods have fundamental differences . First , NetNorM leverages information about first neighbours in the network only while NSQN spreads mutation information at a more global scale on the gene network . Second , with NetNorM the normalised profiles all have the same value distribution by construction , since they are all binary vectors with k ones , removing the need for further quantile normalisation which , as we discuss below , is critical for NSQN . To assess the relevance of NetNorM , we first explore the capacity of somatic mutations to predict patient survival . We collected a total of 3 , 278 full-exome mutation profiles of 8 cancer types from the TCGA portal ( Table 1 ) , censored survival information and clinical data . In parallel we retrieved a gene network to be used as background information for NSQN and NetNorM: Pathway Commons , which integrates a number of pathway and molecular interaction databases [34] . For each cancer type , we use these data to assess how well survival can be predicted from somatic mutations . For that purpose , we perform survival prediction with a sparse survival SVM ( see Methods ) using either the raw mutation profiles or the profiles processed with NSQN or NetNorM , respectively , and assess their performance by cross-validation using the concordance index ( CI ) on the test sets as performance metric . Fig 2 summarises the survival prediction performances for the 8 cancer types , when the sparse survival SVM is fed with the raw mutation profile , or with the mutation profiles modified by NSQN or NetNorM using Pathway Common as gene network . For two cancers ( LUSC , HNSC ) , none of the methods manages to outperform a random prediction , questioning the relevance of the mutation information in this context . For OV , BRCA , KIRC and GBM , all three methods are significantly better than random , although the estimated CI remains below 0 . 56 , and we again observe no significant difference between the raw data and the data transformed by NSQN or NetNorM . Finally , the last two cases , SKCM and LUAD , are the only ones for which we reach a median CI above 0 . 6 . In both cases , processing the mutation data with NetNorM significantly improves performances compared to using the raw data or profiles processed with NSQN . More precisely , for LUAD the median CI increases from 0 . 56 for the raw data and 0 . 53 for NSQN to 0 . 62 for NetNorM . In the case of SKCM , the median CI increases from 0 . 48 for the raw data to 0 . 52 for NSQN , and to 0 . 61 for NetNorM . For SKCM , both NetNorM and NSQN are significantly better than the raw data ( P < 0 . 01 ) . In our experiments , silent mutations are systematically filtered out . To evaluate whether this preprocessing step is actually detrimental or beneficial for the survival prediction task , we performed further experiments where silent mutations are not filtered out ( S1 Fig ) . We find that considering silent mutations does not improve survival prediction performances compared to the case where they are filtered out . In fact , the performance of NetNorM on LUAD is significantly decreased when silent mutations are taken into account . To assess the influence of the gene network used on the survival prediction performances , we also repeated our experiments with four gene networks instead of Pathway Commons: BioGRID [35] , HPRD [36] , HumanNet [37] and STRING [38] ( S2 Fig ) . For HumanNet and STRING , only the 10% most confident interactions were retained . We observe that no gene network clearly stands out as the best network for all cancers . For two cancers , LUSC and HNSC , performances remain very low , close to a concordance index of 0 . 5 , whatever the method or network used . For three cancers , OV , BRCA and KIRC , NetNorM is the only method to significantly outperform the raw data with at least one network ( HumanNet and STRING for OV , HPRD for BRCA , and STRING for KIRC ) with a median concordance index above 0 . 55 . For GBM , NSQN is the only method to outperform the raw data ( with HumanNet and STRING ) with a median concordance index above 0 . 55 . For the two remaining cancers , LUAD and SKCM , the best performances are those obtained with NetNorM using Pathway Commons , with median CI of 0 . 62 and 0 . 61 respectively . Across all cancers , methods , and networks combinations , these two cases are the only ones where the median CI obtained exceeds 0 . 60 . Finally , as mutations in some genes are known to be associated with survival , such as TP53 in BRCA and HNSC which is associated with worsened survival [39] , we evaluate the prediction ability of individual genes’ mutation status . For each cross-validation fold , the gene giving the best concordance index on the training set is selected and its performance evaluated on the test set . We find that for 5 cancers , the performances of individual genes are similar to those of the survival SMV applied to the whole raw mutations datasets ( S3 Fig ) . However for BRCA and HNSC , better survival predictions are obtained using a single gene than the whole raw mutational profiles . Yet these predictions are not better than those obtained with NetNorM . For these two cases , TP53 is the gene selected in the majority of folds ( 17/20 for HNSC and 19/20 for BRCA ) , which is in accordance with existing literature ( S1 Table ) . Lastly , the survival SVM applied to the whole dataset yields significantly better performances than the single gene approach for LUAD . This means that contrary to the BRCA and HNSC cases , the linear combinations of genes which are found for LUAD have a predictive power that generalises well to unseen data . In summary , these results show that for at least 6 out of 8 cancers investigated , somatic mutation profiles have a prognostic value , and that for two of them ( SKCM and LUAD ) it is possible to improve the prognostic power of mutations by using gene networks and to reach a CI above 0 . 6 . In both cases , NetNorM is significantly better than NSQN . To test whether the biological information contained in the gene network plays a role in the improvement of survival predictions for LUAD and SKCM , we evaluate again NetNorM and NSQN using 10 different randomised versions of Pathway Commons for these two cancers . Random networks were obtained by shuffling the nodes’ labels of the real network while keeping the structure unchanged . The results , shown on Fig 3 , demonstrate that NetNorM performs significantly better with a real network . More precisely , the real network significantly outperforms all random networks for SKCM and 8 out of 10 random networks for LUAD ( Wilcoxon signed-rank test with correction for multiple hypothesis testing , FDR ≤ 5% ) . NSQN also performs significantly better with a real network for SKCM ( 7 out of 10 cases ) but not for LUAD ( 0 out of 10 cases ) . This last observation is not surprising since NSQN does not improve over the raw data for LUAD , which suggests that the method may have failed to leverage network information in this case . In summary , these results indicate that the improvements obtained with NetNorM and NSQN compared to the raw data do rely on biological information encoded in the network . In order to shed light on the reasons why NetNorM outperforms the raw data and NSQN on survival prediction for SKCM and LUAD , we now analyse more finely the normalisation carried out by NetNorM on the mutation profiles , and why they lead to better prognostic models . For that purpose , we focus on the genes that are selected at least 50% of the times by the sparse survival SVM during the 20 different train/test splits of cross-validation , after NetNorM normalisation . This leads to 21 frequently selected genes for LUAD and 10 for SKCM ( Fig 4 ) . Remembering that NetNorM either removes mutated genes for patients with many mutations , or adds proxy mutations for patients with few mutations , we can assess for each frequently selected gene whether it tends to exhibit proxy mutations or whether it tends to be actually mutated in the tumour . This is done by comparing how frequently it is marked as mutated on the raw data and after NetNorM normalisation ( Fig 4 , top plot ) . For both cancers , we observe two clearly distinct groups of frequently selected genes: those that concentrate proxy mutations ( which we will call proxy genes , in red in Fig 4 ) , and those to which NetNorM brings only few modifications compared to the raw data , meaning they are usually actually mutated in the tumours ( in black in Fig 4 ) . We assess whether the combination of both mutations and clinical features can improve performances for LUAD and SKCM compared to using clinical data alone . For this purpose , two sparse survival SVM models are trained independently: one on the raw mutation data or mutations preprocessed with NSQN or NetNorM and one on the clinical data . Then the survival predictions from both models are simply averaged ( after being standardised to unit variance ) . The resulting predictions are again evaluated in a 4 times 5 folds cross-validation setting . First , the results show that mutations preprocessed with NetNorM and the clinical data yield similar performances ( P = 0 . 52 , Wilcoxon signed rank test ) for LUAD while the clinical data performs significantly better than NetNorM in the case of SKCM ( P ≤ 1 × 10−2 ) ( Fig 6 ) . Moreover , we observe that combining mutations preprocessed with NetNorM with clinical features allows improving survival predictions compared to the clinical data alone for both LUAD ( P = 4 . 8 × 10−2 ) and SKCM ( P = 5 . 7 × 10−2 ) . More precisely , the median CI increases from 0 . 64 with the clinical data to 0 . 66 with the combination of NetNorM and the clinical data for LUAD and from 0 . 66 to 0 . 70 in the case of SKCM . We also tried to concatenate the mutation profiles with the clinical data before training a unique model and observed that it did not improve the results compared to the previous strategy ( S5 Fig ) . Overall , these results suggest that mutations could provide useful prognostic information that is complementary to the clinical information available . We now assess the possibility to stratify patients into a small number of groups in an unsupervised way , meaning without using survival information , in order to identify distinct subgroups of patients in terms of mutational profiles . For that purpose , we use a standard unsupervised clustering pipeline based on nonnegative matrix factorisation ( NMF ) , and apply it to the different cohorts of patients represented by the raw mutation profiles , or the profiles normalised by NSQN or NetNorM . The hyperparameters k ( NetNorM ) and α ( NSQN ) were set to default values chosen as the median number of mutations in a cohort for k and α = 0 . 5 as recommended in [31] . As we have no ground truth regarding “true” groups of patients , we assess the quality of clustering by two factors: ( i ) the stability of the clusters , assessed by the proportion of ambiguous clustering ( PAC ) which is the rate of discordant cluster assignments across 1 , 000 random subsamples of the full cohort; and ( ii ) the significance of association between clusters and survival . With the raw data , NMF tends to stratify patients into very unbalanced subtypes with typically one subtype gathering the majority of patients ( Fig 7b ) . LUSC , HNSC and SKCM are extreme cases where one cluster contains 95% of the patients , whatever the number of clusters . In addition , in cases where the obtained clusters are reasonably balanced as for KIRC , the clustering stability is low . These results are coherent with [31] who highlighted the difficulty to cluster raw mutation profiles . These undesirable behaviours disappear with both NSQN and NetNorM ( Fig 7 ) . With NetNorM the obtained clusters are reasonably balanced across all cancers and the clusters are stable ( PAC ≤ 30% ) . NSQN also provides stable clusters ( PAC ≤ 30% ) when the number of clusters is set between 4 and 6 however for 2 or 3 clusters the stability is not as good ( PAC ≤ 50% ) . To assess the clinical relevance of the obtained subtypes , we test whether they are associated with significantly distinct survival outcomes ( Fig 7a ) . With the raw data , patient stratification is never significantly associated with clinical data . With NetNorM , significant associations of patient subtypes with survival times are achieved for HNSC , OV , KIRC and SKCM ( Fig 7c ) , while with NSQN , a significant association is only achieved for OV . The stratification based on NetNorM remains prognostic beyond clinical data for SKCM ( Likelihood ratio test , P = 2 . 4 × 10−2 ( SKCM , N = 5 ) ) . It can be surprising at first sight that no signal is recovered for LUAD with NetNorM and for SKCM with NSQN since some signal was observed in the survival prediction setting in these cases . We hypothesized that this could be due to a bad choice of the hyperparameters k and α for these cancer types . Therefore additional experiments were run for LUAD and SKCM with k and α set to their values learned by cross-validation for the survival prediction task ( S3 Table ) . This corresponds to k = 315 and α = 0 . 6 for LUAD ( instead of k = 189 and α = 0 . 5 as defaults ) and k = 140 and α = 0 . 25 for SKCM ( instead of k = 243 and α = 0 . 5 as defaults ) . With these new values for the hyperparameters , significant associations with survival are detected for LUAD with NetNorM ( for 4 , 5 and 6 clusters ) and for SKCM with both NetNorM ( for any number of clusters ) and NSQN ( for 4 clusters ) ( S6 Fig ) . The recovery of a signal in these cases is in accordance with the results in the supervised setting . Overall , these results confirm the findings of [31] that network-based normalisation with NSQN allows stratifying patients better than the raw mutation profiles , and also show that the stratification obtained from NetNorM normalisation is both more stable and more clinically relevant than the one obtained with NSQN . We now assess whether the biological information contained in Pathway Commons is crucial to obtain subtypes with significantly distinct survival outcomes . For that purpose , we carry out patient stratification with NSQN and NetNorM using 10 randomised versions of Pathway Commons for HNSC , OV , KIRC and SKCM . As for the survival prediction experiment , the randomisation involves shuffling the vertices’ labels so as to keep the structure of the network unchanged . Surprisingly , network randomisation does not affect the log-rank statistic obtained for HNSC and SKCM . This suggests that although NetNorM generates subtypes with more distinct survival times than NSQN for HNSC and SKCM , it does not benefit from Pathway Commons gene-gene interaction knowledge . Rather it exploits the prognostic information contained in the raw mutation profiles as well as the overall mutational burdens as captured by proxy mutations . Regarding KIRC and OV , NetNorM produces subtypes with significantly different survival times with 4 and 5 clusters for KIRC and for any number of clusters for OV . In the case of KIRC , the real network yields the subtypes with the most distinct survival times ( N = 5 ) ( Fig 8 ) while in the case of OV , most randomized networks ( at least 15 out of 20 for each number of clusters ) produce subtypes with worse association to survival time . This indicates that for KIRC and presumably for OV , NetNorM takes advantage of gene-gene interaction knowledge to stratify patients into clinically relevant subtypes . This is also clearly the case for LUAD with NetNorM when the hyperparameter k is set to its value learned by cross-validation in the survival prediction setting ( S6 Fig ) . To interpret biologically the subgroups of patients identified by automatic stratification after NetNorM normalisation , we look at differentially mutated genes and pathways across subtypes . We focus on LUAD with N = 5 groups as a proof of principle with k set to its value learned by cross-validation in the supervised setting . This choice is motivated by the fact that LUAD is the most promising cancer type for supervised survival prediction and produces interesting results in the unsupervised setting . As the basis vectors or “metapatients” yielded by the NMF summarise the mutational patterns found in the different subtypes , we analyse genes in terms of their weight in the different metapatients , and restrict our attention to the approximately 900 genes displaying highest variance ( variance greater than 0 . 01 ) across basis vectors since these genes are expected to be the most differentially mutated across subtypes . Interestingly , this gene list comprises most significantly mutated genes in LUAD including TP53 , KRAS , KEAP1 , EGFR , NF1 , RB1 [40 , 41] . To analyse these genes we cluster them into groups with similar weights across basis vectors using hierarchical clustering ( Fig 9b ) , and we test for enrichment in known biological pathways the 20 gene clusters ( GCs ) obtained . One first observation is that the 5 patient subtypes have distinct overall mutational burdens with groups 4 and 5 ( resp . 2 and 3 ) gathering patients with many ( resp . few ) mutations ( Fig 9e ) . This confirms the fact that NetNorM-normalised profiles contain information about the initial number of mutations , although they are normalised to a fixed number of mutations . More importantly , most GCs exhibit high weights in one metapatient and low weights in others , suggesting that they are mainly enriched in mutations in one single patient subtype ( Fig 9b ) . χ2 contingency tests ( see methods ) for each GC confirms that for most of them ( 17/20 ) , the distribution of the mutations across patient subtypes is not that expected according to subtypes’ overall mutational burdens ( P < 5 × 10−2 ) ( S4 Table ) . The contribution of each subtype to the test statistic for each GC also confirms that GCs are often enriched in mutations in mainly one patient subtype ( Fig 9d ) . Subtypes could thus easily be associated with one or several GCs , and therefore pathways through pathway enrichment analysis using the KEGG database [54] ( see Methods ) . Consequently , subtype 3 is characterised by an enrichment in mutations in genes associated with ribosomes and spliceosomes ( GCs 2 , 3 , 4 , 5 , 6 , 7 , 8 , 17 , 18 , 19 ) ( S4 Table ) . Subtype 1 is enriched in mutations in two very small gene clusters ( GCs 11 and 16 ) : the first one consists of four genes including KRAS and the second one only includes MUC16 . These two subtypes are those with poorest survival probability . Subtype 4 is mainly enriched in late replicating genes ( GC 10 ) ( Fig 9c ) . This reflects the fact that subtype 4 is enriched in highly mutated patients as there exists a positive correlation between somatic mutation frequency and genes replication time [16] . Subtype 2 is enriched in mutations in genes related to endocytosis and phagosomes ( GCs 16 , 1 , 11 ) . Finally , subtype 5 is very strongly associated with gene clusters 9 and 13 . Gene cluster 9 is enriched in genes from the cAMP and PI3K-Akt signaling pathways . Gene cluster 13 could not be significantly associated to a known biological pathway . However it contains FANCD2 ( Fanconi Anemia Complementation Group D2 ) which is involved in double-strand breaks DNA repair and the maintenance of chromosomal stability [55] . We note that 12 of the 15 patients in subtype 4 present the same 4-nucleotides splice site deletion in FANCD2 , whereas across the rest of the 430 patients FANCD2 is mutated in 6 patients only , and only one of these 6 mutations is the same as that observed in subtype 4 patients .
Exploiting the wealth of cancer genomic data collected by large-scale sequencing efforts is a pressing need for clinical applications . Somatic mutations are particularly important since they may reveal the unique history of each tumour at the molecular level , and shed light on the biological processes and potential drug targets dysregulated in each patient . Standard statistical techniques for unsupervised classification or supervised predictive modelling perform poorly when each patient is represented by a raw binary vector indicating which genes have a somatic mutation . This is both because the relevant driver mutations are hidden in the middle of many irrelevant passenger mutations , and because there is usually very little overlap between the somatic mutation profiles of two individuals . NetNorM aims to increase the relevance of mutation data for various tasks such as prognostic modelling and patient stratification by leveraging gene networks as prior knowledge . One important aspect of NetNorM is the property that , after normalisation , all patients have the same number of 1’s in their normalised mutation profile . Although there is no biological rational for this constraint , we believe that the fact that all normalised samples have the same distribution of values is an important property for many high-dimensional statistical methods such as survival models or clustering techniques to work properly . To support this claim , we notice that the Network-based stratification ( NBS ) method proposed in [31] performs a quantile normalisation step after network smoothing . To investigate whether the quantile normalisation step in NSQN plays an important role , we applied network smoothing without quantile normalisation ( called NS ) and performed survival prediction and patients stratification with this representation of the mutations . Surprisingly , NS does not improve over the raw mutation profiles for both LUAD and SKCM ( Fig 10c ) . Moreover just as the raw data , NS is unable to stratify patients into approximately balanced clusters ( Fig 10b ) . This suggests that quantile normalisation plays a crucial role in the performances obtained with NSQN , in spite of non obvious biological justification for this step . Another important difference between NSQN and NetNorM is the fact that NetNorM only exploits mutation information about direct neighbours in the network , while NSQN can potentially diffuse a mutation further than the direct neighbours . However , we found that NSQN does not benefit from this possibility . Indeed , we tested a simplified version of NSQN where the network propagation is stopped after one iteration , and assessed the performance of the corresponding method which we call SimpNSQN . For survival prediction , we observe no significant difference between NSQN and SimpNSQN ( Fig 10c ) . For patient stratification , SimpNSQN produces subtypes that are vey similar to those produced by NSQN ( Fig 10d ) . Therefore the subtypes generated by both methods associate equally well to clinical data , and even slightly better for SimpNSQN in the case of LUAD ( Fig 10a ) . Overall , these pieces of information indicate that the useful information created by NSQN is mostly concentrated on shared mutated order 1 neighbourhoods , and explain why we observe no loss in performance with NetNorM which explicitly restricts the diffusion of mutations to direct neighbours only . More generally , these elements also indicate that diffusion to indirect neighbours is still difficult with current methods . This is a likely consequence of the small world property of biological graphs [56] . Because the path between any two genes is usually short , diffusion even to order-2 neighbours reaches a substantial number of genes , and therefore the resulting signal observed for one gene is the superposition of a large number of signals originating from close mutations . NetNorM encodes information about patients’ total number of mutations in the raw data , and potentially can exploit it if this information is relevant for the problem at hand . However we found that the total number of mutations is a poor predictor or survival ( Fig 10c ) , and a poor feature for LUAD patient stratification ( Fig 10a ) . This confirms that NetNorM conserves useful information regarding both the total mutational burden of a patient and the distribution of the mutations on the gene network , and manages to leverage both types of information . In addition to mutational burdens , NetNorM also encodes information about genes’ NMB which proved to carry some prognostic power . The fact that NMB might reveal new insights into mutation profiles is an emerging idea supported by this study . Further support has been formalised with two recently published methods [57 , 58] which rely on NMB to achieve state-of-the-art performances for cancer gene discovery . We emphasize that randomised gene networks lead to significantly worse performances than the real network for survival prediction as well as for patient stratification for several cancers . While it is not always clear whether incorporating gene networks as prior knowledge does help for a given task , this provides a sound argument that such prior knowledge is effectively leveraged with NetNorM . Increasing the relevance of mutation data to various tasks is a broad project and NetNorM could be extended in many ways . First , although NetNorM was successful for LUAD and SKCM , we note that the method brings few improvements compared to the raw data for the remaining cancer types . Therefore extensive efforts are needed to determine whether it is possible to design representations of mutations that would increase the statistical power of models learned on these datasets . Second , NetNorM does not integrate further information about mutations such as their predicted functional impact . A possible extension could therefore include this type of information . Finally , the distribution of values for the normalised profiles is defined as the mean distribution of the original profiles in the case of NSQN , and simply a binary vector with a fixed number of 1’s in the case of NetNorM , however these choices are empirical . This suggests that an interesting future work may be to assess more precisely the effect of this distribution and , perhaps , optimise it for each specific task .
Whole exome somatic mutation calls ( MAF files ) were downloaded from TCGA data portal ( https://tcga-data . nci . nih . gov/tcga ) for 8 cancer types ( LUAD , SKCM , GBM , BRCA , KIRC , HNSC , LUSC , OV ) ( Table 1 ) . The data include point mutations ( single nucleotide polymorphism as well as di/tri/oligo-nucleotide polymorphism ) and indels . Silent mutations were filtered out and mutations profiles were defined as binary vectors with ones whenever a patient is mutated in a given gene and zeros otherwise . Pathway Commons ( http://www . pathwaycommons . org/pc2/downloads ) was used throughout this work ( Pathway Commons v6 , SIF format ) . It integrates gene networks from several public databases and aggregates both genetic and protein-protein interactions ( PPIs ) . PPIs refer to physical contacts established between proteins while genetic interactions refer to interactions through regulatory and signalling pathways . To remove interactions involving small molecules in Pathway Commons , the following interaction types were filtered out: “consumption-controlled-by” , “controls-production-of” , “controls-transport-of-chemical” , “chemical-affects” , “reacts-with” , “used-to-produce” , “SmallMoleculeReference” , “ProteinReference;SmallMoleculeReference” , “ProteinReference” . We obtained a network with 16 , 674 nodes ( genes ) and 2 , 117 , 955 edges ( interactions ) . For the survival prediction task , we also tested the following gene networks: BioGRID v3 . 4 . 131 , HPRD release 9 , HumanNet v1 and STRING v10 . For HumanNet and STRING , only the top 10% most confident interactions were retained . NetNorM is a method that integrates patients mutation profiles with a gene network to produce normalised mutation profiles where all patients have the same number k of mutations . The target number of mutations k is a tuning parameter . In the context of survival prediction ( supervised setting ) , it is learned by cross-validation while for patient stratification ( unsupervised setting ) , it is set as the median number of mutations in a cohort , or alternatively to the median best k learned across cross-validation folds for survival prediction . Concretely , NetNorM defines a ranking over genes separately for each patient and then use this ranking to normalise mutation profiles . The ranking defined in NetNorM is obtained with a simple two-step procedure . First , genes are ranked according to their mutation status with mutated genes ranked higher than non mutated genes . Then , mutated genes are ranked according to their degree ( i . e . their number of neighbours ) and non mutated genes are ranked according to their number of mutated neighbours . The normalisation is then obtained by considering the k highest ranked genes as mutated while the rest of the genes will be considered non mutated . By construction , mutated genes are always ranked higher than non-mutated genes . Therefore patients with a lot of mutations will have mutations removed while patients with few mutations will hold artificial proxy mutations . Note that when the obtained ranking contains ties , all genes are given distinct ranks according to the order in which they occur in the mutation matrix . Network smoothing propagates the influence of mutations over gene-gene interaction networks . It was implemented according to the following update function [31]: X t + 1 = α X t D - 1 2 A D - 1 2 + ( 1 - α ) X 0 where Xt is the patient × genes mutation matrix at iteration t , X0 is the initial binary mutation matrix , A is the adjacency matrix representing the network used and D is the diagonal degree matrix where Dii=∑jAij . α is a tuning parameter controlling the length of diffusion paths over the network . Similarly to the parameter k in the context of NetNorM , it is learned by cross-validation for survival prediction ( supervised task ) while for patient stratification ( unsupervised task ) it is set as α = 0 . 5 as recommended in [31] with Pathway Commons or alternatively to the median best α learned across survival prediction cross-validation folds . The update function is applied until convergence , and the resulting smoothed matrix is then quantile normalised so that all patients have the same mutation distribution . The simplified version of NSQN does not propagate mutations further than to order 1 neighbours in the network . More precisely , the SimpNSQN score of a gene is equal to its number of mutated neighbours normalised by its degree and by the degrees of its neighbours , plus a constant if the gene is mutated . This is obtained by computing: X = α X 0 D - 1 2 A D - 1 2 + ( 1 - α ) X 0 where X0 is the initial binary mutation matrix , A is the adjacency matrix representing the network used , D is the diagonal degree matrix where Dii=∑jAij and α ∈ R is a tuning parameter . Note that SimpNSQN uses the same update equation as NSQN but it is run only once . To estimate a survival model from high-dimensional mutation profiles , we use a survival SVM model [59] combined with a sparsity-inducing regularisation to automatically perform gene selection . Let δi = 1 ( resp . δi = 0 ) if patient i is deceased ( resp . censored ) , and y i ∈ R be the observed survival time of patient i . It corresponds to either a failure or a censoring time depending on whether the patient is deceased or censored . Define Z ∈ {0 , 1}n×n which indicates whether a pair of patients is comparable , i . e , Z i j = { 1 if ( y i < y j and δ i = 1 ) or ( y j < y i and δ j = 1 ) , 1 if ( y i = y j and ( δ i = 1 or δ j = 1 ) ) , 0 otherwise . Finally , let xi ∈ {0 , 1}p be the mutation profile of patient i . The survival time of patient i is modelled as si = wT xi where w ∈ R p is the model parameter learned using ranking Support Vector Machines ( rSVM ) as in [59] . However to get a sparse w we introduce an ℓ1 regularisation instead of the ℓ2 regularisation in [59] and thus solve the following optimisation problem: minimise w 1 2 | | w | | 1 + C ∑ i , j Z i j ℓ h i n g e ( w T ( x j - x i ) ) , where ℓhinge ( u ) = max ( 1 − u , 0 ) is the hinge loss and C ∈ R is the regularisation parameter . To solve this problem we used the support vector classification algorithm svm . LinearSVC from the Python package scikit learn [60] . This optimisation problem maximises a convex relaxation of the Concordance Index ( CI ) which measures how well the predicted survival times s are in accordance with the observed survival times y for the comparable pairs of patients . Formally , CI=1| Z |∑yi≤yjZijI ( sj−si ) where I ( x ) = { 1 if x > 0 , 1 2 if x =0 , 0 otherwise , and | Z | = ∑ y i ≤ y j Z i j . To evaluate the CI obtained on a given dataset , samples were split in 80% train and 20% test sets 20 times using 4 five-fold cross-validation . Each time , a model was learned on the training set and tested on the test set . The CI was computed according to a python implementation of the function estC from the R package compareC . Hyperparameters were learned thanks to an inner 5-fold cross-validation on the training set . The values tested for C ranged from 1 × 10−4 to 1 × 102 included in log scale . The values tested for α ranged from 0 . 1 to 0 . 9 included with steps of 0 . 1 . Finally the values tested for k were chosen to span a grid from kmin and kmax with steps of 2 , where kmin and kmax are the first and third quartiles of the distribution of patients’ total number of mutations . kmin and kmax differ for each cohort ( S2 Table ) . Let X ∈ R n × p be the matrix with patient mutations profiles as rows . To cluster the patients we perform a non-negative matrix factorisation ( NMF ) on X , i . e . , solve the following optimisation problem: minimise W , H > 0 | | X - W H | | 2 2 , where H ∈ R N × p defines N basis vectors or “metapatients” and W ∈ R n × N defines basis vectors loadings . Patient i was then assigned to the group j ∈ {1‥N} that represents him best i . e . a r g m a x j W i j . To promote robust cluster assignments , NMF was applied 1000 times to subsamples of the dataset composed of 80% of the samples and 80% of the features chosen at random without replacement . A consensus matrix C ∈ R n × n was then derived from the 1000 cluster assignments obtained where each entry Cij corresponds to the frequency at which two patients where clustered in the same group over all samplings where both patients were retained . The final cluster assignment was obtained by applying hierarchical clustering to the consensus matrix with euclidean distance and average linkage . To assess the stability of the obtained clusters , we computed the proportion of ambiguous clustering ( PAC ) which is the proportion of discordant cluster assignments obtained through consensus clustering . Cluster assignments for a pair of patients ( i , j ) were considered discordant when 0 . 25 ≤ Cij ≤ 0 . 75 . In the case where only the total number of mutations was used for stratification , NMF is not applicable and kMeans was used instead with 1000 restarts and initialisation by kMeans++ [61] . Several proxy genes have a prognostic power according to log-rank tests performed for each gene separately and which compare patients with mutations ( proxy or not ) versus those without ( P ≤ 1 × 10−2 ) . The difference in survival outcomes observed may be due to at least two types of information encoded in proxy genes: patients’ overall mutational burden and genes’ neighbourhood mutational burden ( NMB ) . To clarify the contributions of each effect , we investigate whether such distinct survival outcomes can be obtained with proxies for the total number of mutations only , regardless of NMBs . To this end , we simulate proxy mutations for each gene separately according to a model that only depends on patients’ total number of mutations . Let T i ∈ N be the total number of mutations of patient i , i ∈ {1 , … , n} . Let M o ⊂ { 1 , . . . , n } and M p ⊂ { 1 , . . . , n } indicate which patients have original and proxy mutations respectively . For a given proxy gene whose mutations are described by the sets Mo and Mp , we leave the original mutations untouched and reallocate the proxy mutations according to P ( i ∈ M p | T i ) = { 0 if ( T i ≥ k ) or ( i ∈ M o ) k - T i α otherwise where α is chosen so that the probabilities sum to 1 . Proxy mutations are drawn from this model 1000 times . Each time we compute the log-rank statistic between the mutated and non mutated patients which yields a distribution of the log-rank statistic under the null hypothesis . The actual log-rank statistic obtained using NetNorM is then compared to this distribution to accept or reject the null hypothesis . Rejecting the null hypothesis means that the difference in survival outcomes observed between the patients with and without artificial mutations is not only driven by patients’ total number of mutations . To determine whether the obtained patient subtypes are predictive of survival beyond clinical data , we fitted a Cox proportional hazards regression model to the clinical data and to the clinical data augmented with a variable describing patients’ subtypes . We then performed a likelihood ratio test to compare the two models . The clinical variables used were downloaded from TCGA . It includes age , gender , stage , extent of spread to the lymph nodes , presence of metastasis , histology for both LUAD and SKCM and further variables such as smoking history , history of prior malignancy , residual tumour after surgery , tumour dimensions for LUAD and clark level at diagnosis , primary melanoma mitotic rate , new tumour event after initial treatment ( yes/no ) , primary melanoma tumour ulceration ( yes/no ) , primary melanoma known ( yes/no ) for SKCM . We obtain gene clusters by applying hierarchical clustering with centroid linkage and Euclidean distance to the columns of the metapatients matrix ( restricted to high variance genes ) . To obtain a reasonable number of gene clusters to analyse , we cut the hierarchical cluster tree at a distance threshold of 5 . 5 , yielding 20 clusters . Gene clusters can be categorised into two types: those that contain a lot of proxy mutations ( ≥ 80% of the total mutational load of the cluster ) and whose genes form a dense subgraph , and those that have neither of these two features . The presence of dense subgraphs with many proxy mutations results from the fact that NetNorM tends to add proxy mutations to all genes in a dense subgraph or none since they all have roughly the same number of mutated neighbours . The association of a gene cluster with one subtype can therefore indicate two things: either the subtype is expected to be enriched in proxy mutations in the corresponding gene cluster , which in turn indicates that the subgraph in which the cluster lies is expected to be enriched in mutations , or the gene cluster itself is expected to be enriched in mutations in the corresponding subtype . The enrichment or depletion in mutations of one gene cluster across patient subtypes was therefore tested slightly differently according to the gene cluster type . In the first case , we first define the neighbourhood of the gene clusters as all genes lying in the same dense subgraph . Specifically , we include in the subgraph all genes sharing an edge with at least 90% of the genes in the cluster , thus keeping subgraphs very dense . The obtained set of genes is the one tested for enrichment in mutations across subtype . In the second case , the gene cluster is directly tested for enrichment . Enrichment is assessed with a χ2 contingency test , where the contingency table is defined by the following marginals: the total number of raw mutations in each subtype , and the total number of raw mutations in and outside the gene cluster ( generalised to the embedding of a dense subgraph if it is relevant ) . Gene clusters are searched for pathway enrichment using DAVID online tool [62] ( https://david . ncifcrf . gov/summary . jsp ) with the KEGG database [54] . They are also tested for enrichment in late replicating genes thanks to a permutation test using data downloaded from http://www . broadinstitute . org/cancer/cga/mutsig_run . For each gene cluster c of length lc , lc genes are chosen uniformly at random without replacement from the list of genes with replication time information . This sampling is performed 1000 times and the null distribution was obtained by computing the median replication time of these 1000 gene sets . The median replication time of cluster c is then compared to the null distribution to yield a p-value , i . e . the probability to observe a set of genes of length lc with median replication time at least as extreme . | The transition from a normal cell to a cancer cell is driven by genetic alterations , such as mutations , that induce uncontrolled cell proliferation . With the advent of next-generation sequencing technologies ( NGS ) in the last decade , thousands of tumours have been sequenced and their mutation profiles determined . However , the statistical analysis of these mutation profiles remains challenging . Indeed , two patients usually do not share the same set of mutations and can even have none in common . Moreover , it is difficult to distinguish the few disease-causing mutations from the dozens , often hundreds of mutations observed in a tumour . To alleviate these challenges , it has been proposed to use gene-gene interaction networks as prior knowledge , with the idea that if a gene is mutated and non-functional , then its interacting neighbours might not be able to fulfil their function as well . Here we propose NetNorM , a method that transforms mutation data using gene networks so as to make mutation profiles more amenable to statistical learning . We show that NetNorM significantly improves the prognostic power of mutation data compared to previous approaches , and allows defining meaningful groups of patients based on their mutation profiles . | [
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"an... | 2017 | NetNorM: Capturing cancer-relevant information in somatic exome mutation data with gene networks for cancer stratification and prognosis |
Solute carrier family 7 member 2 ( SLC7A2 ) is an inducible transporter of the semi-essential amino acid L-arginine ( L-Arg ) , which has been implicated in immune responses to pathogens . We assessed the role of SLC7A2 in murine infection with Citrobacter rodentium , an attaching and effacing enteric pathogen that causes colitis . Induction of SLC7A2 was upregulated in colitis tissues , and localized predominantly to colonic epithelial cells . Compared to wild-type mice , Slc7a2–/–mice infected with C . rodentium had improved survival and decreased weight loss , colon weight , and histologic injury; this was associated with decreased colonic macrophages , dendritic cells , granulocytes , and Th1 and Th17 cells . In infected Slc7a2–/–mice , there were decreased levels of the proinflammatory cytokines G-CSF , TNF-α , IL-1α , IL-1β , and the chemokines CXCL1 , CCL2 , CCL3 , CCL4 , CXCL2 , and CCL5 . In bone marrow chimeras , the recipient genotype drove the colitis phenotype , indicative of the importance of epithelial , rather than myeloid SLC7A2 . Mice lacking Slc7a2 exhibited reduced adherence of C . rodentium to the colonic epithelium and decreased expression of Talin-1 , a focal adhesion protein involved in the attachment of the bacterium . The importance of SLC7A2 and Talin-1 in the intimate attachment of C . rodentium and induction of inflammatory response was confirmed in vitro , using conditionally-immortalized young adult mouse colon ( YAMC ) cells with shRNA knockdown of Slc7a2 or Tln1 . Inhibition of L-Arg uptake with the competitive inhibitor , L-lysine ( L-Lys ) , also prevented attachment of C . rodentium and chemokine expression . L-Lys and siRNA knockdown confirmed the role of L-Arg and SLC7A2 in human Caco-2 cells co-cultured with enteropathogenic Escherichia coli . Overexpression of SLC7A2 in human embryonic kidney cells increased bacterial adherence and chemokine expression . Taken together , our data indicate that C . rodentium enhances its own pathogenicity by inducing the expression of SLC7A2 to favor its attachment to the epithelium and thus create its ecological niche .
L-arginine ( L-Arg ) is a semi-essential amino acid whose metabolism can be dysregulated under diseased conditions . Transport of L-Arg is primarily dependent on the y+ transport system , which includes the cationic amino acid transporter ( CAT; SLC7A ) family of proteins [1] . SLC7A1 is constitutively expressed and involved in basic metabolism [2 , 3]; SLC7A2 is the inducible isoform , and includes the alternatively spliced isoforms SLC7A2A , a low-affinity transporter primarily in liver , and SLC7A2B , the high-affinity L-Arg transporter known to be abundant in macrophages [4–6] . Thus , SLC7A2-dependent cellular bioavailability of L-Arg controls the activity of two major enzymes of the innate response under pathophysiological processes: first , L-Arg is converted into nitric oxide ( NO ) by the inducible NO synthase ( NOS2 ) ; second , arginase 1 and arginase 2 catabolize L-Arg into urea and L-ornithine , which serves as substrate for ornithine decarboxylase ( ODC ) and ornithine aminotransferase for the synthesis of polyamines and proline , respectively . In this context , our work has highlighted that SLC7A2 is a critical regulator of the inflammatory processes of the gastrointestinal tract [6–8] . We reported that generation of antimicrobial NO by macrophages exposed to the gastric pathogen Helicobacter pylori required SLC7A2 [6] , and that mice deficient in Slc7a2 exhibit attenuation of innate and adaptive immune responses to chronic infection with H . pylori , leading to less gastric inflammation [9] . In contrast , expression of macrophage SLC7A2 in the colonic tissue protected mice from dextran sulfate sodium ( DSS ) -induced colitis [10] . However , the expression and the role of SLC7A2 in colonic inflammation following infection with intestinal pathogenic bacteria has not been identified . Bacterial pathogenesis is orchestrated by the targeted deleterious effects of virulence factors and by the molecular crosstalk between the host and the pathogen . In this aspect , non-invasive attaching-effacing ( A/E ) enteropathogens represent exquisite examples of bacteria that have acquired the ability to highjack host metabolism and responses . The A/E phenotype shared by enteropathogenic Escherichia coli ( EPEC ) , enterohemorrhagic E . coli , and by a natural mouse pathogen , Citrobacter rodentium , is characterized by intimate adherence of bacteria to intestinal epithelial cells and by effacement of microvilli . Most of the proteins involved in the A/E phenotype are encoded on a pathogenicity island called the locus of enterocyte effacement ( LEE ) encoding a type III secretion system ( T3SS ) that allows bacteria to directly inject effector proteins into the underlying colonic epithelial cells ( CECs ) [11] . Thus , once in CECs , the bacterial translocated intimin receptor Tir is integrated into the plasma membrane and forms a hairpin-loop; the extracellular domain of Tir then binds intimin , an adhesin present at the bacterial cell surface [12] . The binding of intimin induces clustering of Tir , resulting in phosphorylation of Tir at tyrosine 471 [13 , 14] and subsequent recruitment of proteins involved in actin polymerization and pedestal formation [15] . Of note , the phosphorylation of Tir is required for pedestal formation in vitro , but is not essential for colonization of mice by C . rodentium [14] . It has been also described that the intracellular N-terminus and C-terminus domains of EPEC Tir are also connected to focal adhesion proteins including vinculin , α-actinin , and talin [16] . These proteins , which are recruited independently of Tir phosphorylation [17] , are required for pedestal formation [18] and thus have been immunolocalized beneath the attached bacteria [16] . Hence , focal adhesion proteins are essential for the attachment of EPEC to the host cell surface . Moreover , the bacterial effectors injected into cells through the T3SS , components of the translocation machinery , and/or T3SS-secreted non-LEE-encoded effectors , interfere with the host signal transduction and may thus modulate tight junction disruption [19] and innate immune response [20–22] . Therefore , the capacity of A/E bacteria to attach intimately CECs is a critical step in pathogenesis . Here we determined that C . rodentium induces Slc7a2 mRNA and SLC7A2 protein expression in CECs of infected mice . Genetic ablation of Slc7a2 caused a significant reduction of colonization of the colonic mucosa , associated with reduced expression of Talin-1 , resulting in improved clinical , histopathological , and immunological parameters . Finally , we also demonstrated that SLC7A2 favors the intimate attachment of C . rodentium and EPEC to epithelial cells and , consequently , the induction of the innate immune response .
When compared to uninfected mice , there was a significant increase in Slc7a2 mRNA levels in colonic tissues from C . rodentium-infected mice ( Fig 1A ) . To further determine the cellular localization of C . rodentium-induced Slc7a2 mRNA , we analyzed the expression of this gene in various cells purified from the colon . Slc7a2 mRNA levels were increased in isolated CECs to the same extent as in whole tissues ( Fig 1A ) ; in contrast , this gene was not induced in F4/80+ ( macrophages ) or F4/80− lamina propria cells ( Fig 1A ) . We also examined mRNA levels of Slc7a2 by in situ hybridization ( RNAscope ) , and found increased Slc7a2 mRNA levels in infected mice , predominantly in CECs ( Fig 1B ) , but some cells from the lamina propria were also stained ( Fig 1B ) . Moreover , Slc7a2 mRNA levels were not detectable in infected Slc7a2−/−mice ( S1A Fig ) . Importantly , Slc7a1 mRNA expression was not increased in colonic tissues from infected WT and Slc7a2–/–mice when compared to the respective uninfected control animals ( S1B Fig ) . Immunohistochemistry demonstrated that SLC7A2 protein was mainly increased in CECs from mice infected with C . rodentium compared to those that were uninfected ( Fig 1C ) . This was confirmed by Western blot analysis showing increased SLC7A2 protein levels in colonic epithelial cells of wild-type mice infected with C . rodentium compared to uninfected animals ( S1C Fig ) ; SLC7A2 was not detected in uninfected and infected Slc7a2–/–mice ( S1C Fig ) . As expected , L-Arg uptake in colonic tissue was increased in C . rodentium-infected wild-type mice compared to uninfected animals ( S1D Fig ) ; this was completely suppressed in Slc7a2–/–mice ( S1D Fig ) . Consistent with the decreased tissue L-Arg uptake , L-Arg concentration was significantly higher in the serum of Slc7a2–/–mice compared to wild-type mice , for both uninfected and infected mice ( S1E Fig ) . Lastly , we analyzed Slc7a2 mRNA expression in the conditionally-immortalized young adult mouse colon ( YAMC ) cell line . The expression of Slc7a2 was induced at the same level by C . rodentium or by mutant strains lacking escN , espF , or espG ( S2A Fig ) , suggesting that the T3SS and the injected effector proteins are not involved in Slc7a2 induction . However , the escN mutant failed to stimulate Cxcl1 and Cxcl2 expression , whereas the espF and espG mutants induced the expression of these genes at the same level as the wild type C . rodentium ( S2B Fig ) . Because Slc7a2 was induced in colonocytes by C . rodentium , we sought to determine the effect of Slc7a2 genetic deletion on the course of the infection . As shown in Fig 2A , Slc7a2–/–mice survived throughout the experiment while death of WT mice began on day 7 and continued until day 14 , indicating a significant protective effect of Slc7a2 deletion . Mice of both genotypes began losing weight on day 1 , but Slc7a2–/–mice started gaining weight after day 3 , indicating recovery , and had less weight loss on days 6–14 ( Fig 2B ) . We also measured the weight of the colons , as thickening of the colon is an indicator of colitis severity in this model . Colon weight was increased in WT mice infected with C . rodentium compared to uninfected controls ( Fig 2C ) , and this was significantly attenuated in infected Slc7a2–/–mice compared to infected WT mice by 15 . 5 ± 3 . 8% and 20 . 1 ± 3 . 2% at day 7 and 14 , respectively . Photomicrographs of H&E staining of the colons of C . rodentium-infected WT mice demonstrate a complete effacement of the brush border microvilli , massive crypt hyperplasia , severe mucosal inflammation , and submucosal edema ( Fig 2D ) ; however , the epithelial injury and the inflammation were both markedly attenuated in infected Slc7a2–/–mice ( Fig 2D ) . Using a comprehensive scoring system to quantify the degree of inflammation and epithelial damage , there was a significant decrease in overall histologic injury in Slc7a2–/–mice compared to WT animals at both day 7 and at day 14 ( Fig 2E ) . Furthermore , loss of colonic goblet cells is another characteristic in mice infected with C . rodentium [23]; when assessed by using periodic acid–Schiff staining , we found that infection resulted in a 47 . 5 ± 6 . 5% loss of goblet cells in WT mice compared to only a 25 . 6 ± 5 . 3% loss in Slc7a2–/–mice ( S3 Fig ) . There was no detectable inflammation or epithelial injury in uninfected Slc7a2–/–or WT mice ( Fig 2D and S3 Fig ) . While SLC7A2 is known as a macrophage L-Arg transporter [4] , our data indicate that upregulation of SLC7A2 during infectious colitis was mainly observed in the epithelium . Therefore , to further refine the contributions of different cells to the disease process , we infected WT and Slc7a2–/–mice with C . rodentium 8 weeks following bone marrow transplants . We confirmed by genotyping the efficiency of the transplantation ( S4 Fig ) . Similar to what we had observed with non-transplanted mice , Slc7a2–/–receiving Slc7a2–/–marrow had less body weight loss ( Fig 3A ) , colon weight ( Fig 3B ) , and histological damage ( Fig 3C ) than WT mice receiving WT marrow . Importantly , each of these clinical parameters were significantly improved in infected Slc7a2–/–mice that received WT marrow , when compared to WT mice that received Slc7a2–/–marrow ( Fig 3 ) . There was no detectable weight loss , inflammation or epithelial injury in any of the uninfected control mice in the bone marrow transplant experiments . Taken together , these data show that protection from colitis in the C . rodentium model by Slc7a2 deletion is mediated by the recipient mouse genotype; this indicates that exacerbation of colitis due to SLC7A2 derives from the non-hematopoietic cells , consistent with the predominantly epithelial SLC7A2 localization that we have observed . To further investigate differences in inflammatory responses , we conducted cytokine profiling in colonic tissues by Luminex analysis . In WT mice , the chemokines CCL2 , CCL3 , CCL4 , CCL5 , CXCL1 , and CXCL2 , the innate pro-inflammatory cytokines TNF-α , IL-1α , IL-1β , and G-CSF , as well as the prototype Th1 and Th17 cytokines , IFN-γ and IL-17 , respectively , were all significantly increased above the levels in infected mice in the colonic mucosa during C . rodentium infection ( S1 Table ) . Notably , all of these immune effectors were produced in lower quantities by infected Slc7a2–/–mice when compared to the WT animals ( Fig 4A ) . These results were confirmed by analysis of the mRNA levels of the genes encoding theses chemokines and cytokines , showing a significant induction of gene expression in C . rodentium-infected WT mice vs . uninfected animals , and a significant reduction in these genes in Slc7a2–/–animals ( Fig 4B ) . In contrast , the Th2 cytokines IL-4 , IL-5 , and IL-13 and the immunoregulatory cytokine IL-10 showed no significant increase in C . rodentium-infected mucosa of WT or Slc7a2-deficient mice compared to the respective controls ( S1 Table ) . To establish the nature of the inflammatory cell populations in infected colonic tissues , we conducted immunophenotyping by flow cytometry . There were more F4/80+ cells ( macrophages; Fig 5A ) , GR1+ cells ( granulocytes; Fig 5B ) , and CD11c+ cells ( dendritic cells; Fig 5C ) in C . rodentium-infected WT animals compared to uninfected mice; each of these myeloid cells of the innate immune system were less abundant in the mucosa of infected Slc7a2−/−mice ( Fig 5A–5C ) . Similarly , CD4+ , IFN-γ+ cells ( Fig 6A ) and CD4+ , IL-17+ cells ( Fig 6B ) were more abundant in the colonic lamina propria and in the mesenteric lymph nodes ( Fig 6C ) of WT mice compared to Slc7a2−/−mice during C . rodentium infection . During the time course of the infection , there was less C . rodentium excreted into the stool by Slc7a2–/–mice in comparison to infected WT animals ( Fig 7A ) . Importantly , the colonization of the colon by C . rodentium was decreased by more than a Log-order in Slc7a2–/–mice compared to WT mice , at day 1 , 3 , 7 and 14 ( Fig 7B ) . This was confirmed by detection of the bacterial protein EspB , a part of the T3SS [11] , in the colonic tissues: By Western blot ( Fig 7C ) and immunohistochemistry ( Fig 7D ) there was markedly less EspB in Slc7a2–/–versus WT tissues . Of importance , when WT animals were infected with C . rodentium , we found by immunofluorescence and confocal microscopy that the bacteria co-localized with SLC7A2 ( Fig 7E ) . We then reasoned that the decreased colonization level observed in Slc7a2–/–mice could be linked to attenuation of the innate immune function of CECs . To verify this , we isolated CECs from mice and analyzed Cxcl1 and Cxcl2 mRNA expression . Both genes were induced in CECs of infected WT mice , but this was significantly reduced in Slc7a2–/–mice compared to uninfected animals ( Fig 7F ) . To understand the mechanism by which SLC7A2 may regulate colonization , we assessed the expression of host focal contact proteins , which are involved in the attachment of C . rodentium to epithelial cells independently of Tir phosphorylation [16 , 17] . We observed that α-actinin ( ACTN1 ) protein expression in the colon was not affected by C . rodentium infection nor by Slc7a2 deletion ( Fig 8A ) ; in contrast , Talin-1 protein was less abundant in colonic tissues of Slc7a2-deficient mice , infected or not with C . rodentium ( Fig 8A ) . This was confirmed by immunofluorescence ( Fig 8B ) , showing an induction of Talin-1 protein expression in WT mice , but not in Slc7a2–/–mice , infected with C . rodentium compared to uninfected controls . We then established that Tln1 mRNA was induced in colonic tissues , and mainly in CECs , when WT mice were infected with C . rodentium ( Fig 8C ) ; in accordance to the proteins levels , we found that this gene was less upregulated in infected Slc7a2–/–mice ( Fig 8C ) . Because the in vivo data suggested that SLC7A2 favors colonic colonization by C . rodentium , we further investigated the role of this protein in the attachment of the bacteria to the cells . First , we used HEK 293T cells transfected with a pLX304 plasmid harboring the human SLC7A2 gene; when compared to the cells transfected with empty vector , there was a concomitant increase in SLC7A2 mRNA expression ( S5A Fig ) and formation of A/E lesions on the cells , assessed by fluorescence actin staining ( FAS ) test ( Fig 9A ) . Moreover , the induction of the human CXCL8 gene observed in infected HEK 293T cells was further increased in SLC7A2-overexpressing cells ( Fig 9B ) . Second , we used YAMC cells transduced with Slc7a2 short-hairpin RNA ( shRNA ) , and found more than 70% knockdown of Slc7a2 mRNA expression compared to control shRNA-transduced uninfected cells ( S5B Fig ) ; the Slc7a2 gene was induced by ~ 15 fold in C . rodentium–infected YAMC cells transduced with control shRNA , and the expression was essentially abolished in infected cells transduced with Slc7a2 shRNA ( S5B Fig ) . Similarly , C . rodentium-stimulated SLC7A2 protein expression was completely inhibited in YAMC cells expressing Slc7a2 shRNA ( S5C Fig ) . Slc7a2 knockdown was accompanied by a marked inhibition of bacterial adhesion to cells ( Fig 9C ) and by a complete inhibition of C . rodentium-induced Cxcl1 and Cxcl2 mRNA expression ( Fig 9D ) . To determine whether these effects of SLC7A2 were mediated by L-Arg , we performed experiments with L-lysine ( L-Lys ) , a competitor of L-Arg uptake by SLC7A2 [24] . As shown in Fig 9E , the attachment of C . rodentium to YAMC cells was decreased in the presence of L-Lys . In addition , the expression of the genes encoding CXCL1 and CXCL2 in C . rodentium-infected cells was inhibited by L-Lys ( Fig 9F ) . Together , these data establish that SLC7A2 favors the attachment of C . rodentium by a mechanism involving L-Arg . Because Talin-1 protein is less abundant in C . rodentium-infected mice ( Fig 8 ) , we reasoned that endogenous L-Arg may affect Talin-1 expression and consequently bacterial adherence . First , we found that the gene Tln1 that encodes Talin-1 was induced by 5–fold in YAMC cells expressing Ctrl shRNA and infected with C . rodentium compared to uninfected cells ( Fig 10A ) . This induction of Tln1 was abolished in cells transfected with Slc7a2 shRNA or Tln1 shRNA ( Fig 10A ) . Further , C . rodentium-induced Tln1 mRNA expression was also abolished in cells treated with L-Lys ( Fig 10B ) . As depicted in Fig 10C , Tln1 mRNA was more stable in YAMC cells in the presence of L-Arg ( t1/2 = 80 . 32 min ) than in cells starved of L-Arg ( t1/2 = 19 . 02 min; P = 0 . 019 ) . Then , we observed a diminution of the attachment of C . rodentium to YAMC cells expressing Tln1 shRNA compared to cells transfected with Ctrl shRNA ( Fig 10D ) , demonstrating that Talin-1 is involved in the intimate binding of the bacteria to epithelial cells . A concomitant reduction of C . rodentium-induced Cxcl1 and Cxcl2 mRNA expression was also evidenced in cells with Tln1 knockdown ( Fig 10E ) . We then recapitulated our findings in the human colonic epithelial cell line Caco-2 infected with the human pathogen EPEC . We observed that the gene SLC7A2 was induced during infection with EPEC ( Fig 11A ) ; this expression was completely suppressed in cells transiently transfected with small interfering RNA ( siRNA ) directed against SLC7A2 ( Fig 11A ) . The adhesion of EPEC to Caco-2 cells ( Fig 11B and 11D ) and the EPEC-induced CXCL8 mRNA expression ( Fig 11C and 11E ) were both inhibited in cells treated with SLC7A2 siRNA or with L-Lys .
SLC7A2 is a master regulator of macrophage function notably by controlling the activity of the innate enzymes that use L-Arg as substrate , namely NOS2 and arginase [6 , 25 , 26] . Consequently , this transporter orchestrates the susceptibility of the host to parasites , including Toxoplasma gondii and Schistosoma mansoni [27] , and to pathogenic bacteria such as H . pylori [9] , and regulates adaptive immunity during infectious [9 , 27] and inflammatory processes [10] . Herein , we now demonstrate that epithelial SLC7A2 plays a critical role in the attachment of intestinal pathogenic bacteria to colonocytes and to the formation of A/E lesions , thus triggering the initiation of the inflammatory cascade through chemokine production . This occurs through the effect of intracellular L-Arg that is required to increase mRNA stability of Talin-1 , a focal adhesion protein involved in the intimate attachment of A/E pathogens ( Fig 12 ) . Using the murine intestinal pathogen C . rodentium , we showed that SLC7A2 is induced during the infection mainly in CECs and that colonization and inflammation were attenuated in Slc7a2–/–mice , resulting in improved clinical and histological parameters . Our results showed that the difference in colonization between WT and Slc7a2-deficient animals was observed as soon as one-day post-infection and during the complete time course of the disease; this indicates that the improved colitis observed in Slc7a2–/–mice is likely to be due to decreased colonization , since the level of bacterial burden is generally associated with the level of inflammation in this model [28] . Our data also highlight a previously unappreciated role for epithelial SLC7A2 in initiating the recruitment of myeloid cells in the infected mucosa and the development of Th1 and Th17 populations during the infectious process . We evidenced herein that Slc7a2–/–mice exhibit less colonic inflammation than WT animals after C . rodentium infection , whereas our previous work showed that Slc7a2–/–mice have worsened colitis when treated with DSS when compared to WT mice [10]; this suggests that different mechanisms orchestrate A/E bacteria-induced mucosal inflammation and experimental colitis initiated by a chemical that destroys the surface of the epithelium . Similar differences in epithelial injury versus infectious models have been described . As examples , mice lacking the TNF-α receptor TNFRp55 or IFN-γ display an attenuation of DSS-induced colitis [29 , 30] , but when infected with C . rodentium , they exhibit enhanced colonic bacterial burden , worsened clinical and histological parameters , and more mucosal inflammation than WT mice [31 , 32] . In the same way , the Th17 lineage is required for protection against C . rodentium [33] , but Il17a−/− mice show reduced severity of colitis in the DSS model [34] . Moreover , we have described that SLC7A2 is induced in colonic macrophages after DSS treatment [10] , but here we show that expression is in CECs , and is not significant in the lamina propria cells , during C . rodentium infection . Further , bone marrow transfers between WT and Slc7a2–/–mice resulted in a C . rodentium-induced phenotype driven by the recipient genotypes of the animals , demonstrating that the hematopoietic pools do not play a major role in SLC7A2-mediated disease development . These data emphasize that SLC7A2 may play a different role in the gastrointestinal tract according to the type of cells expressing this protein and to the etiology of the inflammation , i . e . epithelial injury or infectious processes . Although in vitro analysis has evidenced that epithelial restitution is supported by SLC7A2–dependent L-Arg uptake by increasing L-proline synthesis through the arginase 1 metabolic pathway [24] , our data herein indicate that Slc7a2-deficient mice displayed less colitis than WT after C . rodentium infection . Because arginase 1 is induced during C . rodentium infection [35] , our new findings suggest that the deleterious effect of SLC7A2 in this model exceeds its potential beneficial effect and is not dependent on arginase activity . We therefore reasoned that SLC7A2 may play a role in the adherence of C . rodentium to CECs . Indeed , decreased C . rodentium colonization and epithelial cell EspB content in Slc7a2-deficient mice , co-localization of SLC7A2 and C . rodentium in the infected mucosa , and enhanced intimate attachment of C . rodentium to cultured cells overexpressing SLC7A2 provide strong evidence to support this postulate . Moreover , our in vivo and in vitro studies ( summarized in Fig 12 ) have demonstrated that SLC7A2 activity and L-Arg concentration are coupled with regulation of transcription and mRNA stability of Tln1 , the gene encoding Talin-1 that is essential for the intimate attachment of A/E pathogens to CECs [16 , 17] . In summary , we have shown that SLC7A2 facilitates the binding of C . rodentium to CECs in vitro and in vivo , thus initiating a strong innate immune/inflammatory response of enterocytes . Intriguingly , in addition to its role in metabolic L-Arg uptake by numerous cells [36 , 37] , constitutive SLC7A1 has been also shown to be the receptor for ectopic murine leukemia viruses [38] . However , in the present report , we demonstrate that SLC7A2 regulates bacterial attachment through a mechanism that depends on L-Arg , as we were able to recapitulate effects of Slc7a2 interference by using L-Lys , a competitive inhibitor of L-Arg uptake . Because the difference in colon colonization with C . rodentium between WT and Slc7a2–/–mice occurs as soon as day 1 post-infection , we propose that SLC7A2 plays a crucial role in the early stage of the disease , which is essential for C . rodentium pathogenesis . It has been reported that bacterial adhesion to CECs is associated with the activation of enteric innate Th17 response in the first few days of the disease [39] . Supporting the possibility that our data has a relevance to this immunological event , we showed that Slc7a2-deficient mice had decreased Il-17 mRNA levels and less recruitment of CD4+ , IL-17+ cells in the infected mucosa than in WT animals . Our investigations lead us to propose that C . rodentium favors its own pathogenesis by inducing the expression of SLC7A2 in epithelial cells , representing a striking example of the manipulation of cell signaling by a pathogenic bacterium .
Experiments were conducted under protocol M/08/124 approved by the the IACUC of Vanderbilt University and the Research and Development Committee of the Veterans Affairs Tennessee Valley Healthcare System . Procedures were performed in accordance with institutional policies , AAALAC guidelines , the AVMA Guidelines on Euthanasia ( CO2 asphyxiation followed by cervical dislocation ) , NIH regulations ( Guide for the Care and Use of Laboratory Animals ) , and the United States Animal Welfare Act ( 1966 ) . EPEC strain E2348/69 was maintained on Luria-Bertani ( LB ) agar plates . C . rodentium DBS100 [40] and the isogenic mutants escN , espF , and espG [19 , 41 , 42] were maintained on McConkey agar plates and cultured overnight at 37°C in LB broth; this culture was then diluted to OD600 = 0 . 1 in LB broth or in cell culture medium to the exponential growth phase . These bacteria were used to infect mice or cells [35 , 43] . The bacterial concentration was estimated to be 5 X 108 bacteria/ml per OD unit at 600 nm , as calculated by plating . C57BL/6 and C57BL/6 Slc7a2–/–mice were house-bred as described [9 , 10] . Age-matched male WT and mutant mice ( 7–11 weeks ) were infected by oral gavage with 0 . 1 ml of LB broth containing 5 X 108 bacteria . Animals were monitored daily and mice that showed extreme distress , became moribund , or lost more than 20% of initial body weight were euthanized . After sacrifice , colons were removed , measured , cut longitudinally , cleaned , weighed , and Swiss-rolled for histology . Three proximal and distal 2 mm pieces were used for RNA and protein extraction and to determine colonization by plating serial dilution of ground tissues . Bone marrow was harvested from the femurs and tibias of WT and Slc7a2–/–mice as described [44] . Hematopoietic cells ( 106 ) were injected retro-orbitally into 5-week-old WT and Slc7a2−/−mice after each were irradiated with a single dose of 900 rads . The mice were fed autoclaved food and water , with the latter supplemented with 1 . 1 g/L neomycin sulfate and 125 mg/L polymyxin B for one-week pre-transplant and two-weeks post-transplant . Mice were infected with C . rodentium eight weeks after transplantation . For genotyping , DNA extraction from a sample tissue of spleen and PCR were performed using EZ Fast PCR Genotyping Kit ( EZ Bioresearch ) and the primers for Slc7a2 and neo genes ( S2 Table ) . CECs were isolated by a dissociation and dispersion method [24 , 43 , 45] . Briefly , colons were removed , cut open longitudinally , cleaned , cut into 2–3 mm pieces , and incubated in DTT ( 3 mM ) and EDTA ( 0 . 5 mM ) . After one hour , samples were vigorously shaken in PBS and filtered through 70-μm nylon mesh . The purity of the epithelial cells was assessed by flow cytometry as described [24] . Cells from the lamina propria were isolated by enzymatic digestion using a dispase and collagenase method as reported [10 , 44] . Briefly , mouse colons were removed , opened longitudinally , cleaned , cut into 1 mm pieces , and incubated in dispase ( 1 mg/ml ) and collagenase A ( 0 . 25 mg/ml ) for 20 min . Cells were filtered through a 70-μm strainer , centrifuged , and counted . Cells were then labeled with a biotin-conjugated anti-mouse F4/80 antibody ( 1:200; CALTAG Laboratories ) , washed , and incubated with streptavidin conjugated with magnetic beads ( BD Biosciences ) . Cell suspensions were applied to an IMagnet ( BD Biosciences ) for 6 min at room temperature; F4/80− fractions were carefully removed and this selection procedure was repeated 3 times , before collecting the F4/80+ fraction . Isolated cells from the lamina propria were analyzed by flow cytometry using labelled Abs ( S3 Table ) . For intracellular proteins the cell suspensions were incubated in complete RPMI 1640 medium containing GolgiPlug ( BD Biosciences ) for 4 h before staining [10 , 46] . Single cell suspensions from mesenteric lymph nodes were cultured in complete RPMI 1640 medium with 5 μg/ml anti-CD3 . After 24 h , 1 μg/ml soluble anti-CD28 was added for 2 more days; cells were then stimulated with 20 ng/ml phorbol 12-myristate 13-acetate and 1 μg/ml ionomycin in the presence of GolgiPlug for 4 h [10 , 46] . Swiss-rolled colons were formalin-fixed and paraffin-embedded , and 5 μm sections were stained with H&E and examined in a blinded manner by a gastrointestinal pathologist ( M . B . P . ) . The histologic injury score ( 0–21 ) was the sum of acute and chronic inflammation ( 0–3 for each ) scores multiplied by extent of inflammation ( 0–3 ) plus the epithelial injury score ( 0–3 ) , as described [43] . Histological sections were also stained with Periodic acid–Schiff that is specific for goblet cells [47] . YAMC cells were maintained under permissive growth conditions in DMEM medium supplemented with 10% fetal bovine serum , 2 mM glutamine , 50 μg/ml gentamicin , 100 U/ml penicillin , 100 μg/ml streptomycin , and 5 U/ml IFN-γ in a humidified incubator with 5% CO2 at 33°C [24 , 43] . These cells were plated in 96-well plates at 33°C for 20 h and hexadimethrine bromide ( 8 μg/ml ) was added to the cells to enhance transduction efficiency . Lentiviral particles containing Slc7a2 or control shRNAs ( Sigma ) were added at an MOI of 5 and incubated for an additional 20 h at 33°C . Transduced cells were selected in complete medium with 10 μg/ml puromycin , which was replaced every 3–4 days for 3 weeks [24] . YAMC cells , transduced or not , were then transferred at 37°C in IFN-γ-free medium for 24 h before infection with C . rodentium . Caco-2 and HEK 293T cells were grown in DMEM containing 10% fetal bovine serum , 2 mM glutamine , 100 U/ml penicillin , and 100 μg/ml streptomycin . These cells were plated at 5 X 105 cells per well in 6-well plates at 37°C for 20 h . Medium was changed to Opti-MEM I Reduced-Serum Media ( Invitrogen ) and cells were transfected using Lipofectamine 2000 ( Life Technologies ) with i ) 100 nM ON-TARGETplus siRNAs ( Dharmacon ) directed against human SLC7A2 or scrambled siRNAs , or ii ) 1 μg of the pLX304 plasmid vector expressing or not the human SLC7A2 gene . After 6 h , cells were washed , maintained 24–48 h in fresh medium , and then infected with C . rodentium or EPEC , in the presence or absence of 40 mM L-Lys . RNA from colonic tissues and cells was extracted using the RNeasy Mini Kit ( Qiagen ) as described [10] . RNA from isolated CECs , F4/80+ , and F4/80− cells was purified using the 5 PRIME PerfectPure RNA 96 Cell CS Kit ( 5 PRIME ) . Total RNA ( 1 μg ) was reverse transcribed using an iScript cDNA synthesis kit ( Bio-Rad ) and oligo ( dT ) primers . Each PCR was performed with 2 μl of cDNA , iQ™ SYBR Green Supermix ( Bio-Rad ) , and primers described in S2 Table . RNA in situ hybridization was performed using the 2 . 0 HD Brown FFPE Reagent Kit ( Advanced Cell Diagnostics ) . Briefly , 5 μm sections from formalin-fixed paraffin embedded Swiss-rolled tissues were heated for 1 h at 60°C , incubated twice in xylene for 5 min , washed twice in 100% ethanol for 1 min , dried for 5 min at room temperature , treated with H2O2 for 10 min , boiled in target retrieval reagent , and incubated with protease solution for 30 min at 40°C . Sections were then incubated with a custom-made Slc7a2 probe overnight , washed , and signals were amplified using a six-step protocol according to the manufacturer’s instructions . Color was developed using 3 , 3'-diaminobenzidine substrate and slides were counterstained with hematoxylin . To study Tln1 mRNA stability , YAMC cells were first maintained in L-Arg-free DMEM ( GIBCO Life Technologies ) supplemented with 0 . 1 mM L-Arg for 4 h , washed once , and switched to fresh L-Arg-free DMEM containing or not 0 . 1 mM L-Arg . Actinomycin D ( 10 μg/ml ) was added directly to the cells cultured with or without L-Arg . After different times , Tln1 mRNA was analyzed by RT-real time PCR . Sections ( 5 μm ) from formalin-fixed paraffin-embedded Swiss-rolled tissues were deparaffinized , treated with citrate buffer for antigen retrieval , and blocked in 5% goat serum for 1 h at room temperature . For immunohistochemistry , slides were subsequently incubated at 4°C for 16 h with mouse monoclonal anti-EspB ( 1:100; tgcBIOMICS ) and MACH2 Mouse HRP polymer was used ( Biocare Medical ) . Color was developed using 3 , 3'-diaminobenzidine substrate and slides were counterstained with hematoxylin . Slides were washed , dried , mounted using mounting medium , and visualized using a Nikon E800 fluorescence microscope and Spot RT Slider digital camera ( Diagnostic Instruments ) ; images were analyzed using Spot Advanced software ( Diagnostic Instruments ) . For immunofluorescence , slides were blocked with Background Sniper ( Biocare Medical ) and subsequently incubated with i ) rabbit polyclonal anti-SLC7A2 Ab ( Sigma; 1:200 ) , Alexa Fluor 555-labeled goat anti-rabbit IgG ( 1:400; Life Technologies ) , Fab Fragment Donkey anti-rabbit IgG ( Jackson ImmunoResearch ) in 5% goat serum , rabbit polyclonal anti-C . koseri , which has been shown to cross-react with C . rodentium ( Abcam; 1:50 ) , and Alexa Fluor 488-labeled goat anti-rabbit IgG ( 1:400; Life Technologies ) , or ii ) mouse monoclonal anti-EspB ( 1:200; tgcBIOMICS ) , Alexa Fluor 555-labeled goat anti-murine IgG ( 1:500 ) , rabbit monoclonal anti-Talin-1 Ab ( Cell Signaling; 1:200 ) , and Alexa Fluor 488-labeled goat anti-rabbit IgG ( 1:700 ) . Slides were washed , dried , mounted using DAPI ( for nuclear staining ) containing mounting medium , and then visualized by confocal microscopy ( FV1000; Olympus ) . YAMC cells and Caco-2 cells were plated on coverslips and infected with C . rodentium or EPEC at an MOI 10 for 6 h , respectively . Cells were washed with PBS , fixed with 2 . 5% glutaraldehyde , permeabilized in 0 . 1% triton X , and stained with Phalloidin CF543 ( 1:50; Biotium ) for 1 h . Coverslips were washed with PBS , mounted on glass slides , and visualized by confocal microscopy . Colonic tissues were lysed in Cell Lytic Mammalian Tissue Lysis Extraction Reagent ( Sigma ) and analyzed using the 25-analyte MILLIPLEX MAP Mouse Cytokine/Chemokine Magnetic Bead Panel ( EMD Millipore ) on a FLEXMAP 3D instrument ( Luminex ) [9 , 10] . Data were standardized to tissue protein concentrations measured by the BCA Protein Assay Kit ( Pierce ) . Colonic tissues or cells were lysed in Cell Lytic Mammalian Tissue Lysis Extraction Reagent ( Sigma ) containing the EDTA-free Protease Inhibitor Cocktail Set III , and the Phosphatase Inhibitor Set I ( Millipore ) , and disrupted by sonication at 40 W . Protein concentrations were determined by the BCA Protein Assay Kit ( Pierce ) . Total proteins ( 80 μg per lane ) were separated on 10% polyacrylamide gels ( Bio-Rad ) and transferred onto PVDF membranes . Monoclonal anti-EspB ( 1:1000 ) , rabbit monoclonal anti-Talin-1 Ab ( 1:2000 ) , rabbit polyclonal anti-ACTN1 Ab ( 1:4000; Cell Signaling ) , rabbit polyclonal anti-SLC7A2 Ab ( 1:2000; Abcam ) , and mouse monoclonal anti-β-actin ( 1:10 , 000; Sigma ) were used as described [9 , 10] . Freshly collected colonic tissues from uninfected and C . rodentium-infected mice were maintained in DMEM containing 5 μM [14C]-L-Arg ( specific activity , 346 mCi/mmol ) for 5 min . Tissues were then washed three times with PBS , lysed with RIPA buffer , and supernatants were collected . Tissue lysates were mixed with scintillation fluid and the [14C] content was determined in a scintillation counter . Protein was measured in tissue lysates by the bicinchoninic acid method as described previously [10] . L-Arg transport values were expressed as pmol [14C]-L-Arg/min/mg protein . Blood was collected from WT and Slc7a2–/–mice at the time of euthanasia via cardiac puncture and serum was separated as described [8 , 10] . Serum were diluted 1:5 with acetonitrile and derivatized by sequential addition of 50 mM sodium carbonate and 2% benzoyl chloride ( v/v in acetonitrile ) . An internal standard solution ( 13C6-benzoyl chloride derivatized arginine at a concentration of 8 . 7 pg/μl in 20% acetonitrile in water with 2% sulfuric acid ) was also added to the samples . LC–MS analysis was performed using a Waters Acquity UPLC ( Milford , MA , USA ) coupled to a SCIEX 6500+ QTrap mass spectrometer ( Framingham , MA , USA ) operating in multiple reaction monitoring mode [48] . Quantitative data are shown as the mean ± SEM . Statistical analyses were performed with Prism version 5 . 0c ( GraphPad Software ) . Student’s t test was performed for comparison between two groups . Analysis of variance with the Student-Newman-Keuls post-hoc multiple comparisons test was performed to compare multiple groups . Survival was analyzed by the Log-rank ( Mantel-Cox ) Test . | Intestinal infections by attaching and effacing ( A/E ) bacteria widely impact human health , with major social and economic repercussions . Mucosal immunity plays a critical role in determining the outcome of these infections . The amino acid L-arginine regulates inflammatory responses to bacterial pathogens . We studied the role of the L-arginine transporter solute carrier family 7 member 2 ( SLC7A2 ) during infection with the A/E pathogen Citrobacter rodentium . SLC7A2 is induced in colonic epithelial cells during the infection and facilitates the intimate attachment of the bacteria , thus initiating the inflammatory response of the infected mucosa . These data were confirmed in vitro using C . rodentium-infected mouse cells and human colonic epithelial cells infected with enteropathogenic Escherichia coli . Our work describes a mechanism by which A/E bacteria manipulate host response to favor their colonization , thereby positioning SLC7A2 as an unrecognized therapeutic target to limit infection with enterobacteria . | [
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"dev... | 2016 | The L-Arginine Transporter Solute Carrier Family 7 Member 2 Mediates the Immunopathogenesis of Attaching and Effacing Bacteria |
The tsetse fly Glossina fuscipes s . l . is responsible for the transmission of approximately 90% of cases of human African trypanosomiasis ( HAT ) or sleeping sickness . Three G . fuscipes subspecies have been described , primarily based upon subtle differences in the morphology of their genitalia . Here we describe a study conducted across the range of this important vector to determine whether molecular evidence generated from nuclear DNA ( microsatellites and gene sequence information ) , mitochondrial DNA and symbiont DNA support the existence of these taxa as discrete taxonomic units . The nuclear ribosomal Internal transcribed spacer 1 ( ITS1 ) provided support for the three subspecies . However nuclear and mitochondrial sequence data did not support the monophyly of the morphological subspecies G . f . fuscipes or G . f . quanzensis . Instead , the most strongly supported monophyletic group was comprised of flies sampled from Ethiopia . Maternally inherited loci ( mtDNA and symbiont ) also suggested monophyly of a group from Lake Victoria basin and Tanzania , but this group was not supported by nuclear loci , suggesting different histories of these markers . Microsatellite data confirmed strong structuring across the range of G . fuscipes s . l . , and was useful for deriving the interrelationship of closely related populations . We propose that the morphological classification alone is not used to classify populations of G . fuscipes for control purposes . The Ethiopian population , which is scheduled to be the target of a sterile insect release ( SIT ) programme , was notably discrete . From a programmatic perspective this may be both positive , given that it may reflect limited migration into the area or negative if the high levels of differentiation are also reflected in reproductive isolation between this population and the flies to be used in the release programme .
Control of Human African Trypansomiasis ( HAT ) has largely been based upon the detection and treatment of human cases [1] . Anti-vector interventions , whilst hugely successful in reducing transmission of Animal African Trypanosomiasis ( AAT ) , have rarely been implemented on a programmatic scale [2] , [3] . Part of the explanation for the relative neglect of anti- vector interventions is that the majority of cases of HAT are transmitted by flies within the Glossina palpalis group which are less amenable to control using natural ( insecticide-treated cattle ) or artificial ( traps and insecticide-treated targets ) baits . The recently launched Pan African Tsetse and Trypanosomiasis Eradication Campaign ( PATTEC ) has placed anti-vector interventions back on the agenda for HAT control . This initiative aims to identify , then eradicate discrete populations of tsetse flies . The programme is not reliant upon a single intervention but will take an integrated vector management ( IVM ) approach which tailors the interventions to the ecology and bionomics of the target species . Most interventions , such as aerial spraying , bait and trap methods and release of sterile irradiated-males ( SIT ) , require a detailed understanding of the biology and population genetics of the target species . As discussed in two recent papers by Solano et al we are beginning to see population genetic data being used to target and tailor control strategies for some species within the palpalis group [4] , [5] . However , for Glossina fuscipes s . l . , which is thought to vector approximately 90% of cases of HAT [6] , very few molecular genetic studies have been conducted [7] , [8] , [9] , . Consequently , at present , our understanding of the taxonomy and population structure of this “species” is too incomplete to fully inform intervention strategies . A recent initiative to develop improved bait technologies for G . fuscipes spp . flies has revealed marked geographical differences in the response of flies to both odour and trap design . In Kenya G . f . fuscipes were unresponsive to any mammalian odour whilst in the Democratic Republic of Congo ( DRC ) G . f . quanzensis was responsive to pig odour [12] . Similarly , studies investigating the optimal orientation for the insecticide-treated , oblong cloth traps which are commonly used to control tsetse suggest that the visual responses of the putative sub-species may differ . Glossina f . fuscipes was equally attracted to traps in which the longest axis of the oblong was either parallel ( horizontal ) or orthogonal ( vertical ) to the ground [13] whereas G . f . quanzensis was apparently more attracted to horizontal oblongs ( S . Torr , unpublished ) . If these , and other , differences in vision and odourant-mediated behaviour between the putative fuscipes subspecies reflect genetic differences population genetic approaches may be used to target interventions to populations with specific behaviours . Glossina fuscipes s . l . has an extensive distribution centralised on the Congo basin but also extending as far north as Ethiopia/Sudan and as far south as Angola ( Figure 1 ) . The sister group to Glossina fuscipes is the predominantly parapatric Glossina palpalis complex [9] whose species range lies largely to the west . Machado revised the systematics of the palpalis group , [14] , and described three G . fuscipes subspecies on the basis of morphology . The first , G . fuscipes fuscipes inhabits the most humid , equatorial forest habitats across the northern part of the species range . The second subspecies , G . f . martinii , inhabits the south Eastern part of the range , around Lake Tanganyika , and in the drainage of river Lualaba from the south up to where it is joined by the Luama , and was described as the most tolerant of low humidity levels of the three subspecies . The third subspecies , G . f . quanzensis , is distributed in the south western part of the species range , in the drainages of the tributaries joining the Congo River south of Mushie . Machado asserted that the habitat of G . f . quanzensis is intermediate in character between fuscipes and martinii . Whether the present distributions are limited by the tolerance of the flies to different humidity levels is unknown , since only G . f . fuscipes has been empirically tested for desiccation tolerance [15] . The three subspecies are thought to have contiguous , non overlapping distributions . Machado concluded that the three fuscipes subspecies are probably the result of vicariant ( allopatric ) speciation events . From the work of Vanderplank there is evidence for barriers to mating between some of the subspecies [16] . Glossina fuscipes fuscipes ( then called palpalis fuscipes ) from Uganda were reciprocally crossed with G . fuscipes martinii from Zambia [16] . In the female G . f . fuscipes×male G . f . martinii the superior claspers of the male genitalia punctured the female abdomen leading to death of the female . The reciprocal cross showed partially sterility , with approximately10 times fewer pupae produced than in intraspecific crosses . The area the subspecies inhabit has long been problematic to sample due to a combination of physical and socio-political difficulties and hence classical approaches of crossing different putative species are scant . In this paper by collecting samples over a wide geographical range and using molecular genetic approaches we attempt to determine whether the subspecies of G . fuscipes sensu Machado [14] are supported or if there is evidence for alternative genetic stratification within G . fuscipes . Given that methods of tsetse control often exploit species-specific behaviours there is a pressing need to establish the taxonomic status and ranges of the taxa within G . fuscipes s . l .
G . fuscipes were collected using biconical traps or pyramidal traps [17] , [18] at the locations and dates shown in table 1 and figure 1 . After preliminary morphological identification in the field , flies were stored in either acetone or 90% ethanol . In the laboratory samples were assigned to the three morphological subspecies proposed by Machado [14] using the identification key of Jordan [19] . For mitochondrial and nuclear sequence data , DNA was extracted from three legs per tsetse using a modified version of the Ballinger-Crabtree protocol [20] , [21] . The same method was used to extract DNA from tsetse abdomens for the amplification of DNA from the tsetse symbiont Wigglesworthia glossinidia . The abdomen was used because Wigglesworthia is concentrated in a specialized organ , the bacteriome , on the tsetse midgut . Table S1 details which loci were examined in each specimen ( Accession numbers HQ387026–HQ387133 ) . The sequencing-based analyses were conducted at the Liverpool School of Tropical Medicine whilst microsatellite analyses were conducted at the Institut de Recherche pour le Développement . A Chelex method [22] was used to extract DNA from 3 legs of individuals used solely for microsatellite analysis . An 850 bp fragment of the 3′ end of the Glossina mitochondrial Cytochrome Oxidase 1 gene , a 764 bp fragment of the Glossina mitochondrial NADPH dehydrogenase 2 gene , and a 618 bp fragment of Glossina ribosomal internal transcribed spacer 1 ( ITS1 ) were amplified as described previously [9] using primer pairs COI-CULR , TW-N1284- N2-J586 and Glossina ITS1for- GlossinaITS1rev respectively . Putative Glossina period gene sequences were identified from genome reads produced by the Wellcome Trust Sanger Institute available from http://www . sanger . ac . uk/resources/downloads/vectors/glossina-morsitans-morsitans . html using tBLASTn with the Drosophila melanogaster period protein sequence ( NP_525056 ) as a query seqeuence . period was selected as it is a single copy nuclear gene in Drosophila and other insects and has been previously used to study closely related taxa [23] , [24] . tBLASTn hits to the G . m . morsitans genome ( downloaded from Sanger website in February 2008 , cut off probability 1×e−20 ) were assembled using CodonCode aligner ( CodonCode corporation ) together with the cDNA GMsg-3911 found using a description search of “period” in GeneDB ( http://old . genedb . org/genedb/glossina ) [25] . A possible intron-exon structure was inferred by comparison with Drosophila cDNA and genomic DNA , and the protein sequences of other insect period genes . Primers Perfor1 ( GATTTCGTTCATCCCAAGGA ) and Perrev1 ( GAGGCTAAAGCCTGACAACG ) were designed to amplify a fragment at the 5′ end of the putative Glossina period gene up to the highly conserved PAS domain ( due to a gap in the blast hits , the precise length of the fragment was determined by PCR and sequencing to be 1026 bp ) . This fragment was initially amplified and sequenced from G . m . morsitans genomic DNA , and the same primers were subsequently used to amplify the same region from G . fuscipes genomic DNA . 25 µl reactions contained 1 µl template , 0 . 8 mM dNTP , 3 mM MgCl2 , each primer at 0 . 5 µM and 0 . 08 µl ( 0 . 4 units ) Kapa Taq polymerase . 30 amplification cycles of 94°C for 30 seconds , 60°C for 30 seconds and 72°C for 2 minutes were used . Primers and reaction conditions for the less variable 3′ region are given in the table S2 and Text S1 . Wigglesworthia . Genes for use as G . fuscipes genotyping markers were identified by comparative genomics between W . glossinidia - G . brevipalpis [26] and W . glossinidia - G . m . morsitans genomes ( Serap Aksoy , pers . comm . ) . No W . glossinidia - G . fuscipes genome was available and differential gene loss in symbiont lineages was anticipated . To allow for the different gene content orthologous single copy genes were identified between E . coli ( K1 ) , W . glossinidia from G . brevipalpis and G . m . morsitans , using ORTHOMCL [27] . A total of 355 genes were found to be single copy and present in all three genomes . The gene orthologous groups where aligned using MUSCLE [28] , only five genes showed high levels of divergence at the nucleotide level in genome regions with conserved synteny . Degenerate primers were designed to all regions , but the hypothetical protein YcfW was the only one which yielded amplicons of the selected size . The gene encoding the hypothetical protein , YcfW , was initially amplified using degenerate primers DG11F ( 5′-ACWTGGATKTYAAAATACGG-3′ ) and DG11R ( 5′-ACWCCTGAWAARTAYATTGG-3′ ) based upon sequences of W . glossinidia from G . brevipalpis ( genome accession number: NC_004344 ) and G . m . morsitans ( Serap Aksoy , pers . comm . ) . The degenerate primers amplified a 600 bp fragment from G . fuscipes derived material which was then sequenced and used to design specific primers for G . fuscipes Wigglesworthia ( Gp11fusc_for 5′-GCGCTATTTTAATATCTTTTATTTTTG-3′; Gp11fusc_rev 5′-TGGATTWTCAGAACAAATDGTTAATC-3′ ) . YcfW was amplified for 35 cycles of 94°C for 30 seconds , 58°C for 30 seconds and 72°C for 30 seconds from roughly 40 ng of template DNA extracted from either abdomen or the whole fly , with primers 0 . 5 µM each , MgCl2 3 mM , dNTP , 0 . 8 mM . These primers amplified a 499 bp fragment and were also used for direct sequencing . Sanger sequencing of PCR products was performed by Macrogen Korea using an ABI3730XL sequencer . PCR products were purified prior to sequencing using Sureclean ( Bioline ) according to the manufacturer's instructions . All the individuals studied were genotyped using 5 microsatellite loci with dinucleotide repeats , using a LI-COR sequencer . All these microsatellite loci were originally isolated by Alan Robinson ( Entomology Unit , Food and Agricultural Organization of the United Nations/International Atomic Energy Agency , Austria ) . GfA3 , GfB8 , and GfB101 were redesigned to produce smaller amplicons [29] . The PCR reactions were carried out in a thermocycler ( MJ Research , Cambridge , UK ) in 20 µl final volume using 10 µl of the diluted supernatant from the extraction step . After PCR amplification , allele bands were routinely resolved on a 4300 DNA Analysis System from LI-COR ( Lincoln , NE ) after migration in 96-lane reloadable ( 3× ) 6 . 5% denaturing polyacrylamide gels . This method allows multiplexing by the use of two infrared dyes ( IRDye™ ) , separated by 100 nm ( 700 and 800 nm ) , and read by a two channel detection system that uses two separate lasers and detectors to eliminate errors due to fluorescence overlap . To determine the different allele sizes , a large panel of about 70 size markers was used . These size markers had been previously generated by cloning alleles from individual tsetse flies into pGEM-T Easy Vector ( Promega Corporation , Madison , WI , USA ) , by sequencing the cloned alleles to determine their exact size . PCR products from these cloned alleles were run in the same acrylamide gel as the samples , allowing the allele size of the samples to be determined accurately [30] . Allele sizes were scored twice by two independent readers using the LI-COR SagaGT genotyping software . Primers , repeat motifs , allele size ranges and the dye used are given in table S2 . Sequence data: An incongruence length difference ( ILD ) /partition homogeneity test [31] was performed in PAUP [32] to determine whether Cytochrome oxidase 1 and NADH dehydrogenase 2 sequences could be used together for estimating phylogenetic trees . No significant difference was detected between tree lengths of the COI∶ND2 partition compared to random partitions of the same size , so subsequent tree inference was performed on the combined data set . JModeltest [33] , [34] was used to perform a hierarchical likelihood ratio tests on all markers to find which substitution model best describes their evolution . Using the Akaike information criterion ( AIC ) , the Tamura Nei 1993 model [35] was specified for the COI+ND2 dataset . This model was used to make maximum likelihood ( ML ) trees using PhyML online [36] . Neighbour joining trees were inferred using PAUP version 4 . 0 [32] . Other than specifying the substitution model and a gamma distribution of rates among sites , PAUP settings for distance trees were default except that base frequencies were determined empirically from the data , tree searching was heuristic with a random order of sequence addition repeated 10 times , and 2000 bootstrap replicates were performed . Using JModeltest , the “Transversion” model was specified for YcfW ( AIC ) . This model is equivalent to the Generalised time-reversible ( GTR ) model but with only one transition rate . We therefore used the similar GTR model for neighbour joining trees in PAUP and for ML tree inference in PhyML . Bayesian phylogenies of COI+ND2 , YcfW and Period and ITS1 were made using MrBayes [37] , in each case the substitution model was selected using the Bayesian information criterion in JModeltest [33] , [34] . For COI+ND2 each gene was designated as a partition of the dataset . Both YcfW and COI+ND2 were allowed 6 substitution rates with a gamma distribution of rates across sites and a proportion of invariable sites allowed . Period was permitted 2 substitution rates and no variation of rates among sites or invariable sites . ITS1 was permitted one substitution rate and no variation of rates among sites or invariable sites . The rate prior was set to variable ( dirichlet ) , with other priors left on default . Two runs of four chains were run for 2000000 generations , sampling every 100 generations . The first 100000 generations ( 1000 samples ) were discarded as burn-in . Runs and burn-in of this length gave good convergence as assessed by examining plots of log probability against generation and observing that potential scale reduction factor for all parameters was close to 1 . The analysis was repeated three times with different seeds for the random number generator . For period full length sequences Jmodeltest specified the TPM3uf+G [38] substitution model under the Bayesian information criterion , and TIM3+G under the AIC . The TPM3uf+G model was used to infer a NJ tree using PAUP . The GTR model was used to infer a maximum likelihood phylogeny using PhyML online as this does not implement the TPM3uf or TIM3 models . Molecular clock calculations on COI data were performed using divergence rate of 1 . 5% per million years appropriate for insect COI [39] . The assumption of uniform rates across the tree was not rejected by the two cluster test implemented in Lintre [40] . Hypotheses about monophyly were tested in a Bayesian framework by observing the frequency of particular groups being monophyletic in the posterior distribution of trees , which is the posterior probability of monophyly [41] , [42] . The probability of monophyly of the three morphological subspecies ( fuscipes , quanzensis and martinii ) were tested , and we also tested the monophyly of Ethiopian G . f . fuscipes since these flies are geographically separated from other G . fuscipes by a discontinuity in their distribution , and the monophyly of Lake Victoria Basin ( LVB ) and Tanzanian specimens , since this seemed like a possible taxonomic unit in the COI+ND2 tree . This was done by using PAUP [32] to filter the posterior distribution of trees excluding the burn-in ( i . e . 19001 trees from each run ) to find the trees which agree with the hypothesis of monophyly . The Shimodaira and Hasegawa ( SH ) test [43] was also used to test the Maximum Likelihood tree topology under the constraints of the three morphological subspecies and the monophyly of Ethiopian G . fuscipes , estimating the bootstrap probabilities by bootstrap resampling the estimated log likelihoods of sites 1000 times ( the RELL method ) [44] . The monophyly of LVB+Tanzanian flies , a taxonomic unit noticed only after tree construction , was not tested using the SH test because hypotheses for this test should be a priori hypotheses , independent of the observed data [45] . Linkage disequilibrium ( LD ) between microsatellite loci was tested in each population using Genepop V [46] . A log likelihood ratio statistic ( G test ) was calculated for contingency tables of genotypes of each pair of loci in each sample . A global test for each pair of loci across all sample sites was also performed using Fisher's method . The Ethiopian sample sites were all considered as one due to their geographic proximity ( <10 Km ) . Although the straight line distance was shorter between Manga and Rusinga islands ( <5 km ) , they were considered separately because this distance is over open water . FST [47] was estimated with correction for null alleles , [48] . Null allele frequency was estimated using the expectation maximization algorithm of [49] using FreeNA [48] , and was also estimated simultaneously with the inbreeding coefficient as described by Chybicki and Burczyk [50] . After re-coding positions in the matrix containing no data with a unique code , the ‘excluding null alleles’ ( ENA ) corrected and uncorrected genotype data was converted into PHYLIP format for further analysis with programmes within the PHYLIP package [51] . Recoding of missing data genotypes with a code unique for each locus was necessary to make the sum of allele frequencies 1 . This makes the assumption that all missing data at a particular locus are the result of a single mutation that results in a null allele , which is an oversimplification . However , trees made using the original ( non-recoded ) dataset using populations [52] results in similar topology of the well supported clades , with only the poorly supported nodes changing . Allele frequencies were bootstrapped over loci using seqboot . The Cavalli-Svorza chord distance [53] was calculated using gendist and neighbour-joining trees made for each of the bootstrapped datasets using neighbour . An extended majority rule consensus tree of the bootstrap replicates was calculated using consense , the tree converted to an unrooted tree using retree , and branch lengths based on the non bootstrapped Cavalli-Svorza distance matrix were imposed on that tree topology using fitch , where negative branch lengths were not allowed . Hierfstat [54] was used to test the contribution of hierarchical levels of population structure on departures from Hardy Weinberg equilibrium . Specifically , we aimed to test whether the morphological subspecific classification ( Fsubspecies/total ) accounts for a significant level of genetic differentiation once the geographical sampling is taken into account ( Fcluster/subspecies , Fsample site/cluster and Findividual/sample site ) . Hierfstat tests the significance of higher levels of the hierarchy by permuting predefined units at a lower level between the bigger units defined by the higher level . Since G . f . martinii was only sampled from one site ( Kigoma ) , this sample was removed from the dataset for Hierfstat analysis . Three levels of structure were considered above “individual” , which were sample site , geographic cluster ( Kinshasa , Madimba and Kisantu were grouped into one cluster , Ungoye , Manga and Rusinga into another , and Busime and Buvuma into another , with the remaining sample sites classified individually . 1000 permutations were used to test the significance of F statistics at each level of the hierarchy , for all 5 autosomal loci and across all loci . STRUCTURE 2 . 3 . 1 [55] , [56] was used to infer population structure without prior information about sample locations . STRUCTURE assigns individuals to each of K clusters with different probabilities . STRUCTURE was run with K = 1 to K = 12 , using 10 replicate runs for each value of K with sequential random seeds . A burn-in period of 12000 iterations and a subsequent 60000 iterations were used to estimate parameters . The admixture model was used , which assumes that a fraction of the genome of each individual can come from each of the K populations . Allele frequencies were allowed to be correlated between clusters , as each cluster is thought to have undergone genetic drift away from a common ancestral population . The optimal value of K was assessed using the DeltaK method of Evanno et al [57] . When the whole dataset was entered , K = 2 was the optimal number of clusters using this criterion , which is the uppermost level of hierarchical structure . We then aligned the results of the 10 runs with K = 2 using the full search algorithm implemented in CLUMPP [58] . The proportionate assignment of each individual output by CLUMPP was then used to assign each individual to one of three groups: 1 . Assigned to cluster 1 with >90% probability , 2 . Assigned to cluster 2 with >90% probability and 3 . Assigned to neither cluster with >90% probability . Data from the third group was discarded for further analysis . Groups 1 and 2 were analysed separately in STRUCTURE as above , except that only K = 1−K = 10 was considered . For group 2 , the greedy method , which selects the locally optimal solution at each stage in the hope of finding the global optimum , was used on CLUMPP since the full search algorithm took >5 minutes to run . STRUCTURE analysis was run with the original genotypes , and also with missing data genotypes replaced with a code unique for each locus .
Bayesian tests of monophyly were performed for all sequence data sets ( Table 2 , figures S3 , S4 , S5 ) . No marker supported the monophyly of G . f . fuscipes or G . f . quanzensis , although ITS1 did provide weak support for the monophyly of G . f . fuscipes ( P = 0 . 917 ) . All the markers give support to the monophyly of G . f . fuscipes from Ethiopia . The monophyly of G . f . martinii was supported by the nuclear marker ( Period ) , but neither of the maternally inherited markers . The hypothesis of the monophyly of flies inhabiting Lake Victoria basin down to Tanzania ( LVB+martinii ) is supported by mitochondrial DNA but rejected by the nuclear marker period , with Wigglesworthia YcfW being inconclusive . This contrast between nuclear and maternally inherited markers may reflect the repeated adaptive sweeps to which maternally inherited markers are prone which can result in dissociation between nucleotide diversity and population demography [59] . The more conservative Shimodaira Hasegawa ( SH ) test of monophyly was performed on the same data sets . SH tests rejected monophyly for G . f . quanzensis only for the full COI+ND2 dataset ( P = 0 . 003; n = 29 ) , but when only the individuals genotyped at other loci were considered , monophyly could not be rejected ( P = 0 . 729; n = 16 ) . Monophyly was also rejected for G . f . fuscipes ( P = 0 . 029 ) for the YcfW dataset ( table S3 ) . No hypothesis could be rejected with the period or ITS1 data sets . No pair of loci showed significant LD after Bonferroni correction for multiple testing . Exact tests of heterozygote deficit [60] and highly variable FIS values suggested the presence of null alleles ( table S4 ) . Estimated null allele frequencies and the population inbreeding coefficient ( F ) for each population are shown in table S5 . For each locus estimated null allele frequency was >0 . 1 in at least one population . Once the data set was adjusted to account for the presence of null alleles , the population inbreeding coefficient was low ( <0 . 1 ) for all populations except Ungoye and Bena Tshibangu . If the three morphological subspecies , sensu Machado are valid phylogenetic entities , subspecific classification should account for a proportion of the genetic differentiation between populations . However , using a hierarchical analysis of F-statistics morphological subspecific classification was not found to be a major determinant of genetic differentiation among G . fuscipes . Subspecific classification was defined as one level of the hierarchy , and sampling site/clusters of sampling sites as other levels . It was not possible to test G . f . martinii using this method since this subspecies was only sampled at one site . With uncorrected genotypes , significant levels of genetic differentiation were accounted for by sample site ( Fsample site/cluster = 0 . 020–0 . 113 , P = 0 . 001–0 . 003 ) and sample site cluster ( Fcluster/subspecies = 0 . 050–0 . 210 , P = 0 . 001–0 . 003 ) but not by subspecific classification ( Fsubspecies/total = −0 . 045–0 . 056 , P>0 . 05 ) at all 5 autosomal loci . P values and F statistics were similar for both uncorrected and ENA corrected [48] genotypes . To test further the morphological subspecies hypothesis we used STRUCTURE software in the expectation that the genotypes would separate into three main clusters corresponding to the martinii , quanzensis and fuscipes subspecies within which there might be additional geographical sub-structuring . The optimal number of clusters based upon the DeltaK statistic was K = 2 [57] , with a local peak at K = 7 . Genotypes from Kinshasa , Madimba , Kisantu , Ethiopia , Kigoma fell into cluster 1 , whereas those from Ungoye , Manga , Rusinga , Busime and Buvuma fell into cluster 2 ( figure 4A ) . Bunghazi and Bena Tshibangu populations showed admixture between the two clusters , and Moyo was sometimes assigned to cluster 1 and sometimes to cluster 2 . When K = 7 , 5 of the clusters correspond to clades seen in the mitochondrial DNA trees , with the fifth mtDNA clade ( Kenya and south Eastern Uganda ) corresponding to the fifth ( Kenya ) and seventh clusters ( SE Uganda ) ( figure 4B ) . To further test the ability of the microsatellites to distinguish the morphological subspecies , genotypes assigned to cluster 1 or cluster 2 when K = 2 , with probabilities greater than 0 . 9 were pooled into two separate data sets and the analysis re-run . For cluster 1 , an optimal number of 2 sub clusters was found , which corresponded to i . West DRC ( Kinshasa , Madimba and Kisantu ) , with ii . Ethiopia and western Tanzania ( Kigoma ) ( figure 4C ) . Cluster 2 was separated into 3 subclusters , corresponding to i . Non admixed individuals from Bena Tshibangu , ii . eastern Lake Victoria Basin ( Ungoye , Manga and Rusinge ) and iii . northern Lake Victoria Basin ( Busime and Buvuma ) in the other , although Ungoye and Busime did show a moderate level of admixture ( figure 4D ) . Thus the first level of clustering split G . f . fuscipes into two groups , one of which clustered together with martinii ( Kigoma population ) , and the other of which corresponds to fuscipes living in the lake Victoria basin . G . f . quanzensis individuals ( Western DRC and Bena Tshibangu ) also failed to cluster together . At the next level of clustering , martinii was resolved as separate from quanzensis , but still grouped together with a fuscipes population ( although at K = 3 or higher , martinii did cluster alone ) . When the analysis was run with null homozygotes recoded as homozygous for recessive alleles , the results were largely similar , except that Bena Tshibangu showed a higher level of admixture and therefore contributed very few individuals to the second runs on clusters 1 and 2 . Cluster 1 split into an optimal ( Max DeltaK ) 3 clusters , which were i . West DRC , ii . Ethiopia and iii . Kigoma . STRUCTURE analysis does not support the hypothesis that the subspecies account for the deepest level of structuring amongst fuscipes populations . Trees made using ENA corrected or uncorrected datasets were very similar in topology , only differing at nodes with <70% bootstrap support . As demonstrated by the low bootstrap values at internal nodes in this tree ( figure S6 ) , the phylogenetic relationships of widely geographically distributed G . fuscipes populations are not well resolved by this method . The only well supported clades are the Lake Victoria Basin ( blue ) and south west DRC ( green ) . The distance of the morphologically similar Bena Tshibangu population from the other quanzensis flies is great , and they are not resolved as sister taxa in this tree .
There was not strong support for the three morphological subspecies proposed by Machado [14] . With the exception of ITS1 , sequence data from both nuclear , mitochondrial and endosymbiont genomes rejected one or more of the morphological subspecies in tests of monophyly . Microsatellite data lends little support to the monophyly of G . f . fuscipes: in the STRUCTURE analysis the major subdivision between two clusters split G . f . fuscipes between these two clusters . The Hierfstat analysis showed that once the population differentiation due to sampling sites has been taken into account , subspecific identity does not contribute significantly to differentiation . Also , in the neighbour joining tree there was no clear separation into three clades according to Machado's subspecies . Both sequence and microsatellite data does however support Machado's statement that the subspecies are allopatrically distributed; no mixed taxonomic units or admixture between morphological subspecies is observed in any population . However , microsatellite and mitochondrial DNA , and to a lesser extent Wigglesworthia DNA and single copy nuclear DNA did reveal strong support for marked genetic discontinuities within G . fuscipes s . l . Taking the results from the various markers together , five clear sub divisions were observed: The status of the central DRC population from Bena Tshibangu was harder to resolve , despite being well supported by mtDNA , and forming a sister taxa to all other fuscipes in both the ML and NJ distance trees . The Wigglesworthia YcfW gene did not amplify from samples in this population using the same primers used to amplify the rest of the G . fuscipes specimens , which may suggest they harbour a very divergent sequence with mutations in the primer binding site . Judging by the number of locus specific non amplifications and heterozygote deficit , the microsatellite markers that were optimized on G . f . fuscipes populations from Uganda worked particularly poorly on the most divergent populations: G . f . quanzensis from central DRC , G . f . fuscipes from Ethiopia and G . f . martinii . Now that these samples are available , PCR primers to study these more divergent populations in more details could be designed for future studies . One of the major difficulties in inferring the interrelationships of the fuscipes clades defined here was the lack of samples from the central and northern part of the species range , which lies mostly in DRC . The huge genetic distance between the central DRC population ( Bena Tshibangu ) and the Western populations ( Kinshasa , Madimba and Kisantu ) hints at large amounts of unexplored genetic structure within the morphological quanzensis types . Whether this marked genetic difference is associated with differing vectorial capacity is unknown but it is worth noting that there are marked differences in the epidemiology of HAT in the two locations . In central DRC HAT prevalence is very high whilst in western foci ( Kinshasa and Bas Congo ) HAT prevalence is very low [61] , [62] . It also seems likely that the vast tracts of unstudied range of the fuscipes and martinii morphological types harbour yet more genetic structure . The G . f . martinii specimens used in this study were all collected from Kigoma , which likes on the Eastern shore of Lake Tanganyika , Tanzania . This is the eastern extreme of the range of the martinii type , and it is plausible that the martinii found to the West of Lake Tanganyika may be highly diverged from the specimens examined in this study . These unsampled populations will not change the conclusions of this study about the polyphyletic nature of the morphologically defined G . f . fuscipes and G . f . quanzensis . However , the dearth of samples from parts of the species range means that it would be premature to put forward a replacement for Machado's morphological subspecies theory . We observed possible introgression between Lake Victoria Basin and Tanzanian flies: although we observed no shared mtDNA haplotypes between Tanzanian and LVB flies , the level of sequence divergence of as little as 2 substitutions ( 0 . 26% ) between haplotypes is much less than would be expected from the high level of divergence observed at nuclear loci . This could indicate an introgression event that has later been obscured by genetic drift , or insufficient sampling of Tanzanian haplotypes . If the high similarity in maternally inherited but not biparentally inherited markers between the Lake Victoria Basin and martinii is due to introgression , it remains to be determined whether more westerly distributed martinii populations have been affected in the same way . Future studies of G . fuscipes genetic structure focussing in DRC , especially at the boundaries between the ranges of the morphological subspecies proposed by Machado will be essential to finally answer the question of fuscipes subspecies interrelationships . Since the mtDNA and Wigglesworthia DNA have been subject to at least one possible introgression event , nuclear microsatellites would be the most appropriate markers for this type of study , provided that sufficient numbers of unlinked polymorphic microsatellites can be found that amplify well in the more divergent and unstudied fuscipes populations . Using molecular clock calculations on COI data , the most ancient divergence in the fuscipes group occurred between central DRC and all other G . fuscipes between 0 . 8 and 1 . 2 million years ago . This is much more recent than the split between G . p . palpalis and G . p . gambiensis which occurred between 4 . 2-2 . 2 Mya according to the same divergence rate of 1 . 5% per million years [63] . If behavioural divergence is correlated with genetic divergence within the Glossina then this relatively low level of differentiation may mean that similar control measures may be successful across the range so far studied . However , this conclusion must be viewed cautiously with respect to vector control , as it is already known that flies from Kinshasa and Kenya show different behaviours with respect to pig odour [12] , and large tracts of G . fuscipes habitat in the DRC have not yet been studied . Therefore , it is advisable that any populations showing the same level of divergence as that observed between West DRC and Kenyan populations ( Microsatellite null allele corrected FST = 0 . 26 , 95% CIs = 0 . 17–0 . 36 ) may need to be tested separately for the efficacy of control measures . We would recommend that of the G . fuscipes populations studied so far , flies from Ethiopia and northern Uganda , Central DRC and morphological G . f . martinii ( Tanzania ) may require separate testing . | Glossina fuscipes s . l . tsetse flies are responsible for transmission of approximately 90% of the cases of Human African Typanosomiasis in Sub Saharan Africa . It was previously proposed on the basis of morphology that G . fuscipes is composed of three sub-species . Using genetic evidence from G . fuscipes nuclear , mitochondrial and symbiont DNA , we show that the morphological subspecies do not correspond well to genetic differences between the flies and morphologically similar flies may have arisen more than once in the evolution of this species . Instead , we found at least 5 main allopatrically distributed groups of G . fuscipes flies . The most genetically distinct group of flies originated from Ethiopia , where a sterile insect release programme is planned . Given that tsetse control often exploits species-specific behaviours there is a pressing need to establish the taxonomic status and ranges of these five groups . Moreover given that we were only able to perform limited sampling in many parts of the species distribution further groups within G . fuscipes are likely to be awaiting discovery . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"taxonomy",
"medicine",
"infectious",
"diseases",
"african",
"trypanosomiasis",
"molecular",
"systematics",
"neglected",
"tropical",
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"biology",
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"biology",
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] | 2011 | Cryptic Diversity within the Major Trypanosomiasis Vector Glossina fuscipes Revealed by Molecular Markers |
Aspergillus fumigatus is responsible for a disproportionate number of invasive mycosis cases relative to other common filamentous fungi . While many fungal factors critical for infection establishment are known , genes essential for disease persistence and progression are ill defined . We propose that fungal factors that promote navigation of the rapidly changing nutrient and structural landscape characteristic of disease progression represent untapped clinically relevant therapeutic targets . To this end , we find that A . fumigatus requires a carbon catabolite repression ( CCR ) mediated genetic network to support in vivo fungal fitness and disease progression . While CCR as mediated by the transcriptional repressor CreA is not required for pulmonary infection establishment , loss of CCR inhibits fungal metabolic plasticity and the ability to thrive in the dynamic infection microenvironment . Our results suggest a model whereby CCR in an environmental filamentous fungus is dispensable for initiation of pulmonary infection but essential for infection maintenance and disease progression . Conceptually , we argue these data provide a foundation for additional studies on fungal factors required to support fungal fitness and disease progression and term such genes and factors , DPFs ( disease progression factors ) .
Microbial pathogenesis is a complex , multifactorial process in which interactions between a microbe and host shape disease development and outcome [1 , 2] . While many studies have focused on defining the molecular determinants required for fungal pathogenesis , molecular pathogenesis studies often fail to address the spatial and temporal dynamics of an infection . These dynamics are the result of local changes in nutrients , stressors , and even substrate phase ( solid tissue versus liquid environments ) that occur over the course of a host-microbe interaction . How fungi navigate these rapidly changing infection dynamics to promote disease is largely unknown and difficult to study with traditional molecular pathogenesis approaches . However , identifying fungal and host factors essential for infection maintenance and disease progression may yield novel and potent therapeutic targets and approaches . With regard to invasive pulmonary aspergillosis ( IPA ) , initiation of disease caused by A . fumigatus involves conidial deposition into the airways , conidia germination , and subsequent hyphal extension into the lung parenchyma that induces host damage as a result of both fungal growth and inflammation . Many fungal genes and attributes have been identified to be associated with virulence through robust animal model studies [3 , 4] . However , patients are typically diagnosed after this initiation phase of the infection when invasion of the lung parenchyma and even vasculature has already occurred . Consequently , it is unclear if fungal virulence factors and attributes identified through animal model virulence studies are relevant at these later stages of the host-pathogen interaction that must be targeted by clinically relevant therapeutics . It has long been hypothesized that metabolic flexibility significantly contributes to A . fumigatus virulence; though specific tests of this hypothesis are difficult to achieve due to genetic redundancy in the fungus and the largely uncharacterized dynamic infection microenvironments [5–8] . While the role of several core metabolic pathways has been largely studied in the context of infection initiation ( reviewed in [8 , 9] ) , the carbon metabolism regulatory mechanisms required for fungal proliferation within the host as the infection and disease progresses are ill defined . Previously , we observed that established infection microenvironments in murine models of invasive pulmonary aspergillosis ( IPA ) are depleted in oxygen and contain fungal metabolic by-products such as ethanol [10] . This result was surprising given that A . fumigatus and related filamentous fungi are obligate aerobes that use glucose as the preferred carbon source and raises important questions about fungal metabolism and bioenergetics throughout the course of an infection . These observations led us to question whether ill-defined mechanisms of A . fumigatus infection metabolism dynamics are critical for in vivo fungal persistence , virulence , and ultimately disease progression . One mechanism widely employed by microbes to regulate and optimize nutrient acquisition and metabolism is carbon catabolite repression ( CCR ) [11–13] . Transcriptional regulation of CCR controls central carbon metabolism in many microorganisms and allows prioritization of preferential carbon source usage to yield maximum energy and fitness . The role of CCR in microbial pathogenesis is established in bacteria , and in many species this system plays a role in virulence ( reviewed in [14] ) . For example , in Streptococcus pyogenes , the transcriptional regulator CcpA is required for both colonization of the nasopharynx and virulence in invasive disease [15] . Intriguingly , transcriptional regulation of CCR seems dispensable for virulence in human pathogenic yeast based on host survival outcomes . Mig1 , the CCR transcriptional regulator in Candida albicans , is dispensable for virulence in a systemic murine infection model of candidiasis [16] . Similarly , a mig1-null mutant in the basidiomycete yeast Cryptococcus neoformans causes wild type levels of disease in a murine inhalation model with no effect on growth within the host though an undefined role for Mig1 in macrophage-yeast interactions was suggested [17] . Yet , given our and other’s previous in vivo observations in murine models of IPA , we questioned whether CCR would be dispensable for virulence in an environmental filamentous fungus capable of extreme metabolic flexibility . To test this hypothesis , we generated a genetic null mutant of the predicted A . fumigatus CCR transcriptional repressor , CreA . Interrogation of this genetic null mutant in vivo and in vitro supports the hypothesis that CreA is a CCR transcriptional repressor in A . fumigatus and surprisingly revealed a crucial role for this system in established infection microenvironments . Our data suggest a model whereby a clinically relevant steroid treatment promotes release of alternative de-repressing carbon sources utilized by A . fumigatus through CreA independent mechanisms during infection initiation . However , for the infection to proceed and cause life-threatening host damage , dynamic spatial temporal changes in nutrient and oxygen availability at the site of infection drive a requirement for CreA activity in A . fumigatus . Taken together , our data emphasize the critical importance of spatial temporal dynamics in fungal-host interactions and reveal a novel role for fungal CCR in navigation of infection dynamics and disease progression . We propose that CreA and CCR represent a new class of fungal virulence factors with significant clinical relevance we term DPFs for disease progression factors .
Previously , we observed that oxygen levels within pulmonary fungal lesions are dynamic with oxygen tensions reaching ~1 . 5% or less by 3 days post inoculation concomitant with detection of ethanol in these animals [10] . Thus , we posited that in the face of this changing nutrient environment , A . fumigatus must adjust its metabolism to persist and cause additional host damage . To explore this hypothesis , we identified a predicted ortholog of the master fungal CCR transcriptional regulator , CreA , in Aspergillus fumigatus . A BLASTP search using the Aspergillus nidulans CreA protein sequence as a query against the A . fumigatus A1163 proteome reveled one homolog of CreA in the A . fumigatus genome ( 78% identity; e-value = 4e-175 ) , encoded by the gene AFUB_027530 . A protein alignment of AfCreA with homologs from A . nidulans ( Anid_CreA ) , C . albicans ( Calb_MIG1; 32% identity ) , Cryptococcus neoformans ( Cneo_MIG1; 32% identity ) , Trichoderma reesei ( Trees_Cre1; 56% identity ) and Saccharomyces cerevisiae ( Scere_MIG1; 29% identity ) , reveals little conservation outside of the zinc-finger domains of these 6 CCR transcriptional regulator homologs ( S1 Fig ) . Outside of this region , there is large divergence in the amino acid sequences among these proteins , both in sequence and total length that may suggest the existence of functional differences across species . To study the role of this regulator in A . fumigatus , we generated genetic null mutant and reconstituted strains of creA ( ΔcreA and creAR respectively; S2 Fig ) . To determine whether CreA controls CCR in A . fumigatus , we used allyl alcohol as a measure of glucose repression of alcohol dehydrogenases that are well characterized targets of the CCR system in A . nidulans [18 , 19] . Allyl alcohol is metabolized by the alcA/aldA gene cluster [20 , 21] and broken down into the toxic byproduct , acrolein . When grown on 1% glucose media in the presence of 0 . 1% allyl alcohol the wild type strain shows a 27% inhibition in radial growth . However , under the same conditions ΔcreA fails to germinate resulting in 100% growth inhibition ( Fig 1A ) . Re-introduction of creA ( creAR ) restored growth on glucose in the presence of allyl alcohol to wild type levels . As expected , growth of all strains on 1% lactate with 0 . 1% allyl alcohol was inhibited 100% ( S3A Fig ) , supporting the conclusion that de-repression of glucose-repressed alcohol dehydrogenases is responsible for growth inhibition on allyl alcohol . We also tested the growth of wild type and ΔcreA on media containing the glucose analog 2-deoxyglucose ( 2-DG ) . While the wild type , ΔcreA , and creAR strains are not impaired on 1% glucose media with 0 . 1% 2-DG , the wild type and creAR strains are partially inhibited on 1% ethanol or 1% lactate with 0 . 1% 2-DG . However , ΔcreA growth is significantly less inhibited than the wild type strain , further supporting the conclusion that CreA controls CCR in A . fumigatus ( S3B Fig ) . Collectively , these results support regulation of CCR by CreA in A . fumigatus . As expected for a transcriptional regulator of CCR , loss of creA results in de-repression of ethanol utilization genes as observed with increased allyl alcohol sensitivity ( Fig 1A ) . Given this role in CCR we tested growth of ΔcreA on several carbon and nitrogen sources . We observed that fitness of ΔcreA on solid media is reduced on both repressing ( glucose , acetate ) and de-repressing carbon sources ( ethanol , lactate ) , as well as on a variety of rich ( glutamine , ammonium ) and poor nitrogen sources ( nitrate , urea ) with glucose as a carbon source ( Fig 1B; S4A Fig ) . This result contrasts with CreA homologs in other environmental but non-human pathogenic fungi , such as CRE1 of T . reesei and Cre-1 of Neurospora crassa , where growth defects of the respective CCR mutant are only apparent on particular carbon sources [22 , 23] . We also observed comparable growth defects on ethanol , lactate , acetate , and glycerol when glutamine was supplied as the nitrogen source ( S4A Fig ) and on complete media ( S4B Fig ) . Re-introduction of the creA coding sequence ( creAR ) fully restored fitness of ΔcreA on all tested conditions . Taken together , these data suggest that CreA is critical for A . fumigatus fitness on solid substrates in both repressing and de-repressing conditions . Given the role of CreA in CCR and growth in vitro , we hypothesized that this transcription factor would be critical for virulence . To test the contribution of CreA to A . fumigatus virulence , we used a triamcinolone-induced immune suppression murine model of IPA [24 , 25] . Surprisingly , given the in vitro phenotypes of CreA loss , after 2 days post-inoculation , mice inoculated with the wild type , ΔcreA , or creAR strains all exhibited symptoms of A . fumigatus infection . In fact , on day 3 post-inoculation the mortality between all 3 groups was essentially identical with 50% of mice having succumbed to the fungal challenge irrespective of the presence of CreA and a functioning CCR system ( Fig 2A ) . One potential hypothesis to explain this result is a difference in immunopathogenesis in the presence and absence of CreA . However , no obvious qualitative difference in the inflammatory response was observed between each strain with histopathology . Inflammation observed in all animals by H&E staining of sectioned lung tissue collected 48 hours post inoculation ( hpi ) was mainly centered upon major airways with extension into the surrounding alveolar parenchyma ( Fig 2B ) . In support of the qualitative data , we observed no significant differences in expression of TNF-α , IL-10 and CXCL1 between mice inoculated with wild type or ΔcreA conidia , with the exception of increased CXCL1 in response to WT at 72 hpi , a result driven by two animals that had much greater cytokine expression ( S5A Fig ) , indicating that the immune response to each strain is similar . Consistent with the survival curve and observed similar levels of inflammation , Gomori methenamine silver ( GMS ) staining indicated that the fungal burden of mice inoculated with wild type and ΔcreA was indeed similar . Moreover , the amount of tissue involvement in mice inoculated with ΔcreA both in terms of lesion size and frequency was similar to wild type ( Fig 2B ) . The histopathology data is supported by quantitative measurement of fungal burden using quantification of 18S rDNA 48 hpi that showed no significant difference between wild type and ΔcreA inoculated mice ( Fig 2C ) . Similarly , we found that ΔcreA showed no defect in germination at 8 hpi in vivo , which further suggests that there is no defect in initiation of infection in the absence of CreA ( Fig 2D ) . These data suggest that the initial in vivo airway microenvironment complements the loss of CreA in A . fumigatus and therefore CCR as mediated by CreA is not necessary for establishment of infection and disease in the lung . To test whether the in vivo nutrient environment could complement the loss of CreA as suggested by the in vivo data , we tested the growth of ΔcreA in liquid lung homogenate and observed that growth was similar to the wild type strain ( Fig 2E ) . These data suggest the nutrients available within the steroid treated lung prior to initiation of infection and inflammation are adequate to support the germination and growth of ΔcreA . At day 4 post inoculation a marked difference in the infection course emerged between animals inoculated with ΔcreA compared to the wild type and reconstituted strains . Ninety percent of mice inoculated with the wild-type and reconstituted strain succumbed to the infection at this time point while mice inoculated with ΔcreA had a significant increase in survival compared to wild type ( p = 0 . 0217 ) and creAR ( p = 0 . 0425 ) that extended out to 14 dpi ( Fig 2A ) . Histopathological analysis of the lungs from the surviving animals inoculated with ΔcreA show persistent fungal abscesses that are walled off by a thick layer of immune cells that consisted predominantly of neutrophils ( S5B Fig ) . Taken together , these data indicate that transcriptional regulation of filamentous fungal CCR is dispensable for early growth and initiation of disease but essential for virulence and disease progression after infection establishment . Despite the ability of ΔcreA to establish infection , the observed temporal virulence defect prompted us to examine the in vitro growth phenotypes of this strain more closely to understand underlying mechanisms . In contrast to growth on solid media , ΔcreA shows a partial growth defect in repressing carbon source liquid media , but not under liquid de-repressing conditions . To test growth initiation in liquid conditions that may better reflect the early infection air-surface liquid interface in the lung , we measured absorbance of cultures grown in 1% glucose , 1% lactate and 1% Tween-80 ( Fig 3A , 3B and 3C ) and calculated the change in absorbance over time ( ΔAbs405/min ) . In 1% glucose , the ΔAbs405/min of ΔcreA is significantly lower than the wild type and creAR . However , under carbon source de-repressing conditions ( Tween-80 , lactate ) , the ΔAbs405/min of ΔcreA is strikingly similar to the wild type and creAR strains ( Fig 3D ) . These results suggest that the initial host environment encountered by A . fumigatus is enriched for de-repressing carbon sources that promote fungal growth in the absence of transcriptional CCR regulation . In support of this hypothesis , a UPLC-MS/MS based steady-state metabolite profile of whole murine lungs of steroid-treated mice versus healthy controls revealed a significant increase in alternative carbon and nitrogen sources upon steroid treatment ( Fig 4 ) . Of 630 detected metabolites , 242 metabolites were significantly ( p≤0 . 05 ) altered in the triamcinolone-treated lungs compared to healthy , untreated controls ( Fig 4A , S1 File ) . Some key findings from this comparison include a significant decrease in glucose ( 0 . 71-fold change; p = 0 . 0042; Fig 4B ) and glutamate ( 0 . 77-fold change; p = 0 . 0133; Fig 4C ) , preferred carbon and nitrogen sources , respectively , for A . fumigatus , and a concomitant increase in amino acids ( leucine , asparagine , valine , methionine , alanine , glutamine , threonine , proline and 5-oxoproline ) , long chain fatty acids ( myristate , arachidate , maragarate , oelate; Fig 4D and 4E ) , and the alternative nitrogen source , urea . While the steroid treated lungs represent the lung environment first encountered by conidia upon inhalation , we also hypothesized that the environment would change rapidly upon fungal inoculation due to the host immune response . Thus , we also measured the steady-state metabolite profile of steroid-treated animals inoculated with wild type fungal conidia . At 8 hours post inoculation , 168 of 630 detected metabolites significantly ( p<0 . 05 ) changed between steroid-treated un-inoculated lungs and steroid-treated fungal-inoculated lungs ( Fig 4A ) . While many changes induced by steroid treatment were reversed by inoculation with fungus , glucose remained significantly lower than naïve lungs , and alternative carbon sources such as fatty acids , remained significantly increased ( Fig 4B–4D , S1 File ) . Overall , the nutrient landscape of the lungs at the time of inoculation and during initiation of fungal growth is enriched with alternative carbon sources . These data support the hypothesis that the host environment is complex and dynamic and support the observation that filamentous fungal CCR is dispensable for fungal fitness early in lung infection . To gain a deeper understanding of the function of CreA under repressing and de-repressing conditions we used an RNA-Sequencing based approach . Wild type and ΔcreA were cultured in 1% liquid glucose minimal media ( GMM ) overnight , then shifted to fresh 1% glucose ( repressing ) or 1% ethanol ( de-repressing ) minimal media ( EMM ) for 2 hours before sample preparation for RNA-sequencing . Importantly , this time point was chosen because growth rate is similar between strains ( S4C Fig ) . Moreover , our previous in vivo metabolomics of an IPA murine model revealed significant ethanol levels in the lungs suggesting this alcohol is a potential carbon source seen by A . fumigatus [10] . In the wild-type strain in de-repressing conditions ( EMM ) , the abundance of 860 transcripts significantly ( p<0 . 05 ) increased and the abundance of 741 transcripts significantly decreased by at least two-fold ( S1 File ) . As expected , we observed an increase in transcripts of genes involved in the utilization of alternative carbon sources , including the glyoxylate cycle , gluconeogenesis , succinate-fumarate antiporter , aldehyde dehydrogenase and acetyl-coA synthase encoding genes . The induction of these gene indicates generation of anabolic intermediates through gluconeogenesis and subsequent energy production through respiration . As expected , transcript levels of the ethanol utilization alcohol dehydrogenase , alcA , increased significantly in EMM , while transcript levels of the fermentation associated alcohol dehydrogenase , alcC , decreased [10] . In glucose , loss of CreA resulted in a significant reduction ( ≥ 2 fold ) in transcript levels of 450 genes ( p<0 . 05 ) and significant increase in transcript levels of 402 genes ( S1 File ) . We verified transcript levels of a subset of genes with qRT-PCR and found that patterns of transcript levels between wild type and ΔcreA in both glucose and ethanol conditions were consistent between RNA-sequencing and qRT-PCR results ( S6 Fig ) . We applied FunCat analysis to the genes that significantly increase and decrease in ΔcreA compared to wild type genes using FungiFun2 ( S7 Fig ) [26] . The categories of increased genes include a large number with predicted functions in transport of metabolites , carbohydrate metabolism , and secondary metabolism . Included in these genes are the alcohol utilization genes alcA and alcS , the glyoxylate cycle enzymes , isocitrate lyase , acuD , and malate synthase , acuE , as well as acuF , which encodes PEP carboxykinase , and fbp1 , encoding fructose bisphosphatase aldolase , the rate-limiting steps of gluconeogenesis . Consistent with a role of CreA in regulation of CCR , this transcriptome profile of ΔcreA suggests that the glyoxylate cycle and gluconeogenesis are active in this strain despite abundant glucose in the environment . The representative categories of decreased genes also include secondary metabolism and c-compound and carbohydrate metabolism in addition to degradation of amino acids . Genes that changed significantly in 1% ethanol between ΔcreA and wild type were assigned to similar FunCat categories ( S1 File; S7 Fig ) . These data demonstrating significant reductions in mRNA levels in many genes in the absence of CreA may also suggest that CreA has additional roles as a transcriptional activator , but further analyses are required to test this hypothesis . Consequently , these data suggest that loss of creA in an environment rich in de-repressing carbon sources such as the steroid treated airway would likely be dispensable for fungal fitness . To further explore the mechanism underlying the observed in vitro growth defects of CreA loss we turned to a metabolomics approach . Global steady-state metabolomics analysis of ΔcreA mycelia compared to wild-type revealed striking alterations in glucose metabolism in ΔcreA ( Fig 5A; S1 File ) . We observed a significant increase ( 5 . 31-fold ) in the amount of intracellular glucose in ΔcreA , however no significant changes in metabolites of the early steps of glycolysis ( glucose-6-phoshate and fructose-6-phosphate ) were observed . In contrast , metabolites of the late steps in glycolysis , including the sugar-phosphates 3-phosphoglycerate ( 3-PG ) , 2-phosphoglycerate ( 2-PG ) , and phosphenolpyruvate ( PEP ) , were significantly increased in ΔcreA . In addition , we observed significant changes in tricarboxylic acid ( TCA ) cycle intermediates . The oxidative portion of the cycle shows significantly altered metabolite levels with citrate , isocitrate and aconitate decreased in the absence of CreA . However , fumarate and malate significantly increase in ΔcreA compared to wild type ( Fig 5A ) . This pattern of changes in the TCA cycle is consistent with over-expression of isocitrate lyase in A . niger [27] , suggesting that the glyoxylate cycle is active in ΔcreA despite the presence of glucose . Consistent with this observation , transcript levels of both the predicted isocitrate lyase ( acuD; 4 . 7-fold ) and malate synthase ( acuE; 2 . 39-fold ) in the glucose-grown ΔcreA are significantly increased compared to wild type . Furthermore , transcripts of key gluconeogenic enzymes , PEP carboxykinase ( acuF; 4 . 53-fold ) and fructose bisphosphatase ( fbp1; 3 . 2-fold ) are increased in ΔcreA grown in glucose , suggesting that carbon is being fluxed from the TCA cycle , through the glyoxylate shunt and fed into gluconeogenesis . The over-lap of transcriptomics and metabolomics data sets support the conclusion that CreA is a central regulator of fungal bioenergetics . Given the significant impact of CreA loss on carbon metabolism , we hypothesized that the polysaccharide rich cell wall , closely associated with virulence , would be significantly altered in ΔcreA . In support of this hypothesis , we observed that ΔcreA is more sensitive to the cell wall perturbing agents calcoflour white ( CFW ) , congo red ( CR ) and to a lesser extent caspofungin ( CF ) when on a glucose containing medium , indicating that cell wall integrity of ΔcreA is perturbed in these conditions ( Fig 6 ) . To test whether changes in transcript levels of these metabolic enzymes at the time of harvest for RNA-sequencing are a result of true dysregulation or a product of a shift in kinetics of expression following the shift to fresh glucose media , we examined transcript levels of acuF over two hours following the shift to fresh glucose . Consistent with perturbation in regulation of these enzymes , we observed transcripts of the gene encoding this enzyme are significantly higher than wild type at nearly all time-points ( Fig 5B ) . Thus , we conclude that loss of CreA results in dysregulation of genes encoding glyoxylate shunt and gluconeogenic enzymes . Moreover , transcript levels of a putative fructose bisphosphate aldolase are 12 . 72-fold reduced in ΔcreA , rendering transcript levels of this enzyme nearly absent . We conclude from these data that carbon is being moved through the glyoxylate shunt back into gluconeogenesis despite the already high levels of glucose intracellularly . Consequently , the lack of an FBP-aldolase generates a block in glycolysis/gluconeogenesis , which is responsible for the accumulation of the sugar-phosphate intermediates 3-PG , 2-PG and PEP . The accumulation of these intermediates not only traps cellular carbon , but also lowers available phosphate for cellular bioenergetics and fitness . In support of this hypothesis , we observed a 5 . 79-fold increase in the ADP/ATP ratio in ΔcreA as compared to wild type ( Fig 5C ) . From our metabolomics data , we noted a 5 . 67-fold increase in the amount of AMP present in ΔcreA compared to wild type ( S1 File ) . Together , both the increase in AMP and ADP/ATP ratio suggest a defect in mitochondrial output resulting in insufficient cellular bioenergetics . In further support of these data , ΔcreA is significantly ( p<0 . 0001 ) more sensitive to the respiratory inhibitors targeting complex II ( thenoyltrifluoroacetone; TTFA ) , complex III ( Antimycin A ) , complex V ( Oligomycin A ) and the alternative oxidase ( Salicylhydroxamic acid; SHAM ) when grown on both 1% glucose and 1% ethanol minimal media ( S8 Fig ) . Overall , transcriptomics and metabolomics data suggest that CreA is critical for fungal fitness in environments with repressing carbon sources such as glucose . With the observed increase in alternative carbon sources upon steroid treatment , we sought to test the hypothesis that reduced repressing carbon sources early during A . fumigatus host interaction results in de-repression of CreA-regulated genes . We extracted RNA from triamcinolone treated mice inoculated with fungal conidia 24 and 72 hpi . In support of the model , the CreA-regulated genes , acuD and acuF , do not increase over time from 24 to 72 hours and the trend was for higher levels early in the infection consistent with increased levels of alternative carbon sources ( Fig 7A and 7B ) . In contrast , the transcript levels of acuF and acuD , are significantly increased in ΔcreA compared to wild type at 24 and 72 hpi , which strongly supports the conclusion that these CreA-target genes identified in our RNA-sequencing data are regulated in part by CreA in vivo ( Fig 7A and 7D ) . To further test our model , we measured the transcript levels of genes expressed in response to severe oxygen depletion , a known characteristic of fungal lesions in the triamcinolone model of IPA [10] . These genes , including the pyruvate decarboxylase , pdcA , and alcohol dehydrogenase alcC , involved in ethanol production , are significantly increased from 24 hpi to 72 hpi in both wild type and ΔcreA ( Fig 7C and 7D ) . This increase in hypoxia responsive genes led us to hypothesize that SrbA and SrbB activity , known regulators of the hypoxia response and carbohydrate metabolism in A . fumigatus [28 , 29] , increases over the course of infection . However , we did not observe any change in srbA mRNA levels from 24 to 72 hpi in the wild type strain perhaps consistent with the absolute requirement of SrbA for A . fumigatus virulence ( Fig 7E ) . Strikingly , we observed a significant increase in the mRNA levels of srbA and srbB over the course of the ΔcreA infection ( Fig 7E and 7F ) . We interpret these data to suggest that ΔcreA experiences increased reductive stress due to an inability to engage the fungal hypoxia response . Taken together , these data suggest that CreA is critical for maintaining fitness in the low-oxygen environment of established fungal lesions in the lung . To test this hypothesis , we measured the growth rate of point inoculated colonies that were germinated in normoxia for 24 hours and then shifted to low oxygen ( hypoxia inducing ) environment for 96 hours . The ratio of colony growth in hypoxia to normoxia was significantly higher for ΔcreA compared to wild type and creAR at the first day post-shift to hypoxia , however , after 48 hours , hypoxia/normoxia growth ratio of ΔcreA was dramatically lower than wild type , and continued to decrease over time ( Fig 8A ) , which indicates that increased exposure to low oxygen conditions in the presence of a repressing carbon source results in a strong reduction in ΔcreA growth . Based on our model of CreA interactions with the host and the inability to adapt to the severe low oxygen conditions of the host environment , we tested the susceptibility of ΔcreA to killing by neutrophils . We grew wild type , ΔcreA and creAR germlings in both repressing ( glucose ) and de-repressing ( Tween-80 ) conditions , then shifted cultures to a low oxygen environment and added bone marrow derived neutrophils . After 2 hours of incubation with neutrophils , fungal damage was measured by XTT . While we observed no significant difference between damage of wild type and ΔcreA germlings , or germlings grown in repressing versus de-repressing conditions , all strains , including ΔcreA , are very sensitive to neutrophil damage under these conditions ( Fig 8C ) . As our data suggest infection site hypoxia plays an important role in disease progression and a requirement for CreA activity , we utilized the observation that loss of leukocytes reduces the severity of oxygen depletion at the site of fungal infection [10] . Consistent with our data , in this chemotherapy model of IPA , where mice are largely leukopenic , inoculation with ΔcreA results in 100% mortality by 4 dpi , while wild type results in 100% mortality by 3 dpi ( Fig 8B ) . Therefore , in a host environment with reduced hypoxic and immune cell mediated stress , ΔcreA is more fit , and produces mortality with near wild type kinetics . Together , these data suggest that fungal cells are responding to the infection site microenvironment in part through CreA activation which is essential for supporting fungal bioenergetics in the face of oxygen depletion , the dynamic nutrient environment and host immune cell interactions . Therefore , we conclude that CreA represents a novel class of fungal virulence attributes that are dispensable for the initiation of infection but required in established infection microenvironments , which we term disease progression factors ( DPFs ) . Further interrogation of CreA direct genes is thus likely yield additional DPFs in future studies . Spatial temporal mechanisms of filamentous fungal pathogenesis are under studied . These mechanisms have underappreciated clinical relevance as current antimicrobial therapies largely must thwart pathogens in complex established infection microenvironments to ameliorate disease progression . Yet , in animal models of invasive fungal infections , an emphasis is placed on survival curves with fungal mutants that exhibit marked virulence attenuation from the initiation of infection . Consequently , it is unclear to what extent fungal virulence factors identified from animal model studies mediate infection maintenance and disease progression . In this regard , discovery and critical analysis of regulatory mechanisms essential for fungal fitness and persistence after the initiation of infection and host damage is a promising approach to identify new virulence factors and consequently new and perhaps more clinically relevant drug targets . Here , we propose that metabolic flexibility allows an environmental microbe , A . fumigatus , to navigate infection site dynamics and allow disease progression . Our model proposes that during an invasive fungal infection there are at least two distinct fungal metabolic phases driven by changes in nutrient and oxygen availability . For full pathogenic potential and disease progression , cells must undergo metabolic reprogramming to adapt to the changing microenvironments to support proliferation and continued host damage . Our model is not the first to consider this type of adaptation to changing infection dynamics . Saville et al . ( 2003 , [30] ) used an engineered C . albicans strain to show that yeast-locked cells could initiate infection , as evidenced by the ability to disseminate to target organs , however , the transition to hyphal morphology was required to cause disease , as measured by murine survival . This study elegantly demonstrates that distinct fungal morphologies are required during different stages of infection and that a morphological transition is essential for C . albicans virulence . Our data suggest that early in infection , the steroid treated airway microenvironment contains sufficient gluconeogenic carbon sources and oxygen for fungal conidia germination and growth ( Fig 9 ) . In this environment , CCR as mediated by CreA is dispensable . As the infection and disease progresses , carbon source and oxygen availability changes drive the fungal response toward a more glycolytic and hypoxia based metabolism . In this established infection microenvironment , CCR as mediated by CreA significantly contributes to virulence and disease progression . CreA contributes to disease by modulating carbon metabolism and loss of this regulator results in persistent activity of the glyoxylate shunt , which causes metabolic dysregulation in the presences of repressing carbon sources [31] . Importantly , the creA-mutant is unable to thrive in the low oxygen environment within the host , as demonstrated through the increase in mRNA levels of the hypoxia response transcription factors , SrbA and SrbB , and the decrease in growth rate upon shift to hypoxia . A critical consequence of hypoxia in eukaryotic cells is the increase in reducing equivalents . Consequently , the increase in these hypoxia associated gene transcript levels in vivo in the absence of CreA strongly suggests a role for CreA in mitigating reductive stress that occurs in these conditions . Although the signals for SREBP activation in mammalian systems are well characterized and depend on cellular sterol levels sensed by the sterol sensor , SCAP [32] , SREBP activation mechanism ( s ) in A . fumigatus remain unknown [29 , 33] . The lack of a SCAP homolog in A . fumigatus , suggests a distinct mechanism of activation from mammals . An intriguing possibility is regulation of this transcriptional network in response to carbohydrate metabolism and cellular redox status . Support for this hypothesis comes from the observed role of SrbA and SrbB in carbohydrate metabolism and the tight connections between hypoxia and central carbon metabolism in many organisms [34 , 35] . Notably , CreA homologs are not required for virulence in the pathogenic yeasts C . albicans and C . neoformans as measured by murine mortality [16 , 17] . There are several potential reasons for the contrasting results between the yeast systems and our results which highlight differences between the biology and pathogenesis of yeasts and filamentous fungi . First , the models used to test the virulence of the C . albicans and C . neoformans mig1-null mutants are significantly different than IPA models . Neither yeast study uses a model that involves the use of corticosteroid immune suppression , which we observed to dramatically alter the metabolite and oxygen landscape of the lungs . Furthermore , C . albicans murine models often use tail vein delivery of inocula , where yeasts enter the blood , an environment that is very different from the airways in terms of available nutrients and structural landscapes . In particular , the airways contain far less glucose compared to the bloodstream [36–38] . Beyond the models utilized in each study , some observed phenotypes between yeast Mig1 mutants and our CreA mutant are also different , underscoring differences in basal metabolism between these fungi . In C . albicans , the loss of Mig1 does not affect growth rate in glucose , galactose or glycerol containing media [16] and similarly , growth of C . neoformans Δmig1 shows wild type growth on solid YPD and YES media [17] . Thus , although some observed stress phenotypes between A . fumigatus and C . neoformans are similar , the loss of Mig1/CreA has different effects on fungal metabolism across species . For example , in some filamentous fungi , loss of CCR leads to lethality . For example , clean full gene replacements of the CCR transcriptional regulator CreA has not been possible in Fusarium oxysporum [39] , Penicillium chrysogenum [40] and Colletotrichum gloeosporoides [41] and results in extremely severe growth defects in Aspergillus nidulans [42 , 43] . Consequently , the ability to generate a creA null mutant in A . fumigatus and its subsequent phenotypes on de-repressing carbon sources further highlights the diversity of metabolism and bioenergetics regulation in fungi . Whether yeast and other filamentous fungal CCR homologs are DPFs would require additional experiments including assessment of disease in relevant immune compromised animal models . Although CreA homologs have been shown to be dispensable for virulence in pathogenic yeasts [16 , 17] , alternative regulation of metabolism through catabolite inactivation , the proteolytic degradation of metabolic enzymes in response to glucose , is important for growth of C . albicans in vivo . C . albicans isocitrate lyase ( ICL ) is subject to transcriptional regulation in response to glucose [44] , however , contrary to S . cerevisiae , ICL is not subject to catabolite inactivation , allowing simultaneous assimilation of glucose and lactate [45] . Consequently , the ability to simultaneously assimilate multiple carbon sources results in higher fungal burden in kidneys during disseminated infection and in feces and kidneys in a gastrointestinal colonization model [46] . Intriguingly , our results suggest that like C . albicans , A . fumigatus can integrate both lactate and glucose simultaneously , as inferred from the ability of A . fumigatus to grow on lactate or ethanol in the presence of 2-DG , suggesting that this fungus is metabolically plastic , an attribute which likely contributes to spatial temporal fitness within the host and ultimately disease maintenance and progression . Therefore , a further understanding of the divergence of the CCR pathway across fungi can yield insights into the mechanistic differences of pathogenicity and virulence across pathogenic fungi for a better understanding of how to treat these infections . Lastly , we argue that further studies to identify DPFs in pathogenic microbes will identify novel therapeutic targets distinct from existing virulence factors . Though existing virulence factors may certainly be critical for disease progression throughout the course of infection , our data strongly suggest additional factors that may not always be evident from traditional animal model survival curve studies exist and remain to be identified . Moreover , if the metabolic state of the fungus or microbe can be precisely identified during disease progression , in vitro systems that induce the same metabolic state are expected to enhance antifungal drug discovery . Moving forward pathogenesis studies should consider the spatial temporal dynamics of infections and leverage developing tools to probe fungal and microbial gene function in established infection microenvironments in vivo for high impact therapeutic target identification [47 , 48] .
A . fumigatus CEA17 was used to generate ΔcreA . CEA17 is a uracil/uridine auxotrophic mutant of CEA10 , therefore , CEA10 was used as the wild type control for all experiments . Strains were stored as conidia in 50% glycerol at -80°C and maintained on 1% glucose minimal media ( GMM; 6g/L NaNO3 , 0 . 52g/L KCL , 0 . 52g/L MgSO4•7H2O , 1 . 52g/L KH2PO4 monobasic , 2 . 2mg/L ZnSO4•7H20 , 1 . 1mg/L H3BO3 , 0 . 5mg/L MnCl2•4H2O , 0 . 5mg/L FeSO4•7H2O , 0 . 16mg/L CoCl2•5H2O , 0 . 16mg/L CuSO4•5H2O , 0 . 11mg/L ( NH4 ) 6Mo7O24•4H2O , 5mg/L Na4EDTA , 1% glucose; pH 6 . 5 ) . All growth assays use the above minimal media with indicated carbon and nitrogen sources; for experiments with alternative nitrogen sources , NaNO3 and ( NH4 ) 6Mo7O24•4H2O are omitted . Solid media was prepared with addition on 1 . 5% agar ( unless otherwise noted ) before autoclaving . For all assays , conidia were grown on solid GMM , harvested in 0 . 01% Tween-80 , and counted with a hemacytometer , then diluted to desired concentration in sterile water , unless otherwise noted . CreA-null mutants were generated by replacing ~1 kb of the creA coding sequence ( ~1 . 2 kb ) with A . parasiticus pyrG in the CEA17 background . The replacement construct was generated using overlap extension PCR [49] to join ~1 kb of creA 5’ and 3’ UTR with pyrG . The resulting construct was used for transformation of protoplasts for selection on media without uracil and uridine supplements . Reconstitution of creA in the null mutant was achieved by amplification of the entire creA coding sequence including ~1 kb of the flanking 5’ and 3’ regions and the dominant selectable marker ptrA , which confers resistance to pyrithiamine . The creA coding sequence and ptrA were joined with overlap extension PCR , and the linear construct was used to transform ΔcreA for ectopic expression . The reconstituted strain is denoted as creAR . Protoplast transformations were carried out as previously described [10 , 29] . Briefly , 5-10ug of construct was incubated on ice with protoplasts generated with Lysing Enzyme from Trichoderma harzianum ( Sigma ) . PEG/CaCl2 solution was added to protoplasts , then incubated at room temperature ( RT ) . Protoplasts were plated on sorbitol stabilized media plates embedded in sorbitol stabilized media agar . Transformants were selected on appropriate media , and screened using PCR to determine if correct integration of the creA replacement construct with primers designed to bind within the pyrG sequence and outside the integrated construct . Single spores were isolated from positive colonies , and correct integration was confirmed using Southern blotting with the digoxigenin-anti-digoxigenin system ( Roche Diagnostics ) , as previously described [10 , 29] . For alternative carbon and nitrogen plates , the indicated nutrients were added to our minimal media base ( nitrogen-free trace elements and salt solution ) then plates were inoculated with 1x103 conidia and incubated at 37°C for 72 hours . Allyl alcohol ( Sigma ) experiments were completed using 1% agar , which was allowed to cool substantially before addition of allyl alcohol to prevent excess evaporation . 1x103 conidia were spotted on plates and incubated at 37°C for 48 hours . Control plates were incubated in a separate incubator , under the same conditions , to prevent inhibition of growth by volatile allyl alcohol . Mitochondrial inhibitors were used in the following concentrations: 0 . 1mM thenoyltrifluoroacetone ( TTFA; Sigma ) , 15ug/mL Antimycin A ( Sigma ) , 10uM Oligomycin A ( Sigma ) , or 5mM Salicylhydroxamic acid ( SHAM; Sigma ) in GMM or 1% ethanol minimal media ( EMM ) agar . Plates were inoculated with 1x103 conidia and incubated at 37°C for 72 hours . Percent inhibition was calculated by measuring radial growth of colonies in the presence of drug , compared to GMM/EMM plates without drug in biological triplicate . All growth assays were completed in biological triplicates . Liquid growth assays were performed with conidia adjusted to 2x104 conidia in 20uL 0 . 01% Tween-80 in 96-well dishes , then 180uL of minimal media with indicated carbon source was added to each well . Plates were incubated at 37°C for 7 hours , then Abs405 measurements were taken every 15 minutes for the first 24–36 hours of growth with continued incubation at 37°C . Lung homogenate media was prepared as follows: lungs were harvested from healthy CD-1 female mice ( 20-24g ) and homogenized through a 100uM cell strainer in 2mL PBS/lung . Homogenate was diluted 1:4 in sterile PBS , then filter sterilized through 22uM PVDF filters . Each curve represents six technical replicates . ΔAbs405/min was calculated using the linear regression of each individual technical replicate . Hypoxia shift experiments were performed by inoculation of 1x103 conidia on GMM plates , which were incubated at 37°C for 24 hours in normoxia ( 21% O2 , 5%CO2 ) for 24 hours then moved to hypoxia ( 0 . 2% O2 , 5% CO2; INVOVO2 400 hypoxia chamber [The Baker Company , Sanford , ME] ) for 4 days . Radial growth was measured every 24 hours and compared to normoxia controls . Growth is presented as a ratio of hypoxia colony diameter to normoxia colony diameter for three biological replicates . ADP/ATP ratios were measured from cultures ( 1x106 conidia/mL ) grown in liquid GMM at 37°C for 16 hours with shaking at 250rpm . Fungal tissue was washed with sterile water , and transferred to fresh GMM for 2 additional hours at 37°C with shaking at 250rpm . Tissue was lyophilized , ground with glass beads with a Mini-BeadBeater ( BioSpec Products , Inc . ) . Metabolites were extracted using buffer from ApoSENSOR ADP/ATP Ratio Bioluminescent Assay Kit ( K255-200 , BioVision ) and quantified following manufacturers protocol . Tissue lysates were filtered through Ultracel 10K centrifugal filters ( Millipore ) prior to processing to remove proteins . Cultures ( 1x106 conidia/mL ) were grown in GMM at 37°C for 16 hours with shaking at 250rpm . Fungal tissue was washed with sterile water , and transferred to fresh media ( GMM or EMM ) for 2 additional hours at 37°C with shaking at 200rpm . Mycelia were collected with vacuum filtration and immediately frozen with liquid nitrogen . Tissue was lyophilized , then ground with glass beads with a Mini-BeadBeater ( BioSpec Products , Inc ) . RNA was extracted in 1mL of TriSure ( Bioline Reagents ) , then 200uL of chloroform was added , and the aqueous phase was collected after centrifugation . The aqueous phase was in an equal volume of 80% ethanol , then eluted using RNeasy columns ( Qiagen ) following manufacturer’s instructions . 5ug of RNA was treated with Ambion Turbo DNAse ( Life Technologies ) according to manufacturer’s protocol . 500ng of DNAse treated RNA was used for cDNA synthesis with QuantiTech Reverse Transcription kit ( Qiagen ) , then cDNA was diluted 1:5 with ddH2O . qRT analysis was performed in 20uL reactions using 2uL of dilute cDNA per reaction with iQ SYBR Green Supermix ( BioRad ) . Gene expression was measured with a BioRad MyiQ Real Time PCR Detection System . For all expression studies , gene expression was normalized to actA and tub2 expression . RNA , collected as described above , was analyzed by Qubit and a Fragment Analyzer ( Advanced Analytical ) for quality control . cDNA libraries were prepared with Illumina’s TruSeq Directional polyA+ library prep kit following the manufacturer’s protocol , with TruSeqLT sequencing adapters/indices . cDNA libraries were multiplexed and loaded at 1 . 6pM on a NextSeq500 for sequencing . The Illumina raw RNA-seq reads for each replicate were downloaded and then aligned to the A . fumigatus A1163 genome ( CADRE 30 ) using Tophat v2 . 1 . 0 [50] under default parameter settings . Transcripts were then assembled using Cufflinks v2 . 2 . 1 [51] and annotated using the A1163 ( CADRE 30 ) GTF file from ENSEMBLFungi . Differential expression analysis was performed for each experimental group separately using DESeq2 [52] and p-values were corrected for multiple testing using Benjamini-Hochberg . NCBI GEO accession number pending . Spores were inoculated in GMM at a concentration of 106 conidia/mL and incubated for 16 hours at 37°C with shaking at 250 rpm , then collected with vacuum filtration and washed with sterile water . Fungal tissue was transferred to fresh GMM and further incubated at 37°C , with shaking at 200rpm , for 2 hours . Mycelia were collected with vacuum filtration , washed with sterile water and frozen in liquid nitrogen and stored at -80°C until samples were sent for processing and analysis . Murine metabolomics samples were prepared from whole lungs . CD-1 female mice , 20–24 grams , were administered 40mg/kg Kenalog-10 ( Bristol-Myers Squibb ) subcutaneously . Lungs of steroid-treated mice were collected 24 hours post injection . For fungal inoculation , immune suppressed mice were inoculated with 2x106 CEA10 conidia in 40uL PBS and lungs were collected 8 hours post inoculation . Healthy controls were given no drug . All lung samples were immediately frozen in liquid nitrogen and stored at -80°C until processing . Samples were prepared using the automated MicroLab STAR system from Hamilton Company . Several recovery standards were added prior to the first step in the extraction process for QC purposes . To remove protein , dissociate small molecules bound to protein or trapped in the precipitated protein matrix , and to recover chemically diverse metabolites , proteins were precipitated with methanol under vigorous shaking for 2 min ( Glen Mills GenoGrinder 2000 ) followed by centrifugation . The resulting extract was divided into five fractions: two for analysis by two separate reverse phase ( RP ) /UPLC-MS/MS methods with positive ion mode electrospray ionization ( ESI ) , one for analysis by RP/UPLC-MS/MS with negative ion mode ESI , one for analysis by HILIC/UPLC-MS/MS with negative ion mode ESI , and one sample was reserved for backup . Samples were placed briefly on a TurboVap ( Zymark ) to remove the organic solvent . The sample extracts were stored overnight under nitrogen before preparation for analysis . All methods utilized a Waters ACQUITY ultra-performance liquid chromatography ( UPLC ) and a Thermo Scientific Q-Exactive high resolution/accurate mass spectrometer interfaced with a heated electrospray ionization ( HESI-II ) source and Orbitrap mass analyzer operated at 35 , 000 mass resolution . The sample extract was dried then reconstituted in solvents compatible to each of the four methods . Each reconstitution solvent contained a series of standards at fixed concentrations to ensure injection and chromatographic consistency . One aliquot was analyzed using acidic positive ion conditions , chromatographically optimized for more hydrophilic compounds . In this method , the extract was gradient eluted from a C18 column ( Waters UPLC BEH C18-2 . 1x100 mm , 1 . 7 μm ) using water and methanol , containing 0 . 05% perfluoropentanoic acid ( PFPA ) and 0 . 1% formic acid ( FA ) . Another aliquot was also analyzed using acidic positive ion conditions , however it was chromatographically optimized for more hydrophobic compounds . In this method , the extract was gradient eluted from the same afore mentioned C18 column using methanol , acetonitrile , water , 0 . 05% PFPA and 0 . 01% FA and was operated at an overall higher organic content . Another aliquot was analyzed using basic negative ion optimized conditions using a separate dedicated C18 column . The basic extracts were gradient eluted from the column using methanol and water , however with 6 . 5mM Ammonium Bicarbonate at pH 8 . The fourth aliquot was analyzed via negative ionization following elution from a HILIC column ( Waters UPLC BEH Amide 2 . 1x150 mm , 1 . 7 μm ) using a gradient consisting of water and acetonitrile with 10mM Ammonium Formate , pH 10 . 8 . The MS analysis alternated between MS and data-dependent MSn scans using dynamic exclusion . The scan range varied slighted between methods but covered 70–1000 m/z . Peaks were quantified using area-under-the-curve . For studies spanning multiple days , a data normalization step was performed to correct variation resulting from instrument inter-day tuning differences . Essentially , each compound was corrected in run-day blocks by registering the medians to equal one ( 1 . 00 ) and normalizing each data point proportionately . For studies that did not require more than one day of analysis , no normalization is necessary , other than for purposes of data visualization . ‘Scaled Input’ values represent the data rescaled with wild type values set to have a median of one , for ease of visualization . For in vitro fungal samples , metabolites were normalized to protein content as determined by a Bradford assay . In vivo lung samples represent metabolite content of whole lung without normalization . Outbred female CD-1 mice ( Charles River Laboratory , Raleigh , NC ) , 20–24 grams , were given subcutaneous injections of Kenalog-10 ( triamcinolone acetonide , Bristol-Myer Squibb ) at 40mg/kg to induce immune-suppression . 24 hours post injection; mice were inoculated with 2x106 fungal spores in 40uL PBS via intranasal inoculation . Mock mice were given 40uL sterile PBS . Mice were sacrificed 48 hours post inoculation and lungs were collected for DNA extraction . Lung tissue was freeze-dried and homogenized with 2 . 3mm zirconia/silica beads on a Mini-BeadBeater ( BioSpec Products , Inc . ) . Total DNA was extracted using a phenol-chloroform extraction and RNAse treated DNA was used for quantitative PCR , as previously described [24] , with primers to amplify 18S region . Outbred female mice were immune-suppressed as described above . Inocula of 2x106 CEA10 , ΔcreA , creAR conidia were prepared in 40uL PBS and delivered via intranasal inoculation . Mock mice were given 40uL PBS in the absence of fungal spores . For the chemotherapeutic murine model outbred CD-1 female mice , 6 weeks old , were immunosuppressed with intraperitoneal ( i . p . ) injections of 175mg/kg cyclophosphamide ( Baxter Healthcare Corporation ) 48 hours before fungal inoculation and subcutaneous ( s . c . ) injections of 40 mg/kg Kenalog-10 ( triamcinolone acetonide , Bristol-Myer Squibb24 hours before fungal inoculation . Mice were inoculated with 1x106 conidia in 40uL PBS intranasally or PBS alone for mock . n = 12 mice/group; n = 4 mice/mock . Mice were housed 4 per cage and had access to food and water ad libitum . Mice were monitored for 14 days following challenge with A . fumigatus . Percent survival was plotted on a Kaplan-Meier curve and a Log-rank test was used to assess statistical significance of the curves . CD-1 female mice , 20–24 grams , were immune suppressed as described above . Mice were inoculated with 2x106 conidia of CEA10 , ΔcreA , or creAR and lungs were harvested 48 hours post inoculation for histopatholoical sectioning and staining . For early germination studies , immune suppressed mice were inoculated with 1x107 conidia and lungs were harvested 8 hpi for sectioning and staining . Briefly , lungs were perfused with 10% buffered formalin solution upon collection , then fixed in 10% buffered formalin overnight . Lungs were blocked in paraffin , sectioned , and stained with Gömöri methenamine silver ( GMS ) and hematoxylin and eosin ( H&E ) stains . Images were obtained with a Zeiss Axioplan 2 imaging microscope ( Carl Zeiss Microimaging , Inc . ) fitted with Qimiging RETIGA-SRV Fast 1394 RGB camera using Phylum Live 4 imaging software . Outbred female mice were immune-suppressed as described above and given 5x106 CEA10 or ΔcreA conidia intranasally in 40uL PBS , 24 hours post injection with steroids . Lungs were harvested 24 and 72 hours post inoculation , and immediately frozen in liquid nitrogen . Lungs were freeze dried and homogenized with 2 . 3mm zirconia/silica beads on a Mini-BeadBeater ( BioSpec Products , Inc . ) . RNA was extracted using TriSure ( Bioline Reagents ) then eluted using RNeasy columns ( Qiagen ) following manufacturer’s instructions . cDNA was synthesized with Qiagen Quantitect Reverse Transcription kit ( Qiagen ) using 500ng of DNAse treated RNA , and 25ng of resulting cDNA was used in each qRT-PCR reaction . Gene expression was measured with qRT-PCR using BioRad iQ SYBR Green Supermix with a BioRad MyiQ Real Time PCR Detection System ( BioRad ) . To ensure our primers amplified A . fumigatus genes specifically , we used cDNA from mock mice that had been treated with steroids but given PBS in lieu of fungal spores . All primer pairs tested failed to amplify from mock cDNA . The Guide for the Care and Use of Laboratory Animals of the National Research Council was strictly followed for all animal experiments . The animal experiment protocols were approved by Institutional Animal Care and Use Committee at Dartmouth College ( protocol: cram . ra . 1 ) . | Medical treatment advances such as organ transplants and chemotherapies that suppress the immune system have increased the number of patients susceptible to invasive fungal diseases . The most common filamentous fungus isolated from these infections is the environmental mold , Aspergillus fumigatus , the causative agent of invasive aspergillosis ( IA ) . Despite medical intervention , mortality from IA remains high , underscoring the need to understand A . fumigatus pathogenesis mechanisms to uncover new therapeutic targets and strategies . Here , we show that regulation of central metabolism in A . fumigatus is critical for infection maintenance and progression of disease . The dysregulation of carbon catabolite repression results in reduced virulence in an animal model at later stages of the infection because of an inability to navigate infection microenvironment dynamics driven in part by oxygen depletion and alterations in nutrient availability . These results likely not only apply to IA , but are broadly applicable to other infection models stressing the need to understand spatiotemporal dynamics of individual microbial infections particularly at the level of metabolism . We propose that microbial genes which support disease progression , in contrast but not mutually exclusive to disease initiation , be termed DPFs ( disease progression factors ) and as such represent a novel class of antimicrobial drug targets . | [
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"metabolis... | 2017 | Filamentous fungal carbon catabolite repression supports metabolic plasticity and stress responses essential for disease progression |
Hookworm-related cutaneous larva migrans ( CLM ) is a common but neglected tropical skin disease caused by the migration of animal hookworm larvae in the epidermis . The disease causes intense pruritus and is associated with important morbidity . The extent to which CLM impairs skin disease-associated life quality has never been studied . A modified version of the Dermatology Life Quality Index ( mDLQI ) was used to determine skin disease-associated life quality in 91 adult and child patients with CLM , living in resource-poor communities in Manaus , Brazil . Symptoms and signs were documented and skin disease-associated life quality was semi-quantitatively assessed using mDLQI scores . The assessment was repeated two and four weeks after treatment with ivermectin . Ninety-one point five percent of the study participants showed a considerable reduction of skin disease-associated life quality at the time of diagnosis . The degree of impairment correlated with the intensity of infection ( rho = 0 . 76 , p<0 . 001 ) , the number of body areas affected ( rho = 0 . 30; p = 0 . 004 ) , and the presence of lesions on visible areas of the skin ( p = 0 . 002 ) . Intense pruritus , sleep disturbance ( due to itching ) and the feeling of shame were the most frequent skin disease-associated life quality restrictions ( reported by 93 . 4% , 73 . 6% , and 64 . 8% of the patients , respectively ) . No differences were observed in skin disease-associated life quality restriction between boys and girls or men and women . Two weeks after treatment with ivermectin , skin disease-associated life quality improved significantly . After four weeks , 73 . 3% of the patients considered their disease-associated life quality to have returned to normal . CLM significantly impaired the skin disease-associated life quality in child and adult patients living in urban slums in North Brazil . After treatment with ivermectin , life quality normalised rapidly .
Hookworm-related cutaneous larva migrans ( CLM ) is a parasitic skin disease caused by the migration of animal hookworm larvae such as Ancylostoma braziliense , A . caninum or Uncinaria stenocephala in the epidermis . The infection occurs when third-stage larvae come into contact with human skin and penetrate into the epidermis . Since animal hookworm larvae cannot penetrate the basal membrane of the human host , they remain confined to the epidermis where they migrate for several weeks or months , and eventually die in situ [1] . CLM is frequent in impoverished rural and urban communities in countries with hot climates [2] , [3] , [4] , [5] , [6] . In these settings the prevalence of CLM can reach 4% in the general population and 15% in children <4 years . [6] , [7] , [8] . CLM belongs to the category of neglected tropical diseases [9] , [10] . The main symptom of CLM is severe pruritus , which intensifies at night . The itching leads to sleep disturbance and day somnolence [6] . Scratching may cause extensive excoriations and subsequent bacterial superinfection of the lesions , typically by Streptococcus pyogenes or Staphylococcus aureus . Bacterial superinfection by group-A streptococci may induce the development of post-streptococcal glomerulonephritis [11] . A recent study on knowledge , attitudes and practice among mothers of children with CLM highlighted the psychosocial stress associated with this parasitic skin disease and its negative impact on family life ( H . Lesshafft 2010 , unpublished data ) . This prompted us to investigate the impairment of skin disease-associated life quality in patients with CLM in a semi-quantitative manner .
The study was carried out in Manaus , the capital of Amazonas State , North Brazil , from October 2008 to February 2009 . Patients were actively recruited in resource-poor neighbourhoods , so called invasões . Patients were identified via word-of-mouth advertising through primary health care centres , neighbourhood organisations and community leaders . Twenty-three patients were recruited in Barrio da União and 28 in Nova Vitória; 40 patients came from five further resource-poor communities scattered in the city of Manaus . All communities were situated along small tributaries of the Amazon River ( igarapés ) . In these communities , most houses are built on stilts ( palafitas ) and made of wooden planks or recycled materials . Streets are unpaved , access to drinking water is precarious , sanitation is deficient and garbage is usually disposed in the adjacent igarapé or on the street . Dogs and cats stray around and feed on garbage found below and around the houses . In the rainy season , the communities are regularly inundated and animal faeces are widely dispersed . Usually , households include two to six children . Blended family constellations , single mothers , adult illiteracy and unemployment are frequent . Alcoholism , psychological and physical violence and drug abuse are common . The setting in which the study was carried out shares many social and economical characteristics with numerous other impoverished urban communities in South America . Most households in which the patients lived benefitted from the national Bolsa Familia and Bolsa Escola programs which support families with a monthly per capita income <140 Brazilian Reais ( equivalent to 54 Euros at the time of study ) with regular financial contributions . The study is part of a larger research project on the epidemiology , morbidity , and control of CLM in North Brazil . Individuals aged ≥5 years with a diagnosis of CLM were eligible for the study . The investigation was performed as a prospective study with active case detection . Pregnant women and children <5 were excluded from the study because ivermectin treatment is contra-indicated in these groups . The study took place between October 2008 and July 2009 . The diagnosis of CLM was made clinically . The whole skin was examined in a room where privacy was guaranteed and good lighting was available . The genital area was only inspected when the patient or his/her carer gave verbal consent . Children were always examined in the presence of their mothers . CLM was diagnosed when the characteristic elevated linear or serpingious track was visible and the lesion had moved forward during the preceding days [6] , [12] . The number and the topographic localisation of each lesion was documented . Each track was defined as a single lesion , irrespective of the distance between the tracks . Tungiasis ( jigger flea ) and scabies , parasitic skin diseases also characterized by itching skin lesions , were excluded by careful clinical examination . In order to determine the topographic distribution of the lesions and the affected area of the skin , the body surface was divided into right and left . As in previous studies each side was subdivided into 14 areas as follows: head , upper arm , forearm , hand , thorax , abdomen , back , buttock , genital/inguinal area , thigh , lower leg , ankle , back and sole of the foot [13] . Body areas were further classified into clearly visible areas ( head , forearm , hand , lower leg , back and sole of the foot ) , partially visible areas ( upper arm , thorax , abdomen , back , thigh ) and non-visible areas ( buttock , genital/inguinal area ) according to local dress codes . Lesions were differentiated into papular , crusted-papular , and nodular [13] . The presence and dimensions of excoriations were documented . A simple lesion was defined as a track without bacterial superinfection , excoriations , or an significant inflammation presenting nodular lesion or an extended erythema . Bacterial bacterial superinfection was diagnosed when pustules , suppuration , or an abscess were present [6] . The severity of CLM was determined semi-quantitatively , using a severity score . This score combines the following variables: number of tracks ( 1–2 tracks = 1 point , 3–5 tracks = 2 points , 6–10 tracks = 3 points , >10 tracks = 4 points ) ; presence/absence of secondary infection ( 0/2 points ) ; signs of local inflammation ( erythema , warmness or swelling = 1 point , pain = 2 points , nodular lesions = 3 points ) ; presence of lymphadenopathy proximal to the lesion ( 0/1 point ) . Hence , the severity score can vary between 1 and 10 points . Immediately after diagnosis patients were treated with ivermectin ( 200 µg/kg ) in a single oral dose ( Revectina; Solvay Farma Ltda , São Paulo , Brazil ) . Two and four weeks after treatment , the patients were re-examined and the mDLQI was determined again . The Dermatology Life Quality Index ( DLQI ) was developed by Finlay and Khan in 1994 [14] . It is a validated instrument to assess skin-associated life quality impairment and it is the most frequently used tool to determine skin disease-associated life quality in patients with skin diseases of infectious and non-infectious origin [15] , [16] , [17] . The original DLQI questionnaire is available in English and in several other languages ( www . dermatology . org . uk ) . In the present study , the Brazilian Portuguese translation was used . First , the wording was adapted to local culture and attitudes according to guidelines described by Cestari et al . [18] . Second , the questions were modified to focus on characteristic sequelae of parasitic skin diseases , and their impact on life quality in the setting of resource-poor communities in Brazil . Third , questions not applicable to children , such as the impact of skin disease on sexual life , were omitted in accordance with the original questionnaire for children [19] . This resulted in a modified dermatology life quality index ( mDLQI ) with eight items and a score varying between 0 and 24 points . The items were the following: pruritus , sleep disturbance , feeling of shame , need to adapt clothing in order to cover up skin lesions , problems faced at work or in school , impairment of leisure activities , impairment in personal relationships , teasing ( only children ) , and problems concerning sexual relationships ( only adults ) . The mDLQI has been validated by Worth et al . in patients with scabies living in a similar setting in northeast Brazil [20] . Since illiteracy was widespread , each statement was read out loud to the patient by one of the investigators ( AS or HL ) and its meaning explained in a standardized manner . The answers to each statement were weighted as follows: not at all = 0 points , a little = 1 point , quite a lot = 2 points , very much = 3 points [14] . The points for each statement were added up and formed the mDQLI for each patient . The mDLQI scores were categorised as shown in table 1 . The data were entered twice into a database using Epi Info software package Version 3 . 4 . 3 ( CDC Atlanta , USA ) and checked for errors which may have occurred during data entry . Data analysis was performed using SPSS for Windows ( Version 16 . 0; SPSS Inc . , Chicago , Illinois ) . Since data did not follow a normal distribution , the median and the interquartile range ( IQR ) were used as an indicator of central tendency and dispersion of the data , respectively . The Spearman rank correlation coefficient was calculated for correlations between mDLQI scores and other ordinal variables . The Mann-Whitney-U test was used to compare mDLQI scores between subgroups of patients . Relative frequencies were compared using the chi-squared test . The study was approved by the Ethical Committee of the Fundação de Medicina Tropical do Amazonas ( FMT-AM ) , the reference institution for tropical diseases of Amazonas State . The objectives of the study were explained to each participant in simple and comprehensible Portuguese . The right to withdraw at any time was described in plain words . Patients had time to meditate about their decision and were given the possibility to discuss any doubts with the researchers . Each participant , or in the case of minors , their legal guardian , signed the written informed consent form . In case of illiteracy consent was given via fingerprint . The informed consent form was written and read out loud , and after each paragraph , the participant was asked whether she/he understood its meaning . Patients with other skin diseases than CLM were referred to the nearest primary health care centre or to the outpatient department of the FMT-AM , where treatment was provided free of charge .
Ninety-one patients were included in the study , 63 of them were male and 28 female . The median age was 10 years ( IQR 7-12 , range 5–44 years ) . The demographic and clinical characteristics of the patients are summarized in Table 2 . Forty-four point eight percent of the patients had more than two lesions . The maximal number of lesions was 51 . 88% of the patients had noted the appearance of the oldest track during the last four weeks . Figure 1 shows a typical example of an inflamed and superinfected track at a visible body part . Nearly all study participants showed a reduction of life quality ( mDLQI≥2 points ) at the time of diagnosis ( Table 3 ) . The majority of the patients ( 51 . 6% ) showed a moderate life quality impairment . At baseline , the median mDLQI score was 5 ( IQR 3-8 ) . 6 ( IQR 3-9 ) for adults and 5 ( IQR 3-8 ) for children ( p = 0 . 7; Table 4 ) . Pruritus , sleep disturbance , feeling of shame and the need to dress differently were the most frequent restrictions . Significant differences in perceived restrictions between adult and child patients existed for problems faced at work/school and impairment in social relationships ( p = 0 . 040 and p = 0 . 026 , respectively ) . There was no difference in mDLQI scores between boys and girls ( 5 [IQR 3-8] versus 6 [IQR 3-7]; p = 0 . 86 ) and men and women ( 6 [IQR 3 . -9] versus 4 [IQR 2-9]; p = 0 . 63 ) . The degree of skin disease-associated life quality impairment correlated strongly with the severity of the infection ( rho = 0 . 76; p<0 . 001 ) ( Figure 2 ) and the number of affected body areas ( rho = 0 . 30; p = 0 . 004 ) ( Figure 3 ) . A significant correlation existed between the presence of lesions in clearly visible body areas and the mDQLI score ( p = 0 . 002 ) . Skin disease-associated life quality impairment did not depend on the number of CLM episodes experienced previously ( p = 0 . 88 ) , the duration of the infection ( p = 0 . 52 ) , or the presence or absence of bacterial superinfection ( p = 0 . 80 ) . The follow–up examinations showed an improvement of skin disease-associated life quality two weeks after treatment with ivermectin ( median mDLQI = 5 [IQR 3-8] versus 1 [IQR 0-3; p<0 . 001] Table 5 ) . Four weeks after treatment , the median mDLQI score was zero and 82% of the patients reported a normalization of their skin disease-associated life quality . The normalization of skin disease-associated life quality was paralleled by a drastic reduction of the CLM severity score from a median of 4 points ( IQR 3-6 ) to 1 point ( IQR 1-1 ) two weeks after treatment with ivermectin and to 1 point ( IQR 0-1 ) at the end of the study ( both p<0 . 001 ) . Figures 4 and 5 show the resolution of the inflammatory skin reactions around embedded hookworm larvae before and four weeks after treatment with ivermectin .
Diseases of the skin lead to various levels of suffering . First , they cause defined clinical pathology , such as visible inflammation , pruritus or pain . Second , skin diseases are frequently chronic in nature and patients have to take drugs , either topically or orally , for a protracted period of time . Third , if gross alterations of the skin are located on visible body parts , they may , at worst , lead to social withdrawal and/or to exclusion from society , as it is the case , for instance , with leprosy [21] . Additionally , patients may be confronted with ignorance or misconceptions regarding the aetiology of their skin disease , such as the fear that the condition is contagious or related to poor personal hygiene – assumptions which may lead to stigmatisation [22] , [23] . Lymphatic filariasis with gross lymphoedema is a paradigmatic example of this category of skin diseases [24] , [25] , [26] . CLM is an extremely itchy skin condition characterized by signs of inflammationm such as erythema . Since lesions are frequently located at visible body parts they are difficult to hide from the public [13] and negative impact on emotional well-being of the patient is possible . . In our study 94 . 5% of patients with CLM reported reduction of their skin disease-associated life quality with a median mDLQI score of 5 ( Table 3 ) . The degree of skin disease-associated life quality impairment was positively correlated with the intensity of the infection ( Figure 2 ) , the number of body areas affected ( Figure 3 ) , and the presence of lesions at clearly visible body parts . In contrast to a study in patients with scabies [20] we did not find different degrees of impairment between women and men . This could be due to the fact that scabies lesions usually are less obvious to the patient and external observers/third parties than highly inflamed larval tracks . Besides , in scabies the lesions are frequently located at “hidden” topographic areas , such as the interdigital spaces . Finally , the preponderance of male participants in the study – a consequence of the higher prevalence of CLM in males in the area where the study was conducted – may have blurred the differences between the sexes . The most common finding associated with an impairment of skin disease-associated life quality was pruritus ( 93 . 4% of the patients ) . Pruritus causes the patient to scratch repeatedly- a behavior which does not pass unnoted by other members of society [27] . In addition , since the intensity of itching increases at night , it causes alterations in the sleep pattern . The affective aspect of pruritus may induce a vicious cycle in which increasing mental harm and distress lead to increased itching which , in turn , augments scratching [27] , [28] . Insomnia was reported as a cause of life quality impairment by 73 . 6% of the patients . A previous study has shown that CLM related insomnia manifests itself as a sleep maintenance disorder [13] , probably due to an increased perception of pruritus during the night . In patients with pruritus-induced perturbation of sleep , quality and duration of sleep are reduced as a consequence of shorter non-REM sleeping periods [29] . This may cause daytime somnolence , irritability and psychological problems such as anxiety disorders [30] , [31] . It seems paradoxical that insomnia has been cited as most important restriction by people living in an invasão . From an outside observer's point of view , getting rest and sleep in this setting seems to be very difficult anyway: poor housing and a high population density allow noise to enter the house almost unaltered and loud music is heard even late at night . However , our patients seem to have adapted to the extremely noisy environment of an invasão and considered sleep and recreation to be severely impaired by the CLM-related pruritus . In fact , after treatment , insomnia was reduced significantly already after two weeks ( Table 3 ) . The feeling of shame was noted by 64 . 8% of the patients . In our study on knowledge , attitudes and disease perception , it was found that shame frequently resulted from the concept that the occurrence of CLM reflects poor personal hygiene and lack of care ( H . Lesshafft , unpublished observation ) . Another commonly noted restriction is related to the necessity of patients with CLM to dress differently . In the hot climate of northern Brazil a great part of the body remains uncovered . Hence , skin lesions are difficult to hide and the effort to cover them up with extra clothes or bandages may lead to a reduction of self-esteem and provoke shame and stigmatisation [23] , [32] , [33] . These somato-psychological interactions were confirmed by our finding that mDLQI scores were highest in patients in whom lesions were present at clearly visible parts of the body . Problems faced at work or at school and impairment of personal relationships were also a frequently noted restriction of skin disease-associated life quality ( Table 4 ) . Several mechanisms may underlie these psycho-social consequences . First , and similarly to other skin diseases such as psoriasis , the erroneous assumption that CLM is contagious leads to alterations in personal relationships and eventually to social exclusion [23] . Second , as shown in a previous focus group discussion in the study area ( unpublished data ) , mothers frequently ban affected children from playing outside , partly to prevent a new infection and partly to avoid teasing by other children , which may cause boredom and or lead to a feeling of social exclusion . Thirdly , bullying and interrupted personal relationships may provoke a feeling of disgust and shame about the skin condition and reinforce an active withdrawal from social networks due to the fear of stigmatisation [23] , [22] . With regard to personal relationships , the significantly lower impairment of skin disease-associated life quality in children compared to adults might be explained by the fact that consciousness about their own appearance interferes less in children's relationships . The higher impairment perceived by adults at work is presumably related to a similar mechanism . At work , adults are confronted with the “outside world” in which CLM reflects a life in poverty . In contrast , children - going to school in the community - do not leave their social environment and consequently may perceive less life quality impairment . Hitherto , only a few studies have attempted to determine skin disease-associated life quality impairment in tropical parasitic skin diseases . While in patients with active cutaneous leishmaniasis or onchocerciasis , the average impairment was found to be higher than in the CLM patients of our study , skin disease-associated life quality restrictions in lymphatic filariasis caused a similar or higher impairment depending on the severity of lymphoedema [24] , [25] , [26] , [32] , [33] , [34] . In contrast , patients with scabies living in an invasão in Northeast Brazil percieved less impairment than our patients with CLM [20] . In scabies the duration of infection , but not the number of infested body areas , correlated with skin disease-associated life quality impairment . This is probably due to the rather slow development of the clinical pathology in scabies , where the degree of skin alteration increases gradually , whereas in CLM inflammatory skin reactions develop within a couple of days . We think that our data clearly indicate a cause-effect relationship between cutaneous larva migrans and impaired quality of life . First , the severity of disease was significantly correlated to the degree of impaired quality of life ( rho = 0 . 76; p<0 . 001 ) and number of body areas affected ( rho = 0 . 30; p = 0 . 004 ) , indicating positive “dose-response” relationships . Second , already two weeks after the regression of skin lesions due to treatment with ivermectin , the degree of life quality impairment decreased significantly . Taken together , these findings provide substantial evidence that the impairment of life quality is the consequence of the parasitic skin disease as it has been observed in patients suffering from other parasitic infections [24]–[26] [32]–[34] . These findings also suggest that a treatment that costs approximately 40–80 eurocents , not only abrogates clinical pathology , but also averts stressful psycho-social consequences and prevents the development of secondary morbidity when given promptly . When interpreting our results one has to take into account that skin disease-associated life quality of people living in misery in an urban slum is very low per se [35] . Housing is poor , sanitary infrastructure is deficient , crowding is common and social problems such as unemployment , alcoholism , illiteracy , and violence prevail . Obviously , these characteristics should mitigate perceived restrictions on skin disease-associated life quality in our patients . In fact , the results of another study in the same setting showed that members of the community considered parasitic skin diseases negligible in comparison to the existential problems of daily life ( H . Lesshafft , unpublished observation ) . In conclusion , CLM impairs the physical and mental wellbeing as well as social interaction of patients in a setting where skin disease-associated life quality is generally low . A single dose of ivermectin caused a complete resolution of the lesions within one month and restored skin disease-associated life quality to the normal level . | Hookworm-related cutaneous larva migrans ( CLM ) is a parasitic skin disease common in developing countries with hot climates . In resource-poor settings , CLM is associated with considerable morbidity . The disease is caused by animal hookworm larvae that penetrate the skin and migrate aimlessly in the epidermis as they cannot penetrate the basal membrane . Particularly in the rainy season , the intensity of infection is high with up to 40 larval tracks in an affected individual . Tracks are very itchy and are surrounded by a significant inflammation of the skin . Bacterial superinfection is common and intensifies the inflammation . The psychosocial consequences caused by CLM have never been investigated . We showed that CLM causes skin disease-associated life quality impairment in 91 patients with CLM . Skin disease-associated life quality was significantly impaired . The degree of impairment correlated to the intensity of infection and the number of body areas affected . After treatment with ivermectin , life quality was rapidly restored . | [
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] | 2011 | Life Quality Impairment Caused by Hookworm-Related Cutaneous Larva Migrans in Resource-Poor Communities in Manaus, Brazil |
Sensitivity to pain varies considerably between individuals and is known to be heritable . Increased sensitivity to experimental pain is a risk factor for developing chronic pain , a common and debilitating but poorly understood symptom . To understand mechanisms underlying pain sensitivity and to search for rare gene variants ( MAF<5% ) influencing pain sensitivity , we explored the genetic variation in individuals' responses to experimental pain . Quantitative sensory testing to heat pain was performed in 2 , 500 volunteers from TwinsUK ( TUK ) : exome sequencing to a depth of 70× was carried out on DNA from singletons at the high and low ends of the heat pain sensitivity distribution in two separate subsamples . Thus in TUK1 , 101 pain-sensitive and 102 pain-insensitive were examined , while in TUK2 there were 114 and 96 individuals respectively . A combination of methods was used to test the association between rare variants and pain sensitivity , and the function of the genes identified was explored using network analysis . Using causal reasoning analysis on the genes with different patterns of SNVs by pain sensitivity status , we observed a significant enrichment of variants in genes of the angiotensin pathway ( Bonferroni corrected p = 3 . 8×10−4 ) . This pathway is already implicated in animal models and human studies of pain , supporting the notion that it may provide fruitful new targets in pain management . The approach of sequencing extreme exome variation in normal individuals has provided important insights into gene networks mediating pain sensitivity in humans and will be applicable to other common complex traits .
Chronic pain has a prevalence of nearly 20% in Europe [1] and similar estimates are reported for North America . The symptom is poorly controlled by existing therapies and the resulting personal and socio-economic burden is considerable . While many analgesic drugs are available , the vast majority of analgesic prescriptions are drawn from two classes of drug , opiates and nonsteroidal anti-inflammatory-like drugs , and have either limited efficacy or significant side effects . There is , therefore , a considerable need to develop novel analgesic treatments . The use of human genetics for identification of intrinsic factors that contribute to chronic pain states is attractive for several reasons . Chronic pain conditions as well as experimentally induced pain have been shown to have a considerable genetic component [2] . Twin studies have shown observed heritabilities of about 50% for different pain traits [3] . The manifestation of pain in response to experimental stimuli such as skin heating , or to clinical pathologies such as joint degeneration , is known to vary markedly . It is clear that a range of factors , including personality , expectation and mental state modulate the expression of chronic pain and these features are themselves genetically mediated . Modelling in twins , however , suggests that there are two separate predisposing genetic factors [4] including variants that modulate sensitivity to pain , as well as those mediating anxiety and depression . A number of approaches to pain sensitivity genetics have been adopted including the examination of rare ( monogenic ) syndromes of pain insensitivity ( reviewed in [5] ) and candidate genes identified from transcriptional profiling in animal models [6] . Candidate gene studies in humans with chronic pain have been unconvincing , and confirmed candidate gene associations are still lacking ( reviewed in [4] and [7] ) . The aim of the present study was to examine the influence of genetic variation , particularly rare variants having minor allele frequency <5% , on pain sensitivity in normal human volunteers . Two hypotheses were tested; that a single rare variant having large effect influences pain sensitivity and that the burden of variation would differ between sensitive and insensitive individuals . Attempts to standardise and quantify pain sensibility in humans have led to the introduction of standardised thermal , mechanical or chemical stimuli that activate the nociceptive ( pain signalling ) system . Such quantitative sensory testing ( QST ) has been used to show that an individual's sensitivity to experimental pain predicts risk of developing chronic pain after surgical interventions such as hernia repair [8] and arthroscopy [9] . That pre-operative pain sensitivity is a major risk factor for chronic post-operative pain suggests that exploration of genetic variation underlying experimental pain might be a useful approach . The pain stimulus , its site of application and methods of rating have all been standardised - unlike spontaneous pain in a disease state . A further benefit is that the genetic influence on pain sensitivity is studied , rather than its influences on disease and disease progression . In the present study , we sought to determine whether rare variants associate with extremes of pain sensitivity in healthy volunteers . Using heat as the stimulus for QST in a large sample of healthy twin volunteers ( www . twinsuk . ac . uk ) we observed the normal variation in pain sensitivity using two objective tests , the heat pain threshold ( HPT ) and the heat pain suprathreshold ( HPST ) . From a study population of >2500 individuals having QST , we compared approximately 200 individuals categorised as having high and low sensitivity to HPST ( approximately 100 from each; TUK1 set ) then repeated the process in a further 200 individuals ( TUK2 set ) . Our initial analysis sought to identify genes harbouring single nucleotide variants ( SNVs ) in either pain sensitive or insensitive subjects , with a focus on non-synonymous exonic and nonsense mutations . A large number of methods have been proposed for such an analysis [40]–[44] . We employed a battery of such tests including both old and new techniques , as well as tests examining a range of hypotheses; a difference between pain groups ( sensitive vs . insensitive ) in the proportion of subjects harbouring rare variants; a difference in abundance of rare variants , weighted by function; and a multivariate difference in variant patterns between the two groups , allowing simultaneous excess in either pain group for any single rare variant within a gene . We found no single rare variant to have a statistically significant association to heat sensitivity , after multiple testing correction . The strongest signal was found for GZMM , a serine protease from immune cell granules . However , our network analysis identified up to 30 genes harbouring rare SNVs as belonging to the Angiotensin II pathway , which has previously been linked to the pain phenotype in a number of settings .
The 2nd lowest p-value among 6 gene-centric variant burden tests was used as a cut-off to prioritise genes for pathway analysis ( see Methods , statistical analysis ) . After merging TUK1 and TUK2 datasets , we identified 138 unique genes harbouring a rare variant with a 2nd lowest p-value<0 . 01 . First we examined the functional annotations of these 138 genes using the online functional annotation tool DAVID ( http://david . abcc . ncifcrf . gov/ ) [10] . Nine high level GO terms were nominally significantly enriched in the gene list eg . “plasma membrane” and “intracellular signalling cascade” . None reached significance after multiple testing correction or offered obvious insights into mechanisms of altered pain sensitivity ( results not shown ) . We applied causal reasoning to our data [11] , which uses a large curated database of directed regulatory molecular interactions to identify the most plausible upstream regulators of a gene set . Of the 138 genes 86 were present in our database of causal interactions , from which we identified 4 nominally significant regulatory networks ( Table 4 ) . One of the regulatory networks , angiotensin II ( Figure 3 ) , was highly enriched for a pain signal with 12 out of 204 genes in the network also in the set of 86 genes with a nominal genetic burden . This yields an odds ratio of 7 . 6 , an enrichment p = 3 . 4×10−7 and a correctness p = 1 . 2×10−8 . Since 1108 pathways were tested , this adjusts to enrichment p = 3 . 8×10−4 and correctness p = 1 . 4×10−5 under multiple test correction . We also investigated whether the genes identified were known to interact physically with proteins playing a role in pain . For this we used the BioGrid database of protein-protein interactions . Notable connections included the binding of synaptotagmin-9 ( SYT9 ) , a membrane trafficking protein activated by calcium , to TRPV1 , the capsaicin receptor , which plays a key role in thermal nociception [12] . The extracellular matrix glycoprotein laminin B1 chain ( LAMB1 ) interacts with the voltage dependent calcium channel Cav2 . 1 ( CACNA1A ) [13] . The receptor activity modifying protein 3 ( RAMP3 ) binds to the calcitonin receptor ( CALCRL ) , for transport to the membrane . Here the calcitonin receptor recognises the calcitonin gene related peptide ( CGRP ) , a hormone proposed to contribute to pain transmission and inflammation [14] . Finally , the sodium-hydrogen exchanger regulatory factor 1 ( SLC9A3R1 ) , binds the beta-2-adrenergic receptor ( ADRB2 ) [15] , nitric oxide synthase 2 ( NOS2 ) [16] , membrane metallo-endopeptidase ( MME ) [17] and the opioid receptor kappa 1 ( OPRK1 ) [18] .
Patients with chronic pain have increased sensitivity to noxious stimuli such as heat and pressure compared to controls [19] as well as to non-noxious stimuli such as sound [20] . These observations support the notion that the processing of external stimuli is heightened or exaggerated in chronic pain states . Thus , people harbouring gene variants associated with greater sensitivity to heat pain stimulus are thought to be at increased risk of developing chronic widespread pain . The premise of this work was that understanding better the genetic influence on normal pain processing would shed light on the biological pathways underlying the pathology of chronic pain . In this project we adopted novel methods - biotechnological and statistical - to identify rare sequence variation contributing to pain sensitivity in normal individuals . The advent of high throughput genotyping technologies has helped to unravel the aetiology of many complex diseases and quantitative traits . In particular , genome-wide association ( GWA ) studies have uncovered many common variants associated with quantitative phenotypes . However , GWA is underpowered to detect association of rare variants , and the common variants identified so far explain only a fraction of the trait heritability . As whole-genome sequencing has become more cost-efficient it is now feasible to examine the effect of rare variants . The hypothesis that multiple rare variants explain a proportion of the missing heritability is gaining more attention [21] . Rare variants with moderate to high penetrance have been associated with a number of extreme phenotypes ( summarised in [22] ) . For quantitative phenotypes , sampling and comparing the extremes of traits has become an accepted strategy for identifying disease-causing rare variants in exome sequencing [23] . In this novel exome project of pain perception in normal individuals , no genetic variants of large effect were identified . Considering that the statistical power after applying stringent multiple test correction was limited , we can't exclude moderate or small contributions by individual SNVs to the experimental pain phenotype . Indeed , we have noted a differential distribution of rare variants between the pain sensitive and insensitive subjects ( Figure S2 ) , which suggests enrichment of multiple SNVs of small effect at the extremes of the normal distribution . This study also provides proof of principle of the utility of the exome sequencing method . Such an approach has been used successfully in the , albeit more limited , setting of sequencing ion channel genes in epilepsy [24] . The authors highlighted the need for cell and network analysis to optimise information obtained from such a study . A variety of statistical methods have been developed for analysis of association of rare variants with complex traits , but there remains a paucity of data regarding the genetic architecture underlying complex traits such as pain perception . For this reason we elected to use a variety of tests based on different underlying assumptions so that no rare variant associated with pain perception would be missed . GZMM was the only gene classified as having “very high” evidence of association to thermal nociception ( Table 3 and Figure 2: see Methods: statistical analysis for classification definitions ) . It encodes granzyme M , one of the serine proteases produced and stored in the granules of immune cells such as lymphocytes and natural killer cells [25] . While we could not find reports of association with pain in the literature , granzymes are known to play an important role in apoptosis [26] and in the initiation of inflammation: elevated levels have been detected in rheumatoid synovial fluid [27] and granzyme B expression increased in lesional atopic dermatitis skin [28] . In the “high” evidence category , the enzyme encoded by the seventh gene , DDAH1 , plays a role in nitric oxide generation by regulating cellular methylarginine concentrations , which in turn inhibit nitric oxide synthase . Although both anti-nociceptive and pro-nociceptive roles of NO have been reported , overproduction of NO - together with free radicals - contribute to central sensitisation and the pathogenesis of abnormal pain states via association with NMDA receptor mediated signalling events . In support of this , circulating NO has been shown to be elevated in chronic widespread pain patients [29] . The links between pain and other genes listed in Table 3 ( such as CCNJL and TBK1 ) are tenuous at present . To explore further the interplay between the SNV-containing genes identified we applied causal reasoning , an algorithm using directed molecular relationships between biological entities to identify up-stream regulators of a set of input genes [30] . We identified 4 regulatory networks that were nominally significant , one of which ( angiotensin II ) remained significant after correction for multiple testing ( correctness p = 1 . 4×10−5 , enrichment p = 3 . 8×10−4 ) . Angiotensin II is a peptide hormone involved in the control of blood pressure . This network connected 12 of our identified genes into a causal network ( Figure 3 ) . Angiotensin II has been already been implicated in central pain: it has been shown to facilitate pain-related behaviours in experimental animals [31] including responses to thermal stimuli similar to those employed in the current studies . The mechanism appears to be via the modulation of descending brainstem pathways . Blocking the receptors for angiotensin II ( so called AT1 receptors ) reverses some pain-related behaviours in models of chronic pain , suggesting a role for endogenous angiotensin II . For example , AT-1 receptor antagonist telmisartan has been shown to abrogate pain in the sciatic nerve constriction model in rats [32] . The data from several small clinical studies in humans have been conflicting [33] , [34] but a recent phase II clinical trial of a AT2 receptor antagonist ( AT2 receptors are expressed by primary afferent nociceptors ) found a significant improvement in the pain of a group of patients with post-herpetic neuralgia ( http://www . spinifexpharma . com . au/DRUG-DISCOVERY . html ) . Our causal reasoning analysis allowed for only one interaction upstream of the genes in our dataset to be included . However , allowing two interactions increased the number of genes from this study that may be causally linked to angiotensin II to 30 genes . Angiotensin II can also be causally linked to known pain relevant processes . For example , PTGS2 , the gene encoding cyclooxygenase 2 ( COX-2 , the target of the non-steroidal anti-inflammatory drugs ) is regulated by angiotensin II [35] . COX-2 produces prostaglandin E2 ( PGE2 ) , which is released in damaged or inflamed tissues and binds to nociceptive nerve terminals via PGE2 receptors ( so called EP receptors ) , leading to cAMP production . This leads to post-translational modification of several target proteins within nerve terminals that regulate nociceptor excitability , including voltage-gated sodium channels [36] . The current study using novel exome sequencing methods supports the notion that the angiotensin II pathway is important in pain regulation in man and suggests that genetic variation in the pathway may influence sensitivity to heat pain , at least in the Northern European population . A third form of analysis examined the target genes in a network of all human protein-protein interactions from the BioGRID database . We asked if any of the proteins encoded by the genes identified in this study were known to interact directly with proteins having a role in pain . We found known physical interactions with several pain-relevant proteins including ion channels ( TRPV1 and Cav2 . 1 ) , the CGRP receptor and the kappa opioid receptor . It is clear therefore that although we did not identify any genes immediately associated with nociception , several play key roles in processes linked to the reception and transduction of pain signals by their physical and biochemical interactions with important pain mediating complexes . This study highlights the potential of using a combination of sophisticated analytical methods to identify associations underlying rare variants in quantitative traits . While the predicted effect sizes are relatively small and require large samples , we have made progress in understanding the genetic architecture underlying heat pain sensitivity . Despite recent advances in both DNA sequencing technology and the statistical methods to analyse such complex datasets , the identification and follow-up of associations of individual gene variants remains a challenge . Our results lend weight to the notion that angiotensin II plays in important role in signal transduction in pain and this pathway merits further biological investigation .
HPST score was selected as the primary metric because reproducibility was greater ( intra-class correlation coefficients , HPST = 0 . 59 ( 0 . 51 , 0 . 68 ) ; HPT = 0 . 34 ( 0 . 23 , 0 . 46 ) ) . HPST was also found to have greater heritability ( HPST h2 = 0 . 44; HPT h2 = 0 . 29 ) . The two phenotypes were correlated ( r = 0 . 64 ) . To select subjects who were relatively pain sensitive/insensitive for exome sequencing , the following protocol was adopted: a subject was included only if their HPT score was in the same half of the distribution as the HPST and , in the case of MZ twin pairs , the co-twin also resided in the same HPST tail . For DZ twins , the entire pair was excluded if they fell into opposite tails; if both were in the same tail , the more extreme twin was selected . In no case were two members from a twin pair selected . In addition , three samples provided by HapMap were analysed twice – in TUK1 and TUK2 – to enable comparison of the methods . Additional detail is provided in Text S1 . DNA extracted from whole blood was sent to BGI for exome sequencing [38] . The qualified genomic DNA sample was randomly fragmented by Covaris technology with resultant library fragments 250–300 bp . Adapters were ligated to both ends of the fragments . Extracted DNA was amplified by ligation-mediated PCR ( LM-PCR ) , purified and hybridized to the NimbleGen human exome arrays for enrichment; non-hybridized fragments were then washed out . The target enrichment of the TUK1 samples were performed using hybridization to the NimbleGen 2 . 1 M array , while the shotgun libraries of the TUK2 samples were enriched using NimbleGen EZ v2 library . The captured LM-PCR products were subjected to quantitative PCR to estimate the magnitude of enrichment . Each captured library was then loaded on Illumina platforms and high-throughput sequencing was performed on each library . The BGI used Illumina GAIIx for sequencing of the TUK1 samples and a Hiseq2000 platform for TUK2 samples . Raw image files were processed by Illumina base-calling software v1 . 6 ( and v1 . 7 ) , and the sequences of each individual were generated as 75 bp ( and 90 bp ) paired-end reads for TUK1 ( and TUK2 ) sets respectively . The fastq files were generated from the raw data after removing the adapters and low quality reads . Both datasets were mapped to the NCBI Human Reference ( GRCh37; hg19 ) using BWA v0 . 5 . 5 ( v0 . 5 . 9 ) . We considered the default parameter –q 15 for read clipping , and a maximum insert size of 600 bp for proper pairing of the short reads . The alignment files for each lane were sorted and indexed by SAMtools [39] before constructing the library-level bam files . We also tried to improve the accuracy of the base quality scores by running a recalibration stage using Genome Analysis Toolkit ( GATK ) v1 . 0 . 5777 [40] . On average 5% of each library was contaminated with duplicate fragments , which were removed before variant calling . An extra step of local re-alignment was applied only to the TUK2 data to improve the sensitivity and specificity of mismatches near indel sites . For quality control ( QC ) of the TUK1 data , we studied the histogram of depth distribution , the distribution of inferred insert sizes in the bam files , the GC content distribution for reads mapped to forward and reverse strands , the depth of coverage as a function of percentile of unique sequences ordered by GC content , and the fraction of each chromosome covered by the exomes . The distribution of per-base sequencing depth for each sample was evaluated as was the cumulative depth distributions in target regions , and sequencing depth and coverage of the target region per chromosome . The TUK2 dataset had a slightly higher depth of coverage over the capture target region ( CTR ) , with average 71× depth ( compared to 69× for TUK1 ) , whereas average coverage of the CTR was 97 . 5% for TUK2 ( compared to 96 . 5% in TUK1 ) . In the TUK1 panel we discarded and re-sequenced a few lanes , which showed very low target coverage; hence requiring all the exomes to cover more than 70% of the CTR by at least 20× in both datasets . We observed that although the mean depth was comparable , the fraction of CTR covered at a given depth was generally lower in TUK2 set , e . g . CTR coverage at ≥20× was 80 . 3% for TUK2 compared to 89 . 1% for TUK1 . This alludes to the greater coverage uniformity of the 2 . 1 M array compared with that of the solution-based EZ sequence capture . For TUK1 , we ran SAMtools v0 . 1 . 8 ‘pileup’ while limiting maximum depth for indels to 500 . Then we filtered the SNVs ( with ‘varFilter’ ) with SNV and indel Phred-scale quality scores less than 20 , and minimum and maximum depth at 8 and 300 respectively . The GATK v1 . 0 . 5777 was run for TUK1 using default values and a minimum confidence threshold 30 and minimum read mapping quality at 10 . We subsequently filtered the GATK SNVs by keeping only those with alternative allele quality score ≥ 20 and depth within [8 , 300] interval . For TUK2 , we ran SAMtools v0 . 1 . 16 ‘mpileup’ together with ‘bcftools’ using default parameters , but requiring the SNV quality score and depth interval to satisfy the same criteria of the TUK1 set ( i . e . QUAL≥20 and 300≥DP≥8 ) . The GATK calling for TUK2 data followed the same procedure as for TUK1 . We further filtered all the variants outside the capture target region . Overlapping results SAMtools and GATK were extracted . The discordance ( about 5% ) was largely attributed to unique calls , however we observed a small fraction ( less than 1% ) of SNVs called by both algorithms were assigned mismatching genotypes ( homozygous non-reference vs heterozygous ) . We ran the GATK on the coordinates of the overlap to determine the non-variant genotypes hence adjusting the missing rates . The single-sample variant files were then merged ( using ‘merge-vcf’ ) into two large variant call files ( VCF ) each containing the entire sample variants . Table S1 compares the SNV statistics for TUK1 and TUK2 samples . We evaluated the genotype concordance between the exome and pre-existing GWAS datasets . We observed greater agreement between GWAS and TUK2 ( average 99 . 8% ) than GWAS and TUK1 ( 99 . 3% concordance ) ( Table S1 ) . Three samples were identified as highly discordant with GWAS ( 52% , 54% and 51% rates ) . A multi-dimensional clustering analysis of these three exomes together with the entire GWAS dataset for 5 , 654 twins , confirmed that they were true outliers so were excluded from statistical analysis . Duplicate samples in TUK1 allowed estimation of genotype error rate . Out of ∼35 M bases on the 2 . 1 M array which had been genotyped , 295 and 374 sites were discordant between duplicates . This sets a type 1 error rate for genotyping of approximately 1 . 0e-05 , or 0 . 001% . The wide variety of methods to analyse rare variants generally fall into three broad categories: “collapsing” methods , which test for differences in rare variant accumulation; “carrier-based” tests , which test for differences in the number of subjects carrying a certain class of variant ( usually at least partially based on frequency thresholds ) ; and “multivariate” tests , which test for differences in variant patterns , and is further subdivided into kernel-based and regression-based methods . Using several tests from each category we ran 21 different gene-centric variant burden tests on the TUK1 set and the results correlated ( and displayed as a “heat” map , Table S2 ) . A pair of tests was selected from each category/correlation block based on the correlation matrix and the QQ plots ( Figure 1 ) . The six statistical methods selected for this project were:- A primary list of genes harbouring rare variants was drawn up based on combining the p-values from TUK1 and TUK2 sets using Fisher's method . To identify signals from genes with concordant variant patterns across TUK1 and TUK2 datasets , the top genes from the merged raw TUK1 and TUK2 datasets were also considered as relevant signals . This combination did not comprise the primary list because the TUK1 and TUK2 sequencing were performed on different capture platforms: some regions did not overlap between the two . Further details are provided in Supporting information . In addition to the issue of combining TUK1 and TUK2 was the challenge of combining and sorting the results of the 6 gene-centric variant burden tests which were relatively new and not well understood . Because the 6 tests comprised 3 pairs of similar test methods ( Table S2 ) we considered that a result was not robust if it was significant for only 1 test category . Significance in more than 1 category added confidence that a result was less likely to be a false signal . To prioritize genes that were either significant in more than one category or consistently significant for both tests within a pair , we prioritized genes based on the 2nd lowest p-value from the 6 selected tests . This approach also ensured that the top gene list could not be dominated by anomalies from a single test . Significant results using the 2nd lowest p-value were obtained in two ways: from combining the TUK1 and TUK2 p-values via Fisher's formula , and by merging the datasets ( Table 3 ) . A gene was classified as “High” evidence if its 2nd lowest p-value achieved p<0 . 00044 ( the p-value such that replication would achieve a genome-wide significant meta-analytic p-value ) , and “Very High” if this occurred with the combined dataset being more significant than the combination of the p-values across the two halves . “Medium” priority was given any gene which achieved p<0 . 001 for its 2nd lowest p-value in either the merged dataset or the combination of the p-values across the two halves . After removing genes showing an opposite direction of effect and after merging the datasets , we identified 138 unique genes having a 2nd lowest p-value<0 . 01 . These were considered for more detailed analysis . We looked first for enriched Gene Ontology categories within these genes using DAVID [10] with an EASE p-value<0 . 05 . Then we undertook causal reasoning [11] which uses a large curated database of directed regulatory molecular interactions to identify the most plausible upstream regulators of a gene set . Consequently it allows the recapitulation of regulatory networks/pathways associated with genes of interest . The method offers two measures of statistical significance . The enrichment p-value corresponds to a standard gene set enrichment test on the set of downstream genes , whereas the correctness p-value takes the direction of regulation into account . For the latter , each associated gene was considered as a down-regulated transcript in the causal reasoning network ie . assumed loss-of-function mutations . As a background set for the significance calculations we considered the intersection of the set of all genes covered in either the TUK1 or TUK2 study and all transcripts in our causal reasoning database . This set consists of 9275 genes . A regulatory hypothesis was considered nominally significant with a p-value<0 . 05 and significant at a 0 . 05 level after application of the Bonferroni correction for multiple testing . As we are considering 1108 potential upstream regulators in the underlying database , a Bonferroni corrected p of 0 . 05 corresponds to a nominal p-value of 4 . 5×10−5 . Finally , we searched for direct physical interactions between proteins identified in this study and proteins known to have a role in pain using protein interaction data from the BioGrid database [46] . | Chronic widespread pain is a complex clinical problem . Identification of underlying genetic factors would shed light on the biology of pain and offer targets for novel therapies . We aimed to identify rare genetic variants in the normal population associated with pain sensation by performing exome sequencing on individuals who were more or less sensitive to heat pain . While we did not identify any single variants having large effect , we did observe major group differences between the sensitive and insensitive individuals . Network analysis suggested a role for the angiotensin pathway , which previous work in animal models has suggested is important in pain mediation . Our results cast light on the genetic factors underlying normal pain sensation in humans and the utility of exome analyses . It suggests that further exploration of the angiotensin pathway may reveal novel targets for the treatment of pain . | [
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Perfusion bioreactors regulate flow conditions in order to provide cells with oxygen , nutrients and flow-associated mechanical stimuli . Locally , these flow conditions can vary depending on the scaffold geometry , cellular confluency and amount of extra cellular matrix deposition . In this study , a novel application of the immersed boundary method was introduced in order to represent a detailed deformable cell attached to a 3D scaffold inside a perfusion bioreactor and exposed to microscopic flow . The immersed boundary model permits the prediction of mechanical effects of the local flow conditions on the cell . Incorporating stiffness values measured with atomic force microscopy and micro-flow boundary conditions obtained from computational fluid dynamics simulations on the entire scaffold , we compared cell deformation , cortical tension , normal and shear pressure between different cell shapes and locations . We observed a large effect of the precise cell location on the local shear stress and we predicted flow-induced cortical tensions in the order of 5 pN/μm , at the lower end of the range reported in literature . The proposed method provides an interesting tool to study perfusion bioreactors processes down to the level of the individual cell’s micro-environment , which can further aid in the achievement of robust bioprocess control for regenerative medicine applications .
The culture of stem cell populations in dynamic set-ups , for example in perfusion bioreactors , holds great potential for the production of tissue engineered constructs [1] . The use of bioreactors permits automated seeding and expansion of progenitor cells [2 , 3] , facilitating the production of clinically relevant cell populations in close systems , while maintaining their phenotype and bone forming potential [4] . Additionally , these systems can provide controlled biomechanical stimuli , such as fluid flow-induced shear stresses , that might significantly affect stem cell properties during dynamic culture in bioreactors . For example , mechanical stimuli have been associated to early stem cell lineage commitment [5] and osteogenic priming in the absence of inductive growth factors [6–8] . Moreover , they have been shown to further promote osteogenic differentiation of bone marrow , periosteum and adipose derived osteochondroprogenitor cells in the presence of osteoinductive growth factors [9–13] . Osteogenic differentiation has been linked to the magnitude of shear stress , showing dose dependent enhancement of extra-cellular matrix deposition and subsequent mineralization by the cultured cells [14–17] . In order to characterize the dynamic environment throughout cell seeded scaffolds in perfusion bioreactors , many Computational Fluid Dynamics ( CFD ) modeling studies have been presented in the past decade [18–22] . However , the majority of these studies considered empty scaffold geometries without incorporating a cell domain . Recently this issue was addressed by representing the growing neotissue as a porous medium in order to model the effect of neo-tissue growth on the flow profile [23–27] . Still , these models predict the local distribution of shear stress and pressure throughout a volume averaged porous domain and do not take into account the local mechanical and geometrical environment of individual cells . Mechano-transduction of stress induced by shear flow conditions is highly localized at specific areas of the cell’s interface with its environment , such as focal adhesions , FAs [28] , and primary cilia [5] . The latter have been shown to be involved in the osteogenic response of bone cells to dynamic shear flow conditions [29] , as well as in remodeling of the extracellular matrix [30] . The amount of force perceived at the level of FAs as a result of external flow conditions is influenced by the cell’s mechanical properties , cell shape and the geometry of its microscopic environment , e . g . location of attachment points , and presence of extracellular matrix ( ECM ) . In this respect , the concept of cell cortical tension has gained a renewed interest in the last years as a mediator of mechano-transduction processes [31] . Cortical tension is created by the cell itself through active acto-myosin contractility , resulting in a prestressed cytoskeleton . This self-generated stress is an essential aspect of the tensegrity theory as introduced by [32] , which posits that the cytoskeleton constitutes a tensegrity structure , with tension generating cortical stress fibers as ‘ropes’ and load bearing capacity provided by other cytoskeletal elements , the substrate or the ECM . Next to additional structural integrity , the mechanically stressed state of a cell can boost the mechano-sensitivity of a cell [33] . External flow however , can also contribute to locally elevated levels of cortical tension , especially close to attachment point such as FAs . This passive source of cortical tension , as well as its importance relative to the cell-generated active tension and prestress , has not yet been investigated for perfusion cell culture systems . Therefore , a scaling gap exists between small scale i . e . ‘single cell’ and ‘neotissue/whole scaffold’ macro-scale that needs to be bridged . Computational models using realistic single cell geometries are a prime candidate for facilitating this task . Similar concepts of cross-scale model integration in order to capture mechanical interactions across scales from a whole organ to single cell level have been already described and envisaged [34 , 35] and have served as a paradigm for the current study focusing mostly on a scaffold-based in vitro process . Computational models of cell deformation due to shear flow have been developed considering the cell as a 2D Gaussian interface [36] or a 3D linear elastic solid [23 , 37–47] . The latter use a mixed Lagrangian-Eulerian formulation to solve the Fluid-Structure Interaction ( FSI ) problem , with a coupling through continuity boundary conditions . Additional numerical methods have been recently developed for modeling fluid-flow driven solid deformations in a biomechanical context . Immersed finite element methods have been used for modeling soft tissue deformation under the influence of blood flow [47] and within the walls of the aortic root [48] . In addition cell motility and deformation through contracted channels reminiscent of microfluidic experiments were also captured using a similar method operating with a single analysis mesh for solid and fluid that was not subjected to any deformation [49] . For larger deformations , the interaction between cell and fluid has been resolved by means of the level-set method [50] . Alternatively , the Immersed Boundary Method ( IBM ) is able to explicitly take into account discrete entities in the cell’s cortex and , possibly , its internal cytoskeletal structure . It has been used to model the movement and deformation of vesicles , red blood cells and bacteria under flow conditions [51 , 52] . An FSI model for osteoblasts attached to scaffold struts was recently published [53] , with a rigid single cell consisting of a half-sphere with two focal adhesion points . In the work presented in this study , more realistic cell shapes are introduced , which are not rigid but deform due to the fluid flow . Still , the cytoskeleton constitutes a highly complex , mechanoadaptive material [54–56] and its mechanical behavior differs between various temporal and spatial scales , [57 , 58] . Hence at present , only a strongly simplified mechanical representation of a complete attached cell is considered computationally feasible . The main purpose of this study is to use the IBM to investigate fluid-induced mechanical stimuli on progenitor cells used for bone tissue engineering ( human periosteal derived cells , hPDCs ) attached to regular pore titanium scaffolds inside a perfusion bioreactor set-up . Each cell is represented by a simplified model of the cortical shell , similar to [59] , supplemented with discrete Focal Adhesions ( FAs ) and an elastic nucleus . A multi-scale modeling approach is presented , consisting of a CFD analysis at the scaffold macroscopic ( tissue ) scale in order to determine suitable input boundary conditions at the microscopic scale ( single cell scale ) where the fluid-structure interaction is modeled by means of the IBM . The impact of the spatial location of the cells within the scaffold during flow perfusion on a number of key mechanical quantities at the cellular scale was investigated . To illustrate how ( location-induced ) geometrical differences might affect the biomechanical environment of single cells , three characteristic locations and corresponding cell geometries were chosen: one cell spread along the direction of the flow ( A ) , one facing the flow ( F ) and one bridging between two struts ( B ) . Furthermore , the presented model was used to assess how small clusters of cells attached to the scaffold are mechanically affected by perfusion flow . In order to investigate mechanical effects of mutual shielding [60] , a ‘three cells configuration’ ( T ) facing the flow was investigated .
The IBM has been developed for simulating moving , deformable membranes immersed in a fluid , based on a combination of an Eulerian and a Lagrangian approach [61] . The deformable object ( the cell in this study ) , is represented by a discretized membrane/cortex Γ ( t ) and is able to move freely through the fixed Eulerian mesh Ω on which the flow is computed . The interconnection between both lattices is accomplished by means of a smoothed Dirac function δ . In the 3D mesh Ω , the equations for incompressible Stokes flow are solved ( as appropriate for the low Reynolds numbers typically encountered in bioreactors , see also in the supplementary information ) : −μΔu+∇p=F , ( 1 ) ∇⋅u=0 , ( 2 ) with suitable boundary conditions which are explained in following section . In Eqs ( 1 ) and ( 2 ) , u represents the fluid velocity , p the pressure and μ the viscosity . The influence of the cell boundary Γ ( t ) immersed in the fluid is taken into account through the distributed force density F and can be expressed as: F ( x , t ) =∫Γ ( t ) f ( s , t ) δ ( x−X ( s , t ) ) ds . ( 3 ) Here , x are the Eulerian coordinates and X ( s , t ) are the discretized cell membrane coordinates indicating the position of the membrane at time t . As mentioned previously , the interaction between both meshes is realized through the introduction of a Dirac function δ defined by the following continuous function of the distance r: δ ( r ) ={14 ( 1+cos ( π|r|2 ) ) , |r|≤20 , |r|>2 ( 4 ) Using Eq ( 4 ) , Eq ( 3 ) can be rewritten in a discrete formulation F ( x , t ) =∑i=1Nfi ( s , t ) δh ( x−Xi ( s , t ) ) ds , ( 5 ) with N the number of nodes of the cell membrane , h the Eulerian mesh size and δh ( x ) =1h3δ ( xh ) δ ( yh ) δ ( zh ) . ( 6 ) Once the flow is computed , the membrane positions are updated using the following equation of motion: dX ( s , t ) dt=U ( X ( s , t ) , t ) , ( 7 ) with U the interpolated flow velocity on Γ ( t ) which can be expressed as follows: U ( X ( s , t ) , t ) =∫Ωu ( x , t ) δ ( x−X ( s , t ) ) dx . ( 8 ) The mechanical representation of a cell in the Eulerian domain relies on the work presented in [62] where the underlying mechanisms and assumptions are discussed in detail . Briefly , the model assumes that most of the cytoskeletal material is present in a relatively thin cortical shell and that an elastic description of deformations at short timescales is adequate . Hence , the mechanical properties of this cortical shell will account for the mechanical response of the complete cytoskeleton . The immersed boundary which represents the cell is composed of a triangulated surface with a stretching stiffness ks and a bending energy kb . Finally , the cell nucleus is represented as a submerged solid elastic sphere with Young’s modulus En . –see Fig 1 . The linear spring force of node i for each connected node j at distance dij and resting length d0ij is expressed as: fsij=ks ( d0ij−dij ) eij , ( 9 ) where eij denotes a unit vector pointing from i to j . A moment of bending is computed between all adjacent triangles k and m with angle θkm and resting angle θ0km: Mbkm=kbsin ( θkm−θ0km ) . ( 10 ) A force corresponding to this moment is applied to the non-common points of each of the two triangles , and a compensating force is applied to the common edge points , ensuring that the total force on the cell remains unchanged , i . e . for two triangles with common nodes c1 and c2 and non-common nodes lk and lm: fc1=fc2= −Mbkm2 ( nkLk+nmLm ) , flk=MbkmLknk , flm=MbkmLmnm . ( 11 ) Lk and Lm are the distances from resp . node lk and lm to the line containing the common edge , and nk and nm are the normal unit vectors of triangle k and m . We denote the total bending force contribution of all adjacent triangle pairs of node i as fbi . This type of bending stiffness is commonly found in the literature for Red Blood Cell models [63] . The cell’s volume is maintained through an effective bulk modulus K . For this , an internal pressure Pv is computed based on a cell’s volume V and equilibrium volume V0: Pv=KV0−VV . ( 12 ) Subsequently , a force fvi=PvAini is obtained for each node i with Ai and ni respectively the area and outward normal unit vector of each node , both of which are calculated using a discrete version of the Laplace-Beltrami operator . Furthermore , the nucleus is represented as a solid , elastic sphere with Young’s modulus En for which contact with the cortical nodes is considered Hertzian , i . e . fni={ 4EnRn3δni3/2eni , δn>00 , δn≤0 ( 13 ) for node i indenting a nucleus with radius Rn and overlap distance δni , and with eni a unit vector pointing from the nucleus center to node i . Discrete attachment points serve as Focal Adhesions ( FAs ) . These points are placed outside of the fluid domain and are therefore not displaced by the fluid . Finally , the total force per node fi ( s , t ) is computed as the sum of all aforementioned partial forces: fi ( s , t ) =fsi+fbi+fvi+fni . ( 14 ) Cell cortical stiffness was measured using Atomic Force Microscopy ( AFM ) . Measurements were performed using a Nanowizard 3 BioScience AFM ( JPK ) with a working range of 100×100×15 μm mounted on the stage of an inverted microscope ( Olympus 1 ) placed on a vibration-isolation table . A V-shaped gold-coated silicon nitride cantilever with a four-sided pyramidal tip ( Budget Sensors ) with a nominal tip radius rtip of 15 nm and an opening angle θ of 35 degrees was used as the probe . The spring constant kspring of the cantilever was ca . 0 . 3 Nm−1 . Exact values have been calibrated using the thermal fluctuation method . Force curves have been recorded at 5 μm/s approach and retract speed , of which only the approach curves have been analyzed to arrive at the instantaneous Young’s modulus using the Sneddon model for forces > 200 pN . We neglect the information at low indentations , since according to [64] , the Sneddon model is accurate at higher indentation δ , allowing us to extract the cortical Young’s modulus Ec as: F=Ectan ( θ ) 2 ( 1−ν2 ) δ , ( 15 ) where F is the measured force . Assuming a Poisson’s ratio ν of 0 . 5 [65] , we can fit this formula to the typical force-indentation curves obtained by AFM for every pixel on the cell’s surface ( we use the Levenberg-Marquard algorithm in MINPACK through its python-interface provided by SciPy for curve-fitting ) . To extract the stiffness of the cortical layer , we select regions on the cell away from the nucleus where the average cell height is very low so that we can assume that the measured stiffness is indeed the compressive stiffness of the cell’s cortex and not dominated by effects from bending of the cortical layer or the intra-cellular fluid—see S1 Text and S2 Fig . To limit the influence of the underlying substrate , the maximal force was chosen to keep the indentation depth to less than 20–30% of the height of cortex cell thickness . The full procedure to select usable patches within the AFM stiffness maps is detailed in the supplementary information . The global average over all measured cells and all patches yields an estimated cortical stiffness Ec = 3 . 5 ± 2 kPa ( Fig 2 ) . For a relatively thin cortical ‘sheet’ , and assuming that the cortex consists out of some homogenous elastic material , the stretching stiffness ks and bending energy kb can be related to the cortical Young’s modulus Ec and the cortical thickness tc: kb=Ectc312 ( 1−νc2 ) ( 16 ) ks=2Ectc3 ( 17 ) where we usually assume the Poisson’s ratio of the actin cortex vc to be close to 0 . 5 [66] . Having determined the effective stiffness of the cortical shell and its thickness from AFM , these equations allow us to calculate the parameters of the mechanical cell model . To evaluate our procedure , these estimated mechanical parameters can be compared to simulated Micropipette Aspiration ( MA ) . Hereto , a simulation was set up where a spherical cell is aspirated into a thin cylindrical structure with a rounded tip and radius Rp—Fig 3 . The relationship between the applied under-pressure in the pipette and the aspirated length Lp of the cell expresses an effective equilibrium Young’s Modulus E∞ which can be compared to experimental values obtained using the same technique [67]: ΔP=2π3E∞LpRpΦ ( 18 ) where Φ ≈ 2 . 1 is a scaling factor . The cell’s Young’s modulus obtained by applying this procedure—see Fig 3—from the parameter values estimated from AFM measurements ( Table 1 ) is 194 . 7 Pa , which compares well to measured values from MSCs; e . g . [68] report Young’s moduli in the range of 150–350 Pa . The flow profile around a single cell attached to the scaffold is computed by solving the immersed boundary problem at the scale of the investigated cell . For this purpose , the Eulerian computational domain Ω corresponds to a box of a few hundred microns wide/long containing the cell and not the whole scaffold pore—see Fig 4F . In order to obtain the magnitude of flow velocity which is to be used as a Dirichlet boundary condition on the microscopic domain Ω , see Eqs ( 1 ) and ( 2 ) , Stokes’ equation was solved on an entire scaffold pore . An inlet velocity corresponding to the bioreactor flow rate Qin was set at the entrance of the pore and symmetry boundary conditions were applied on each sides of the pore–Fig 4A . Next , the calculated flow velocities vms at specific locations in the scaffold pore were used to extract suitable boundary conditions vib for the IBM problem–Fig 4D . These locations are indicated in Fig 4E and correspond to characteristic positions inside a pore: a cell on a cylindrical strut with flow parallel to the cylinder axis ( A ) , a cell on a cylindrical strut with flow perpendicular to the cylinder axis ( F ) , a small cluster of three interconnected cells on a cylindrical strut with flow perpendicular to the cylinder axis ( T ) and a single cell attached on a strut junction , forming a bridge between two perpendicular struts ( B ) . At each of these locations , a spread out cell was positioned that conforms to the local geometry of the scaffold strut ( s ) –see Fig 5 . The procedure that was used to obtain the detailed cell shapes is explained in the supplementary information . All cells were attached with discrete FAs which are located on the surface of the struts and of which the position did not change in time . The Immersed Boundary implementation was realized using the Finite Element software FreeFEM++ [70] , which solves the Stokes flow problem , with the Lagrangian forces computed in a coupled module implemented in the particle-based simulation platform Mpacts [71] . This procedure was approved by the ethics committee for Human Medical Research KU Leuven ( ML7861 ) . Patient informed written consent was provided by the legal guardian .
In the presented work , a novel application of the immersed boundary method was developed , representing a deformable cell exposed to microscopic flow and attached to a 3D scaffold inside a perfusion bioreactor . Cells were represented by a deformable Lagrangian surface mesh , which was immersed in an Eulerian fluid domain , with flow in the Stokes regime . We demonstrated the effect of shear flow for multiple realistic geometrical cell configurations and strut locations inside a regular pore scaffold . This tool can be used to estimate shear flow conditions directly on the surface of individual cells , and assess the micro-scale variability of mechanical conditions inside single scaffold pores . The instantaneous cell stiffness was measured using AFM experiments on hPDCs , and the mechanical model was calibrated using micro-pipette aspiration simulations in the range of short term deformations . Simulations confirmed that mechanical cues originating from the flow are highly dependent on the exact geometry of the cell and its environment . For example , a cell on a cylindrical strut with flow perpendicular to the strut will experience a much larger shear stress than a cell on a similar strut with parallel flow . This should be a major consideration when designing novel scaffold designs . Moreover , it was found that wall shear stress calculated in the empty scaffold would underestimate the actual maximal wall shear stress experienced by the cells by a factor of two in the investigated cases . Next , the model was used to estimate the additional instantaneous flow-induced cell deformation , tension and pressure . Compared to the cell-generated deformation and tension due to acto-myosin activity these values are very small for the applied realistic flow conditions . For example , the predicted magnitude of additional cortical tension due to flow is much lower than the pre-stress of a spread out cell . Hence , it is not expected to affect the tensional homeostasis of a cell in steady-state conditions . Nonetheless , in cyclic conditions , small deviations from the resting state can still have pronounced biological effects , as is explained by the tensegrity model [32 , 33] . In the same vein , small cytoskeletal deformations away from the cell's resting configuration might alter the configuration of several intracellular mechanosensing molecules , which are linked to downstream targets in pathways such as the mitogen-activated protein kinase ( MAPK ) or phosphoinositide-3-kinase ( PI3K ) pathways [84] . Since the predicted strain of the cell is highest near its attachment points on the substrate , which also constitute focal sites of mechanotransduction machinery , a discernible biological response could result even from small deformations , as long as the dynamics of the perturbations in flow conditions are faster than the relaxation time of tensional homeostasis . In the current work , a strongly simplified model for the mechanical behavior of single cells was used , which limits its predictive value to small deviations from the resting state . A more in-depth computational analysis of the mechanisms at play involved in large cell deformations would require a more detailed model of the mechanoadaptive behavior of the cytoskeleton and will be reserved for future research . Similarly , the effect of shear flow on the long timescale viscous-like deformation of living cells remains to be investigated . This would also require a more elaborate description of the cell’s mechanical behavior , which for this study was greatly simplified and limited to linearly elastic deformation . The model’s limitation of small deformations and short timescales will mainly restrict in scope the predictions of cell deformation and cortical tension , whereas predictions of shear stress and local pressure , being surface properties , are expected to remain largely unaffected . Finally , to simulate adhesion and detachment behavior ( e . g . in very high shear flows ) , the presented methodology has to be extended since adhesion is only implicitly captured by placing FAs out of the fluid domain thereby fixing them in space independently of applied forces . For this , an adhesion force formulation as proposed in [62] could be included . A parameter study ( see supplementary information ) showed linear behavior in the relevant cell-mechanical and flow parameters , showing that the model can be used to inter-/extrapolate to different cell types and flow conditions . For a cell of thickness 5 μm facing flow on a strut of diameter 200 μm , the wall shear stress can be estimated as: τ ≈ 0 . 08·Qin . Evidently , the maximal wall shear stress experienced by a cell does not depend strongly on the cell’s mechanics . This implies that even though a cell may undergo structural changes ( e . g . migration , re-alignment ) , it can still reliably ‘sense’ the shear flow ( e . g . with its primary cilium ) . This study constitutes an important step towards model-based control of a cell’s biophysical micro-environment ( stem cell niche engineering ) in a perfusion bioreactor . | Tissue Engineering involves the combination of cells , growth factors and biomaterials into artificial constructs which , upon implantation , can improve the healing capacity of the human body . A remaining challenge involves providing physical stimuli to individual cells , thereby guiding them towards the properties of the desired tissue type . Perfusion bioreactors try to control the local concentration of oxygen , nutrients and growth factors and mechanical stresses by varying the fluid flow . In this work , we predict the shear stress that individual cells experience at the microscopic scale , as a function of the bioreactor inlet flow velocity , by making use of the immersed boundary method . This method combines an Eulerian grid ( fixed in space ) with a Lagrangian grid ( moving with the flow ) to model the deformation of cells due to flow inside a scaffold pore . Our simulations show that the local shear stress levels on specific , realistic cell geometries are different from the shear stress levels on empty scaffolds , which are often still used as a reference . Finally , we predict and discuss the additional effect of realistic flow on other mechanical cell properties , such as its deformation and its cortical tension . | [
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... | 2016 | Immersed Boundary Models for Quantifying Flow-Induced Mechanical Stimuli on Stem Cells Seeded on 3D Scaffolds in Perfusion Bioreactors |
Understanding the mechanism of infection control in elite controllers ( EC ) may shed light on the correlates of control of disease progression in HIV infection . However , limitations have prevented a clear understanding of the mechanisms of elite controlled infection , as these studies can only be performed at randomly selected late time points in infection , after control is achieved , and the access to tissues is limited . We report that SIVagm infection is elite-controlled in rhesus macaques ( RMs ) and therefore can be used as an animal model for EC HIV infection . A robust acute infection , with high levels of viral replication and dramatic mucosal CD4+ T cell depletion , similar to pathogenic HIV-1/SIV infections of humans and RMs , was followed by complete and durable control of SIVagm replication , defined as: undetectable VLs in blood and tissues beginning 72 to 90 days postinoculation ( pi ) and continuing at least 4 years; seroreversion; progressive recovery of mucosal CD4+ T cells , with complete recovery by 4 years pi; normal levels of T cell immune activation , proliferation , and apoptosis; and no disease progression . This “functional cure” of SIVagm infection in RMs could be reverted after 4 years of control of infection by depleting CD8 cells , which resulted in transient rebounds of VLs , thus suggesting that control may be at least in part immune mediated . Viral control was independent of MHC , partial APOBEC restriction was not involved in SIVagm control in RMs and Trim5 genotypes did not impact viral replication . This new animal model of EC lentiviral infection , in which complete control can be predicted in all cases , permits research on the early events of infection in blood and tissues , before the defining characteristics of EC are evident and when host factors are actively driving the infection towards the EC status .
A minority of HIV-1-infected patients , defined as elite controllers ( ECs ) , demonstrate that effective control of viral replication and disease progression is possible in the absence of treatment [1] . Deciphering the mechanisms of natural infection control in these patients is a critical step for designing successful vaccines and is considered a priority in the field [2] . Research on human ECs , however , is restricted to studies of the chronic infection , samples collected at random time points , and limited access to tissues , all precluding identification of early events responsible for virus control . These shortcomings have likely prevented a clear understanding of the immune mechanisms driving infection towards elite-controlled status . An animal model in which complete control of viral replication can be achieved in all cases has strong potential to effectively complement EC research in humans . Pathogenic HIV and SIV infections of humans and RMs are characterized by progression to AIDS in a variable time frame [3] and are associated with: ( i ) massive , continuous viral replication [4] , [5] , with VL set-points being predictive for the time of progression to AIDS [6]; ( ii ) continuous depletion of CD4+ T cells in peripheral blood [7] which is more pronounced at mucosal sites [8] , [9] , [10] , and ( iii ) high levels of T cell immune activation [11] , the magnitude of which has been reported to be predictive of disease progression [11]; ( iv ) only partial control of viral replication through immune responses [12] . The interaction among these factors cripples the immune system and eventually results in severe immunodeficiency and death [7] . Antiretroviral ( ARV ) treatments have improved survival , but they do not always provide complete control of viral replication [13] and are plagued by the issues of ARV resistance and multiple side effects , which limit their long-term use [14] . On the other hand , effective HIV/SIV vaccines are not yet available to prevent the spread of HIV [15] , [16] . A fraction of HIV-infected patients ( 1–5% ) are long-term nonprogressors ( LTNP ) , with ECs being a subset of long-term nonprogressors . The main characteristics of LTNP infections are ( i ) infection for more than 7 years; ( ii ) stable CD4+ T cell counts greater than 600 cells/µl; ( iii ) low/undetectable levels of HIV in the peripheral blood; ( iv ) no symptoms of HIV-induced disease; ( v ) apparent control of viral replication through vigorous CD4+ and CD8+ T cell responses against HIV suggesting , but not proving that these cells may be causally related to virus control [1] . These vigorous immune responses against HIV are characterized by multifunctional , persistent CD8 responses , vigorous HIV-specific interferon ( IFN ) -γ and antiviral β-chemokines ( including RANTES ) , CD4+ T cell responses and macrophage inflammatory protein MIP-1α and MIP-1β responses [17]; and ( vi ) lower expression of PD-1 and CTLA-4 in LTNPs than in normal progressors , with the ECs having the least expression [18] . All these features occur in the absence of any ARV therapy . Similarly , a minority of SIVmac-infected RMs is characterized by better control of SIV infection , and these are also referred to as ECs [19] , [20] . Some EC infections may either be intrinsic to the infecting viral strain ( i . e . , nef-defective strains in humans or RMs ) , or host ( i . e . , heterozygosity for the CCR5Δ32 allele ) . However , the most relevant category for understanding the mechanisms of protection in EC infection is the one in which control is achieved through effective host responses . There is a consistent association between certain class I alleles and EC status , however the mechanistic role of some of these alleles ( i . e . , HLA-B5701 ) in the control of HIV remains an open question [1] . Conversely , viral control is immune-mediated in human EC infections associated with B27 allele , or those associated with B*08 allele in RMs [1] . Finally , about 40% of both human and RM ECs have no identified host genetic traits associated with viral control [1] . Therefore , our understanding of the mechanisms of EC infection would greatly benefit from the possibility to study an EC infection before the control is actually achieved , but when factors driving infection to EC status are likely acting . The mechanisms underlying the spontaneous control of SIV infection in ECs can provide clues for the design of effective vaccine strategies or for the development of a functional cure of HIV infection ( defined by complete and durable control of the HIV infection in the absence of virus eradication ) . Here we report the development of an animal model of elite controlled infection in which control occurs in 100% of cases and thus can be predicted at the stages of infection in which the virus is still actively replicating . This animal model is based on RM infection with SIVagm . sab , which is characterized by robust acute viral replication and immune activation , massive acute mucosal CD4+ T cell depletion , followed by complete control of viral replication during the chronic stage , which results in complete recovery of immunologic injuries inflicted during the acute infection . We also report that complete control of SIVagm infection in RMs can be reversed following CD8 depletion in vivo , demonstrating persistence of the SIVagm in RMs and suggesting control through immune responses . This new animal model of “super elite” controlled infection can be use to understand a functional cure of HIV infection .
Twelve RMs were intravenously infected with 100 TCID50 of SIVagm . sab92018 directly derived from an acutely-infected AGM [21] . Four of them were followed for more than 6 years post-infection ( p . i . ) . Two RMs were followed for 450 days p . i . , while the remaining RMs were serially sacrificed during acute and early chronic infection . During the acute infection , RMs infected with SIVagm showed significant lymphadenopathy . One RM developed a rash , while 6 of them experienced weight loss and fever . However , after three months , SIVagm-infected RMs controlled viral replication ( see below ) and none of them showed any clinical or biological signs of disease progression during the follow-up . SIVagm . sab showed active replication in RMs during primary infection , with peak plasma VLs ( 107–109 copies/ml ) occurring by day 10 p . i . ( Figure 1a ) . These VL levels are in the range of those reported for pathogenic SIVmac and HIV-1 infections in RMs and humans , respectively . In contrast to the high viral replication during the acute infection , the postpeak SIVagm . sab VLs continuously declined in RMs until VLs became undetectable , by days 72–98 p . i . and remained so during the follow-up ( Figure 1a ) . SIVagm RNA VL dynamics in the intestine generally paralleled that observed in plasma ( Figure 1b ) . The high levels of viral replication in the intestine during the acute SIVagm infection of RMs were confirmed by in situ hybridization ( Figure S1 ) . Blips of very low levels of viral replication could be documented at mucosal sites during the first stages of chronic infection , up to day 400 p . i . ( Figure 1b ) . Thus , SIVagm . sab replication in RMs during chronic infection is clearly different from both the replication patterns described in pathogenic SIV/HIV infections ( where set-point VLs are established that have predictive values for the duration of disease progression , and increases in VLs occur with disease progression ) and in nonpathogenic natural infections of African NHPs ( where set-point VLs are maintained indefinitely ) [22] , [23] . We concluded that ( i ) the undetectable VLs that last up to 4 years define 100% of SIVagm-infected RMs as ECs; and ( ii ) SIVagm . sab replication is not restricted in RMs and its control during the late stages of SIV infection is not due to the inability of the virus to replicate in RMs . The complete control of SIVagm replication in chronically-infected RMs was further suggested by the dynamics of anti-gp41 SIVagm antibodies in RMs , as detected by a specific SIVagm . sab peptide mapping the immunodominant epitope of gp41 using an in-house ELISA [21] . Seroconversion occurred in all animals by day 21 p . i . ( Figure 2a ) . With the complete control of viral replication during chronic infection , the animals seroreverted starting from day 360 p . i . ( Figure 2a ) . Anti-SIVagm . sab neutralizing antibodies showed a similar dynamics ( Figure 2b ) , also supporting the control of viral replication . The levels of anti-SIVagm . sab92018 in RMs were not substantially different from those observed in experimentally-infected AGMs [24] . Both the magnitude and the dynamics of NAbs suggest that humoral responses are peripheral to the control of SIVagm . sab in rhesus macaques . Finally , nested PCR for different SIV genomic regions on RNA and DNA from serial samples of plasma , PBMCs , LNs or intestinal lymphocytes collected during chronic infection ( days 720 , 1080 and 1260 p . i . ) were all negative , suggesting the complete control of SIVagm replication by RMs . However , the negative PCRs on these late time point samples may be due to the fact that the PCRs were carried out on a limited number of cells ( in general , less than 5×105 cells per assay ) , which might have been insufficient for the detection of very low amounts of SIVagm . A viral replication restricted to certain anatomical sites was reported in the past to occur during SHIV infection [25] . In such a pathogenic scenario , CXCR4-tropic SHIVs would predominantly replicate in lymphoid tissues resulting in a very active acute infection followed by the control of viral replication during the chronic stage of infection [26] . To assess the extent of SIVagm . sab replication in RMs , we performed serial necropsies at the peak of viral replication ( Days 9 and 10 p . i . ) , at the set-point ( Days 35 and 42 p . i . ) and during early controlled infection ( Day 180 p . i . ) . Plasma viral loads are presented in Figure 3a . SIVagm . sab was then quantified on snap frozen tissue fragments collected at the necropsy . RNaseP was used to quantify the number of cells in each tissue . As shown in Figure 3b , during acute infection the virus was present at very high levels in both lymphoid and nonlymphoid tissues . Viral loads dropped by 3–4 logs at the set point ( Figure 3b ) , while during the chronic infection viral replication was generally controlled in all the analyzed tissues , with very low blips being observed in the mesenteric lymph node , rectum and testis in RM CM44 and in the submandibular LN and colon in RM CE26 ( Figure 3b ) . These very low levels of residual tissue viral replication may explain why the residual increased immune activation may persist after the control of plasma VL below detection limits in SIVagm-infected RMs . Similar widespread SIVmac replication was previously reported in RMs , albeit in a more limited number of tissues [27] , [28] . The generalized , massive acute viral replication in SIVagm-infected RMs demonstrates that control of viral replication during chronic infection is not due to a preferential viral replication to certain anatomical sites . All SIVagm-infected RMs were MHC-typed and no correlation between a given MHC type and control of SIVagm replication could be found for RMs ( data not shown ) . With the exception of RM V492 , which harbored a B*17 allele , none of the remaining RMs harbored alleles usually associated with control of SIVmac infection ( A*01 , B*08 or B*17 ) [19] , [20] . Mamu-A*02 allele , a relatively frequent allele in RMs which is not associated with SIV control , was present in 60% of our RMs . We therefore concluded that the control of SIVagm replication in RMs is independent of MHC types . CD4+ T cell dynamics were investigated in peripheral blood , lymph nodes ( LNs ) and intestine of SIVagm . sab-infected RMs using both flow cytometry and immunohistochemistry ( IHC ) . The dynamics of blood CD4+ T cells were consistent with the pattern of viral replication . Thus , the high SIV VLs observed during acute infection were accompanied by a moderate ( ≈30–40% ) but significant ( p = 0 . 03 ) peripheral CD4+ T cell depletion after the VL peak ( Figure 4a ) . During the chronic phase , peripheral CD4+ T counts rebounded to preinfection levels starting from day 200 p . i . on ( Figure 4a ) , in contrast to pathogenic infections . In the LNs , CD4+ T cell depletion was also moderate and persisted up to 200 days p . i . ; both flow cytometry ( Figure 4b ) and IHC on serial LN samples ( data not shown ) showed complete restoration at late time points . A dramatic ( up to 95% ) acute depletion of CD4+ T cells occurred by days 14–28 p . i . at the immune effector sites in the lamina propria of the intestine ( Figure 4c ) . IHC on serial samples confirmed marked gut-associated lymphoid tissue ( GALT ) CD4+ T cell loss during primary SIVagm infection of RMs ( Figure S2 ) . During chronic SIVagm infection , after the control of SIVagm . sab replication , a gradual immune restoration was observed in RMs ( Figure 4c ) . IHC confirmed this gradual GALT CD4+ T cell recovery ( data not shown ) , which resulted in a complete restoration of the mucosal CD4+ T cells after 4 years p . i . The mucosal CD4+ T cell recovery was incomplete in SIVagm-infected RMs that were followed for 450 days p . i . However , a similar trend for mucosal CD4+ T cell recovery was observed in these animals ( Figure 4c ) . This pattern of CD4+ T cell changes observed in SIVagm-infected RMs is unprecedented and significantly differs from SIVsmm/mac-infected RMs that show continuous , persistent loss of mucosal CD4+ T cells during chronic infection and progression to AIDS [25] . Therefore , we concluded that ( i ) the outcome of SIVagm infection of RMs is not due to its inability to infect mucosal CD4+ T cells; ( ii ) GALT CD4+ T cell depletion during acute infection of SIVagm-infected RMs was of the same order of magnitude as the CD4+ T cell depletion that usually occurs in pathogenic SIVsmm infections of RMs , thus not being predictive for clinical outcome of SIV infection; ( iii ) the complete control of SIVagm infection in RMs resulted in a slow but complete restoration of CD4+ T cells . We then addressed the question of whether or not the control of SIVagm . sab infection in RMs is due to a particular biology of the virus that may restrict infection to particular CD4+ T cell subsets . We have previously characterized SIVagm . sab and showed no significant deletion in any of the accessory genes ( particularly the nef gene ) that might be associated with impaired viral replication [29] , [30] . Moreover , in numerous comparative pathogenesis studies in AGMs and pigtailed macaques , we have shown robust levels of in vivo replication of SIVagm . sab , as well as progression to AIDS in pigtailed macaques [21] , [29] , [31] [Pandrea , unpublished] . We have previously reported , based on in vitro studies , that SIVagm . sab92018 has the ability to use CXCR4 in addition to CCR5 as a coreceptor for virus entry [29] . Note , however , that the in vivo dynamics of SIVagm . sab infection in its natural host , the AGM , is very similar to other infections produced by CCR5-tropic SIVs that infect vervets , SMs and mandrills [32] , [33] . We therefore investigated whether or not the in vivo biology of SIVagm in RMs has particularities that may explain virus control and established multiple lines of evidence that SIVagm . sab is CCR5 tropic in vivo: ( i ) during acute infection of RMs , SIVagm . sab depleted up to 95–99% of CD4+ T cells in the intestine ( Figure 4c ) , in agreement with the high CCR5 expression on CD4+ T cells at the mucosal sites . Such a massive depletion clearly involved all the CD4+ T cell subsets ( data not shown ) . This pattern was similar to all other CCR5-tropic SIVs and HIVs [25] and differed from the in vivo pathogenicity of the CXCR4-tropic SHIVs , which show only minimal depletion of mucosal CD4+ T cells [25] . ( ii ) In vivo , SIVagm . sab depleted CCR5+CD4+ T cells and not CXCR4+CD4+ T cells in macaques , suggesting the in vivo usage of CCR5 as the coreceptor of SIVagm in RMs ( Figure 5 ) . In addition , phenotyping of the SIVagm-infected cells in RMs also showed that the in vivo SIVagm . sab replicated predominantly in lymphocytes in intestine and LNs ( Figure S3 ) , very similar to SIVmac [34] . In addition , the effector memory CD4+ T cell subset was the cell population most depleted during the SIVagm infection in RMs ( Figure 5 ) . We therefore concluded that SIVagm has an in vivo biology that is similar to pathogenic lentiviruses able to induce AIDS in NHPs . We further confirmed these results by assessing the coreceptor usage of the replicating SIVagm during the acute stage of infection ( day 10 p . i . ) of RMs by testing plasma collected at the peak of viral replication in RMs and comparing it to that observed for the virus in the supernatant of RM PBMC cultures . SIVmac239 ( R5-tropic ) and SHIV 162P3 ( R5-tropic ) were used as controls . This experiment confirmed our previous results [29] , and showed that in vitro passaged SIVagm . sab is dual tropic ( CCR5 and CXCR4 ) , similar to other SIVagm . sab strains ( Figure S4 ) . However , SIVagm . sab from the plasma during acute infection ( i . e . , the replicating virus ) used only CCR5 as a coreceptor ( Figure S4 ) . Therefore , these experiments demonstrate that the control of SIVagm . sab in RMs is not due to a particular biology of the virus . One of the major differences between pathogenic and nonpathogenic SIV infections relies on the mechanisms of CD4+ T cell depletion . In pathogenic infection , destruction occurs through direct killing and indirect mechanisms , such as bystander apoptosis [34] . Conversely , in nonpathogenic infections , the levels of apoptosis do not significantly change during SIV infection [32] . We therefore assessed the mechanisms of CD4+ T cell destruction in SIVagm-infected RMs by flow cytometry and IHC . Flow cytometric analysis revealed increases of both necrosis and apoptosis of GALT CD4+ T cells that paralleled viral replication ( Figure 6a ) . These results were confirmed by IHC for active caspase-3 that showed increased apoptosis in both intestine and LNs during acute SIVagm . sab infection ( Figure 6b , middle panels ) compared to preinfection levels ( Figure 6b , upper panels ) and late chronic infection ( Figure 6b , lower panels ) . These increases in apoptosis persisted after the plasma VLs became undetectable , suggesting bystander apoptosis as a significant contributor to the delay of several months in the restoration of CD4+ T cells after viral control ( Figure 3 ) . Therefore we concluded that the mechanism of CD4+ T cell destruction during acute SIVagm infection of RMs was through both direct viral lysis and bystander apoptosis , being similar to that reported for pathogenic SIVmac-infected RMs [34] . Moreover , the correlation between the normalization of bystander apoptosis and mucosal CD4+ T cell recovery during the chronic stage of SIVagm . sab infection in RMs suggests that the main mechanism of CD4+ T cell destruction during the chronic infection is through bystander apoptosis . Microbial translocation from the intestinal lumen to the general circulation may result in increased immune activation and contribute to the CD4+ T cell depletion . This event occurs as a result of damage to the mucosa during SIV/HIV infection . During the acute SIVagm infection of RMs , we observed both high viral replication and increases in apoptosis levels of both CD4+ T cells and intestinal epithelial cells ( Figure 6b ) , we assessed the dynamics of microbial translocation in RMs by measuring the levels of sCD14 [35] . sCD14 testing was preferred over the LPS testing because it was reported to be a robust test to measure microbial tranlocation [35] , while plasma LPS measurements are less reliable ( they show 25% interassay variability ) [35] . Also , serum LPS levels depend on other factors ( i . e . , levels of LPS binding , degradation ) . The massive SIVagm replication during acute infection of RMs led to high levels of immune activation and increases in apoptosis resulted in damage to the intestinal barrier as illustrated by the transient increase in plasma sCD14 ( Figure 7 ) . However , after the onset of control of viral replication and normalization of immune activation and apoptosis , sCD14 levels returned to baseline values which were maintained during the follow-up ( Figure 7 ) . Therefore , we concluded that if SIV replication is completely controlled , the integrity of the mucosal barrier can be restored and the immune system , although “crippled” by the acute infection , does not progress to “exhaustion” during the chronic phase , as in pathogenic HIV/SIV infections [7] . During the acute SIVagm infection of RMs , increases in immune activation ( HLA-DR ) and cell proliferation ( Ki-67 ) were observed for both CD4+ and CD8+ T cells ( Figure 8 ) . These levels started to decrease after the peak of infection , but immune activation and proliferation remained increased for approx . 200 days p . i . , paralleling the increased levels of apoptosis ( Figure 6 ) . Indeed , the decrease in apoptosis levels and proliferation during this period were highly correlated ( r = 0 . 87 , p = 0 . 016 ) . Overall , both markers of proliferation and apoptosis were highly correlated with CD4+ T cell levels in GALT ( p<0 . 0001 ) . Later on , both T cell activation/proliferation ( Figure 8 ) and apoptosis ( Figure 6 ) returned to baseline , which corresponded to significant GALT CD4+ T cell restoration ( Figure 2 ) . These profiles correlated with those of sCD14 and differed from those observed in pathogenic infections , in which continuous viral replication is associated with persistent damage of intestinal mucosa resulting in continuous and rampant immune activation and cell proliferation , which are associated with disease progression [25] . Therefore , we concluded that ( i ) the complete control of SIVagm viral replication resulted in normalization of T cell immune activation , proliferation , programmed cell death and subsequent restoration of CD4+ T cells to normal levels; ( ii ) the control of immune activation , cell proliferation and apoptosis occurred after plasma VLs became undetectable and explains delays observed in CD4+ T cell restoration; ( iii ) normalization of T cell immune activation , proliferation and apoptosis provide further evidence for the complete control of SIVagm replication in RMs . Lentiviruses can infect new host species [32] in spite of the formidable barriers imposed by host defenses . In these cases , a transient viremia may be due to host immune defenses . However , host restriction factors ( i . e . , APOBEC ) might play a role in the control of cross-species transmitted infection through catastrophic GA mutations during reverse transcription that either results in a nonfunctional cDNA or in a cDNA that is targeted for degradation [36] . Therefore , we have investigated the possibility that cross-species transmission of SIVagm . sab to RMs was controlled by a partial host restriction through APOBEC . We have sequenced env of SIVagm . sab sampled at different time points from RMs and compared sequence variability to that observed in SIVagm-infected AGMs . Results are shown in Figure S5 . No significant accumulation of GA hypermutations was observed in SIVagm-infected RMs . We also evaluated the accumulation of synonymous ( ds ) versus nonsynonymous ( dn ) substitutions in SIVagm strains infecting RMs and AGMs . Evaluation of the ds/dn ratio gives an indication of the type of selection pressure that contributes to the evolution of viral sequences . A majority of synonymous mutations ( ds/dn>1 ) would indicate the predominance of a purifying type of selection , which is associated with a preferential elimination of viruses with variant amino acids . Conversely , a majority of nonsynonymous mutations ( ds/dn<1 ) reflects the predominance of a diversifying type of selection . A comparison between ds/dn ratios showed no significant differences between SIVagm-infected RMs ( average ds/dn ratio: 7 . 1677; range: 2 . 13–10 . 78 ) and SIVagm-infected AGMs ( average ds/dn ratio: 6 . 89; range: 1 . 98–9 . 96 ) . It was recently reported that TRIM5 suppresses cross-species transmission of SIV and that TRIM5 genotype correlate with 100-fold to 1 , 000-fold differences in viral replication levels [37] . Therefore , we investigated the impact of TRIM5 on the levels of SIVagm . sab replication in RMs as a potential host restriction mechanism of viral replication and we report that no such correlation could be established . SIVagm-infected RMs belonged to different TRIM5 genotypes , such as: TFP/TFP ( P373 ) ; TFP/Q ( BA38; EK15 ) ; TFP/CypA ( DJ52; CV08; DG50 ) and Q/CypA ( DD16 ) . No difference in viral replication could be observed between these groups . Finally , we performed an in vitro study of SIVagm . sab92018 replication in RM cells . As controls , we used SIVagm . sab replication in African green monkey PBMCs , as well as the highly pathogenic SIVmac239 on RM PBMCs . Our results showed that SIVagm . sab replicated in peripheral blood mononuclear cells ( PBMC ) from RMs at similar levels as in AGM PBMCs ( Figure 9 ) , in agreement with previous reports [38] . There was a robust and persistent SIVagm in vitro replication in both CD4+ T cells and monocyte-derived macrophages ( MDMs ) from AGMs , RMs and humans ( data not shown ) . Persistent SIVagm replication on RM PBMCs , ( Figure 9 ) suggests that the virus is not controlled by host restriction factors . To ascertain if the lack of detectable VLs in SIVagm-infected RMs is due to control by cellular immune responses , we treated three long-term infected RMs with a single 50 mg/kg dose of cM-T807 anti-CD8 depleting antibody . Two EC SIVagm-infected RMs were sampled according to the same schedule and were used as controls . CD8 depletion was successful for 28–42 days in the periphery in all treated RMs ( Figure 10a ) and induced downregulation of CD8+ T cells in the intestine ( not shown ) . Prior to CD8 depletion , viral replication was undetectable for 4 years ( Figures 1 and 2 ) . Postdepletion , a rebound in plasma VLs was observed in 3/3 RMs ( BA38 , P373 and V492 ) ( Figure 10b ) . By day 42 post-cM-T807 administration , VLs became undetectable in all RMs . VL control was coincidental with the rebound of CD8+ T cells ( Figure 10a and b ) . Viral replication was accompanied by depletion of CD4+ T cells in blood and in intestine ( Figure 10b ) . Interestingly , peripheral CD4+ T cell depletion was abrupt and preceded the rebound of viral replication ( Figure 10b ) , probably as a consequence of the use of an anti-CD8α depleting antibody [39] . Indeed , a fraction of CD4+ T cells that also express the CD8α molecule and may be therefore targeted by the depleting antibody . At the mucosal site , although a significant number of intestinal CD4+ T cells express the CD8 molecule , the anti-CD8 antibody only downregulate CD8+ cells [39] and therefore mucosal CD4+ T cell depletion paralleled the rebound of viral replication ( Figure 10b ) . In all three RMs , the administration of anti-CD8 antibody significantly increased CD4+ T cell activation , as defined by -DR expression in periphery ( Figure 10c ) and intestine ( not shown ) as well as CD4+ T cell proliferation , as defined by Ki-67 expression ( Figures 10c ) . Note that the dynamics of VLs correlated with the CD8+ T cell depletion and rebound and not with increased CD4+ T cell activation and proliferation , which persisted longer than CD8+ T cell depletion . Therefore , we concluded that SIVagm replication appears to be controlled in RMs at least in part by cellular immune responses .
In this study , we showed that rhesus macaque infection with SIVagm results in an infection pattern that models that of elite controlled HIV infection in 100% of cases . Acute SIVagm infection of RMs was similar to pathogenic infection , being characterized by robust acute viral replication and massive mucosal CD4+ T cell depletion . However , during the chronic stage of infection , SIVagm was eventually completely controlled in all RMs in blood and tissues . Inflammation and apoptosis were resolved at mucosal sites , microbial translocation was controlled , immune activation returned to baseline levels and mucosal CD4+ T cells were completely restored in RMs infected with SIVagm . We also report that SIVagm elite controlled infection in RMs could be reverted by experimental depletion of CD8+ cells , suggesting that , similar to HIV-infected human elite controllers , cellular immune responses are involved in the control of SIVagm infection in RMs . Therefore , this new animal model of elite controlled infection may be used to model the viral and host factors involved in the achievement of long-term control of HIV replication in the absence of antiretroviral therapy , i . e . , the “functional cure” of HIV infection . The use of this new animal model for SIV infection allowed us to address some of the most important open questions of SIV/HIV pathogenesis , which may have immediate implication for the management of HIV-infected patients . First , in agreement with our previous reports [31] , [40] , [41] , our model of functional cure for HIV infection demonstrates that acute mucosal CD4+ T cell depletion has no prognostic value for the chronic outcome of infection . Second , we report that residual apoptosis and immune activation in subjects with undetectable plasma VLs are due to an incomplete control of viral replication . In SIVagm-infected RMs , low levels of viral replication persisted in tissues several months after plasma VL became undetectable and prevented the control of the apoptosis , microbial translocation and immune activation , hence the immune restoration was quasi inexistent during this time . Conversely , after the achievement of the complete control of virus replication in tissues , apoptosis and immune activation were resolved and normalization of these parameters was followed complete restoration of the mucosal CD4+ T cells . These findings suggest that in human ECs , as well as in patients treated with HAART , incomplete immune restoration and persistent elevated levels of immune activation which are observed in spite of undetectable plasma VLs may be due to very low levels of residual viral replication in tissues [42] , [43] , calling for a therapeutic control of viral reservoirs to restore and preserve the levels of CD4+ T cells [44] . Furthermore , our model demonstrates that only the long-term normalization of immune activation and inflammation may lead to complete immune restoration in HIV-infected patients . In our study , mucosal CD4+ T cells restoration only occurred four years after the return of apoptosis and immune activation to near baseline levels . Finally , our study showed that sustained complete control of viral replication may result in seroreversion . Seroreversion was not reported in ECs , but occurred in a patient that achieved the functional cure after being treated with allogeneic CCR5Δ32/Δ32 stem cell transplantation for relapsed acute myeloid leukemia ( the “Berlin patient” ) [45] . As for the SIVagm-infected RMs , the functional cure in this patient was characterized by lack of disease progression for 45 months in the absence of antiretroviral therapy , CD4+ T cell reconstitution to normal values in peripheral blood and at mucosal sites , undetectable HIV RNA and DNA in plasma and tissues and seroreversion [45] . These striking similarities between RMs infected with SIVagm and the “Berlin patient” , which is currently the standard for the functional cure of HIV infection , reinforce the value of our new animal model . In the currently available animal models of EC infection , control is only achieved in a fraction of monkeys or it is due to significant attenuation of the virus . Thus , viral replication of attenuated strains of SIVmac , such as Δ-nef or Δ-3 strains [46] is impaired in all phases of infection by significant alterations in virus structure . This pattern is in sharp contrast with the majority of the EC infection in humans in which the viruses are replication-competent without major deletions [47] . Second , suppression of viremia in pathogenic macaque infections through highly active antiretroviral treatments [48] does not fulfill the definition criteria for EC infection , as the animals are ARV-treated and low levels of viral replication , immune activation and incomplete immune restoration persist under treatment [48] . Third , during SIVmac infections in Chinese ( Ch ) RMs long-term nonprogression occurs only in a subset of monkeys [49] , cannot be predicted based on in vitro testing [50] and is associated with persistent viral replication in tissues [28] . Reagents are not yet fully tested in ChRMs and MHC characterization is incomplete , as opposed to IndRMs . Finally , a fraction of SIVmac239-infected Indian RMs with particular MHC profiles is defined as “elite-controlling” infection [19] , [20] . Note , however , that “EC” SIVmac-infected RMs show persistent low levels of viral replication ( 102–103 copies/ml ) [19] . Moreover , as for ChRMs , control of infection cannot be predicted before it is achieved . The advantage of the EC model reported here over the existing models , is that in SIVagm-infected RMs , a robust acute viral replication is followed by complete control in all cases , therefore , to date , this animal model is the only one that can be used for the study of early events of SIV infection leading to EC infection and to define the biomarkers that will allow early identification of the HIV infected patients that have the potential of controlling infection and the delineation of the parameters of a successful functional cure of HIV infection ( i . e . , control of viral replication in tissues , normalization of apoptosis and immune activation , seroreversion etc ) . One may argue that , since SIVagm infection is cross-species transmitted to RMs , the proposed model might not be relevant because control is dependent on host restriction factors . We have several lines of evidence that the role of host intrinsic immunity may be peripheral in achieving control of infection in this model: ( i ) SIVagm replicated at high levels in RMs during acute infection; ( ii ) in vitro data showed persistent high levels of SIVagm . sab replication ( comparable to that of SIVmac ) on RM PBMCs; ( iii ) the analysis of SIVagm evolution in RMs showed no evidence of hypermutation that might have been the result of a partial host restriction through the deaminase system; ( iv ) the high ds/dn ratios observed in RMs suggest that SIVagm was under purifying selection in the postacute phase of SIVagm . sab infection; finally , ( v ) Trim5 genotypes of the infected monkeys were not associated with particular viral replication profiles; ( vi ) CD8 depletion experiments in controllers resulted in rebounds of VL , thus pointing to an immune control of viral replication . Another critique to this new animal system may be that it only models a fraction of controlled HIV infections . LTNPs are a heterogeneous group of patients , in which different mechanisms may lead to various levels of viral control [1] , [51] . Thus , viremic controllers have residual levels of viral replication . Elite controllers , on the other hand , control viral replication to undetectable levels of plasma viral load , but the majority of ECs have persistent levels of increased immune activation [52] , [53] , inflammation-associated vascular dysfunction [54] and declining CD4 counts over time [52] , [53] . Only a fraction of EC patients ( the “super-elites” ) , achieve control of immune activation close to baseline levels , in addition to the control of viral replication . These patients sustain CD4+ T cell counts and preserved CD8+ T cell function [Landay , unpublished] and are probably the best examples of functional cure of HIV infection . Even if these patients represent a minority of the HIV infected patients , understanding the mechanisms through which the functional cure of HIV infection occurs in the absence of antiretroviral therapy is probably the most important information that can be derived from the study of controlled HIV/SIV infections for both vaccine development and HIV eradication efforts . Our new animal system models these super-elite controllers , which are the most difficult to model in animal systems and therefore it is a major achievement in the field , as it can be used to identify the factors driving the infection to elite controlled status overcoming the most important limitation to the study of the functional cure , which is that control cannot be predicted at the time when the virus actively replicate during the early stages of infection . We conclude that SIVagm infected RMs represent a valuable model of super elite controlled infection which can be used to: ( i ) examine virologic and immunologic changes during early infection that may lead to the infection control; ( ii ) perform invasive studies facilitating concomitant investigations in a large array of tissues collected at critical time points of infection; ( iii ) perform in vivo manipulation of SIV pathogenesis by selective depletion of different cellular subsets , or experimental modulation of immune activation to assess their contribution to the control of VL . Such experiments have not been and cannot be pursued in studies of human HIV-1 ECs and thus , this new model addresses an immediate need in AIDS research: deciphering the mechanisms and biomarkers of durable and effective control of SIV replication .
The animals were fed and housed according to regulations set forth by the Guide for the Care and Use of Laboratory Animals [40] and the Animal Welfare Act . The animal experiments in this study were approved by the Tulane University Institutional Animal Care and Use Committee ( IACUC ) . Twelve male RM ( Macaca mulatta ) aged 5–11 years were used . Animals were intravenously inoculated with plasma equivalent to 150 tissue culture infectious doses ( TCID50 ) of SIVagm . sab92018 [21] . The first group of 6 RMs were followed for up to 6 years p . i . ( the pathogenesis group ) to characterize the clinical outcome of SIVagm infection , the dynamics of viral replication and the impact of SIVagm infection on the major immune cell populations; the remaining 6 RMs were serially sacrificed at days 9 , 10 , 35 , 42 and 180 p . i . to assess the dynamics and the levels of tissue replication . Blood was collected from all the animals in the pathogenesis group at 2 time points preinfection ( days -30 , -7 p . i . ) , then at the time of SIVagm . sab inoculation , twice per week for the first two weeks p . i . , weekly for the next four weeks , every two weeks for the next two months and then every three months , up to 6 years p . i . LN biopsies were sampled on days 0 , 8 , 28 , 200 , 400 , 800 , 1200 and 1400 p . i . Intestinal endoscopies ( proximal jejunum ) consisting of approximately 10–15 , 1–2 mm2 pieces were obtained by endoscopic guided biopsy were performed on days 0 , 8 , 14 , 21 , 28 , 42 , 72 , 84 , 100 , and then every three months up to 6 years p . i . Intestinal resections ( five to ten cm ) were performed at days preinfection ( D-30 ) and at days 10/42 and 200/600 p . i . , as previously described [31] , [55] . Additional intestine pieces were obtained at necropsy . The serially sacrificed RMs were sampled following the same sampling schedule . Plasma and peripheral blood mononuclear cells ( PBMCs ) and mononuclear cells from the intestine and LNs were isolated as described [10] , [21] . The serially sacrificed RMs were euthanized and up to 38 different tissue samples were collected . Mononuclear cells were separated from all mucosal and lymphatic tissues as described [10] , [21] , while total RNA was extracted directly from 100 mg of snap frozen parenchimatous tissues . Plasma VLs were quantified as described [29] . Assay sensitivity was 100 RNA copies/ml of plasma . For quantification in tissues , viral RNA was extracted from 5×105–106 cells originating from multiple tissues with RNeasy ( Qiagen , Valencia , CA ) . Snap frozen tissues were ultrasonicated prior to RNA extraction . VLs were quantified in blood and tissues as described [21] . Simultaneous quantification of RNase P ( TaqMan Copy Number Reference Assay RNase P , Applied Biosystems , Carlsbad , CA ) normalized the RNA input from cells [31] . Assay sensitivity was 10 RNA copies/106 cells . An in-house SIVagm . sab-specific primate immunodeficiency virus enzyme immunoassay was used for the titration of anti-gp41 and anti-V3 antibody titers , as described previously [56] , on serial plasma or serum samples to investigate the dynamics of anti-SIVagm . sab seroconversion and seroreversion . SIVagm . sab neutralization was measured using a new neutralization assay . An SIVagm . sab-specific molecularly cloned Envpseudotyped virus containing full-length gp160 of SIVagm . sab92018 ( clone 28 ) was prepared as described previously [24] . Neutralization titers were then measured as 50% reductions in luciferase reporter gene expression in TZM-bl cells , as reported previously [24] . Immunophenotyping of lymphocytes isolated from the blood , LNs and intestine was performed by using fluorescently conjugated monoclonal antibodies using a seven-color staining technique . The samples were run using a LSR-II flow cytometer ( Becton Dickinson ) and the data were analyzed using FlowJo ( Tree Star , Inc ) . The following mAbs were used for flow cytometry: CD3-Pacific blue ( clone no . SP34 ) , CD4-APC ( clone no . L200 ) , CCR5-PE ( clone no . 3A9 ) , HLA-DR-APC-Cy7 ( clone no . L243 ) , Ki-67-FITC ( clone no . B56 ) ( BD Bioscience ) , CD95-FITC ( clone no . DX2 ) , CD28-PE-Cy7 ( clone no . CD28 . 2 ) CD8αβ-Texas Red ( clone no . 2ST8 . 5H7 ) ( Beckman Coulter ) . All antibodies were validated and titrated using RM PBMCs . Samples were stained for apoptosis using Annexin V: PE Apoptosis Detection kit I ( BD Pharmingen ) as per manufacturer instructions . Apoptotic CD4+ T-cells were defined as Annexin Vpos7AADneg , whereas the necrotic CD4+ T-cells were defined as Annexin Vpos7AADpos . CD4+ and CD8+ T-cell percentages were obtained by first gating on lymphocytes , then on CD3+ T-cells . Memory , activation , proliferation and apoptosis markers were determined by gating on lymphocytes , then on CD3+ T cells and finally on CD4+CD3+ or CD8+CD3+ T cells . IHC was performed on formalin-fixed , paraffin-embedded tissues using an avidin-biotin complex horseradish peroxidase technique ( Vectastain Elite ABC kit , Vector Laboratories , Burlingame , CA ) and either mouse monoclonal anti-human CD4 ( NCL-CD4-1F6 , Novocastra , Newcastle , UK ) or rabbit polyclonal Activated Caspase-3 ( Abcam , Cambridge , MA ) . For SIV in situ hybridization ( ISH ) , sections were subjected to high-temperature unmasking , treated with 0 . 2 N HCl , and hybridized overnight at 45°C with either sense or antisense SIVagm digoxigenin-UTP labeled riboprobe , blocked with normal sheep serum , incubated with sheep anti-digoxigenin-alkaline phosphatase ( AP ) and incubated with Ferangi blue . SIVagm-infected cell phenotype was determined after ISH by incubating sections with: rabbit anti-human CD3 ( DAKO , Carpinteria , CA ) ; mouse anti-human macrophage ( HAM56; DAKO ) ; followed by the appropriate anti-mouse or anti-rabbit secondary antibodies . ABC method ( Vectastain Elite ABC kit ) and amino-ethylcarbazole ( AEC ) ( DAKO ) were also used to detect HAM56 and CD3 . Negative controls included an antisense probe with uninfected tissues , a sense probe with infected tissues , an antisense probe with infected tissues and anti-rabbit or anti-mouse secondary antibodies only . Microbial translocation was monitored by measuring sCD14 levels , as previously described , using a commercially available ELISA ( R&D systems ) [35] . Plasma samples were diluted 5 fold with endotoxin-free water and then heated to 70°C for 10 minutes to inactivate plasma proteins . Plasma was diluted 1∶300 and assay was performed in duplicate according to the manufacturer's protocol . Coreceptor usage was determined as described previously [50] . Human osteosarcoma ( GHOST ) cells expressing CD4 and one of the following coreceptors were obtained through the NIH AIDS Research and Reference Program , Division of AIDS , AIAID contributed by Dan Littman and Vineet KewalRamani: CCR1 , CCR2 , CCR3 , CCR4 , CCR5 , CCR8 , CXCR4 , BOB and Bonzo . These cells were cultured in complete Dulbecco's minimal essential medium containing G418 ( 5 µg/ml ) , hygromycin ( 1 µg/ml ) , and puromycin ( 1 µg/ml ) . GHOST cells expressing only CD4 ( GHOST-CD4 cells ) served as controls; these cells were cultured in the same medium without puromycin . GHOST cells ( 105/ml; 500 µl per well ) were maintained in 24-well plates for 24 h . The medium was then removed , and 200 µl of fresh medium was added , along with a viral inoculum of 10 MOI . On the next day , residual virus was removed and the cells were washed once with 1 ml of medium . Then , 750 µl of fresh complete medium containing the selection antibiotics was added . Productive viral replication was monitored by measuring SIV P27 Gag antigen in the culture supernatants on days 0 , 2 , 4 , 6 , and 9 by ELISA ( Zeptometrix Corp . , Buffalo , NY ) . In all cases , the amount of antigen produced in control GHOST-CD4 cells was subtracted from the amount produced in coreceptor-transfected GHOST-CD4 cells . Viral evolution in vivo in RMs was investigated as follows: SIVagm RNA was extracted as described above from serial plasma samples ( collected between days 10–72 pi ) and subjected to PCR amplification . Then , viral RNA was retro-transcribed by using superscript II RNAse H- Reverse Transcriptase ( Invitrogen ) according to the manufacturer's instructions using primers EnvASab and EnvBSab [29] . Reverse transcription was done at 25°C for 10 min , 50°C for 30 min and 70°C for 15 min . Resulted cDNA was amplified by PCR using the proof-reading DNA polymerases TAKARA Ex Taq ( Takara Bio ) or Platinum Taq ( Qiagen ) following the manufacturer's instructions . The PCR conditions consisted of 40 cycles of denaturation ( 95°C for 10 s ) , of hybridization ( 55°C for 30 s ) and elongation ( 72°C for 1 min ) . A seminested PCR was then performed using the same conditions as for the first round PCR . Primers used were EnvAsab and EnvBsab for the first round and EnvAsab and NS3asVerTYO ( 5′ GAA GCC TAA GAA CCC TAG CAC AAA 3′ ) for the seminested reaction [29] . PCR products were visualized by agarose gel electrophoresis . PCR products were cloned using the TOPO TA Cloning Kit ( Invitrogen ) . Plasmids with the correct insert were sequenced with the universal primer and env sequences were aligned , translated and analyzed . The TRIM5α genotypes were determined for RM by isolating genomic DNA from PBMCs and directly sequencing the 526 nucleotides PCR product of the B30 . 2/SPRY domain of TRIM5α . The sequence of the primers utilized both for PCR and for the sequencing reaction are CAGTGCTGACTCCTTTGCTTG for the forward primer and GCTTCCCTGATGTGATAC for the reverse primer . The obtained sequences were characterized by polymorphisms at nucleic acid position 997 , 1015–1020 , 1022 of TRIM5α . PBMCs were isolated from RM and AGM blood as described earlier [50] . Freshly isolated PBMCs were stimulated with 10 µg PHA per ml of medium for 2 days followed by overnight incubation in IL-2 media . Activated PBMCs ( 5×106 ) were infected with SIVagm stocks containing 4 , 000 pg of P27 at 37°C for 4 h; cells were then washed extensively to remove any cell-free virus . Cells were maintained in IL-2 media for 8 weeks . Virus production in culture supernatants was monitored weekly by SIV P27 antigen capture assay . Three SIVagm-infected RM controllers were treated intravenously with cM-T807 , a mouse anti-human monoclonal anti-CD8 antibody . Treatments consisted of an initial dose of 50 mg/kg followed by administration of 10 mg/kg after 6 and 13 days , respectively . cM-T807 administration was followed by a frequent sampling of blood ( days 0 , 4 , 7 , 10 , 14 , 21 , 28 , 35 , 42 , 56 , 72 post cM-T807 administration ) as well as intestinal biopsies ( days 0 , 7 , 14 , 21 , 28 , 42 , 72 post cM-T807 administration ) . Data comparisons were done using two-tailed non-parametric tests ( Mann-Whitney ) . Correlation analyses among time-varying variables were done by comparing slopes or using gee with working independence [57] . Analyses were performed using R ( R Foundation for Statistical Computing , Vienna , Austria ) . | A small proportion of HIV-infected patients control viral replication and disease progression in the absence of any antiretroviral treatment . Understanding the mechanisms of viral control in these elite controllers may help to identify new therapeutic approaches in order to control HIV infection . However , elite controllers are identified AFTER control is established , therefore it is difficult to identify the virus and host factors that drive the infection to the controlled status . We identified an animal model ( the rhesus macaque infection with SIVagm ) in which , after massive acute viral replication and CD4+ T cell depletion , SIV infection is controlled in 100% of cases during chronic infection . This “functional cure” of SIVagm infection in rhesus macaques results in a complete immune restoration after four years and can be reverted by depleting the cellular immune responses in vivo . An animal model of elite controlled lentiviral infection in which complete control can be predicted in all cases permits research on the early events of infection when host factors are actively driving the infection towards the controlled status to understand the pathogenesis of HIV/SIV infections and design of new approaches for controlling HIV infection . | [
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"clinical",
"immunology",
"immu... | 2011 | Functional Cure of SIVagm Infection in Rhesus Macaques Results in Complete Recovery of CD4+ T Cells and Is Reverted by CD8+ Cell Depletion |
Latently-infected CD4+ T cells are widely considered to be the major barrier to a cure for HIV . Much of our understanding of HIV latency comes from latency models and blood cells , but most HIV-infected cells reside in lymphoid tissues such as the gut . We hypothesized that tissue-specific environments may impact the mechanisms that govern HIV expression . To assess the degree to which different mechanisms inhibit HIV transcription in the gut and blood , we quantified HIV transcripts suggestive of transcriptional interference ( U3-U5; "Read-through" ) , initiation ( TAR ) , 5' elongation ( R-U5-pre-Gag; "Long LTR" ) , distal transcription ( Nef ) , completion ( U3-polyA; "PolyA" ) , and multiple splicing ( Tat-Rev ) in matched peripheral blood mononuclear cells ( PBMCs ) and rectal biopsies , and matched FACS-sorted CD4+ T cells from blood and rectum , from two cohorts of ART-suppressed individuals . Like the PBMCs , rectal biopsies showed low levels of read-through transcripts ( median = 23 copies/106 cells ) and a gradient of total ( 679 ) >elongated ( 75 ) >Nef ( 16 ) >polyadenylated ( 11 ) >multiply-spliced HIV RNAs ( <1 ) [p<0 . 05 for all] , demonstrating blocks to HIV transcriptional elongation , completion , and splicing . Rectal CD4+ T cells showed a similar gradient of total>polyadenylated>multiply-spliced transcripts , but the ratio of total to elongated transcripts was 6-fold lower than in blood CD4+ T cells ( P = 0 . 016 ) , suggesting less of a block to HIV transcriptional elongation in rectal CD4+ T cells . Levels of total transcripts per provirus were significantly lower in rectal biopsies compared to PBMCs ( median 3 . 5 vs . 15 . 4; P = 0 . 008 ) and in sorted CD4+ T cells from rectum compared to blood ( median 2 . 7 vs . 31 . 8; P = 0 . 016 ) . The lower levels of HIV transcriptional initiation and of most HIV transcripts per provirus in the rectum suggest that this site may be enriched for latently-infected cells , cells in which latency is maintained by different mechanisms , or cells in a "deeper" state of latency . These are important considerations for designing therapies that aim to disrupt HIV latency in all tissue compartments .
The major barrier to a cure for HIV is thought to be latently-infected cells that do not produce HIV constitutively but can be induced to produce infectious virus upon activation [1–3] . The latent HIV reservoir cannot be eliminated using currently available antiretroviral drugs , and due to their long half-lives and ability to proliferate [4] , latently-infected cells can persist for many years [5–8] . While an extensive body of research has underscored the importance of peripheral CD4+ T cells as reservoirs for latent HIV , it is becoming increasingly apparent that the gut may play an integral role as a major tissue reservoir for HIV [9] . First , a large proportion of all lymphocytes reside in lymphoid tissue , of which the gut accounts for up to 85 per cent [10] . Second , CD4+ T cells of the gut are likely to be more vulnerable to infection than their peripheral blood counterparts [10] . This increased permissivity to HIV [11 , 12] may be due to factors such as elevated levels of activation or CCR5 expression [13–15] . Consequently , the depletion of CD4+ T cells in the gut during acute HIV [16] and SIV [17–21] infection is both more rapid and severe than peripheral blood . Furthermore , this depletion occurs prior to and is more profound than that in the blood or lymph nodes [17 , 22] . The disproportionate effect of HIV infection on the gut may result in an increased HIV burden in gastrointestinal tissue . Both HIV DNA and RNA are found to be concentrated in the gut [23 , 24] and replication-competent HIV has been recovered from the rectal mucosa [25] , suggesting that a proportion of gut CD4+ T cells harbor replication-competent proviruses . Prior data also suggest differences between blood and gut in infected cell types , levels of T cell activation , HIV DNA levels , relationship to activation , and levels of HIV RNA per cell [23 , 26] , suggesting these tissues differ in the mechanisms that govern HIV transcription and latency . Using a novel panel of reverse transcription droplet digital polymerase chain reaction ( RT-ddPCR ) assays that can simultaneously quantify multiple different blocks to HIV transcription , we recently showed that the major reversible blocks to HIV transcription in peripheral CD4+ T cells from ART-suppressed patients are blocks to proximal elongation , distal transcription/polyadenylation ( completion ) , and splicing [27] . We hypothesized that the mechanisms and degrees of HIV transcriptional blocks underlying HIV latency differ between gut and peripheral blood . In this study , we applied our "transcriptional profiling" assays to two cohorts of ART-suppressed individuals to simultaneously assess the mechanisms that govern HIV transcription in the gut and blood . We quantified the levels of different HIV RNAs in PBMCs and intact rectal biopsies ( n = 9 ) , as well as sorted CD4+ T cells from peripheral blood and dissociated rectal biopsies ( n = 7 ) . The relative levels of the different HIV RNAs suggested blocks to distal HIV transcription , completion , and splicing in all samples , and these observations were not explained by mutations in the corresponding HIV DNA primer/probe sequences or differential RNA stabilities . However , in contrast to our findings in peripheral CD4+ T cells [27] , we found a much greater block to HIV transcriptional initiation in the rectum ( both biopsies and sorted cells ) compared to the blood . These differences in HIV transcriptional blocks , which could reflect tissue-specific differences in viral or cellular factors , are important to consider in designing therapies that aim to eliminate or silence HIV-infected cells .
We used a novel panel of HIV “transcription profiling” assays ( Fig 1 ) to quantify HIV transcripts suggestive of transcriptional interference ( U3-U5; "Read-through" ) , initiation ( TAR [Trans-activation Response region] ) , 5' elongation ( R-U5-pre-Gag; "Long LTR” [Long Terminal Repeat] ) , distal transcription ( Nef ) , completion ( U3-polyA; "PolyA" ) , and multiple splicing ( Tat-Rev ) in PBMCs and intact rectal biopsies from nine ART-suppressed individuals . In PBMCs , these assays revealed a reproducible gradient in the relative abundance of HIV transcripts ( normalized to cell equivalents by ddPCR for Telomere Reverse Transcriptase [TERT] ) where total ( TAR ) > elongated ( Long LTR ) > distally elongated ( Nef ) > polyadenylated ( PolyA ) > multiply-spliced Tat-Rev transcripts ( medians: 7289 , 420 , 108 , 44 , and 2 copies/106 cells , respectively; Fig 2A ) . Read-through transcripts ( U3-U5 ) , suggestive of transcriptional interference , were also detected in every individual ( median 155 copies/106 cells ) , but were 30-fold lower than total ( TAR ) transcripts ( median [Read-through/TAR ratio] = 0 . 033 ) . The median level of 5' elongated ( Long-LTR ) transcripts was 17-fold lower than that of total transcripts ( median [Long LTR/TAR] = 0 . 06 ) , suggesting a block to proximal elongation . The median level of Nef ( 3' ) was almost 4-fold lower than that of 5' elongated transcripts ( median [Nef/Long LTR] = 0 . 26 ) , suggesting a block to distal transcription . The median level of multiply-spliced transcripts was 23-fold lower than levels of polyadenylated transcripts ( median [MS Tat-Rev/PolyA] = 0 . 04 ) , in accord with prior data suggesting a reversible block to multiple splicing [27] . A similar trend in relative transcript levels ( albeit at lower overall levels ) was observed in the rectal biopsies , wherein the relative abundance of HIV transcripts was also: total > elongated > Nef > polyadenylated > multiply-spliced Tat-Rev ( medians: 679 , 75 , 16 , 11 , and <1 copies/106 cells , respectively; Fig 2B ) . Just as in the PBMCs , Read-through transcripts were detected in all individuals ( median 23 copies/106 cells ) , but were much lower than that of total transcripts ( 47-fold lower; median [Read-through/TAR] = 0 . 021 ) . The median level of elongated transcripts was 9-fold lower than that of total transcripts ( median [Long LTR/TAR] = 0 . 11 ) . The median level of distally-elongated ( Nef ) transcripts was 4-fold lower than that of 5’ elongated transcripts ( median [Nef/Long LTR] = 0 . 21 ) , and the level of polyadenylated transcripts was nearly 2-fold lower than that of distally-elongated transcripts ( median [PolyA/Nef] = 0 . 66] ) . A block to multiple-splicing is also likely ( PolyA > MS Tat-Rev ) , given that polyadenylated HIV transcripts were detected in rectal biopsies from 6 of 9 individuals , while MS Tat-Rev transcripts were detected in biopsies from only 2 of 9 individuals ( vs . 5 of 9 from PBMCs; Fig 2A and 2B ) . These data suggest that in both PBMCs and rectal biopsies , HIV transcription is blocked at the stages of elongation , distal transcription/polyadenylation ( completion ) , and splicing . To address the possibility that proviral deletions or hypermutations in primer/probe regions could account for the varying levels of HIV transcripts , we quantified the levels of U3-U5 ( "Read-through" ) , TAR , Long LTR and Nef in DNA extracted in parallel with the RNA from the same PBMCs and rectal biopsies ( n = 9 individuals ) using the same primers/probes and ddPCR conditions used to measure each HIV RNA ( S1 Fig ) . Comparisons between DNA from the same tissue revealed no differences in the levels of TAR and Read-through regions ( both present at 2 copies in an intact provirus ) and these levels were ≥2-fold greater than long LTR DNA ( 1 copy per intact provirus ) in rectal biopsies and PBMCs ( median TAR/Long LTR = 1 . 99 and 3 . 49 , respectively; S1 Fig ) . Levels of Nef DNA ( 1 copy per intact provirus ) were similar to Long LTR DNA and tended to be lower than both Read-through and TAR DNA for both tissues ( P<0 . 05 for all comparisons ) . Next , we measured the ratio of each HIV RNA to the corresponding HIV DNA sequence region quantified using the same ddPCR assay ( Fig 2C ) and normalized to 106 cells in the same manner . This measure expresses the average level of transcription per provirus and is independent of normalization to cell numbers . For both PBMCs and intact biopsies , the gradient pattern in the levels of successive HIV transcripts was preserved after normalization of each HIV RNA region to the corresponding HIV DNA region , suggesting that the differences are unlikely to be due to proviral deletions or hypermutations in primer/probe regions . The average level per provirus of each HIV transcript was quantified using two approaches . First , we expressed the ratio of each HIV transcript to HIV DNA measured using the same primers/probe ( Fig 2C ) , which revealed lower levels of Read-through , TAR and Nef transcripts per provirus in intact rectal biopsies compared to PBMCs ( P<0 . 05 for all ) . As an orthogonal method , we expressed the ratio of each HIV transcript to DNA measured using the Long LTR assay alone , which is present in only one copy per intact provirus . This analysis revealed the same trend for all transcripts ( Fig 2D; P<0 . 05 for all ) . Together , these data strongly suggest lower levels of HIV transcriptional initiation and distal transcription , in addition to lower levels of transcriptional interference , in the rectal biopsies relative to PBMCs . To determine whether sequence-specific differences in RNA stability/degradation contribute to the divergent levels of HIV transcripts detected , we measured the RNA decay rate of each transcript in peripheral CD4+ T cells isolated from an ART-suppressed patient in the presence or absence of the RNA Pol II inhibitors Triptolide ( Fig 3A ) or Actinomycin D ( Fig 3B ) . In the absence of RNA Pol II inhibitors , levels of each HIV transcript over 16 hours ( h ) remained relatively stable ( S2 Fig ) . In contrast , the presence of either Triptolide ( 100nM ) or Actinomycin D ( 5 mg/mL ) resulted in decay of all HIV transcripts over 16h , irrespective of normalization ( Fig 3 , S1 Table ) . The half-lives of TAR- and Long LTR-containing transcripts were similar ( in the order of ~3–5 hours ) irrespective of treatment ( Triptolide or Actinomycin D ) and generally similar to the half-lives of read-through , Nef , and PolyA transcripts in the presence of Triptolide ( 4 . 58 , 2 . 80 and 2 . 29h , respectively ) , although the latter three transcripts had shorter half lives in the presence of Actinomycin D ( 2 . 54 , 1 . 66 , and 2 . 34h , respectively ) . The half-life of MS Tat-Rev transcripts was longer with Triptolide ( 6 . 25h ) and Actinomycin D ( 5 . 49h ) treatments . The relatively short , similar half-lives of most HIV transcripts ( S1 Table ) suggest rapid decay in vivo , and any differences do not readily account for the measured differences in levels of the various HIV RNAs . Since PBMCs and gut biopsies contain different mixtures of T and non-T cells , we also compared CD4+ T cells ( defined as CD3+CD8- ) sorted from blood and dissociated rectal biopsies from a different cohort of seven ART-suppressed patients . HIV DNA ( as measured by the Long LTR assay ) was higher in CD4+ T cells from the rectum ( 10 , 736 copies/106 CD4+ T cells ) than blood ( 3 , 841 copies/106 CD4+ T cells; P = 0 . 016; Fig 4A ) . HIV DNA can exist in non-integrated or episomal forms , such as 2-LTR circles , which have been interpreted as either labile markers of recent infection or stable forms that decrease only with cell division [28 , 29] . Therefore , we also measured the levels of 2-LTR circles using a new assay ( S3 Fig ) , as well as the levels of 2-LTR circles relative to total HIV DNA , in the blood and rectal CD4+ T cells by ddPCR . 2-LTR circles were detected in both blood and rectal CD4+ T cells from the same 5 of 7 individuals , and in 4 of these individuals , levels of 2-LTR circles/106 CD4+ T cells ( normalized by TERT ) were higher in the rectum ( Fig 4A ) . However , the ratio of 2-LTR HIV DNA to total HIV DNA did not appear to differ in CD4+ T cells from rectum and blood . In order to measure blocks to HIV transcription in the sorted CD4+ T cells , levels of read-through , total , elongated , completed , and multiply-spliced Tat-Rev transcripts were measured by RT-ddPCR ( S4 Fig ) . Since HIV DNA levels differed in the CD4+ T cells from blood and rectum , levels of each HIV RNA were divided by the HIV DNA ( measured using the Long LTR assay ) to express the average transcription per provirus ( Fig 4B ) . In CD4+ T cells from both sites , we observed low average levels per provirus of read-though transcripts compared to total transcripts , suggesting little transcriptional interference , and a gradient where elongated > polyadenylated > multiply-spliced transcripts , suggesting blocks to distal transcription and splicing . For the four individuals for whom ileal CD4+ T cells were available , we observed a similar trend ( S5 Fig ) . As in the rectal biopsies , levels of total ( TAR ) transcripts per provirus were much lower ( median 12-fold ) in rectal CD4+ T cells compared to peripheral CD4+ T cells ( Fig 4B; P = 0 . 016 ) , suggesting less initiation of HIV transcription in the rectum . Unlike the intact rectal biopsies , PBMCs , or blood CD4+ T cells , we observed no difference between levels of elongated ( Long LTR ) and total ( TAR ) transcripts in the rectal CD4+ T cells ( Fig 4B ) . With the caveat that isolation of gut CD4+ T cells requires additional processing , these data suggest little or no block to elongation in the sorted rectal CD4+ T cells . Ratios of one HIV transcript to another are independent of normalization to cell numbers and can be used to measure the presence and degree of different HIV transcriptional blocks [27] . We did not find a difference between PBMCs and rectal biopsies in the proportion of HIV transcripts that are read-through ( read-through/total ) or in the proportion of HIV transcripts that proceed through blocks to elongation ( elongated/total ) , completion ( polyadenylated/elongated ) , or multiple splicing ( MS Tat-Rev/polyadenylated ) . In contrast , the sorted CD4+ T cells from blood and rectum showed a 6-fold difference in the proportion of HIV transcripts blocked at the stage of elongation ( P = 0 . 016; Fig 4C ) , with little block to elongation in the rectal CD4+ T cells ( median total/elongated = 1 . 31 ) and a strong block to elongation in CD4+ T cells from the blood ( median total/elongated = 7 . 50 ) . These data suggest that a block to HIV transcriptional initiation plays a greater role in inhibiting virus expression in the rectal CD4+ T cells , whereas a block to elongation likely plays a bigger role in CD4+ T cells from the blood . Blocks to HIV transcriptional initiation could be due to transcriptional interference caused by transcription from neighboring cellular genes that perturbs assembly of preinitiation complexes at the 5’ HIV LTR [30] . To assess the likely contribution of transcriptional interference to the decreased HIV transcriptional initiation in rectal CD4+ T cells , we measured levels of read-through transcripts in relation to total and elongated HIV transcripts . A trend toward lower read-through/elongated transcripts was observed in sorted CD4+ T cells from rectum compared to blood ( 0 . 03 vs . 0 . 08 , respectively , P = 0 . 078; Fig 4D ) , suggesting less transcriptional interference in the rectal CD4+ T cells . Furthermore , the levels of read-through transcripts tended to be low compared to total transcripts in all samples from both tissues . These findings suggest that transcriptional interference plays a relatively modest role in inhibiting HIV transcription in both sites . It is conceivable that the process of dissociating gut tissue using collagenase may degrade HIV RNA , which could contribute to lower levels of HIV RNAs in the sorted gut CD4+ T cells but not the intact biopsies . Alternatively , the tissue processing could induce HIV transcriptional elongation , which could contribute to the lower block to elongation in sorted gut CD4+ T cells . To address these concerns , we treated PBMCs from an HIV-infected individual using the same protocol employed for dissociation of gut biopsies . HIV RNA transcripts were measured in PBMCs that were untreated , FACS-stained only , or collagenase treated and FACS-stained ( S6 Fig ) . Interestingly , we found that the FACS-staining procedure necessary to sort live cells of interest may itself cause increases in HIV transcription . In PBMCs that were not treated with collagenase but were FACS-stained , an increase was observed in all HIV transcripts in relation to untreated PBMCs ( S6 Fig ) . FACS-staining alone resulted in a 6 . 6- and 4 . 4-fold increase in MS Tat-Rev relative to untreated cells when normalized by RNA mass and TERT , respectively . In contrast , the combination of collagenase treatment and FACS-staining resulted only in an ~2 . 5 fold increase in MS Tat-Rev relative to untreated cells . The effect of collagenase without FACS staining was also determined in PBMCs from a second HIV-infected individual ( S6 Fig ) . Collagenase treatment alone increased levels of all HIV RNA transcripts , although the change was less than two-fold for each transcript irrespective of normalization ( S6 Fig ) . Collagenase treatment did not alter the pattern of differences between various HIV transcripts . Given that the flash-frozen biopsies that were not subject to collagenase treatment also demonstrated lower levels of HIV transcription , these data support our findings that RNA stability or processing disparities do not account for differences in HIV transcription between tissues .
As with any study that attempts to quantify levels of RNA or DNA , differing assay efficiencies can greatly influence the levels of RNA or DNA detected and thus interpretations of these data . Our transcription profiling approach utilizes special methods to minimize bias towards any one sequence region , and we have previously measured the performance characteristics of each assay [27 , 31] . Given that all our HIV assays demonstrate similar efficiencies [27] , it is unlikely that differing assay characteristics account for the marked differences between levels of the various HIV RNAs . Internal deletions and hypermutations are present in a substantial proportion of proviral sequences [32 , 33] and could cause sequence mismatches with primers or probes , which could impair detection of some HIV transcripts [33] . To assess the impact of proviral sequence , we measured the levels of each sequence region ( except Tat-Rev ) in the DNA from the PBMCs and rectal biopsies , and we also normalized levels of each RNA to levels of the corresponding DNA measured using the same assays employed for the HIV RNA and normalized to 106 cells using the same method ( Fig 2C ) . The gradient in levels of different HIV RNAs was preserved even after normalizing to the corresponding DNA , and was similar when all RNAs were normalized to the same Long LTR DNA region ( Fig 2D ) , underscoring that these differences in RNA levels cannot be attributed solely to mutations in the corresponding DNA sequence regions . We were unable to quantify HIV DNA regions other than the Long LTR region in the sorted CD4+ T cells , for which the detection of 2-LTR circles consumed a large proportion of the DNA , but it seems unlikely that these would differ much from the results in PBMCs and gut biopsies . The steady state level of each HIV RNA likely reflects a balance between production ( transcription ) and destruction ( degradation ) . To determine whether sequence-specific differences in RNA stability could contribute to differences in levels of the HIV RNAs , we measured the decay of Read-through , TAR , Long LTR , Nef , PolyA , and MS Tat-Rev transcripts in CD4+ T cells from an ART-suppressed individual using two RNA Pol II inhibitors , Triptolide and Actinomycin D . In Triptolide-treated cells , the half-lives of Nef ( 2 . 80h ) , PolyA ( 2 . 29h ) , and Read-through ( 4 . 58h ) did not vary considerably from TAR and Long LTR ( 3 . 13h and 2 . 86h , respectively ) . MS Tat-Rev seemed to be more stable than the other HIV transcripts after treatment with Triptolide ( 6 . 25h ) and Actinomycin D ( 5 . 49h ) . However , the very low levels of MS Tat-Rev cause considerable imprecision in calculating its half-life , and even if MS Tat-Rev transcripts were more stable , this would not explain the very low levels of MS Tat-Rev relative to other HIV RNAs . The half-lives determined from decay of HIV transcripts in cells treated with Triptolide tended to be higher than those determined from cells treated with Actinomycin D . Triptolide acts by inducing proteasome-dependent degradation of RNA Pol II [34] , whereas Actinomycin D is thought to intercalate into DNA to sterically-inhibit RNA Pol II [35] . Potential discrepancies in the half lives measured with Triptolide and Actinomycin could be due to the different mechanisms by which these agents inhibit RNA Pol II , incomplete arrest of de novo transcription , or imprecision due to the limited number of time points . For both Actinomycin and Triptolide , the most striking observation is that the half-lives of less abundant HIV transcripts , such as Read-through , Nef , PolyA , and MS Tat-Rev , are comparable to those transcripts that are detected at much higher levels ( TAR and Long LTR ) . These data strongly argue that differences in RNA stability alone do not explain the differential abundance of these HIV transcripts . Although we did not have sufficient numbers of rectal CD4+ T cells to assess the stability of these transcripts in the rectum , and cellular factors may contribute to differences between cell or tissue types , it seems unlikely that there are major sequence-dependent differences between the various HIV transcripts . Surprisingly , these data also demonstrate that the half-lives of all HIV transcripts tested were relatively short ( <7 hours for all except MS Tat-Rev ) . The short half-life of the TAR transcripts and the average levels of TAR RNA per provirus ( >2 ) suggest a dynamic transcriptional environment with multiple rounds of HIV transcription initiation per day per provirus in the PBMCs , in contrast to the prevailing model of transcriptionally-silent proviruses in ART-suppressed individuals . Ultimately , however , HIV transcripts may be maintained at low levels , despite their active transcription , because of rapid RNA turnover rates , particularly in activated T cells [36] . Furthermore , blocks to elongation and splicing could lead to low expression of HIV Rev , which is critical in circumventing the degradation of unspliced viral transcripts containing introns and AU-rich sequences that contribute to instability [37 , 38] . The presence of instability elements within gag also contribute to HIV-1 unspliced mRNA instability [39] . The expression of Gag , Pol , Vif , Vpr , Vpu , and Env proteins from unspliced and partially spliced human immunodeficiency virus type 1 ( HIV-1 ) mRNAs depends on Rev protein , and intron-containing HIV-1 transcripts undergo nuclear downregulation as they are further spliced or degraded in the absence of Rev [39 , 40] . This finding may contribute to the short half-lives of unspliced or partially spliced transcripts ex vivo , even those that have been polyadenylated . Our data from collagenase-treated PBMCs provide evidence that collagenase treatment of gut cells is unlikely to promote HIV transcript degradation or selectively alter levels of a particular transcript . Collectively , our RNA stability and collagenase treatment data suggest that the differential expression of HIV transcripts in the gut and blood are not explained by either intrinsic or treatment-mediated changes to HIV RNA stabilities . Although the PBMCs and rectal biopsies differ in cell composition , direct comparison between HIV transcription in these samples was possible using the ratio of HIV RNA to HIV DNA , which yields a measure of average transcription per provirus . In intact rectal biopsies , the average levels per provirus of Read-through , total , elongated , Nef , polyadenylated , and multiply-spliced Tat-Rev transcripts were all lower compared to PBMCs , supporting previous work that reported lower levels of HIV transcription in the rectum [23 , 26] . The sorted CD4+ T cells showed largely congruent results , where the average levels per provirus of Read-through , total , and multiply-spliced Tat-Rev transcripts were all lower in the rectal CD4+ T cells than the blood CD4+ T cells . One notable exception was the average level per provirus of elongated transcripts , which did not appear to differ between rectal and peripheral CD4+ T cells . Moreover , the TAR/Long LTR ratios suggest a considerable block to elongation in the peripheral CD4+ T cells ( TAR/Long LTR = 7 . 5 ) but little block to elongation in the rectal CD4+ T cells ( TAR/Long LTR = 1 . 31 ) . This lack of a block to elongation in the rectal CD4+ T cells seems to disagree with the results from the intact biopsies , where we observed a larger block to elongation . Aside from differences in the patient cohorts , it is likely that the rectal biopsies contain a different mix of infected cell types , including non-T cells . Alternatively , it is possible that the tissue processing ( collagenase digestion and shearing ) used to dissociate the rectal biopsies into single cells could change cellular or viral transcription in ways that selectively induce elongation . The control experiments in PBMCs suggest that collagenase and FACS staining may cause a small increase in all HIV transcripts , likely reflecting an increase in initiation rather than a specific effect on elongation , although it is possible that these effects of tissue processing differ in adherent and nonadherent cells . However , other findings in the sorted gut CD4+ T cells accord with those in the flash-frozen gut biopsies ( which were not subject to such processing ) and suggest that blocks to HIV transcriptional initiation ( low TAR RNA per provirus ) and blocks to later stages of HIV transcription ( distal transcription , completion , and splicing ) may be important for HIV latency in the gut . Our major finding that HIV transcription initiation in the rectal CD4+ T cells is 12-fold lower than that observed in blood CD4+ T cells ( Fig 4B ) provides evidence that differing transcriptional blocks operate in different tissues ( Fig 5 ) . These findings are particularly striking given that the gut harbors a much higher proportion of activated CD4+ T cells , and that T cell activation usually stimulates HIV transcription and reverses latency [10] . It is not clear what mediates the 12-fold greater block to initiation of HIV transcription in the rectum . Blocks to HIV transcriptional initiation have been attributed to integration into heterochromatin , epigenetic modification , transcriptional interference , lack of host transcription initiation factors , or insufficient activity of the viral transcription factor Tat [41–44] . It seems very unlikely that the greater block to HIV transcriptional initiation in the gut is driven by higher levels of transcriptional interference from neighboring cellular genes , since levels of Read-through transcripts per provirus tended to be lower in the gut than blood , the ratio of Read-through to elongated transcripts tended to be lower in rectal CD4+ T cells than the blood CD4+ T cells , and T cell activation is supposed to reverse transcriptional interference . The differences between the blood and gut in the blocks to HIV transcription could be attributable to either the characteristics of the proviral sequences found in each site or the prevailing host cellular conditions and/or cellular environment . It is possible that proviruses in the rectum are more likely to be integrated into transcriptionally-silent regions , although most studies from the blood suggest that HIV is more likely to be integrated in actively-transcribed genes [45 , 46] , and the higher levels of T cell activation in the gut might also correlate with more actively transcribed genes . It is also possible that proviruses in the gut have more or different mutations that could affect transcription , such as those in the LTRs , Tat , or Rev [47–49] . Compartmentalization of HIV could occur in anatomic sites or tissues where viral trafficking may be impaired or restricted , such as the brain , central nervous system and genital tract [50–52] , but prior studies disagree on whether there is compartmentalization of HIV in the gut [47–49] . The HIV DNA levels from the rectal biopsies suggest that many proviruses in the rectum have 2 full LTRs ( containing U3-U5 and TAR ) for every R-U5/pre-Gag and Nef region , but future studies will be needed to determine whether HIV integration sites or full-length proviral sequences differ between blood and gut . Epigenetic modifications , host cell factors , and extracellular milieu at either site may also dictate the basal transcriptional activity and the consequences for silencing integrated HIV [30 , 45 , 46 , 53–57] . Previous reports have shown that CD4+ T cell tolerance and anergy can be mediated by epigenetic modification [58–60] . Given that epigenetic modification ( for example , of the LTR ) can also inhibit HIV transcription [56] , it is possible that the unique environment of the gut favors both induction of CD4+ T cell tolerance and HIV latency through epigenetic modification . The gut and blood also show differences in the phenotypes of HIV-infected and uninfected cells , which likely differ in the cellular factors that govern HIV transcription . Naïve and central memory T helper ( TCM ) cells account for the largest proportion of T lymphocytes in peripheral blood , while effector memory ( TEM ) and transitional memory ( TTM ) constitute the predominant populations in the gut [10] . Most HIV DNA in the blood is found in central and transitional memory CD4+ T cells [4] , whereas in the gut , most HIV DNA and RNA are found in effector memory CD4+ T cells [26] . The expansive surface area of the GI mucosa is under constant exposure to diverse microbial and food antigens , resulting in sustained immune activation [10] . A higher proportion of gut CD4+ T cells express HIV coreceptors and markers of T cell activation , which may enhance their susceptibility to infection or depletion during acute infection [10] . The gut is also enriched for tissue resident memory cells and different subsets of helper T cells , such TFH [61–64] , TH17 , TH1/TH17 and TH22 , which may serve as reservoirs for latent HIV [10] . In addition , a higher proportion of gut CD4+ T cells express immune checkpoint blockers such as PD-1 and CTLA-4 [65 , 66] , which have been associated with HIV latency [66] . Non-T cells such as macrophages or dendritic cells might also constitute a larger portion of the infected cells in the gut [26] . These differences in infected cell types likely contribute to the discordant progression through various transcriptional blocks observed in these two tissues . It is not clear which cellular or viral factors might explain the differences between HIV transcriptional blocks in the blood and gut . A multitude of cellular factors reportedly interact with the HIV LTR to control transcription , and a regulatory feedback mechanism mediated by Tat and Rev drives HIV transcription through its distinct phases [67 , 68] . MS Tat-Rev was more frequently detected in the blood than the gut , resulting in a lower median level per provirus in the gut , but MS Tat-Rev may have been harder to detect in the gut because of the lower frequency of CD4+ T cells in the rectal biopsies and the lower yield of sorted CD4+ T cells from the rectum . If the lower block to elongation in the rectal CD4+ T cells is not the result of induction of elongation during collagenase digestion , it could reflect higher levels of P-TEFb or lower levels of NELF in the gut CD4+ T cells , which are more likely to be activated . At the same time , activation should also increase cellular factors that increase HIV transcriptional initiation , such as NFAT and NF-κB , but HIV transcriptional initiation was lower in the rectum , even in the flash frozen biopsies . It is possible that "activation" markers have different meanings or that T cell activation results in different changes in the gut , where immune cells are exposed to more microbial products but mechanisms are needed to maintain tolerance to the normal flora . In both blood and rectum , we found evidence suggesting blocks to HIV transcriptional completion and multiple splicing . The block to completion could represent incomplete processivity of the RNA polymerase II , which could be modulated by protein levels or post-translational modifications of the enzyme itself , cellular co-factors that affect transcriptional processivity , or secondary structure in the HIV DNA or RNA . In addition , the block to completion could reflect viral and cellular factors involved in end processing of HIV transcripts , including Vpr [69] , CDK11 [70 , 71] , and members of the polyadenylation complex . We have previously shown that the block to multiple splicing in blood CD4+ T cells is partially reversed by T cell activation , which could change levels of cellular factors involved in splicing , such as spliceosome components , SR proteins , MATR3 , and PSF [67] . However , a difference between polyA and Tat-Rev remained even after activation , which could reflect intrinsic sequence-dependent factors that inhibit HIV splicing ( including multiple inefficient splice donor and acceptor sites , intronic and exonic splice silencers , secondary structure , etc . [67] ) as well as cell-specific differences in activation , proviral mutations affecting Tat-Rev or splice sites [32 , 33] , and the effect of Rev to export unspliced HIV RNA [67] . The blocks to splicing found in both gut and blood suggest that latency could be governed , in part , by post-transcriptional mechanisms . This assertion is supported by the observation that the various latency-reversing agents tested to date fail to completely eradicate infected cells despite inducing viral transcription , albeit to varying degrees [27 , 72] . Post-transcriptional blocks to HIV expression , such as blocks to nuclear export [73] , RNA interference [74–76] , and inefficient translation [77] , have been observed in latency models and patient cells . In a primary cell model of latency , levels of intracellular Gag protein were found to be markedly low despite high levels of gag RNA [77] , and in resting CD4+ T cells from ART-suppressed individuals , both partially- and fully-spliced HIV transcripts were retained in the nucleus [73] , alluding to a block to nuclear export of HIV RNAs . Polyadenylation is important for nuclear export [38] as well as translation , so the block to completion could contribute to lack of export or translation . Because Rev protein facilitates export of unspliced and incompletely-spliced HIV transcripts from the nucleus [67] , the block to multiple splicing could also contribute to very low levels of Rev and therefore blocks to nuclear export of unspliced and incompletely-spliced HIV transcripts . Our methods do not allow us to detect other post-transcriptional blocks , which are possible but would have to be in addition to the blocks described here . Additional studies are needed to determine what cellular factors govern the different blocks to HIV transcription and how cellular gene expression differs between infected and uninfected cells in the blood and gut . Although a limited number of gut biopsies and cells can be obtained from endoscopic procedures , gut cells from HIV-infected patients could be tested ex vivo for their responses to T cell activation or drugs known to act on distinct cellular factors , and gut biopsies could be obtained during clinical trials with interventions designed to disrupt latency . Other techniques of gut cell isolation , such as those that require either no or alternative collagenases [78 , 79] , could be explored in an effort to minimize tissue processing and potentially increase dissociated cell recovery . Even if cell recovery is limited , single cell transcriptomic and/or proteomic studies could also be used to investigate the cellular factors that are associated with different HIV transcripts in blood and gut cells from patients . Moreover , larger cell numbers could be obtained from unused tissue left over from surgical resections in either HIV+ or HIV- patients . Future mechanistic studies could also employ primary cell latency models derived from lymphoid tissue , such as ‘human lymphocyte aggregate culture’ [80] or ‘lamina propria aggregate culture’ [81] systems , which might achieve higher numbers of HIV-infected cells . In addition to the points addressed above , other limitations of this study should be noted . One limitation is the relatively small number of ART-suppressed individuals in each of the two cohorts from whom we had samples from blood and gut . Nonetheless , given the statistically significant findings from two different cohorts with two distinct types of gut samples , and findings that largely concur with previous work [23 , 27] , it is likely that there are differences between blood and gut in the molecular mechanisms that constitutively block HIV transcription . A second limitation is that we did not address the degree to which these blocks are reversible after T cell activation . However , we have previously demonstrated the reversibility of the blocks to HIV transcriptional elongation , completion , and splicing in blood CD4+ T cells [27] , so we also expect some reversibility in gut cells , although the magnitude may differ . Finally , although our transcriptional profiling technique enables us to simultaneously assess multiple blocks to transcription , these assays are unable to distinguish which of these transcripts originate from replication-competent proviruses . Given the many cellular factors that can influence HIV transcription independently of viral fitness , the large proportion of the HIV genome in which mutations could eliminate infectivity without affecting transcription ( or , conversely , could prevent any transcription ) , and prior evidence suggesting these blocks to HIV transcription operate in most infected CD4+ T cells from the blood , it seems likely that the same mechanisms operate in many cells with infectious proviruses . However , this question is extremely important and should be addressed in future studies , although it is an exceedingly challenging question to answer using patient cells and will require novel single cell approaches . Findings from this study have important implications . The large disparities between levels of different HIV RNAs in both blood and gut highlight the importance of critically evaluating the regions targeted when quantifying HIV RNA in different tissue compartments . This fact is particularly important when designing studies to evaluate the effectiveness of interventions designed to reverse latency , since putative "latency reversing" agents ( LRAs ) exert differential effects on the various transcriptional blocks in blood CD4+ T cells [27] , and the same is likely true in other tissues . For such studies , a multi-target approach , such as the transcriptional profiling technique applied in this study , might yield greater insight than quantifying unspliced HIV RNA alone . The lower levels of HIV transcriptional initiation and of most HIV transcripts per provirus in the rectum suggest that this site may be enriched for latently-infected cells , cells in a "deeper" state of latency , or cells in which latency is maintained by different mechanisms . Future studies are needed to determine whether the same finding holds true in other gut regions or tissues , and whether these tissues differ from blood in the proportion of cells that contain infectious proviruses or can be induced by activation to produce infectious viruses . Since infected cells in the rectum make less HIV RNA ( and likely less HIV protein ) [23] , they may be less likely to trigger cell-intrinsic defense mechanisms or extrinsic immune responses that are designed to recognize and kill infected cells . This may facilitate survival of infected cells in the rectum and might contribute to the higher levels of HIV DNA per million CD4+ T cells in the gut . For the same reason , HIV-infected cells in the rectum might be less susceptible to killing by immune-based therapies that require HIV protein or antigen expression , such as broadly-neutralizing antibodies to Env , immunomodulators , CAR T cells , and vaccines designed to elicit B or T cell responses . Given the stronger block to HIV transcriptional initiation , as well as persistent blocks to later stages of HIV transcription ( completion , splicing ) despite higher levels of T cell activation , infected cells in the rectum may also be less susceptible to agents designed to reverse latency or may require different LRAs or combinations . Direct evidence for this idea comes from a recent multi-dose trial of vorinostat in HIV-infected patients , which showed that the median increase in cell-associated HIV RNA in the rectum was 5-fold less than in the blood [82] . Future studies should investigate how gut cells differ from blood in the response to different LRAs , and whenever feasible , the gut should probably be sampled in clinical trials designed to evaluate new therapies aimed at HIV cure . Additional studies should investigate how cellular gene expression differs between CD4+ T cells in the gut and blood , since differentially-expressed genes might suggest new proteins or pathways involved in suppressing HIV transcription . As an alternative to latency reversal , some recent studies have investigated therapies designed to create a "deeper" latency and prevent reactivation from latency [83–85] . Infected gut cells that are activated in vivo but do not transcribe RNA could serve as a model for therapies designed to silence HIV-infected cells . In these ways , an improved understanding of the mechanisms that govern HIV transcription/latency in the gut and blood could help inform new therapies aimed at HIV cure , functional cure , or reducing HIV-associated immune activation and organ damage .
The study was approved by the Committee on Human Research ( CHR ) , the Institutional Review Board for the University of California , San Francisco ( approval #11–07551 ) . All study participants provided written informed consent . The study participants were HIV-infected adults on suppressive ART from two cohorts ( median age = 51; median CD4 count = 611 cells/mm3; median years of suppression = 5 ) . Matched rectosigmoid biopsies and cryopreserved PBMCs were obtained from study participants ( Table 1 ) in the Reservoirs and Drug Levels ( RADL ) study . This study was a prospective , randomized study designed to measure HIV levels and antiretroviral drug ( ARV ) levels after suppression of plasma viremia with ART regimens containing 2 nucleotide inhibitors of reverse transcriptase and either an integrase inhibitor or a protease inhibitor . Inclusion criteria included: confirmed HIV-1 infection , ART suppression for ≥12 months on initial ART regimen , plasma HIV-1 RNA <40 copies/mL , and a willingness to undergo rectal biopsy . Matched blood and gut biopsies were obtained and aliquoted for parallel measurement of ARV levels and HIV levels . Six rectosigmoid biopsies ( flash frozen ) and 107 cryopreserved PBMCs were available from 9 study participants for measurement of HIV levels . Ileal biopsies were available for three of the 9 study participants . While these samples offer the advantages of an inclusive mix of cell types and less processing that could affect HIV RNA or drug levels , the PBMCs and gut biopsies also differ in composition of infected and uninfected cells . For more direct comparison of CD4+ T cells in the two tissues , we also sorted CD4+ T cells ( defined as CD3+CD8- ) from fresh gut biopsies ( rectosigmoid +/- ileum ) and blood from a different group of 7 ART-suppressed study participants recruited prospectively and sequentially from the SCOPE cohort ( Table 1 ) . For flash frozen rectosigmoid tissue , 1 mL TRI Reagent with 2 . 5 μL polyacryl carrier ( both from Molecular Research Center , Cincinnati , OH ) was added to the pooled rectal biopsies ( six rectal biopsies per individual ) , which were homogenized using a Mini Beadbeater ( Biospec Products , Bartlesville , OK ) . PBMCs from the same participants were thawed quickly and pelleted by centrifugation ( 300g for 5 min at 4°C ) . Following centrifugation , cryopreservation medium was removed and 1 mL TRI Reagent with 2 . 5 μL polyacryl carrier was added to homogenize cells . Total cellular RNA and DNA were subsequently extracted per the TRI Reagent protocol with back extraction for DNA[27] . RNA and DNA concentrations and quality were measured using the Nanodrop 1000 spectrophotometer ( Thermo Fisher Scientific , Waltham , MA ) . Up to 1μg of total RNA was used for a polyadenylation-reverse transcription-ddPCR assay for the TAR region , which is found in all HIV transcripts . This assay employs an initial polyadenylation step , since efficient reverse transcription ( RT ) of short , prematurely-terminated TAR transcripts requires RT from a linker molecule [31] . Following an RT reaction employing a combination of oligo ( dT ) and random hexamers , replicate 5μL aliquots of the cDNA were used in a ddPCR reaction containing TAR-specific primers and probe . Up to 5μg of RNA was used for a separate 50μL common RT reaction , from which aliquots of cDNA ( 5μL/well ) were used in ddPCR assays for other sequence regions , including U3-U5 ( "Read-through" ) , R-U5/pre-Gag ( "Long LTR" ) , Nef , U3-R-polyA ( "Poly A" ) , and multiply-spliced Tat-Rev ( MS Tat-Rev ) regions [27] . Since prior studies suggest that the average level per cell of "housekeeping" transcripts and the average RNA content per cell differ between cell types or even between corresponding cell types in different tissues or conditions ( such as T cell activation [27 , 86 , 87] ) , we did not use housekeeping transcripts or total cellular RNA to determine the cell equivalents in the RNA from the PBMCs or rectal biopsies , which contain different mixtures of cell types from different tissues . Instead , we determined the total cell equivalents in the DNA extracted from the same samples as the RNA by measuring the absolute copy numbers of a nonduplicated cellular gene , Telomere Reverse Transcriptase ( TERT ) , using ddPCR [88] . Using the assumption that the extraction efficiency is similar for both DNA and RNA , which we have verified in prior TRI reagent extractions from PBMCs , we normalized the absolute copy numbers of cDNA in each well to copies per million cells by using the total cell equivalents in the DNA and the fraction of the total RNA used for each RT and ddPCR [27] . It should be noted that the same method of normalization was used for each HIV RNA transcript , and therefore would not explain any difference between levels of various HIV RNAs in the same sample . As an additional method of normalization , we also normalized levels of each HIV RNA to the total cell equivalents in the DNA as determined by DNA mass , using the concentration measured by NanoDrop and the total resuspension volume . The findings were essentially identical . We also quantified the levels of the U3-U5 ( "Read-through" ) , TAR , Long LTR , and Nef regions in DNA extracted in parallel with the RNA from the same PBMCs and rectal biopsies ( from n = 9 individuals ) using the same primers/probes and ddPCR conditions used to measure each HIV RNA [27] . Since the PolyA assay does not detect HIV DNA , polyadenylated transcripts were normalized to HIV DNA levels of the U3-U5 ( Read-through ) assay , which contains the U3-R region of the PolyA assay and shares the same forward primer and probe . Note that we excluded MS Tat-Rev from this analysis , since this assay also does not detect HIV DNA and spans the two exons of tat and rev , so there is no DNA equivalent . Cellular DNA was fragmented by passage through a QIAshredder column [88] . Levels of each HIV DNA sequence region were measured by ddPCR in replicate aliquots ( at least 2 ) of 500ng DNA/well and expressed as copies per million cells by levels of TERT ( measured in duplicate ) in other aliquots from the same DNA . To further assess the effect of proviral sequences , we calculated the ratio of each HIV RNA to the corresponding HIV DNA sequence region quantified using the same ddPCR assay and normalized to 106 cells in the same fashion . This measure expresses the average level of transcription per provirus and is independent of normalization to cell numbers . While this measure may be best to assess the impact of proviral mutations in each sequence region , it should be noted that some sequence regions ( TAR , "Read-through" ) are present in 2 copies per intact HIV DNA ( one in each LTR ) , while the others are present in only one copy . As an alternative method to express the average transcription per provirus in the PBMCs and biopsies , we also calculated the ratio of each HIV RNA to HIV DNA as measured using the R-U5/pre-Gag ( Long LTR ) assay , which is present in only one copy per intact provirus . This method also allows comparison of Tat-Rev to the other transcripts . Finally , we calculated the ratios of one HIV transcript to another , which are independent of normalization to cell numbers and can be used to measure the presence and degree of different HIV transcriptional blocks [27] . To determine whether sequence-specific differences in RNA stability could contribute to differences in levels of the HIV RNAs , we measured the decay of Read-through , TAR , Long LTR , Nef , PolyA , and MS Tat-Rev transcripts in CD4+ T cells from an ART-suppressed individual using RNA Pol II inhibitors , Triptolide and Actinomycin D . CD4+ T cells were isolated from blood from an ART-suppressed individual using the Dynabeads Untouched Human CD4 T cells kit ( Thermo Fisher , Waltham , MA ) . Replicate aliquots of CD4+ T cells ( 6x106 cells/well ) were seeded into 6-well tissue culture plates ( Corning Inc . , Corning , NY ) at a concentration of 1x106 cells/ml in complete RPMI with either DMSO ( negative control ) , 100nM Triptolide ( Sigma , St Louis , MO ) , or 5mg/ml Actinomycin D ( Sigma , St Louis , MO ) . Cells were harvested at the following time points: DMSO: 0 , 1 and 16h; Triptolide: 0 , 1 , 3 , 6 and 16h; Actinomycin D: 0 , 1 , 3 and 16h . HIV transcripts ( Read-through , TAR , Long LTR , Nef , Poly A , and MS Tat-Rev ) were quantified using RT-ddPCR as described above . Levels of each HIV RNA were quantified by RT-ddPCR , normalized by alternative measures ( cell counts , DNA mass , RNA mass ) , and expressed as a fraction of the value at time zero . The half-life for each transcript was determined using an exponential one-phase decay model . For more direct comparison of CD4+ T cells in the blood and gut , we also analyzed CD4+ T cells isolated from blood and rectosigmoid ( +/- ileum ) from a different group of 7 ART-suppressed individuals . Fresh gut biopsies ( 15–20 ) were obtained by colonoscopy , placed immediately in RPMI ( supplemented with L-Glutamine , penicillin , streptomycin and 15% fetal calf serum ) , washed , and dissociated into total gut cells using collagenase with DNase and mechanical shearing [26] . Blood was obtained immediately before colonoscopy and PBMCs were isolated using Ficoll as previously described [26] . PBMCs and total gut cells were counted , stained with LIVE/DEAD stain and fluorescently-conjugated antibodies , and sorted for live , single , CD45+CD3+CD8- cells as previously described [89] . Sort yields from the rectum ranged from 83 , 474 to 976 , 738 ( median 360 , 279; S2 Table ) . Cells were sorted into FACS buffer , centrifuged to pellet cells , and immediately frozen . Total cellular RNA and DNA were isolated from the sorted CD3+CD8- cells using TRI Reagent , as described previously [26] . DNA was resuspended in 20μL ( for rectal T cells ) to 75μL ( for blood T cells with higher cell counts ) of QIAGEN buffer EB . 2 . 2μL were used to measure the DNA cell equivalents by ddPCR for TERT in duplicate ( 1μL/well ) . At least one ( and up to 3 ) aliquots ( 5μL/well ) were used to measure HIV DNA by ddPCR for the "Long LTR" region . At least two ( and up to 4 ) aliquots ( 5μL/well ) were used to measure levels of HIV DNA 2-LTR circles using a new ddPCR assay ( see below ) . Levels of HIV DNA ( Long LTR ) and 2-LTR circles were normalized to copies/106 cells using the absolute levels of TERT ( 2 copies/cell ) in the DNA and the volumes used for each assay . Total cellular RNA was resuspended in 20μL of RNase-free water . 5–10μL ( no more than 1μg ) was used for the polyadenylation-RT-ddPCR assay for TAR , while the remainder was used for a common RT reaction from which aliquots were used in replicate ddPCR assays for Read-through , Long LTR , PolyA , and MS Tat-Rev transcripts ( S4 Fig ) [27] . Since the average level per cell of "housekeeping" transcripts may differ in CD4+ T cells from blood and gut ( which consist of different mixtures of TH subtypes with differing proportions of activated cells ) , and we wanted to reserve as much of the RNA as possible for measurement of the 5 different HIV transcripts , we did not attempt to measure levels of housekeeping transcripts . Instead , HIV RNA levels were normalized to copies per million cells using the total cell equivalents in the DNA , as measured using ddPCR for TERT . As an alternative method to normalize the HIV RNA levels to cell numbers , we also used total cell counts from the sorts; findings were the same . To express the average level of each transcription per provirus in the sorted CD4+ T cells , we calculated the ratio of each HIV RNA to HIV DNA ( both expressed as copies/106 cells using normalization to TERT levels ) as measured using the R-U5/pre-Gag ( Long LTR ) assay , which is present in only one copy per intact provirus . Finally , we calculated the ratios of one HIV transcript to another , which are independent of normalization to cell numbers and can be used to measure the presence and degree of different HIV transcriptional blocks [27] . HIV-infected , ART-suppressed individuals were recruited from the SCOPE cohort ( individual 2475 ) and the VA Healthcare System ( individual 129 ) . Following a blood draw , PBMCs were isolated using Ficoll as previously described [26] . For individual 2475 , a proportion of PBMCs ( 40x106 cells ) were untreated . The remaining cells were split for two treatment protocols: 1 ) 40x106 PBMCs were treated with collagenase as described previously [26] and stained using the same antibodies employed for sorting CD4+ T cells from blood and gut ( CD45 , CD3 , CD8 , and LIVE/DEAD ) ; 2 ) 40x106 PBMCs were not treated with collagenase but stained using CD45 , CD3 , CD8 , and LIVE/DEAD . A similar procedure was followed for individual 129 , where 40x106 PBMCs were untreated and 40x106 cells were treated with collagenase , except these cells were not stained with fluorescently-conjugated antibodies . Following treatments , cells were counted and aliquoted into 10x106 cells prior to centrifugation to pellet . After removal of supernatant , cells were directly lysed in TRI reagent and stored at -80°C until RNA and DNA extraction . Total cellular RNA and DNA were extracted using TRI Reagent ( Molecular Research Center , Inc . , Cincinnati , OH ) as per manufacturer’s instructions , with the following modifications: polyacryl carrier ( Molecular Research Center , Inc . , Cincinnati , OH ) was added to TRI reagent prior to lysis , RNA was resuspended in RNase free-water , DNA was extracted using back extraction buffer ( 4M guanidine thiocyanate , 50mM sodium citrate , 1M Tris ) , polyacryl carrier was added to the aqueous phase containing the DNA , and DNA was resuspended in QIAGEN buffer EB . A common RT reaction was used to generate cDNA for all ddPCR assays except TAR [27] . Each 50μL RT contained cellular RNA , 5μL of 10x Superscript III buffer ( Invitrogen , Carlsbad , CA ) , 5μL of 50mM MgCl2 , 2 . 5μL of 50ng/μl random hexamers ( Invitrogen ) , 2 . 5μL of 50μM dT15 , 2 . 5μL of 10mM dNTPs , 1 . 25μL of 40U/μL RNaseOUT ( Invitrogen ) , and 2 . 5μL of 200U/μL Superscript III RT ( Invitrogen ) . Control RT reactions were performed in parallel with participant samples . A ‘6-assay’ synthetic HIV standard was utilized as a positive control [27] . HIV- donor PBMCs and water that was subjected to nucleic extraction by TRI Reagent served as negative controls for each transcript . The thermocycling conditions were as follows: 25 . 0°C for 10min , 50 . 0°C for 50min , followed by an inactivation step at 85 . 0°C for 5min . Reverse transcription from a linker molecule ( which we achieve using polyA polymerase to attach a polyA tail ) is necessary for efficient reverse transcription of short , prematurely-terminated HIV transcripts limited to the TAR loop [31] . Therefore , a polyadenylation step was employed prior to reverse transcription and ddPCR for the TAR region [27 , 31] . Each polyadenylation reaction comprised cellular RNA with 3μL of 10x Superscript III buffer ( Invitrogen ) , 3μL of 50mM MgCl2 , 1μL of 10mM ATP ( Epicentre ) , 2μL of 4U/μL poly-A polymerase ( Epicentre ) , and 1μL of 40U/μL RNaseOUT ( Invitrogen ) in a 20μL reaction . The reaction was incubated at 37μC for 45min prior to addition of RT reaction components , including 1 . 5μL of 10mM dNTPs ( Invitrogen ) , 1 . 5μL of 50ng/μL random hexamers ( Invitrogen ) , 1 . 5μL of 50μM oligo dT15 , and 1μL of 200U/μL Superscript III reverse transcriptase ( Invitrogen ) . Reverse transcription was performed on the final 30μL reaction at 25 . 0°C for 10 min , 50 . 0°C for 50 min , followed by an inactivation step at 85 . 0°C for 5 min . Droplet digital PCR was employed because it enables absolute quantification , circumvents the requirement for external HIV standards , and is more forgiving of differences in PCR efficiency due to sequence mismatches [90] . These assays have been validated previously [27 , 31] . cDNA from each sample was assayed in duplicate wells for Read-through , TAR , Long LTR , PolyA , and MS Tat-Rev regions ( all samples ) and ( for rectal biopsies and PBMCs ) one replicate for Nef . Total cellular DNA was used for the following ddPCR assays: 1 ) TERT ( all samples ) ; 2 ) Long LTR DNA ( all samples ) ; 3 ) U3-U5 ( "Read-through" ) , TAR , and Nef DNA ( PBMCs and rectal biopsies ) ; and 4 ) 2-LTR circles ( sorted CD4+ T cells ) . Each reaction consisted of 20μL containing cDNA ( 5μL ) or DNA , 10μL of ddPCR Supermix for Probes ( no dUTP ) ( Bio-Rad , Hercules , CA ) , 900 nM of primers , and 250 nM of probe . Following production of droplet emulsions using the QX100 Droplet Generator ( Bio-Rad ) , the samples were amplified under the following thermocycling conditions: 10 minutes at 95°C , 45 cycles of 30 seconds at 95°C and 59°C for 60 seconds , and a final droplet cure step of 10 minutes at 98°C , using a 7900 Thermal Cycler ( Life Technologies , Carlsbad , CA ) . Droplets were quantified using the QX100 Droplet Reader ( Bio-Rad Laboratories Inc . , Hercules , CA ) and analyzed using the QuantaSoft software ( version 1 . 6 . 6 , Bio-Rad Laboratories Inc . , Hercules , CA ) in the “Absolute” quantification mode . Gates were set above the negative controls . False positives were rare , generally limited to a single droplet , and usually identifiable by abnormally high fluorescence in both channels . A new ddPCR assay was used to measure levels of 2-LTR circles . Primers were "F Kumar" ( 5’ GCCTCAATAAAGCTTGCCTTGA 3’; HXB2 522–543 ) and "R Butler mod 2-LTR" ( 5' YCCACAGATCAAGGATMTCTTGTC 3’; 51–28 ) . The probe , "P Kumar" ( 5’ CCAGAGTCACACAACAGACGGGCACA 3’; 559–84 , was dual labelled with FAM ( 5' ) and Black Hole Quencher ( BHQ; 3' ) . Each reaction consisted of 20μL containing DNA , 10μL of ddPCR Supermix for Probes ( Bio-Rad , Hercules , CA ) , 900 nM of primers , and 250 nM of probe . Thermocycling conditions and analysis were as described above . A 2-LTR junction standard was created to determine the performance characteristics of the assay . The 2-LTR junction region was amplified from HIV NL4-3 infected PBMCs using the primers "F Buzon mod 2-LTR" ( 5’ CTARCTAGGGAACCCACTGCT 3’; HXB-2 498–518 ) and "R Buzon 2-LTR" ( 5’ GTAGTTCTGCCAATCAGGGAAG 3’; 92–71 ) , then cloned and sequenced . The copy numbers in the standard were determined using the calculated molecular weight and the DNA concentration as determined by NanoDrop . Replicate dilutions of the standard were used in replicate experiments to determine the detection limit , efficiency , and linearity ( S3 Fig ) . Detection frequencies were 2/6 at 0 . 9 copy , 3/6 at 1 . 8 copies , and 6/6 at 3 . 6 copies or above . No apparent inhibition was observed with addition of 1 or 2μg of QIAshredded cellular DNA . The Wilcoxon signed rank test was performed to assess differences between levels of different HIV RNA or DNA sequence regions . Wells with no positive droplets ( most common for MS Tat Rev ) were assigned a value of zero for purposes of calculating the median and p values in Figs 2 and 4 , and S1 Fig . The Wilcoxon signed rank tests were also repeated with no value ( blank ) for the undetectables , which did not change the major findings . GraphPad Prism ( Version 5 . 0 ) was used for the Wilcoxon tests and exponential one phase decay modeling . As an additional method to account for undetectable samples , a negative binomial regression analysis was performed in STATA using the cell equivalents used in each ddPCR well and the number of replicates . The major findings did not change . | Available antiretroviral drugs significantly prolong life expectancy and reduce morbidity in people living with HIV . However , HIV can escape host immune responses and drug treatment by establishing a reversibly silent ( "latent" ) infection in CD4+ T cells . This latent infection represents the major barrier to a cure . While much of the research to date has highlighted the importance of peripheral CD4+ T cells as reservoirs for latent HIV , it is becoming increasingly apparent that the gut may play an integral role as a major tissue reservoir for HIV . In this study , we show that the transcriptional blocks that underlie HIV latency in CD4+ T cells differ in the blood and gut . In HIV-infected people on effective treatment , the major blocks to HIV transcription in blood cells occur at transcriptional elongation , distal transcription/polyadenylation ( completion ) , and splicing . In the gut , the major block to HIV transcription occurs at transcriptional initiation , suggesting that HIV latency is maintained by different mechanisms in the gut , which may be enriched for latently-infected cells and/or cells in a "deeper" state of latency . These differences in the blocks to HIV transcription are important to consider in designing therapies that aim to cure HIV . | [
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"invasiv... | 2018 | Gut and blood differ in constitutive blocks to HIV transcription, suggesting tissue-specific differences in the mechanisms that govern HIV latency |
Elimination of Plasmodium vivax malaria would be greatly facilitated by the development of an effective vaccine . A comprehensive and systematic characterization of antibodies to P . vivax antigens in exposed populations is useful in guiding rational vaccine design . In this study , we investigated antibodies to a large library of P . vivax entire ectodomain merozoite proteins in 2 Asia-Pacific populations , analysing the relationship of antibody levels with markers of current and cumulative malaria exposure , and socioeconomic and clinical indicators . 29 antigenic targets of natural immunity were identified . Of these , 12 highly-immunogenic proteins were strongly associated with age and thus cumulative lifetime exposure in Solomon Islanders ( P<0 . 001–0 . 027 ) . A subset of 6 proteins , selected on the basis of immunogenicity and expression levels , were used to examine antibody levels in plasma samples from a population of young Papua New Guinean children with well-characterized individual differences in exposure . This analysis identified a strong association between reduced risk of clinical disease and antibody levels to P12 , P41 , and a novel hypothetical protein that has not previously been studied , PVX_081550 ( IRR 0 . 46–0 . 74; P<0 . 001–0 . 041 ) . These data emphasize the benefits of an unbiased screening approach in identifying novel vaccine candidate antigens . Functional studies are now required to establish whether PVX_081550 is a key component of the naturally-acquired protective immune response , a biomarker of immune status , or both .
Intensified research and funding have helped to significantly reduce the morbidity and mortality of malaria , and an increasing number of countries are now aiming to eliminate this disease [1–3] . In Asia-Pacific and the Americas , however , interrupting local Plasmodium vivax transmission will be particularly challenging . The ability of P . vivax to form dormant liver hypnozoites , which are responsible for ~80% of all blood-stage infections [4 , 5] , provides a source of new blood-stage infections in the absence of transmission . P . vivax commonly causes low-density asymptomatic infections that often go undetected and thus untreated . Moreover , the early maturation and peripheral circulation of P . vivax gametocytes , coupled with high infectivity and rapid development in mosquitoes , make P . vivax more refractory to control measures [6] . As a consequence , P . vivax is now the predominant Plasmodium species outside Africa [1] . New tools are needed to control and eliminate vivax malaria . Vector control strategies that are broadly effective in reducing P . falciparum transmission , such as insecticide-treated nets ( ITNs ) and indoor residual spraying , seem to be less effective against P . vivax vectors [7 , 8] , which are more likely to bite and rest outdoors , and less likely to bite humans than African P . falciparum vectors [9] . Furthermore , primaquine , the only drug effective against P . vivax hypnozoites , is associated with hemolysis in glucose-6-phosphate dehydrogenase-deficient individuals [10] . Similar effects have been seen for tafenoquine , the only other liver-stage drug in clinical development [11] . Given these challenges , the development of a highly effective vaccine would immensely facilitate P . vivax elimination , perhaps even more so than P . falciparum elimination [12] . Merozoites represent the only extracellular phase of the Plasmodium blood-stage life cycle , and merozoite antigens are therefore appropriate vaccine targets . Several studies have investigated merozoite antigens as targets of natural protective immunity to P . falciparum malaria [13] , and their potential as vaccine candidates [14] . For P . vivax , the availability of the genome sequence [15] and transcriptome [16] have enhanced our understanding of this parasite’s biology , facilitating the identification of many proteins that are homologous to P . falciparum antigens [17–19] . However , the targets of natural immunity to P . vivax malaria remain poorly understood , and systematic screens of multiple antigens are lacking [20] . As a consequence , there are currently only a handful of P . vivax vaccine candidate antigens in pre-clinical development , with only a single blood-stage antigen ( PvDBP ) nearing clinical development [21] . In this study , we investigated 34 recombinant P . vivax protein ectodomains [22] , known or predicted to localize to the merozoite cell surface , micronemes , or rhoptries , as targets of natural immunity . For 12 highly-immunogenic proteins , we investigated associations between levels of antibodies and indicators of current and cumulative malaria exposure in a moderately-endemic area of the Solomon Islands ( SI ) . Using a cohort of young Papua New Guinean ( PNG ) children with well-characterized individual differences in exposure , we identified an association between reduced incidence of clinical disease and antibody levels to 3 proteins , including a novel hypothetical protein that has not been previously studied . These data emphasize the benefits of an unbiased screening approach in identifying vaccine candidates and indicate that these 3 antigens are high-priority targets for further functional studies , and potentially vaccine development .
Proteins were designed , constructed , and expressed as described previously for P . falciparum merozoite proteins [23 , 24]; the P . vivax ectodomain library has been described in detail by Hostetler et al . ( S1 Table ) [22] . Briefly , sequences derived from the P . vivax Salvador-1 strain encoding merozoite ectodomains , excluding their signal peptide , transmembrane domain , and glycosylphosphatidylinositol ( GPI ) anchor sequences ( if present ) , were codon-optimized for expression in human cells and chemically synthesized ( GeneArt AG ) . Soluble recombinant proteins ( S1 Table ) containing a ~25-kDa C-terminal rat Cd4d3+d4 ( Cd4 ) tag were expressed in human embryonic kidney ( HEK ) 293E cells as either biotinylated or 6-His-tagged forms , culture supernatants were collected 6 days after transfection , and biotinylated proteins were dialysed in HEPES-buffered saline . All expression plasmids are openly available at Addgene ( http://www . addgene . org/express/vivax/ ) . 6-His-tagged proteins were purified by immobilized metal-ion affinity chromatography using HisTrap-HP columns on an AKTA Xpress ( GE Healthcare ) following the manufacturer’s instructions . Proteins were then conjugated to Luminex Microplex microspheres ( Luminex Corporation ) as described [25] , using the following concentrations per 2 . 5x106 beads: P41 , 0 . 5 μg/mL; PVX_081550 , 1 . 2 μg/mL; P12 , 0 . 2 μg/mL; GAMA , 0 . 015 μg/mL; ARP , 0 . 09 μg/mL; CyRPA , 1 . 5 μg/mL; and Cd4 , 2 μg/mL . Coupling efficiency was determined by using an immune plasma pool known to be highly reactive with the antigens , with the appropriate antigen concentration resulting in high fluorescence intensity by the reporter fluorochrome . Ethical clearance was obtained from the PNG Medical Research and Advisory Committee of the Ministry of Health , Solomon Islands National Health Research Ethics Committee , and the Walter and Eliza Hall Institute . Informed consent was obtained from all participants and in cases of children from their parents or guardians . As approved by the Australian and Solomon Islands’ ethics committees , only verbal consent , documented on each participant’s case report form , was obtained from participants in the cross-sectional survey in Ngella , Solomon Islands . Written informed consent was obtained from the parents or guardians of all children participating in the PNG cohort study .
We first investigated whether IgG from 46 P . vivax-exposed SI individuals recognized antigens from our library . There was a high degree of variability in IgG levels to the different proteins . The population mean antibody level to 85 . 3% ( 29/34 ) of proteins was significantly higher than to Cd4 alone ( P<0 . 001–0 . 018 ) and these were therefore considered immunogenic ( Fig 1 ) . IgG levels were similar between adolescents and adults , except for 7/34 proteins ( P<0 . 001–0 . 032 ) ( Fig 1 ) . We then selected 12 of the most immunogenic proteins , MSP3 . 3 , MSP10 , MSP7 . 6 , MSP3 . 10 , P12 , ARP , P41 , MSP5 , GAMA , RIPR , MSP1 , and CyRPA , for further analysis . We tested these 12 proteins against a larger panel of 144 SI samples . Antibody seroprevalence ranged from 31 . 3% ( P41 ) to 100% ( MSP1 ) ( S1 Table ) . MSP1 was recognized by 100% of samples , and P12 , GAMA , MSP3 . 10 , and RIPR were each recognized by at least 85% of the children , 89% of the adolescents , and 93% of the adults ( Fig 2A ) . Children recognized fewer proteins ( mean 7 . 06 ) than adults ( mean 9 . 65; P<0 . 001 ) or adolescents ( mean 8 . 08; P = 0 . 051 ) ( Fig 2C ) . Similarly , individuals with a current infection detected by both PCR and LM ( mean 8 . 90; P = 0 . 005 ) , but not those with only PCR+ infections ( mean 8 . 33; P = 0 . 140 ) , had antibodies to significantly more proteins than noninfected individuals ( mean 7 . 56 ) , suggesting a limited effect of recent infections even in adults ( Fig 2B and 2D ) . We applied a serocatalytic model to the seroprevalence data to investigate the kinetic ( seroconversion and seroreversion rates ) of IgG to antigens with <85% prevalence in children . The estimates are shown in S1 Text . The magnitude of the cumulative levels ( i . e . , sum of IgG levels to all antigens , per individual ) , as well as IgG levels to all individual antigens were strongly associated with age , increasing significantly from children to adults ( P<0 . 001–0 . 027 ) ( Fig 3A ) . The magnitude of the cumulative antibody levels was also increased in the presence of current infection at either lower ( PCR+ only , P = 0 . 008 ) or higher parasite density ( PCR+ LM+ , P = 0 . 001 ) ( Fig 3B ) . Individually , lower-density infections were associated with higher IgG levels to CyRPA ( P = 0 . 022 ) , GAMA ( P = 0 . 015 ) , and MSP3 . 10 ( P = 0 . 013 ) only , while higher-density infections were associated with higher IgG levels to a larger number of antigens: CyRPA ( P<0 . 001 ) , GAMA ( P = 0 . 015 ) , MSP3 . 10 ( P = 0 . 032 ) , P12 ( P = 0 . 020 ) , P41 ( P = 0 . 035 ) , MSP1 ( P = 0 . 020 ) , MSP10 ( P = 0 . 039 ) , and RIPR ( p = 0 . 019 ) ( Fig 3B ) . These data indicate that the antigens in our panel are good markers of cumulative exposure , and that some of them are also markers of current infection . In multivariate analysis , age remained strongly associated with increased cumulative and IgG levels to all individual antigens ( P<0 . 001–0 . 023 ) ( S2 Table ) . Lower-density infections remained associated with increased IgG levels to CyRPA ( P = 0 . 012 ) and GAMA ( P = 0 . 007 ) , and higher-density infections remained associated with increased IgG levels to CyRPA ( P<0 . 001 ) , GAMA ( P = 0 . 005 ) , P12 ( P = 0 . 017 ) , P41 ( P = 0 . 031 ) , MSP1 ( P = 0 . 014 ) , and RIPR ( P = 0 . 012 ) . For MSP5 , IgG levels were higher only in adults with a current infection ( P = 0 . 022 ) ( S2 Table ) . We compared IgG levels against a large number of other epidemiological variables ( e . g . , region , clinical symptoms , and socioeconomic indicators ) , but none of them were significantly associated with differences in antibody levels for any antigen ( S2 Table ) . The use of ITNs was the only variable that had any significant association , with the use of ITNs in previous years associated with reduced IgG levels to RIPR ( P = 0 . 030 ) , MSP1 ( P = 0 . 024 ) , and MSP3 . 3 ( P = 0 . 015 ) ( S2 Table ) . If ITN use is considered a marker for exposure , this also indicates that the levels of antibodies targeting these antigens are particularly sensitive to recent exposure . To establish whether these associations were population-specific or more broadly generalizable , we tested IgG levels to a subset of 6 antigens , chosen on the basis of immunoreactivity and expression levels , in a sub-cohort of 230 PNG children . The median age of the population was 1 . 7 years ( IQR 1 . 3–2 . 5 ) , and the prevalence of P . vivax infection at baseline was 55% by PCR . IgG levels to ARP , CyRPA , and PVX_081550 were positively associated with age ( r = 0 . 15–0 . 25; P = 0 . 001–0 . 027 ) . For PVX_081550 , stronger increases in IgG with age were observed in children without current infections ( r = 0 . 33; P<0 . 001 ) than with current infections ( r = 0 . 18; P = 0 . 048 ) ( S3 Table ) . A current P . vivax infection was associated with higher IgG levels to CyRPA ( P<0 . 001 ) , P12 ( P<0 . 001 ) , P41 ( P = 0 . 001 ) , and PVX_081550 ( P = 0 . 001 ) ( S3 Table ) . When considering cumulative exposure as a product of age and the number of P . vivax infections acquired over time ( molFOB ) , increasing IgG levels with cumulative exposure to PVX_081550 ( r = 0 . 41 P<0 . 001 ) and CyRPA ( r = 0 . 14 , P = 0 . 032 ) are observed in children without current infections ( S3 Table ) . During the 16 months of follow-up of the PNG cohort , children experienced an IRR of 1 . 25 ( 95%CI 1 . 08–1 . 45 ) malaria episodes with P . vivax >500 parasites/μL/year at risk . We applied the unadjusted GEE model to test whether responses to specific antigens were associated with a reduced risk of infection . Children with high levels of IgG to PVX_081550 ( IRRH 0 . 41; P<0 . 001 ) and P41 ( IRRH 0 . 63; P = 0 . 019 ) both had a significantly lower risk of clinical P . vivax malaria ( Table 1 ) . When adjusting for confounders , medium and high levels of IgG to PVX_081550 ( IRRM 0 . 74 , P = 0 . 041; IRRH 0 . 46 , P<0 . 001 ) , and high IgG levels to P41 ( IRRH 0 . 56; P<0 . 001 ) and P12 ( IRRH 0 . 65; P = 0 . 012 ) were associated with protection . No association with protection was observed for levels of IgG to GAMA , CyRPA , and ARP ( Fig 4; Table 1 ) . IgG levels to the 3 antigens associated with protection were significantly correlated ( r = 0 . 34–0 . 66; P<0 . 001 ) ( S4 Table ) , suggesting co-acquisition . In multivariate analyses , only high levels of IgG to PVX_081550 remained strongly associated with reduced risk of P . vivax episodes ( IRRH 0 . 54; P = 0 . 001 ) , indicating that this antigen may be a key target of natural immunity or a good marker of immunity . There were no significant associations between levels of IgG to any of these 3 antigens and risk of clinical episodes caused by P . falciparum with any parasite density ( IRR 0 . 92–1 . 18; P>0 . 10 ) ( S6 Table ) . There was a very strong association between increasing antibody repertoire and increase in protection . Each increase in 1 unit of the breadth score ( described in Methods ) was associated with a reduction of approximately 7% in the risk of P . vivax episodes ( IRR 0 . 93; 95%CI 0 . 90–0 . 97; P = 0 . 001 ) . However , once we accounted for differences in IgG to PVX_081550 , the breadth effect was no longer significant ( IRR 0 . 98; 95%CI 0 . 93–1 . 04; P = 0 . 49 ) , while high levels of IgG to PVX_081550 remained associated with protection ( IRRH 0 . 51; 95%CI 0 . 33–0 . 80; P = 0 . 004 ) . This finding suggests that IgG level to PVX_081550 is a key marker of protective immunity .
The discovery and rational prioritization of P . vivax proteins as candidates for inclusion in a future P . vivax vaccine would be greatly facilitated by a comprehensive and systematic characterization of antibody response to P . vivax antigens in exposed individuals . Although epidemiological associations do not necessarily denote causality , the identification of such ‘protective’ antibody targets in naturally exposed individuals can be used to prioritize antigens or antigen combinations before testing their efficacy and thus potential vaccine suitability in functional studies . Such sero-epidemiological discovery and down-selection are particularly important for P . vivax , where the lack of stable in-vitro culture and genetic manipulation techniques [6] make functional studies and biology-drive discovery difficult , low throughput , and thus very expensive . To date , only a very small number of P . vivax antigens , such as DBP , MSP1 , MSP3 , MSP9 , and AMA1 have been investigated [21 , 35 , 36] . The complexity of naturally-acquired immunity against P . vivax [37] and the likelihood that it’s multifactorial and involves antibodies against several antigenic targets , unlikely to be identified in only one study , highlight the importance of conducting more screening studies . Investigating the large number of potential targets found in the parasite proteome , however , has been constrained in large part by the difficulty of producing natively-folded recombinant P . vivax antigens . We have leveraged our recent development of a large library of immunoreactive merozoite surface and secreted entire ectodomain proteins [22] to perform systematic studies of reactivity to P . vivax blood-stage antigens in 2 Asia-Pacific populations . The vast majority of these proteins ( 28/34 ) were recognized by plasma IgG from asymptomatic ( including noninfected ) adolescent and adult Solomon Islanders . Of these , 27 were also recognized by pooled IgG from Cambodian P . vivax malaria patients [22] . Although there are individual differences between study populations ( e . g . , PVX_116675 was only recognized in SI , and PVX_110950 and RhopH3 were only recognized in Cambodia ) , the use of a large protein library for the first time confirms the broad immunogenicity of a large number recombinant proteins , and also that the pool of potential vaccine targets is much deeper than has been studied to date . For 12 highly-immunogenic proteins ( MSP3 . 3 , MSP10 , MSP7 . 6 , MSP3 . 10 , P12 , ARP , P41 , MSP5 , GAMA , RIPR , MSP1 , and CyRPA ) , we confirmed that IgG levels increase more strongly with age , and thus cumulative life-time exposure , than with current infection . Lower and asymptomatic parasitemias are prevalent in SI , a sign that despite significant recent reductions in transmission , residents have acquired significant immunity that is characterized by long-lasting , stable antibody levels . In several studies , antibodies to the P . falciparum homologs of some of the proteins included in our study were shown to be strongly associated with clinical immunity to P . falciparum [38 , 39] . It is therefore likely that the observed high antibody levels to these P . vivax proteins contribute to the strong levels of clinical immunity in the SI community . The associations of clinical immunity with antibodies to 3 antigens ( P12 , P41 , and PVX_081550 ) were confirmed in a cohort of young , semi-immune PNG children . The observed reductions in risk of P . vivax malaria were comparable to those associated with high antibody titers to P . vivax MSP3α and MSP9 [34] . In P . falciparum , P12 is a GPI-anchored rhoptry protein [40] , while P41 is localized to the merozoite surface [41]; together , they form a heterodimer and are thought to be involved in reticulocyte invasion , although neither is essential for parasite growth in vitro [42] . Both are strongly recognized by natural immunity , and antibodies have also been associated with clinical protection [38 , 39] . It is likely that P . vivax P12 and P41 , which also form a heterodimer [22] , have comparable functions . The protein with the strongest association with protection was the hypothetical protein , PVX_081550 . Its P . falciparum homologue has recently been identified as StAR-related lipid transfer protein [43] , able to transfer different lipids between phospholipid vesicles . In P . falciparum , this protein localizes to the parasitophorous vacuole ( PV ) ; there is some evidence that it may be transferred into the apical organelles of mature merozoites , where it may play a role in forming the PV during the invasion process [43] . Although the P . falciparum protein was also found to be immunogenic [44] , it is unclear whether antibodies to it interfere with parasite function ( e . g . , block erythrocyte invasion ) or are simply elicited by proteins released from the PV upon schizont rupture and thus serve only as markers of an individual’s immune status . Both proteins are polymorphic , with nonsynonymous/synonymous SNP ratios of 1 . 9–2 . 3 ( PlasmodDBv26 [45] ) . Further studies are now needed to elucidate the function of both P . falciparum and P . vivax StAR-related lipid transfer proteins , and importantly to determine whether antibodies to P . vivax PVX_081550 are functionally protective or simply a useful marker of a child's overall immune status . Our studies have confirmed that a large array of P . vivax merozoite antigens are targets of natural humoral immunity , and that antibodies to little-studied proteins may have equivalent or even stronger associations with reduced malaria risk in naturally exposed populations in comparison to current leading vaccine candidates . Further studies , including both in-depth evaluations of their association with protection in longitudinal cohort studies in other transmission settings and functional studies ( to the extent this is currently possible for P . vivax ) , will be required to determine the potential of these proteins as vaccine candidates , markers of immune status , markers of cumulative exposure , or some combination thereof . | Plasmodium vivax is now the predominant malaria parasite outside Africa . Because P . vivax can remain dormant in the liver for months , identifying and treating P . vivax in asymptomatic individuals is difficult . Additionally , current widely-used vector control measures are less efficient against mosquitoes that transmit P . vivax . An effective vaccine would therefore immensely facilitate P . vivax elimination . Unfortunately , little is known about P . vivax biology and only a few proteins have been investigated as targets for vaccine development . To address these knowledge gaps , we measured antibody levels to 34 entire ectodomain proteins predicted to be involved in P . vivax invasion of erythrocytes , in samples from individuals living in 2 malaria-endemic Asia-Pacific countries . We found that antibodies in malaria-exposed Solomon Islanders were reactive to the majority of proteins in our panel , and that antibodies to 12 of these proteins strongly reflected cumulative life-time exposure to P . vivax . In samples from Papua New Guinea children , we identified an association between antibodies to 3 proteins and protection against clinical malaria . Our results demonstrate that screening antibodies to a large number of P . vivax proteins is a useful approach in identifying novel targets of immunity . Functional studies are now required to establish whether these proteins are biomarkers of an individual’s immune status , potential vaccine candidates that warrant further development , or both . | [
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"medicin... | 2016 | An Antibody Screen of a Plasmodium vivax Antigen Library Identifies Novel Merozoite Proteins Associated with Clinical Protection |
Buruli ulcer , caused by infection with Mycobacterium ulcerans , is a chronic ulcerative neglected tropical disease of the skin and subcutaneous tissue that is most prevalent in West African countries . M . ulcerans produces a cytotoxic macrolide exotoxin called mycolactone , which causes extensive necrosis of infected subcutaneous tissue and the development of characteristic ulcerative lesions with undermined edges . While cellular immune responses are expected to play a key role against early intracellular stages of M . ulcerans in macrophages , antibody mediated protection might be of major relevance against advanced stages , where bacilli are predominantly found as extracellular clusters . To assess whether vaccine induced antibodies against surface antigens of M . ulcerans can protect against Buruli ulcer we formulated two surface vaccine candidate antigens , MUL_2232 and MUL_3720 , as recombinant proteins with the synthetic Toll-like receptor 4 agonist glucopyranosyl lipid adjuvant-stable emulsion . The candidate vaccines elicited strong antibody responses without a strong bias towards a TH1 type cellular response , as indicated by the IgG2a to IgG1 ratio . Despite the cross-reactivity of the induced antibodies with the native antigens , no significant protection was observed against progression of an experimental M . ulcerans infection in a mouse footpad challenge model . Even though vaccine-induced antibodies have the potential to opsonise the extracellular bacilli they do not have a protective effect since infiltrating phagocytes might be killed by mycolactone before reaching the bacteria , as indicated by lack of viable infiltrates in the necrotic infection foci .
Buruli ulcer ( BU ) is a neglected tropical disease of the skin and subcutaneous tissue reported from over 30 countries worldwide . BU is most prevalent in West African countries like Cote d’Ivoire , Cameroon , Benin and Ghana [1 , 2] . Mycobacterium ulcerans , the causative agent of BU , produces a macrolide exotoxin called mycolactone , which is responsible for extensive necrosis of infected subcutaneous tissue leading to the development of large ulcerative lesions , if not treated at an early stage [3] . While extensive surgical removal of the diseased tissue has been the only treatment approach for a long time , since 2004 the WHO recommends eight weeks of combination chemotherapy with rifampicin and streptomycin [4] . This change in treatment strategy has substantially decreased both the amount of surgery required for treatment of extensive skin lesions as well as recurrence rates [5–7] . Nevertheless , BU has remained a huge socioeconomic burden in endemic regions of Africa . Affected populations are typically living in rural regions with limited access to health care services and limited financial resources , frequently resulting in delayed health care seeking and presentations with large ulcerative lesions , which take long time to heal [8 , 9] . Sero-epidemiological studies have shown that active BU only develops in some of the people exposed to M . ulcerans [10 , 11] . Together with reports on spontaneous healing of BU lesions [12 , 13] and the fact that the risk for young adults to develop BU is much smaller than for children [14] , this observation suggests that development of protective immunity against BU is possible [15] . However , it is not clear which immune effector functions are important for protection . Cellular immunity is expected to play a key role in the early intracellular growth phase of M . ulcerans in macrophages [16–18] . However , induction of TH1 responses by vaccination with Bacillus Calmette-Guérin ( BCG ) or a mycolactone negative M . ulcerans strain conferred only transient protection in an experimental mouse infection model [19] . Likewise , BCG vaccination seems to lead to cross-reactive immunity to severe forms of BU in clinical trials [20 , 21] , but the BCG mediated induction of cellular response was not able to protect completely from M . ulcerans disease in either mice or humans [19–21] . In advanced BU lesions , in which clusters of extracellular bacilli dominate , antibodies against surface proteins of M . ulcerans may be of major importance for conferring protection [18 , 22 , 23] . In order to study this hypothesis , two M . ulcerans surface antigens , MUL_2232 and MUL_3720 , were chosen in this study as vaccine candidate antigens . MUL_2232 , the 18 kDa small heat shock protein of M . ulcerans , is an immunodominant cell wall associated protein with a homologue found in M . leprae , but not in M . bovis or M . tuberculosis [10] . MUL_3720 is a 22 kDa molecule with a predicted N-terminal lectin domain and a C-terminal peptidoglycan-binding domain with a putative role in cell attachment and cell-cell interaction [24] that is highly expressed on the surface of the bacilli [25] . Within the framework of a collaborative project ( BuruliVac ) our goal was to assess whether vaccine induced antibody responses against surface proteins of M . ulcerans are protective against BU . Here we present immunogenicity studies of MUL_2232 and MUL_3720 formulated as adjuvanted recombinant proteins with Alum , Sigma adjuvant ( a squalene oil-in-water emulsion containing Monophosphoryl Lipid A ( MPL ) and synthetic trehalose dicorynomycolate ) or EM048 ( glucopyranosyl lipid adjuvant-stable emulsion ( GLA-SE ) adjuvant system [26 , 27] ) . Further , we assessed the potential of the induced immune responses to confer protection against experimental infection in a murine M . ulcerans infection model .
All animal experiments performed were approved by the animal welfare committee of the Canton of Basel ( authorization number 2375 ) and the Canton of Vaud ( authorization number 2261 ) and were conducted in compliance with the Swiss animal protection law ( Tierschutzgesetz , TSchG , 455 ) . Infection experiments with M . ulcerans were conducted under Biosafety-level-3 conditions at the École polytechnique fédérale de Lausanne ( EPFL ) . The potential protein vaccine candidate antigens MUL_2232 ( GenBank accession number 4550596 ) and MUL_3720 ( GenBank accession number 4553013 ) of M . ulcerans Agy99 were ordered as codon optimized genes for expression in human cells ( GenScript ) and received in pUC57 plasmids . Expression of the antigens as recombinant proteins in E . coli was achieved with the pET28a expression system ( Novagen , modified to contain an ampicillin selection cassette ) . Briefly , restriction sites required for further cloning were attached by the use of specifically designed primers for amplification of the codon optimized sequences by polymerase chain reaction ( PCR ) . Primer sequences for MUL_22232 amplification were 5’-TTCCTTCATATGCTGATGAGAACCGACCCTTTTAGA-3’ and 5’-TTCCTTGCGGCCGCTCAAGCCTCAATCACTTCGGGA . Primer sequences for MUL_3720 amplification were 5’-TTCCTTCATATGAGCGATACTCTGACTGAAGGACAG-3’ and 5’-TTCCTTGCGGCCGCGCTCAAGGAATAGTCAGGACCTCT-3’ . PCR products were cut by the restriction enzymes NdeI and NotI ( New England Biolabs ) and subsequently ligated into pET28 to attach an N-terminal 6xHis-tag . After propagation of the generated plasmids in Top10 E . coli ( Invitrogen ) , control restriction and sequencing of the plasmids ensured selection of appropriate clones for expression of the proteins . Protein expression was induced in E . coli BL21 ( DE3 ) strains ( Invitrogen ) by addition of 1 mM isopropyl thiogalacoside ( Calbiochem ) for 4 h at 37°C in lysogeny broth ( LB ) medium supplemented with Ampicillin . After screening for high level recombinant protein expression by analysis of small induced cultures , larger amounts of recombinant proteins were produced by selected expression clones . Protein lysates were produced by dilution of the bacterial pellet in PBS , the addition of lysozyme and sonication . After removal of cellular debris by centrifugation , the 6xHis-tagged recombinant proteins were purified by nickel-nitrilotriacetic acetic ( Ni-NTA ) metal-affinity chromatography . Proteins were eluted with increasing concentrations of imidazole and the integrity and purity of proteins was assessed by SDS-page separation and Coomassie Blue staining ( S1 Fig ) . Final concentrations of the produced recombinant proteins rMUL2232 and rMUL3720 were determined by BCA assay ( Pierce ) according to the manufacturer’s instructions . Oil-in-Water formulated TLR-4 agonist ( GLA-SE , EM048 ) was produced by the Infectious Disease Research Institute ( IDRI ) [26 , 27] . EM048 was mixed with recombinant protein in PBS to a final concentration of 50 μg/ml EM048 and 200 μg/ml recombinant protein . Sigma adjuvant system ( Sigma ) was reconstituted according to the manufacturer’s instructions and combined with recombinant protein to a final concentration of 200 μg/ml recombinant protein . Imject Alum ( Thermo Scientific ) was mixed with recombinant protein according to the manufacturer’s instructions to a final volume ratio of Imject Alum to immunogen of 1:1 and a final concentration of 200 μg/ml recombinant protein . Immunogenicity of the described vaccine formulations was studied in 8 week old female BALB/c mice ( Janvier ) . Groups of five mice were immunized three times by the subcutaneous ( s . c . ) route in the scruff of the neck with 100 μl of the adjuvanted proteins in three week intervals . Prior to the first immunization as well as before every new immunization mice were bled by the tail vein and serum gained by centrifugation of the blood in SST Microtainer tubes ( Becton , Dickinson and Company ) . An additional blood collection was performed three weeks and six months after the last immunization . Serum immunoglobulin G ( IgG ) antibody titers were determined by ELISA on recombinant protein with all incubation steps performed at room temperature ( RT ) . 10 μg/ml of rMUL2232 or rMUL3720 , respectively , were coated on ELISA plates ( Maxisorp; Nunc ) by incubation overnight . After blocking with 5% skim milk/PBS for 1 hour , plates were incubated with dilution series of sera from immunized mice in 0 . 5% skim milk/PBS for two hours , washed and incubated with alkaline phosphatase-conjugated goat anti-mouse monoclonal antibody ( mAb; Sigma ) as secondary antibodies for 1 hour . Plates were washed prior to development with p-nitrophenyl phosphate ( Sigma ) as substrate . The optical density ( OD ) of the reaction product was measured at 405 nm with a microplate absorbance reader ( Sunrise Absorbance Reader; Tecan ) . The threshold for endpoint titer determination was defined as the double of the mean measurements plus the mean standard deviation of a dilution series done without primary antibody and a dilution series done with pre-bleed serum . Individual serum dilution series were approximated with sigmoidal dose-response curves and the reciprocal dilution of the intersection between the curve and the threshold was defined as individual endpoint titer ( GraphPad Prism software , Graph-Pad Software Inc . ) . For the determination of IgG subclasses , ELISA was performed as described above with the use of subclass specific alkaline phosphate-conjugated secondary antibodies ( Southern Biotech ) . According to the manufacturer’s instructions , 10 μg of M . ulcerans whole cell lysate was loaded on a prefabricated 4–12% gradient gel ( NuPAGE Novex 4–12% Bis-Tris Gel; Invitrogen ) with MES running buffer under reducing conditions . A dry-blotting system ( iBlot; Invitrogen ) was used to electrophoretically transfer the separated proteins to nitrocellulose membranes , which were subsequently blocked in 5% skim milk/PBS over night at 4°C . Membranes were cut into strips and individually incubated with appropriate dilutions of serum of immunized mice in 1% skim milk/0 . 05% Tween20 / PBS for 1 hour at RT . After several washing steps in 1% skim milk/0 . 05% Tween20/PBS , a HRP-conjugated goat anti-mouse IgG γ-chain mAb ( Southern Biotech ) was used as secondary antibody and incubated for 1 hour at RT . Excess antibody and skim milk residuals were washed away with PBS and blots were then developed using ECL Western blotting detection reagents ( ECL Western blotting Substrate; Pierce ) . For the determination of the Western blot endpoint titers , individual dilution series of sera were processed as described above . Development of the entire set of strips by the ECL system was done on one single film . Development time was chosen as the shortest time needed for detecting a specific signal in at least one strip for every serum dilution , e . g . lowest dilution , and for every dilution series of individual sera at least one strip with no specific signal , e . g . highest dilution . Western blot endpoint titers were defined as the reciprocal value of the last dilution that yielded a specific band in Western blotting on the film . Immunofluorescence assays with sera of immunized mice on paraffin embedded M . ulcerans bacteria were performed as previously described [25] . Briefly , African M . ulcerans isolates were embedded into paraffin , cut into 3 μm thin sections and mounted on Superforst Plus glass slides ( Thermo Scientific ) . Sections were then deparaffinised , rehydrated and pre-treated with 1mM EDTA buffer pH = 8 for epitope retrieval as described for tissue sections in immunohistochemistry [28] . Unspecific binding was prevented by incubation of the bacteria in 1 . 5% goat serum in PBS for 1 hour at RT . Appropriately diluted mouse mAbs specific for MUL_2232 and MUL_3720 were used as primary antibodies . Detection of the specific binding of primary antibodies was done with an Alexa488 labelled secondary goat anti-mouse total IgG ( H+L ) antibodies ( Life Technologies ) . Image acquisition was performed on a confocal laser microscope ( Carl Zeiss , Axiovert 200M ) . Active immune protection experiments were conducted with groups of eight female 8 weeks old BALB/c mice . Mice were immunized twice s . c . in the scruff of the neck with 100 μl of the adjuvanted proteins in three week intervals . Three weeks after the second immunization and prior to infection with M . ulcerans , mice were bled by the tail vein and successful immunization was verified by testing the sera for specific antibodies in ELISA and Western blotting as described above . All M . ulcerans infection experiments were conducted under BSL-3 conditions . The M . ulcerans strain S1013 used for the experimental infection of mice was isolated in 2010 from the ulcerative lesion of a Cameroonian BU patient [29] . Bacteria were cultivated in BacT/ALERT medium ( Biomerieux ) for six weeks , recovered by centrifugation and diluted in sterile PBS to 125 mg/ml wet weight . Mice were infected with 1 . 5 x 106 ( high dose ) or 1 . 5 x 105 ( low dose ) bacteria in PBS into the left hind foot pad three weeks after the last immunization . The course of the infection was followed by weekly measurements of the foot pad thickness with a caliper . At days 63 ( high dose ) and 87 ( low dose ) after experimental infection , mice were euthanised , blood samples harvested through cardiac puncture and foot pads aseptically removed for enumeration of M . ulcerans bacteria or histopathology . Mouse foot pads designated for enumeration of M . ulcerans bacteria were dipped in 70% ethanol , dried under the laminar flow , cut into four pieces with a scalpel and transferred to reinforced hard tissue grinding tubes ( MK28-R , Precellys ) containing 750 μl of BacT/ALERT medium . Tissue homogenization was performed with a Precellys 24-Dual tissue homogenizer ( 3 x 20 s at 5000 rpm with 30 s break ) , the lysate was transferred to a clean tube and the lysis tube still containing tissue residuals refilled with additional 750 μl of BacT/ALERT medium . The remains were homogenized a second time as described above and the individual two lysates were pooled [30] . DNA from 100 μl of a 1:50 dilution of the foot pad lysate in PBS was extracted as described by Lavender and Fyfe [31] . Extracted DNA was then analysed for insertion sequence ( IS ) 2404 by quantitative ( q ) PCR as previously described [31] . For graphic representation of the results , cycle threshold ( Ct ) values were converted into genome copy numbers per foot pad by applying a standard curve established for IS2404 by Fyfe et al . [32] . Mouse foot pads designated for histopathological analysis were removed above the ankle and immediately transferred to 10% neutral-buffered formalin solution ( approx . 4% formaldehyde , Sigma ) for fixation during 24 hours at room temperature . Subsequently , the foot pads were decalcified in 0 . 6 M EDTA and 0 . 25 M citric acid for 12 days at 37°C and transferred to 70% ethanol for storage and transport . Foot pad samples were dehydrated and embedded into paraffin . 5 μm thin sections were cut , deparaffinised , rehydrated , and stained with Haematoxylin/Eosin ( HE , Sigma , J . T . Baker ) or Ziehl-Neelsen/Methylene blue ( ZN , Sigma ) according to WHO standard protocols [33] . Stained sections were mounted with Eukitt mounting medium ( Fluka ) . Pictures were taken with a Leica DM2500B microscope or with an Aperio scanner .
The two M . ulcerans vaccine candidate antigens MUL_2232 and MUL_3720 were expressed as 6xHis-tagged recombinant proteins in E . coli and purified via a Ni-NTA column ( S1 Fig ) . Mice were immunized three times with either 20 μg of MUL_2232 or MUL_3720 formulated with the human-compatible GLA-SE adjuvant EM048 . Alum and Sigma adjuvant were used as control adjuvants . ELISA with mouse sera on the respective recombinant proteins showed that all formulations elicited robust antigen specific serum IgG responses ( Fig 1 ) dominated by IgG1 and only a minor proportion of IgG3 ( Fig 2A1 and 2B1 ) . While rMUL2232-specific IgG2a to IgG1 ratios showed no significant differences among the different formulations tested ( Fig 2A2 ) , IgG2a to IgG1 ratio was significantly higher when mice were immunized with rMUL3720 EM048 adjuvanted candidate vaccine than with the two other formulations ( Fig 2B2 ) . Western blotting analyses of sera against M . ulcerans lysates showed specific bands of the expected molecular weight of MUL2232 and MUL3720 ( Fig 3A , S2A Fig ) . Sera of immunized mice also recognized M . ulcerans bacteria in an indirect immunofluorescence assay ( IFA ) performed on paraffin embedded M . ulcerans bacteria ( Fig 3B , S2B Fig ) . For both target antigens the previously demonstrated surface localization [25] was confirmed . Six months after the last immunization , antibody responses in all groups of immunized mice had dropped significantly . However , specific antibody titres were still higher than in the pre-immune sera and sufficient to elicit signals in Western blotting analyses ( Fig 2A3 and 2B3 ) . Given that antigens formulated with the human-compatible EM048 elicited higher total IgG responses after two injections and with similar IgG1 , but higher IgG2a , IgG2b , IgG3 antibody levels compared to formulation with Alum , we determined the protective potential of these vaccine formulations in an experimental M . ulcerans infection mouse model . Because the increase of total IgG titers after a third immunization was not significant ( Fig 1 ) , groups of eight mice were immunized twice with rMUL3720/EM048 . Three weeks after the second immunization mice were infected into the left hind foot pad with an inoculum of 1 . 5 x 106 ( high dose ) or 1 . 5 x 105 ( low dose ) of M . ulcerans bacilli . The course of the infection was followed by weekly measurements of the foot pad thickness with a caliper . Mice in all groups infected with the high dose of bacteria showed first signs of inflammation and foot pad swelling seven weeks after infection ( Fig 4A ) . Swelling gradually increased over time , until mice had to be euthanised at day 63 and the bacterial load was determined by qPCR ( Fig 4B ) . Compared to the amount of M . ulcerans DNA that was contained in the inoculum , a roughly 250 times increase had occurred both in immunized and control animals during the 63 days of infection ( Fig 4B ) . Histopathological analysis of representative foot pads revealed the presence of oedema ( Fig 5B1 and 5B4 ) and slight infiltration at the site of infection ( Fig 5B6 ) as well as at the heel of the foot pad ( Fig 5B2 ) . Acid fast bacilli ( AFB ) were found at all sites where infiltration occurred ( Fig 5B3 , 5B5 and 5B7 ) . In mice infected with the low dose inoculum , foot pad swelling started seven to eight weeks after infection ( Fig 4A ) and the increase in M . ulcerans DNA content was about 9500 fold in 87 days . Also with the lower challenge dose no difference in bacterial load was observed between immunized and control immunized animals ( Fig 4B ) . Large clumps of AFB were found in all infected foot pads irrespective of the immunization status of the mice ( Fig 5C1 , 5C2 and 5D1 . AFB occurred in clumps ( Fig 5C2 ) , organized within filamentous structures principally located in oedematous tissue ( Fig 5C3 and 5D3 ) and in close contact with infiltrating cells ( Fig 5D2 ) . Similarly , rMUL2232/EM048 did not induce any protective effect ( S3 Fig ) . Antibody titers in immunized mice before infection ( S4 Fig ) and after 63 days of infection were directly compared to elucidate whether the immunization-induced antibody responses were increased by exposure to the target antigens in the native context of the infecting bacilli . No booster effect was observed after infection with the high dose inoculum of M . ulcerans ( Fig 6A1 and 6A2 ) . On the contrary , both ELISA and Western blotting analyses demonstrated a significant drop in specific antibody titres after infection ( Fig 6B ) , which represents most probably a natural decrease over time . Furthermore , neither non-immunized nor control-immunized animals raised a specific antibody response against rMUL2232 ( Fig 6A1 ) or rMUL3720 ( Fig 6A2 ) and Western blotting analyses of these control sera revealed a profound lack of any M . ulcerans specific antibody responses in the course of infection ( Fig 6B ) .
Currently , there is no highly effective vaccine against the mycobacterial diseases tuberculosis , leprosy and BU available . BCG was originally developed as a vaccine against tuberculosis , but , dependent on the study site , a protective efficacy ranging from 20% to 90% was also observed against leprosy [34 , 35] . Similarly , BCG was found to offer a short-lasting protection of 47% against BU in a controlled clinical trial in Uganda [20] , reconfirming results of a previous smaller trial [36] . However , case-control studies have failed to provide evidence of a lasting protective effect of routine BCG vaccination against BU [37–39] . Yet in M . ulcerans mouse infection models any other vaccine candidate has so far outperformed the effectiveness of BCG [18] . As a partner in the collaborative research project BuruliVac , we seeked to assess the potential to develop a protein based subunit vaccine against BU . Here we report results obtained with two cell surface exposed M . ulcerans proteins formulated with the adjuvant EM048 . In spite of the development of robust humoral immune responses , none of the vaccination formulations tested conferred protection in the experimental M . ulcerans mouse foot pad infection model . Choosing potential protective antigens for the inclusion into a protein subunit vaccine against BU was difficult , since the nature of protective immune responses against the disease is unclear . Given the mainly extracellular location of M . ulcerans in advanced lesions , the choice of surface exposed proteins seemed most attractive . Additionally , M . ulcerans specific proteins were of preference , because it was already observed that proteins from the closely related pathogens M . bovis and M . leprae were not very effective in conferring cross-protectivity , despite high sequence homology [40–42] . We have chosen MUL_2232 for its known surface localization , its strong immunogenicity and its missing homolog in M . tuberculosis [10] . The second candidate , MUL_3720 , was identified in a screen for potential diagnostic antigens conducted in our laboratory [24] . Homologs of MUL_3720 are absent in other mycobacterial pathogens prevalent in BU endemic areas . Furthermore , it is highly expressed and most importantly it is located on the surface of M . ulcerans [25] . In light of current literature available on protective immunity to BU we have chosen to formulate the selected antigens with the human compatible adjuvant GLA-SE . GLA is a synthetic Toll-like receptor 4 agonist , that has been demonstrated to confer potent adjuvant activity for various antigens [26 , 43–45] when formulated with a squalene based oil-in-water stable emulsion ( SE ) . While SE is an adjuvant on its own , the addition of GLA biases the induced cell mediated immunity ( CMI ) toward a TH1 type immune response , an observation made for several antigens tested so far [43 , 46] . However , formulations of the recombinant proteins rMUL2232 and rMUL3720 with EM048 , the specific GLA-SE adjuvant used in this study , did not lead to such a clear shift of CMI towards TH1 , as we have assessed by the IgG2a/IgG1 ratios . Yet compared to Alum , which is still the adjuvant most commonly used in human vaccines , EM048 induced significantly higher antibody titres with both recombinant proteins investigated . All mice immunized with adjuvanted recombinant protein vaccine-candidates and later challenged with M . ulcerans had mounted strong specific antibody responses , which were cross-reactive with the antigens in the native context on the bacterial cell surface . Nevertheless , these mice were not protected from disease . Opsonisation of the bacteria by the immunization-induced antibodies may not lead to protection , since the clusters of the extracellular M . ulcerans found in advanced BU lesions are imbedded in necrotic tissue . Infiltrating macrophages are therefore not able to reach the bacteria , since infiltrating cells seem to be killed by mycolactone before reaching the infection foci . On the other hand , it is not completely ruled out that antibodies against other target structures may be protective . Neutralizing antibodies against the poorly immunogenic macrolide toxin mycolactone for example could potentially confer protection against disease . The fact that the response of immunized mice was not boosted upon progressive infection with M . ulcerans could hint to another obstacle for vaccine development against BU . The apparent general lack of antibody responses in non-immunized challenged control mice that was observed by us and others [23] is surprising , and requires further investigation . A number of studies have reported systemic T-cell anergy in patients with BU , but development of antibody responses against M . ulcerans was detected in a majority of patients [10 , 47–49] . In the framework of this study we have developed methods to evaluate the protective capacity of candidate vaccines in the M . ulcerans mouse footpad infection model . The fact that we did not see protection in immunized animals could have many explanations; including insufficient bias towards a TH1 type of CMI . Most likely immunization with only one antigen is generally not sufficient for protection against disease . Considering that vaccination with a mycolactone deficient mutant strain of M . ulcerans did not lead to full protection in the mouse model [19] , the development of a multivalent subunit vaccine may be the right strategy to pursue . | Buruli ulcer is a slow progressing ulcerative disease of the skin and subcutaneous tissue that is most prevalent in West African rural communities . Mycobacterium ulcerans , the causative agent of the disease , produces a toxin called mycolactone , which is held responsible for the extensive tissue damage seen in advanced Buruli ulcer lesions . To date , no effective vaccine against the disease exists and it is unclear to what extent antibodies against cell surface antigens of M . ulcerans play a role in protection . To assess whether vaccine induced antibodies against cell surface proteins can protect against Buruli ulcer , we formulated two surface vaccine candidate antigens , MUL_2232 and MUL_3720 , as adjuvanted recombinant proteins and investigated their protective potential in a mouse model of M . ulcerans infection . Despite the induction of strong antibody responses against the surface molecules and cross-reactivity of the induced antibodies with the antigens in their native context , we did not observe protection against the disease . While the vaccine-induced antibodies could opsonize the extracellular bacilli , infiltrating phagocytes might be killed early by mycolactone . | [
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"antibodies... | 2016 | Vaccination with the Surface Proteins MUL_2232 and MUL_3720 of Mycobacterium ulcerans Induces Antibodies but Fails to Provide Protection against Buruli Ulcer |
Intrinsic immunity describes the set of recently discovered but poorly understood cellular mechanisms that specifically target viral pathogens . Their discovery derives in large part from intensive studies of HIV and SIV that revealed restriction factors acting at various stages of the retroviral life cycle . Recent studies indicate that some factors restrict both retroviruses and retrotransposons but surprisingly in ways that may differ . We screened known interferon-stimulated antiviral proteins previously untested for their effects on cell culture retrotransposition . Several factors , including BST2 , ISG20 , MAVS , MX2 , and ZAP , showed strong L1 inhibition . We focused on ZAP ( PARP13/ZC3HAV1 ) , a zinc-finger protein that targets viruses of several families , including Retroviridae , Tiloviridae , and Togaviridae , and show that ZAP expression also strongly restricts retrotransposition in cell culture through loss of L1 RNA and ribonucleoprotein particle integrity . Association of ZAP with the L1 ribonucleoprotein particle is supported by co-immunoprecipitation and co-localization with ORF1p in cytoplasmic stress granules . We also used mass spectrometry to determine the protein components of the ZAP interactome , and identified many proteins that directly interact and colocalize with ZAP , including MOV10 , an RNA helicase previously shown to suppress retrotransposons . The detection of a chaperonin complex , RNA degradation proteins , helicases , post-translational modifiers , and components of chromatin modifying complexes suggest mechanisms of ZAP anti-retroelement activity that function in the cytoplasm and perhaps also in the nucleus . The association of the ZAP ribonucleoprotein particle with many interferon-stimulated gene products indicates it may be a key player in the interferon response .
Host restriction factor proteins are part of the intrinsic immune system of the cell , forming an early line of defense against viral infection . Intrinsic immunity is triggered when viral RNAs are recognized by pattern-recognition receptors , such as Toll-like and retinoic acid-inducible gene ( RIG-I ) -like receptor family members , causing activation of an effector protein ( for example , IRF3 ) and the expression of interferon ( IFN ) and hundreds of IFN-stimulated genes ( ISGs ) . Many viral restriction factors are ISGs that function by diverse mechanisms against a wide range of viral pathogens . For example , Myxovirus ( influenza virus ) resistance 1 , interferon-inducible protein p78 ( mouse ) ( MX1 , also known as MXA ) ) and MX2 ( MXB ) are closely related members of the IFN-induced dynamin family of large GTPases . MX1 is a broad-spectrum inhibitor of many RNA and DNA viruses ( reviewed in [1] ) . IFN-induced transmembrane protein family members ( IFITM1/2/3 ) are also potent inhibitors of a range of viruses including HIV-1 , although their mechanisms of action are unclear ( [2]; reviewed in [3] ) . BST2 ( Tetherin ) is a type II transmembrane glycoprotein capable of trapping enveloped virions at the cell surface ( reviewed in [4] ) . RSAD2 ( Viperin ) is an endoplasmic reticulum-associated protein that inhibits many RNA and DNA viruses at multiple stages of the viral life cycle , and which may be involved in innate immune signaling ( reviewed in [5] ) . RNA helicases and IFIH1 interact with Mitochondrial antiviral signaling protein ( MAVS ) , a mitochondrial outer membrane protein , activating formation of the MAVS signalosome and upregulation of NF-κB and IRF3 signaling pathways [6] . ISG20 is a 3'-5' exoribonuclease that inhibits single-strand RNA viruses including HIV-1 [7] . The transcriptional regulator TRIM28 ( KAP1 ) also limits HIV integration by binding acetylated integrase and inducing its deacetylation by recruiting HDAC1 [8] . While the cell can be infected by a wide variety of viruses , unrestricted activity of endogenous retroelements also poses a threat to genome integrity and cell function . Long-terminal repeat ( LTR ) retrotransposons include the human endogenous retroviruses ( HERVs ) that comprise 8% of the human genome , although no HERVs capable of replication have been identified . However , increased HERV expression has been implicated in multiple sclerosis , lupus , amyotrophic lateral sclerosis , and autoimmune rheumatic disease , although whether the increase is the cause or effect of disease remains to be determined ( [9]; reviewed in [10–12] ) . Non-LTR retrotransposons comprise at least one-third of the human genome and remain an ongoing cause of disease [13 , 14] . LINE-1s ( L1s ) are the only active autonomous mobile DNA remaining in humans , and among ∼500 , 000 copies at least 100 remain potentially active for retrotransposition in any human individual [15 , 16] . L1s have also been responsible for the genomic insertion in trans of thousands of processed pseudogenes and a million SINEs ( Alus and SVAs ) . The current residual activity of human retrotransposons is the background that escapes a variety of mechanisms that have evolved to limit replication of mobile DNA . Phylogenetic analyses suggest that eukaryote non-LTR retrotransposons predate LTR retrotransposons , which in turn gave rise to the retroviruses through the acquisition of an envelope gene [17–19] . The ancient origin and interrelatedness of the major classes of retroelements predicts they will be subject to some of the same host restriction factors . On the other hand , significant differences between their modes of replication suggest that non-LTR retrotransposons and retroviruses could be affected by the same restriction factors in divergent ways . For example , Apolipoprotein B mRNA-editing enzyme , catalytic polypeptide-like 3 ( APOBEC3 ) family members , first shown to inhibit HIV by hypermutation of minus-strand DNA , were then found to potently inhibit retrotransposition but without obvious hypermutation , suggesting a unique pathway ( reviewed in [20 , 21] ) . Recently , it was proposed that APOBEC3A inhibits L1 retrotransposition by deaminating transiently exposed single-strand genomic DNA that flanks the site of L1 integration [22] . The Aicardi-Goutières syndrome ( AGS ) -related anti-retroviral protein SAM domain and HD domain 1 protein ( SAMHD1 ) inhibits retroviruses in non-dividing myeloid cells and resting CD4+ T cells by depleting dNTP levels . However , SAMHD1 also limits non-LTR retrotransposition in dividing cells , suggesting an inhibitory mechanism different from nucleotide depletion [23] . Other innate restriction factors also suppress retrotransposons . LINE-1 activity is upregulated in cells deficient for TREX1 , a gene associated with systemic lupus erythematosus ( SLE ) and , like SAMHD1 , with AGS [24] . RNaseL , a member of the IFN antiviral response to dsRNA , likely restricts retrotransposons in cell culture by cleavage of their mRNAs [25] . Finally , the RNA helicase MOV10 , previously reported to affect replication of several RNA viruses , also limits activity of all human retrotransposons [26–28] . MOV10 may target retroelement complexes for degradation , possibly by RNA-induced silencing complexes ( RISC ) . Clearly , studying factors that restrict both retrotransposons and retroviruses can inform both fields . Therefore , we overexpressed a panel of antiviral ISGs and assayed for their previously untested effects on LINE-1 retrotransposition in a cell culture assay . Several of these factors strongly inhibited retrotransposition of an L1 reporter construct . We report that the zinc finger antiviral protein ZAP ( also called Zinc finger CCCH-type , antiviral 1 , ZC3HAV1 , and Poly ( ADP-ribose ) polymerase 13 , PARP13 ) is a potent inhibitor of not only viruses but also retrotransposons . ZAP targets positive- and negative-strand RNA viruses of several families , including Retroviridae ( HIV-1 , MoLV , and XMRV ) , Filoviridae ( Ebola and Marburg ) , Togaviridae ( alpha- , sindbis , Semliki Forest , and Ross river viruses ) , and Hepadnaviridae ( hepatitis B ) [29] , and has been shown to inhibit the double-stranded DNA murine gammaherpesvirus [30] . However , restriction is not universal: ZAP fails to inhibit vesicular stomatitis , poliovirus , yellow fever , and herpes simplex I viruses [31] . The presence of orthologs in fish , birds and reptiles suggests that ZAP ( like MOV10 ) is ancient in origin [32 , 33] . L1 expresses a 6-kb bicistronic RNA that encodes a 40 kD RNA-binding protein ( ORF1p ) of essential but uncertain function for retrotransposition , and a 150 kD ORF2 protein with endonuclease and reverse transcriptase ( RT ) activities . In the cytoplasm , ORF1p and ORF2p preferentially bind their own encoding RNA in cis to form a functional ribonucleoprotein particle ( RNP ) [34 , 35] . We show that ZAP strongly restricts retrotransposition in cell culture with loss of L1 RNA and RNP integrity . Association of ZAP with the L1 RNP is supported by co-IP and co-localization with ORF1p in cytoplasmic granules . Mass spectrometry ( MS ) analyses of the ZAP proteome revealed proteins that bind ZAP , including MOV10 , and suggest possible mechanisms of ZAP-mediated retroelement restriction .
We asked if increased interferon limits L1 activity in a cell culture assay for retrotransposition . In this assay , an enhanced green fluorescent protein ( EGFP ) reporter gene cassette , interrupted by a backwards intron and inserted in opposite transcriptional orientation into the 3' UTR of L1-RP ( a highly active L1 [36] ) , is expressed only when the L1 transcript is spliced , reverse-transcribed , its cDNA inserted in the genome , and the EGFP reporter gene expressed from its own promoter [37 , 38] . The full-length L1 and reporter cassette are cloned in a modified version of pCEP4 vector ( Invitrogen ) lacking a cytomegalovirus ( CMV ) promoter . Therefore , expression of the L1 is driven by its own 5' UTR . We transfected HEK 293T cells with the L1-EGFP reporter ( 99-PUR-RPS-EGFP ) in the presence of increasing amounts of Universal Type I Interferon ( IFN ) alpha , and at 5 days post-transfection assayed for fluorescent ( i . e . retrotransposition-positive ) cells by flow cytometry . Addition of IFN reduced cell culture retrotransposition in a dose-dependent manner . At 1000 U IFN/ml , retrotransposition was over 90 percent less than non-treated controls , with a loss of cell viability of less than 20 percent as determined by trypan blue staining and the MultiTox-Fluor Multiplex Cytotoxicity Assay ( Promega ) ( Fig 1A , compare lanes 2 and 3 ) . Several interferon-inducible proteins are known to restrict not only viruses but also endogenous retroelements . To extend the list of these proteins , we next screened a panel of ISGs and restriction factors with known antiviral activities for effects of their expression on L1 retrotransposition ( Fig 1B , upper bar chart ) . To reveal differences of transfection efficiency , cytotoxicity or reporter gene expression caused by the test proteins , we followed the approach of Wei et al . [39] and in conjunction with the retrotransposition assays transfected CEP-EGFP , a vector that constitutively expresses EGFP , together with empty vector or test constructs . After 4 days we performed flow cytometry to assay for loss of green cells ( Fig 1B , lower bar chart ) . It should be noted that the CEP-EGFP assay is a snapshot of GFP fluorescence at a single time-point , while the retrotransposition assay measures accumulated EGFP insertions that occur over 5 days . Therefore , the MultiTox-Fluor Multiplex Cytotoxicity Assay was also used as a direct assay for cell toxicity ( assessed at Day 3 post-transfection ) . This dual-detection assay generates a ratio of live to dead cell readings , thereby normalizing for cell number ( Fig 1B , table ) . 293T cells were cotransfected with 99-PUR-RPS-EGFP and empty vector or tagged ISG cDNA constructs . With the exception of MOV10 , none of the proteins shown in Fig 1B had previously been tested for effects on cell culture retrotransposition . Six factors , MOV10 ( as previously reported [26–28] ) , BST2 , ISG20 , MAVS , MX2 , and ZAP , reduced plasmid-directed LINE1 retrotransposition in 293T cells by over 75 percent ( Fig 1B ) . While minimal toxicity was detected by the MultiTox-Fluor assay for all proteins , BST2 and MAVS reduced CEP-EGFP vector expression 44 and 50 percent , respectively . However , loss of CEP-EGFP signal only partly explains the strong inhibition of cell culture retrotransposition observed for these proteins ( 86 and over 98 percent , respectively ) . To assess specificity of BST2-induced reduction of retrotransposition , we coexpressed Vphu , a codon-optimized version of the HIV-1-encoded BST2 antagonist Vpu [40] . Vphu restored L1 retrotransposition over 2-fold ( 11 to 26 percent ) , an amount limited by the fact that overexpression of Vphu itself reduced retrotransposition to 37 percent ( Fig 1C ) . We also tested MX2 mutant proteins for their activity in the cell culture assay ( Fig 1D ) . The N-terminal 25 amino acids of MX2 encode a nuclear envelope targeting domain essential for HIV-1 inhibition . Conversely , while MX2 mutated at K131A fails to bind GTP , it still inhibits HIV-1 as if wild-type [41–44] . Contrary to the results for HIV-1 , deleting the first 25 residues of our MX2 construct failed to block its inhibition of retrotransposition , but the K131A mutant lost ability to restrict L1s . Because the K131A mutant is expressed at a lower level than the wild-type protein ( also noted by [45] ) , we increased the amount of mutant MX2 plasmid transfected fourfold but still failed to restore inhibition of retrotransposition to wild-type levels . Thus , the mechanisms by which MX2 restricts retroviruses and retrotransposons appear to differ in some aspects . Unfortunately , endogenous MX2 is expressed at detectable levels in none of the cell lines we use for our retrotransposition assay ( 293T , HeLa , or human embryonal carcinoma 2102Ep cells ) and is induced by IFN in HeLa cells only ( S1 Fig ) . Consequently , we could not assay endogenous MX2 for effect on retrotransposition , and we pursued MX2-related experiments no further . Finally , we tested for interaction of IGS factors with the L1 RNP ( Fig 1E ) . Expression of pc-L1-1FH , a construct containing L1-RP with a tandem FLAG-HA tag fused to the C-terminus of its ORF1 , permits immunoprecipitation ( IP ) of basal L1 RNP complexes [46] . pc-L1-1FH and V5 epitope-tagged ISG proteins were coexpressed in 293T cells , followed by their co-IP on α-FLAG agarose . TRIM28 and IFITM1 bound non-specifically to the affinity agarose , and their associations with L1 were inconclusive . MOV10 ( as previously reported [27] ) , MX1 , MX2 , and the short isoform of ZAP ( ZAP-S/PARP13 . 2 ) all co-immunoprecipitated with the L1 RNP . These interactions were lost with RNase treatment . We have not confirmed that these proteins directly bind L1 RNA . Rather , proteins might bind non-L1 RNAs or other multi-protein complexes captured within the L1 RNP . Moreover , an unknown amount of tagged ORF1p along with its bound partners , free in solution and not part of retrotransposition-competent RNPs , will have co-purified within immunoprecipitates . Only functional analyses of restriction factors associated with the L1 RNP will ascertain their relevance to L1 biology . Thus , we have expanded the growing list of IFN-stimulated cellular restriction factors that inhibit human retrotransposition . We decided to focus on the zinc finger protein ZAP for further investigation . ZAP ( PARP13 ) is a member of the poly ( ADP-ribose ) polymerase ( PARP ) family of 17 proteins , some of which are capable of poly ( ADP ) ribosylation ( pADPr ) of acceptor proteins using NAD+ as a substrate . Human ZAP is a predominantly cytoplasmic protein that exists in two alternatively spliced isoforms . Long isoform 1 ( ZAP-L ) possesses a defective C-terminal PARP-like domain that lacks a catalytic glutamate residue essential for pADPr ( Fig 2A ) [47 , 48] . This domain is absent in ZAP-S . It was previously demonstrated that the first 254 amino acids of rat ZAP ( NZAP ) , comprising only the four CCCH-type zinc fingers , were sufficient to induce loss of viral mRNA and severely inhibit infection [49] . The zinc finger domain is believed to mediate binding of ZAP to long ZAP-responsive element ( ZRE ) sequences within the RNAs of target viruses [29 , 50–52] . Hayakawa et al . [53] reported that ZAP-S , but not ZAP-L , is an ISG whose endogenous expression is increased by Type I interferon , a fact we confirm for 293T cells ( Fig 1A , bottom panel ) . We cotransfected 99-PUR-RPS-EGFP in 293T cells , together with epitope-tagged ZAP or empty vector , and assayed for fluorescent cells at 5 days post-transfection ( Fig 2A and 2B ) . Coexpressed human HA-tagged HA-ZAP-S and HA-ZAP-L , and SBP-PARP13 . 1 ( ZAP-L with an N-terminal streptavidin binding protein tag [54] ) caused a precipitous decrease in the number of retrotransposition-positive 293T cells relative to the empty vector control . L1 inhibition caused by HA-ZAP-S was greater than by V5-TEV-ZAP-S ( Figs 1B and 2B ) , and it is possible that the long tag of the latter construct partially reduced ZAP-S activity . In turn , decrease in retrotransposition was greater for HA-ZAP-L and SBP-PARP13 . 1 than HA-ZAP-S . ZAP-L inhibited retrotransposition in a dose-dependent manner , and even the lowest levels of ectopically expressed protein caused significant loss of L1 activity ( Fig 2C , lanes 3–6 ) . Our data are in line with previous observations that ZAP-L exerts stronger activity against Semliki Forest virus , Sindbis virus , and Moloney leukemia virus ( MoLV ) than ZAP-S [32 , 55] . This increased antiviral activity of ZAP-L has been attributed to S-farnesylation within the C-terminal PARP-like domain [56] . Overexpression of PARP1 , a nuclear protein , failed to inhibit retrotransposition ( Fig 2B , lane 7 ) . We next tested for the effect of ZAP's N-terminal zinc finger and predicted RNA-binding domain . Expressing only the first 256 residues of human ZAP ( HA-ZAP 1–256 ) reduced L1 retrotransposition to 13 percent of vector control ( Fig 2B , lane 5 ) . The analogous N-terminal domain of rat ZAP ( HA-NZAP ) reduced cell culture retrotransposition even more effectively ( to 2 . 5 percent , although , it should be noted that these truncated ZAP proteins were expressed at higher levels than ZAP-S or ZAP-L; Fig 2B , lower panel ) . ZAP constructions with deletions of the zinc finger domain ( constructs HA-ZAP-L 253–902 and SBP-PARP13 . 1ΔZnF , which lacks residues 77 to 223 ) or mutated for five residues considered important for RNA binding ( SBP-PARP13 . 1VYFHR ) inhibited retrotransposition to a lesser but still significant degree ( 40–65 percent of vector control; Fig 2B , lanes 6 , 9 and 10 ) . Similar results were seen when the retrotransposition assay was performed in HeLa cells ( S2A Fig ) . Together , our data show that the ZAP zinc finger domain alone is sufficient but not exclusive for L1 inhibition . Tagged ZAP constructs had minimal effect on the expression of cotransfected CEP-EGFP plasmid ( Fig 2B , right bar chart ) . As an additional test for cell toxicity , ZAP constructs or empty vector were cotransfected with a plasmid that constitutively expresses the blasticidin S-resistance gene ( bsr ) . Coexpression of HA-ZAP-S , HA-ZAP-L , or rat HA-NZAP did not reduce the number of bsr-expressing foci remaining after 10 days of selection with blasticidin ( S2B Fig ) . We next cotransfected ZAP with ORFeus-HS , a synthetic human LINE-1 construct tagged with the EGFP reporter cassette and containing codon-modified ORF1 and ORF2 sequences and a CMV promoter in place of the L1 5' UTR [57] . Even though ORFeus-HS contains little authentic LINE1 DNA sequence , its retrotransposition in 293T cells was decreased by coexpression of ZAP full-length or N-terminal truncated proteins in the same manner as human L1-RP ( S2C Fig ) . We also assayed ORFeus-Mm [58] , a modified version of L1spa , a mouse L1 element with low retrotransposition activity [59] , possessing both CMV promoter and mouse L1 5' UTR , and codon-optimized to boost its activity to a level similar to that of human L1-RP . ZAP also inhibited cell culture retrotransposition of ORFeus-Mm ( S2D Fig ) . Thus , ZAP suppression of L1s is neither DNA sequence- , promoter- , nor species-specific . We also determined by HIRT preparation plasmid recovery and subsequent PCR that expression of ZAP had no effect on the stability of 99-PUR-RPS-EGFP reporter plasmid DNA ( S2E Fig ) . We next asked whether endogenous ZAP inhibits cell culture L1 retrotransposition . We confirmed by Western blotting that two previously described siRNA sequences [60] efficiently depleted endogenous ZAP protein when transiently transfected in 293T cells ( Fig 2D ) . Depletion of ZAP enhanced L1 retrotransposition 2- to 3-fold compared with control siRNAs or mock-transfected cells . We also determined the effect of ZAP expression on retrotransposition of the non-autonomous Alu SINE retrotransposon . Using the assay of Dewannieux et al . [61] , we cotransfected in HeLA-HA cells an Alu reporter construct tagged with the neomycin phosphotransferase gene ( Alu-neoTet ) , an ORF2 construct ( pCEP-5′UTR-ORF2-No-Neo ) to drive retrotransposition , and ZAP constructs or empty vector . Expression of HA-ZAP-L , HA-ZAP-S , rat HA-NZAP , and MOV10 reduced the number of neomycin-resistant retrotransposition-positive colonies more than 80 percent compared with empty vector control . pCEP-5′UTR-ORF2-No-Neo consists of CMV promoter , the L1 5' UTR , and ORF2 sequences only , suggesting that L1 ORF1 or 3' UTR sequence is not essential for ZAP inhibition of retrotransposition . All human endogenous retroviruses are thought to be incapable of retrotransposition due to inactivating mutations . However , mouse intracisternal A particle ( IAP ) LTR retrotransposons actively retrotranspose and cause new mutations . Using an established cell culture assay [62] , we found that overexpression of HA-ZAP-L and V5-TEV-MOV10 strongly restricted insertion of neo-tagged IAP elements in HeLa-JVM cells ( Fig 2F ) . Inhibition by HA-ZAP-S and HA-ZAP 1–256 was less severe , consistent with our results for L1 retrotransposition . Thus , expression of ZAP inhibits not only retroviruses but also both LTR and non-LTR retrotransposons , including mobile DNA currently active in the human genome . We examined the subcellular distribution of ZAP and its association with the L1 RNP . When expressed from a full-length L1 construct , ORF1p is present as an RNP in cytoplasmic stress granules ( SGs ) together with L1 RNA , ORF2p , and many other RNA-binding proteins . Granules are also detected in cells that express endogenous ORF1p at high levels , and their formation is not dependent upon external stress applied to the cell [63–65] . Stress granules ( SGs ) are cytoplasmic aggregates that contain stalled 48S pre-initiation complexes and are induced by a range of stresses . P-bodies ( PBs ) are constitutively expressed cytoplasmic granules rich in factors of RNA decay , including those of RISC [66] . ORF1p granules generally do not overlap , but may juxtapose PBs [63] . Interestingly , we reported [64] , and others have confirmed [67] , that ORF2p is detected in only a minor subset of ORF1p-positive cells when the two proteins are coexpressed from an L1 construct . The reason for this is unknown . Both ZAP-L and ZAP-S have been reported to colocalize in the cytoplasm with markers of PBs and SGs [52 , 54 , 68 , 69] . Epitope-tagged ZAP-S , coexpressed with ORF1-GFP-L1-RP ( a construct with CMV promoter , ORF1 C-terminally tagged with EGFP and intact downstream L1 sequence [64] ) and detected by immunofluoresence ( IF ) of fixed 293T cells , strongly colocalizes with ORF1-GFP and SG marker proteins eIF3 ( Fig 3A ) and TIA-1 . Endogenous ZAP and ORF1p similarly colocalize in the cytoplasm of 293T and 2102Ep cells ( Fig 3B and 3C ) . To track L1 RNA in fixed cells we used construct 99-PUR-L1-RP-MS2-6X [64] , consisting of L1-RP tagged at its C-terminus with a tandem array of six 19-bp stem loop sequences that bind bacteriophage MS2 coat protein . This construct was cotransfected with GFP-tagged ZAP-S and its RNA was detected in fixed cells with a fluorescent in situ hybridization ( FISH ) probe to the MS2 repeats . ZAP-S colocalized with L1 RNA in cytoplasmic granules ( Fig 3D ) . As noted above , V5-TEV-tagged ZAP-S interacts with L1 RNPs in an RNA-dependent manner ( Fig 1D ) . HA-ZAP-L and endogenous full-length ZAP also co-IP with L1 RNPs expressed from pc-L1-1FH ( Fig 3E , upper and lower panels , respectively ) . Shorter ZAP products , presumed to be degradants , were not recovered in the immunoprecipates . Conversely , FLAG-tagged ZAP-L ( ZAP-L-FL ) coimmunoprecipitates untagged ORF1p expressed from a full-length L1 construct ( Fig 3F ) . Unexpectedly , despite the ability of the zinc-finger domain alone to strongly inhibit retrotransposons ( Fig 2B ) , we failed to co-IP human HA-ZAP 1–256 ( Fig 3G ) or rat HA-NZAP with pc-L1-1FH RNPs . Chen et al . [52] proposed that some residues in the zinc finger domain are involved in ZAP's interaction with protein factors rather than RNA . Perhaps an unknown non-L1 protein or RNA can recruit truncated ZAP to the L1 RNP , although this requires further investigation . In summary , L1 RNPs and ZAP associate and are directed to the same cytoplasmic compartments . We previously identified 96 proteins associated with the L1 ORF1p RNP and confirmed a subset of these by direct co-IP and subcellular colocalization experiments [46] . We wished to determine the ZAP protein interactome and identify its members that are shared with that of the L1 . We transfected ZAP-L-FL and empty vector in parallel in 293T cells and performed IP from whole cell lysates in the presence or absence of RNase . Purification was highly efficient with relatively few proteins identified in vector-only lanes ( Fig 4A ) . Following IP , complex samples were analyzed by tandem mass spectrometry ( MS ) . Excluding 36 ribosomal proteins and likely contaminants ( such as keratins ) , 78 proteins satisfied three criteria: 1 ) predicted by 3 or more peptides , 2 ) present in two independent replicate IPs , and 3 ) unique to ZAP-L-FL isolates . Eleven proteins were shared with the L1 ORF1p RNP interactome defined in our previous paper ( Table 1 , S1 Table ) . To confirm protein interactions , a subset of cDNAs of proteins identified by MS were subcloned with an N-terminal V5-TEV-epitope tag or were obtained as gifts . Following cotransfection in 293T cells , 18 of 23 proteins tested co-IPed with ZAP-L-FL on α-FLAG agarose ( Fig 4B ) . A majority of these interactions were resistant to RNase digestion , suggesting direct protein-protein binding . In contrast , our previous work characterizing the ORF1p interactome found almost all of 41 confirmed protein associations to be lost upon RNase treatment [46] . Our analyses identified most previously described ZAP-interacting proteins . We confirmed RNA-independent binding of ZAP and PPR2R1A ( PR65A ) [70] , a component of the protein phosphatase 2 ( PP2A ) complex , and show for the first time RNase-resistant binding of its catalytic subunit , PPP2CA , as well ( Fig 4B ) . Wang et al . [70] reported that knockdown of PPR2R1A by shRNAs reduced ZAP suppression of an MoLV reporter . It has been reported that several serine residues immediately downstream of the rat ZAP zinc-finger domain are phosphorylated by glycogen synthase kinase 3β ( GSK3β ) , and that overexpression of GSK3β reduces , and its inhibition increases ZAP activity against MoLV [71] . These data are all consistent with inhibition of ZAP antiviral function by phosphorylation . Our MS analyses of human ZAP protein sequence ( 65% coverage ) confirmed two phosphorylated residues ( S257 and S284 ) homologous to those detected by Sun et al . [71] in rats , and identified four additional phosphorylated sites ( S335 , T375 , S378 , S796 ) . However , a role for phosphorylation or PP2A-mediated dephosphorylation in modulating ZAP inhibition of retrotransposition remains to be determined . It has been proposed that ZAP recruits the 3'-5' exosome to degrade target viral RNAs in cytoplasmic granules [51 , 72] . We identified exosome component EXOSC8 ( RRP43 ) in ZAP RNP immunoprecipitates , although we could not confirm its direct binding with ZAP-L-FL ( consistent with [72] ) . According to previous reports , EXOSC5 ( RRP46 ) binds rat but not human ZAP [50 , 72] . However , when tested , we found EXOSC5 to bind weakly with human ZAP-L-FL in the presence or absence of RNase . We also identified as a strong ZAP interactor DHX30 , an RNA helicase believed to be recruited by ZAP to unwind viral RNAs and facilitate their exosome-mediated degradation in stress granules [69 , 73] . We confirmed colocalization of DHX30 with both ZAP-S and ORF1-GFP in cytoplasmic granules , and RNA-independent binding of DHX30 with ZAP-L-FL ( Figs 4B and 5D and S3A ) . In addition to the 3'-5' degradation exosome , 5′-3′ degradation enzymes , including 5'-3' Exoribonuclease 1 ( XRN1 ) and Poly ( A ) -Specific Ribonuclease ( PARN ) , have been reported to bind ZAP and augment antiviral function [51] . We detected the XRN1 paralog XRN2 in the ZAP interactome . XRN2 not being available , we tested XRN1 tagged with RFP and found it to colocalize with GFP-tagged ZAP-S in cytoplasmic granules ( Fig 5N ) . XRN1 is a known SG and PB component and also colocalizes with ORF1p-GFP in SGs [63 , 74] . We also discovered novel ZAP-associated proteins . In addition to PP2A complex members , ZAP RNPs contain several other proteins involved in post-translational modification ( PTM ) . TRIM25 , which functions as an E3 ubiquitin and ISG15 ligase and defends against viruses by mediating ubiquitination of RIG-1/DDX58 [75] , associates with ZAP in the presence of RNA ( Fig 4B ) . Even after RNase digestion , ZAP-L strongly binds two ubiquitin carboxyl-terminal peptidases , USP7 and USP9X . Our MS analyses predicted two ubiquitinated lysines ( K226 , K783 ) and the UbPred and CKSAA_UBSITE algorithms [76 , 77] predicted 6 additional sites of ubiquitination within ZAP-L . Perhaps ubiquitin peptidases associate with the ZAP RNP to enhance its anti-retroelement activity by limiting ubiquitin-mediated degradation , although that remains to be determined . While its own PARP domain is likely catalytically inactive , ZAP-L itself is ADP-ribosylated and known to recruit other PARPs that are capable of pADPr , such as PARP5 and PARP12 [68] . PARP1 is the founding member of the PARP family . We report for the first time the direct binding of ZAP and PARP1 independent of RNase digestion . It is not known if PARP1 ribosylates ZAP . Also identified within the ZAP RNP were almost all components of the cytosolic chaperonin-containing TCP1 ( CCT ) complex . We tested several CCT complex members and confirmed weak binding of CCT2 , but not CCT6B or CCT8 , with ZAP-L-FL ( Fig 4B ) . The CCT complex was first discovered for its critical role in the folding of actin and tubulin , and subsequently many other CCT binding proteins were reported [78 , 79] . This is the first report of the association of CCT and ZAP . CCT4 and CCT6 were also detected in the ORF1p interactome [46] . The ZAP interactome includes several canonical components of SGs ( FXR1 , FXR2 , G3BP1 , and ELAVL1 ) and PBs ( DDX6 ) . We confirmed the association of these proteins with ZAP by colocalization in cytoplasmic granules and/or co-IP ( Figs 4B and 5 ) . Indeed , 65 percent ( 12/18 ) of the proteins tested that directly immunoprecipitated with ZAP-L-FL ( Fig 4B ) also colocalized with GFP-tagged ZAP-S in cytoplasmic granules of 293T cells ( Fig 5 ) . To the best of our knowledge , CCT2 , CCT8 , PPP2R1A , TRIM25 , and USP9X have not previously been reported in granules ( Figs 5A , 5B , 5J , 5L and 5M ) . We screened test proteins for colocalization in cytoplasmic granules with ZAP-S rather than ZAP-L ( Fig 5 ) , and it is possible that additional ZAP-associated proteins , bound only by ZAP-L's PARP-like domain , escaped detection . While ZAP-L and ZAP-S colocalize in cells , it is of interest that overexpression of ZAP-L induces fewer and larger cytoplasmic aggregates , and apparently binds ZAP-S to cause its redistribution to these large foci when the two isoforms are coexpressed; ZAP is known to dimerize ( S3B Fig; [52] ) . Lee et al . [69] reported that murine ZAP-S colocalizes in the cytoplasm with markers of PBs and SGs , but not with markers of mitochondria , endosomes , peroxisomes , or lysosomes . On the other hand , Charron et al . [56] , found that C-terminal S-farnesylation of mouse ZAP-L caused its partial redistribution to lysosomes and late endosomes . Reasons for the differing patterns of ZAP-S and ZAP-L bear further investigation . We previously reported that endogenous L1 ORF1p and antiviral protein MOV10 associate in an RNA-dependent manner and colocalize in SGs of cells ( Fig 5O; [27] ) . We now show that MOV10 is also a component of the ZAP interactome . Recently , Gregersen et al . [80] also detected ZAP by SILAC ( stable isotope labeling by amino acids in cell culture ) analyses of MOV10-interacting proteins . Both endogenous and exogenously expressed ZAP and MOV10 co-IP in a manner partially resistant to RNase digestion ( Fig 4C ) . We could not detect binding of MOV10 with HA-ZAP-L 1–256 or rat HA-NZAP . ZAP and MOV10 proteins closely colocalize in cytoplasmic granules ( Fig 5I and 5P ) . In summary , we present for the first time a comprehensive analysis of the ZAP protein interactome . Most of its components we tested directly bound and/or colocalized in RNA granules with ZAP . ZAP may recruit many cellular proteins to these cytoplasmic structures ( or vice versa ) . Previously , we demonstrated that overexpression of MOV10 strongly reduces the steady state number of L1-encoded RNA and proteins in transfected cells , although the mechanism of this loss remained uncertain [64] . We similarly assayed the effects of ZAP on L1 expression . As with MOV10 , levels of ORF1 protein expressed from pc-L1-1FH were significantly reduced in L1 RNPs immunoprecipitated from cytoplasmic extracts in the presence of cotransfected ZAP-L and ZAP-S . ORF2 activity was almost undetectable in the LEAP assay for L1 reverse transcription ( Fig 6A , lanes 3 and 4; [81] . In whole cell lysates , ORF1p expressed from pc-L1-1FH was significantly lower in the presence of ZAP-S , ZAP-L , or MOV10 but not empty vector or an unrelated protein ( Fig 6B ) . This was not a general protein effect , as overexpression of ZAP did not affect levels of coexpressed EGFP or endogenous heat shock protein 90 ( Fig 6B ) , and was without obvious effect on global protein expression detected by Coomassie blue staining of cell lysates ( Fig 6A , lower panel ) . We next examined L1 RNA transcribed from construct 99-PUR-JM111-EGFP in the presence or absence of ZAP . This construct contains a mutation in L1 ORF1 that prevents genomic insertions . PCR primers flanked the intron of the EGFP reporter cassette allowing products amplified from spliced cDNA to be distinguished from those of contaminating plasmid DNA . Paralleling the loss of ORF1 protein , diminished levels of L1 RNA were detected by RT-PCR in whole cell lysates cotransfected with HA-ZAP-L HA-ZAP-S , or HA-ZAP 1–256 , but not with empty vector or an unrelated protein . Analysis of endogenous HSPA6 RNA showed no such effect ( Fig 6C ) . We failed to determine whether or not ZAP affects expression of endogenous L1s in the genome . The observed reduction of exogenous L1 RNA in the presence of ZAP protein is consistent with previous reports that ZAP inhibits infecting viruses by causing loss of their RNAs at a post-transcriptional step [29 , 49 , 51 , 72 , 82] .
Restriction factor proteins are part of the innate immune defense system of the cell , which often detects infection by receptors that recognize viral nucleic acids . Many of these factors target retroviruses , but some , such as ZAP , act against a wide range of viral families . In certain cell types expression of antiviral genes is induced by type I or type II interferons . We have expanded the list of IFN-stimulated genes that limit LINE-1 retrotransposition when overexpressed , including BST2 , ISG20 , MAVS , MX2 , and ZAP ( Fig 1B ) . Most dramatic was an almost complete loss of retrotransposition in the presence of MAVS protein , which is only partly explained by cytotoxicity . MAVS acts downstream of the RIG-I and IFIH1 cytoplasmic receptors for viral dsRNAs . Receptor activation causes multimerization of MAVS to trigger a signaling cascade and production of type I IFNs [83] . Recent evidence indicates that upon viral detection peroxisomal-localized MAVS rapidly induces interferon-independent expression of defense factors to provide short-term protection . Mitochondrial MAVS signaling occurs later in infection , triggering IFN expression and induction of ISGs , and sustaining the immune response [84] . Thus , the profound inhibition of retrotransposition caused by overexpressed MAVS is likely due to the combined action of a number of interferon-induced genes . ISG20 strongly inhibited L1 retrotransposition without obvious cytotoxicity caused by its overexpression . ISG20 is a 3'-5' exonuclease that inhibits replication of several human and animal RNA viruses , including HIV-1 [7 , 85 , 86] . Its mechanism of action is unknown , although one might assume that it degrades viral or retrotransposon RNA Inhibition of L1 retrotransposition by the dynamin-like GTPase MX2 , but not by its closely related paralog MX1 , parallels recent reports that MX1 , a broad-spectrum inhibitor of many RNA and DNA viruses including influenza virus , fails to inhibit HIV retrovirus . On the other hand , MX2 does not inhibit infuenza virus but inhibits multiple strains of HIV1 at a late post-entry step by targeting the viral capsid and preventing accumulation of viral cDNA in the nucleus . [41–43 , 45] . Our mutation analyses suggest that the mechanism of MX2 inhibition of L1 retrotransposition may differ in some aspects from retroviral inhibition , requiring GTP binding but not nuclear localization . Like MX1 , overexpression of TRIM28/KAP1 had little effect on retrotransposition ( Fig 1B ) . However , in mouse ES cells TRIM28 strongly silences expression of multiple classes of endogenous LTR retroelements and modestly suppresses L1s by recruiting chromatin-remodeling factors [87 , 88] . Thus , TRIM28 may inhibit human retrotransposition by epigenetic modification of endogenous L1 elements , but remain ineffective in our cell culture assay against L1s expressed from a plasmid . Importantly , we showed that transient expression of either the long or short isoform of a general protein inhibitor of viral infection , ZAP , potently restricts genomic insertion of both non-LTR and LTR retrotransposons . Furthermore , siRNA-mediated knockdown of endogenous ZAP increased L1 retrotransposition 2- to 3-fold in 293T cells . Association with the L1 complex is confirmed by close colocalization of ZAP protein with ORF1p and L1 RNA in cytoplasmic stress granules , and by the detection of ZAP in RNP particles captured by immunoprecipitation of a tagged L1 construct . The CCCH-type zinc finger domain of ZAP recognizes MoLV and other viral transcripts and induces their degradation by recruiting RNA decay proteins [50 , 51 , 69 , 72 , 73] . Selected cellular RNAs are also targeted , including the TRAIL receptor , TRAILR4 [54] . Our evidence suggests that RNA degradation is also a characteristic of ZAP-associated loss of retrotransposition . Levels of exogenously expressed L1 RNA and protein are reduced in cell lysates in the presence of ZAP ( Fig 6C ) . Loss of ZAP binding in the L1 RNP upon RNase treatment , and the fact that deletion of the zinc finger domain or mutation of residues considered important for its RNA binding significantly reduced inhibition of retrotransposition , suggests that ZAP binds the L1 RNA to promote loss of RNP integrity . However , we cannot exclude the possibility that ZAP binds some other RNA that itself is recruited to the L1 RNP . Many non-L1 RNA species have been found in association with the L1 ORF1p complex , including mRNAs , Alu , SVA , and small cytoplasmic and nuclear RNAs [46 , 89] . ZAP binds ZREs with a minimum known length of 500 bp , and no common motif or secondary structure has been found [29 , 50 , 51] . A detailed investigation of how ZAP binds L1 RNA is required . The fact that deletion or mutation of the zinc finger RNA-binding domain reduced but did not abolish ZAP inhibition of retrotransposition ( Fig 2B ) , suggests that a second RNA-binding domain may exist , or that protein-protein interactions are also important for retrotransposon inhibition . Our data cannot exclude the possibility that ZAP may also inhibit L1s at the protein level ( perhaps by binding L1 RNA to interfere with ORF translation ) . Effects of ZAP on viral translation have been described [31 , 51] . Inhibition of GSK3β phosphorylation increases ZAP's ability to repress target mRNA translation without increased mRNA degradation [71] . And recently , Zhu et al . [82] demonstrated that ZAP represses translation of HIV-1 and Sindbis virus reporter constructs independently of mRNA decay by directly binding translation initiation factor eIF4A and interfering with its interaction with eIF4G . MOV10 is an RNA helicase that also strongly inhibits retrotransposition in cell culture assays [26 , 27 , 90] . Li et al . [28] showed that overexpression of MOV10 strongly reduced levels of exogenously expressed IAP and L1 RNA at a post-transcriptional step , while inhibition of endogenous MOV10 increased L1 RNA . On the other hand , Lu et al . [90] found that MOV10 decreases IAP RT products but not levels of IAP RNA and protein . The facts that MOV10 and ZAP bind each other independently of RNA , colocalize in cytoplasmic granules , associate with the L1 RNP , and promote similar loss of L1 RNP integrity and retrotransposition in cells , suggest that the two proteins may function in a common pathway , a notion worthy of further investigation . In this study , we also present for the first time a detailed analysis of the ZAP protein interactome , confirming most of its previously known interacting proteins and identifying new member proteins whose association with the ZAP RNP is consistent with its known cellular functions . For example , close colocalization in cytoplasmic granules of many of these proteins with ZAP , including L1 ORF1p , is not surprising in light of ZAP's dual roles in the assembly of RNA granules and the control of microRNA silencing . While itself catalytically inactive , ZAP recruits other PARPs active for poly ( ADP ) -ribosylation which is critical for SG formation [68 , 91] . Furthermore , overexpression of ZAP causes loss of microRNA silencing by targeting Ago2 ( a binding partner of ZAP , although not one detected by our study ) for pADPr [91 , 92] . We detected several PTM-related proteins within the ZAP RNP , including protein phosphatase 2A complex members ( PPP2R1A/B and PPP2CA ) , two related deubiquitinating enzymes ( USP7 and USP9X ) , and TRIM25 ( an E3 ubiquitin/ISG15 ligase ) . In addition to pADPr [68] , Sun et al . [71] reported phosphorylation of ZAP by GSK3β , and Charron et al . [56] found that S-farnesylation at the C-terminus of ZAP-L enhanced its restriction of Sindbis virus . Thus , PTMs likely modulate ZAP antiviral activity and may explain why ZAP efficiently restricts some classes of virus but not others . We also identified epigenetic modifying enzymes in ZAP-L-FL immunoprecipitates ( Table 1 ) . RUVBL1 and RUVBL2 are conserved AAA+ protein ATPases present in histone acetyltransferase complexes NuA4 and Tip60 , chromatin remodelling complexes Ino80 and SWR-C , and the telomerase complex [93] . HDAC1 and CHD4 are components of the NuRD ( nucleosome remodeling and histone deacetylase ) complex , along with HP1 , SETDB1 and TRIM28 ( KAP1 ) . While we detected TRIM28 in ZAP immunprecipitates , it was also present in vector-only samples ( 11 vs 4 peptides , respectively ) and is excluded from Table 1 . TRIM28 is targeted to specific DNA sequences via its interactions with various zinc finger proteins , and mediates gene silencing by recruiting NuRD to target promoters [94 , 95] . Epigenetic modifications involving TRIM28 and SETDB1 have been implicated in silencing both endogenous retroviruses and retrotransposons , including multiple classes of LTR retroelements and L1s [88 , 89 , 96–100] . Reichman et al . [101] also showed in silico that HDAC1 silences LTR retrotransposons in mouse mES cells . A possible link between ZAP and epigenetic silencers deserves investigation . Although ZAP is predominantly a cytoplasmic protein it can also function in the nucleus [29 , 102] . Macdonald et al . [103] showed that an unknown IFN-induced factor ( s ) synergizes with ZAP to control viral infection . Karki et al [104] identified 16 IFN-stimulated genes that act synergistically with ZAP to reduce alphavirus infectivity , including IFIH1 and SAMHD1 . Furthermore , ZAP-S stimulates RIG-1-mediated production of Type I interferon [53] . It is remarkable that 34 of the 78 proteins of the ZAP interactome shown in Table 1 are identified in the Interferome v2 . 01 Database of about 2000 ISGs as being induced at least 2-fold by interferon in either mice or humans ( S1 Table; [105] ) . This is a four-fold enrichment of ISG proteins in the ZAP interactome over what would be expected by chance . Likely we would have detected an even greater number of ISG products within the ZAP-L-FL interactome if prior to IP we had first stimulated their expression with interferon . MX1 and , MX2 , for example , while not detected by our MS analyses of endogenous ZAP-interacting proteins , when overexpressed strongly bind ZAP-L-FL in a manner resistant to RNase treatment ( S5 Fig ) . The association of ZAP with many ISG products suggests it may be a key player in the interferon response . This idea is supported by a recent study showing that ZAP-L-depleted HeLa cells are strongly enriched for expression of genes in the interferon immune response pathway [54] . In addition to MOV10 , other IFN-stimulated proteins associated with the ZAP RNP complex also play roles in viral control . Overexpression of DHX30 , for example , strongly enhances expression of HIV-1 , while restricting its RNA packaging [106] . G3BP1 and G3BP2 regulate expression of ISGs known to have broad antiviral activity , and are required for an IFN response against dengue and yellow fever viruses [107] . Chaperone HSPA4 ( HSP70 ) inhibits viral gene expression and replication [108] . Its co-chaperone DNAJ ( HSP40 ) has been associated with both activation and inhibition of viruses , including HIV [109] . Ubiquitination by TRIM25 increases the ability of RIG-1 to initiate antiviral signaling by facilitating its interaction with MAVS . Influenza A virus nonstructural protein 1 specifically inhibits TRIM25-mediated RIG-I ubiquitination , thereby suppressing its antiviral activity [75 , 110] . USP7 was originally identified by its interaction with the HSV-1-encoded E3 ubiquitin ligase ICP0 [111] . PARP1 is required for efficient replication and integration of HIV-1 [112 , 113] , and has been implicated in repressing Epstein Bar virus , Kaposi's sarcoma-associated herpesvirus , and Drosophila retrotransposons [114–116] . RUVBL2 inhibits influenza virus replication , apparently by interfering with oligomerization of the viral nucleoprotein [117] . Future investigations of the associations of these proteins with the ZAP complex could yield new insights into antiviral restriction . As we showed in Fig 1A , application of Type I interferon represses retrotransposition in cell culture . While this paper was under review , another study was published showing that increased endogenous L1 expression in the testes of MOV10L-deficient mice , or transfection of L1 constructs in mouse embryonic fibroblasts , is marked by increased levels of INFβ [118] . MOV10L is a restriction factor that represses retroelements in the male germline [119] . Conversely , treatment of human cells with INFβ suppressed L1 replication . Functional loss of an anti-retroelement restriction factor can alter the normal metabolism of retrotransposon RNA or its reverse-transcribed cDNA with possible consequences for the organism . Such may be the case with Type I interferonopathies , which include AGS , SLE , spondyloenchondrodysplasia , and STING-associated vasculopathy [120 121] . Indeed , it has been proposed that misregulated nucleic acids , deriving from an as yet unknown endogenous source , but possibly retrotransposons , accumulate and are recognized by sensors , triggering an interferon response and causing Aicardi—Goutières Syndrome [122 , 123] . AGS is a severe Mendelian inflammatory disorder that affects particularly the brain and frequently causes death in childhood . The disease is characterized by progressive encephalopathy , psychomotor regression , and lesions of the skin , together with increased levels of Type I IFN in the cerebrospinal fluid and serum , and induction of ISGs detectable in peripheral blood . AGS is associated with mutations in seven genes , all involved with nucleic acid metabolism or signaling: TREX1 , RNASEH2 A/B/C , SAMHD1 , ADAR1 and IFIH1 [124–126] . Most of these genes also have been linked with the innate immune system that restricts retroviral infection and suppresses endogenous retrotransposons [23 , 123 , 127 , 128] . We predict mutations in additional anti-retroelement genes , perhaps even including ZAP or MOV10 , will be linked to inflammatory diseases involving interferon overexpression .
Constructs CEP-EGFP , pc-L1-1FH , pc-L1-RP , ORF1-GFP L1-RP , 99-PUR-L1-RP-MS2-6X , pCEP-5′UTR-ORF2-No-Neo , and XRN1-RFP have been described [63 , 64] . ZAP-L-FL with a C-terminal FLAG-tag , and HA-ZAP 1–256 and HA-ZAP 253–902 with N-terminal HA-tags were generated by PCR and cloned in pcDNA6 myc/hisB vector . Ultimate ORF cDNA clones ( Invitrogen ) were cloned with V5-epitope tags and tobacco etch virus ( TEV ) protease cleavage sites on their N-termini by shuttling them from pENTR221 vector into pcDNA3 . 1/nV5-DEST vector using Gateway Technology ( Invitrogen ) . Ultimate ORF Clone ID numbers are shown in S1 Table . Clones obtained as gifts included Alu-neoTet and IAP-neoTNF ( M . Dewannieux , Institut Gustave Roussy , Villejuif [61 , 62] ) , DHX30 ( v2 ) -HA and DHX30 ( v2 ) -RFP ( C . Liang , Lady Davis Institute-Jewish General Hospital , Montreal [106] ) , pcDNA3 . 1-V5-His-MOV10 ( Y . -H . Zheng , Michigan State University , East Lansing [129] ) , ORFeus-Mm ( WA-125 ) ( W . An , Washington State Univ . [130] ) , ORFeus-HS ( WA117 ) ( L . Dai , Johns Hopkins School of Medicine , Baltimore [57] ) , pCDNA3 . 1-V5-His full-length PARP1 ( J . Pascal , Thomas Jefferson Univ . , Philadelphia [131] ) , GFP-PARP13 . 2 ( GFP-ZAP-S; A Leung , Johns Hopkins School of Medicine [89] ) , SBP-PARP13 . 1 , SBP-PARP13 . 1ΔZnF and SBP-PARP13 . 1VYFHR ( P Chang , MIT , Cambridge [54] ) , pDest51-USP9X-V5 ( R . Hughes , Buck Institute for Research on Aging , Novato [132] ) , Myc-USP7 ( Y . Sheng , York University , Toronto [133] ) , pcDNA-Vphu ( a codon-optimized version of the native Vpu gene; NIH AIDS Reagent Program [40] ) , pCEP-5′UTR-ORF2-No-Neo ( J . L . García-Pérez , GENYO , Spain ) [134] ) , and HA-ZAP-L , HA-ZAP-S , and Rat HA-NZAP ( H . Malik , Fred Hutchinson Cancer Research Center , Seattle [32] ) . An altered amino acid ( M201K ) was restored to consensus in HA-ZAP-L , and the change was found to have no effect on L1 retrotransposition . pEasiLV-MCS MX2-Flag WT was obtained from M . Malim ( King's College , London [45] ) and the MX2 gene was amplified with C-terminal V5-tag by PCR and recloned in the vector pcDNA3 . The K131A mutant was generated by the Quikchange Site-Directed Mutagenesis method ( Agilent Technologies ) . siRNAs were generated by Sigma-Aldrich based on the following sense sequences: siCNT3 AUGUAUUGGCCUGUAUUAG[dT][dT] , siZC3HAV1-2 1435–1453 UUGGGUCAGCAUCAUCUGC[dT][dT] , siZC3HAV1-3 1637–1655 AUGUGCUCAAAGUCCGUCC[dT][dT] [60] , and siCNT4 UAAGGCUAUGAAGAGAUAC[dT][dT] . For MS sequence determination , HEK 293T cells were transfected in T75 flasks with 15 μg of ZAP-L-FL or pcDNA6 myc/hisB ( Invitrogen ) empty vector and expanded for approximately 45 hr , followed by whole cell lysate preparation by sonication . IP and sample recovery were as previously described [46] . Treatment of samples with 30 μg/ml DNase-free RNase ( Roche ) was in the absence of RNase inhibitors . MS sequencing and database analyses was performed by the Johns Hopkins Mass Spectrometry and Proteomics Facility as previously described [46] . For each co-IP , extracts from approximately 6×106 293T cells in T75 flasks transfected with ZAP-L-FL and test protein constructs were prepared in 750 μl of lysis buffer supplemented with protease , phosphatase , and RNase inhibitors , and immunoprecipitated as previously described [46] . Lysates containing test proteins of predominantly nuclear localization were sonicated . RNase-treated reactions contained 25 μg/ml RNase , DNase-free HC ( Roche ) and 25 μg/ml RNaseA ( Invitrogen ) and no RNase inhibitors . Human 2102Ep embryonal carcinoma cells ( a gift from P . K . Andrews , University of Sheffield ) , HeLa-HA and HeLa-JVM cells ( [135]; gifts from J . L . García-Pérez , GENYO , Spain ) , and HEK 293T cells ( ATCC ) were grown in Dulbecco’s modified Eagle’s medium with 10% FBS ( Hyclone ) , GlutaMax and Pen-Strep ( Invitrogen ) . Plasmid and siRNA transfections used FuGENE HD ( Promega ) and Lipofectamine RNAiMAX ( Life Technologies ) reagents , respectively . The EGFP L1 cell culture retrotransposition assay was conducted as previously described [27 , 38] . 2 . 5×105 HeLa or 293T cells/well were seeded in 6-well dishes . The following day , 1 . 0 μg of 99-PUR-RPS-EGFP , a plasmid containing L1-RP and the EGFP retrotransposition reporter cassette , was cotransfected with 0 . 5 μg of empty vector ( pcDNA3 or pcDNA6 myc/hisB , Invitrogen ) or test plasmid . All transfections were in quadruplicate wells . Five days post-transfection , cells having a retrotransposition event , and hence expressing EGFP , were assayed by flow cytometry . Gating exclusions were based on background fluorescence of plasmid 99-PUR-JM111-EGFP , an L1 construct containing two point mutations in ORF1 that abolish retrotransposition [37] . Within each experiment , results were normalized to fluorescence of 99-PUR-RPS-EGFP cotransfected with empty vector . The Alu retrotransposition assay was carried out essentially as described in Dewannieux et al . [61] . Retrotransposition construct Alu-neoTet was cotransfected in HeLa-HA cells with pcDNA6 myc/hisB empty vector or retrotransposition driver plasmid pCEP-5′UTR-ORF2-No-Neo , together with test plasmids . Eighteen hours post-transfection , HeLa-HA cells were expanded from six-well plates to T75 flasks , and three days later selection for retrotransposition events with 550 μg/ml of G418 was begun . After 15 days of selection , cells were fixed , stained with Giemsa , and colonies were counted . Similarly , 1 . 0 μg of the IAP element reporter plasmid , IAP-neoTNF [62] , was cotransfected with 0 . 5 μg empty vector or test plasmid in HeLa-JVM cells , selected with G418 , and colony numbers were counted . To reveal any differences in transfection efficiencies of test proteins or off-target effects on EGFP reporter expression , we followed the strategy of Wei et al . ( 39 ) . Each test plasmid ( 0 . 5 μg ) was co-transfected in quadruplicate wells of 12-well plates with CEP-EGFP ( 0 . 5 μg ) , a construct that constitutively expresses EGFP from a CMV promoter . Four days post-transfection , EGFP fluorescence was determined by flow cytometry , as previously described [46] . To determine potential cell toxicity caused by test proteins , 18 , 000 293T cells were seeded in 75 μl of Dulbecco’s modified Eagle’s complete medium in 96-well plates . The next day , transfection reactions prepared with 70 ng of test plasmid , 0 . 2 μl of Fugene HD and 25 μl of Opti-MEM Reduced Serum Medium ( Invitrogen ) were added to each well . After 3 or 4 days , a MultiTox-Fluor Multiplex Cytotoxicity Assay kit ( Promega ) was used to assay cell toxicity , as previously described [46] . To further test potential toxicity from expression of ZAP , we co-transfected in HeLa cells pcDNA6 myc/his B , a bsr expression vector , together with either empty vector ( pcDNA3 ) or ZAP expression constructs . On day 2 , cells were expanded to T75 flasks and selection with 2 μg/ml blasticidin was begun . After 12 days , cells were fixed , stained and colonies were counted . Cytotoxicity will reduce total colony counts compared with empty vector control ( S2B Fig ) . Commercial antibodies included mouse ( ms ) α-V5-tag ( Invitrogen ) , ms α-FLAG-tag ( Sigma ) , rabbit ( rb ) α-HA-tag ( C29F4 ) , rb α-HSP90 and rb α-Myc-tag ( 71D10 ) ( Cell Signaling Technology ) , goat ( gt ) α- anti-eIF3η ( N-20 ) , gt α-MX2 ( C-20 ) , ms α-SBP ( SB19-C4 ) , and gt α-TIA1 ( C-20 ) ( Santa Cruz Biotechnology ) , rb α-β-tubulin-2 ( Pierce ) , and rb α-MOV10 and rb α-ZC3HAV1 ( ProteinTech ) . Donkey Cy3- , Cy5- , DyLight 488- , or DyLight 549-conjugated , and HRP-conjugated secondary antibodies were from Jackson ImmunoResearch Laboratories . Purified polyclonal α-ORF1p ( AH40 . 1 ) and monoclonal α-ORF1p ( α-moORF1 ) antibodies were gifts from M . Singer ( Carnegie Institution of Washington [136] ) and K . Burns ( Johns Hopkins School of Medicine [137] ) , respectively . Western blotting , IF , and FISH were performed as described [63 , 64] . L1 ORF2p reverse transcriptase analysis followed the LEAP protocol [81] . Primers used were: 3′RACE adapter NV: GCGAGCACAGAATTAATACGACTCACTATAGGTTTTTTTTTTTTVN 3′RACE outer: GCGAGCACAGAATTAATACGACT bORF2-end2 , GATGAGTTCATATCCTTTGTAGGG The sequence of the antisense RNA-FISH probe Cy2-MS2 was , Cy3-GTCGACCTGCAGACATGGGTGATCCTCATGTTTTCTAGGCAATTA . Cells were lysed and their RNA initially extracted with Trizol ( Life Technologies ) , followed by further purification using an RNeasy Mini Kit ( Qiagen ) . Residual DNA was removed by Turbo DNA-free Kit DNase treatment ( Ambion ) , and cDNA was generated from the RNA using the SuperScript III First Strand Synthesis System ( Invitrogen ) and a polyT primer . Subsequent PCR used GoTaq DNA polymerase ( Promega ) . RT-PCR primers were: 1EGFPcass5P TGTTCTGCTGGTAGTGGTCG 2EGFPcass3P TATATCATGGCCGACAAGCAG , which span the intron of the 99-PUR-JM111-EGFP reporter cassette , and 13HSPA6for CAAAATGCAAGACAAGTGTCG 14HSPA6rev TTCTAGCTTTGGAGGGAAAG , which amplify HSPA6 ( Accession No . NM_002155 ) . | Retrotransposons are mobile DNA elements that duplicate themselves by a "copy and paste" mechanism using an RNA intermediate . They are insertional mutagens that have had profound effects on genome evolution , fostering DNA deletions , insertions and rearrangements , and altering gene expression . LINE-1 retrotransposons occupy 17% of human DNA , although it is believed that only about 100 remain competent for retrotransposition in any individual . The cell has evolved defenses restricting retrotransposition , involving in some cases interferon-stimulated genes ( ISGs ) that are part of the innate immune system that protects the cell from viral infections . We screened a panel of ISGs and found several to strongly limit retrotransposition in a cell culture assay . Our investigations increase understanding of how ZAP , an important restriction factor against positive- and negative-strand RNA and some DNA viruses , also interacts with human retrotransposons to prevent genome mutation . Microscopy and immunoprecipitation show a close association of ZAP protein with the L1 ribonucleoprotein particle , as well as MOV10 , an RNA helicase that also inhibits retrotransposons . A detailed examination of the ZAP protein interactome reveals many other ISGs that directly bind ZAP , and suggests new directions for exploring the mechanisms of ZAP-mediated anti-retroelement activity . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | The Broad-Spectrum Antiviral Protein ZAP Restricts Human Retrotransposition |
Cell intercalation is a highly directed cell rearrangement that is essential for animal morphogenesis . As such , intercalation requires orchestration of cell polarity across the plane of the tissue . CDC-42 is a Rho family GTPase with key functions in cell polarity , yet its role during epithelial intercalation has not been established because its roles early in embryogenesis have historically made it difficult to study . To circumvent these early requirements , in this paper we use tissue-specific and conditional loss-of-function approaches to identify a role for CDC-42 during intercalation of the Caenorhabditis elegans dorsal embryonic epidermis . CDC-42 activity is enriched in the medial tips of intercalating cells , which extend as cells migrate past one another . Moreover , CDC-42 is involved in both the efficient formation and orientation of cell tips during cell rearrangement . Using conditional loss-of-function we also show that the PAR complex functions in tip formation and orientation . Additionally , we find that the sole C . elegans Eph receptor , VAB-1 , functions during this process in an Ephrin-independent manner . Using epistasis analysis , we find that vab-1 lies in the same genetic pathway as cdc-42 and is responsible for polarizing CDC-42 activity to the medial tip . Together , these data establish a previously uncharacterized role for polarized CDC-42 , in conjunction with PAR-6 , PAR-3 and an Eph receptor , during epithelial intercalation .
Understanding how the cellular behaviors that underlie embryonic morphogenesis are regulated is a longstanding and fundamental goal of developmental biology . One such cell behavior that can reshape tissues during embryogenesis is mediolateral intercalation , whereby cells within the same plane interdigitate along a preferred axis ( reviewed in [1] ) . Mediolateral intercalation is a widespread process in animal development; it occurs in tissues derived from all three germ layers during gastrulation and organogenesis . There are multiple mechanisms by which cells can intercalate , including highly directed and coordinated protrusive activity and highly polarized apical junction rearrangement ( reviewed in [1] ) . These mechanisms are unified by a fundamental theme: movement must occur in a highly polarized fashion . One well studied regulator of cell polarity is the Rho family GTPase Cdc42 ( reviewed in [2] ) . As a GTPase , Cdc42 cycles between active , GTP-bound and inactive , GDP-bound states . Cdc42-GTP has multiple molecular functions that allow it to influence cell polarity . Active Cdc42 can modify the actin cytoskeleton to produce long , thin filopodia [3] , a process mediated through its interaction with the actin nucleation promoting factor Wiskott-Alrich syndrome protein ( WASP ) [4] . Furthermore , through interactions with the partitioning defective ( PAR ) complex [5–7] , Cdc42 can regulate the microtubule cytoskeleton to reorient centrosomes in order to polarize cell division and regulate apical junctions in epithelia [8 , 9] . Given these diverse and conserved molecular processes , it is reasonable to think that Cdc42 has roles in polarizing epithelia during intercalation . While Cdc42 has documented roles in other intercalation events , it has not yet been implicated during embryonic epithelial morphogenesis . For example , Cdc42 helps orient intercalation in frog mesenchymal cells during convergent extension through the planar cell polarity pathway [10] , in the ascidian notochord during convergent extension as dorsoventral polarity is established [11] , in the fly mesoderm during gastrulation through fibroblast growth factor signaling [12] , and during transendothelial migration as cancer cells metastasize [13] . While Cdc42 has a documented role in orienting cell divisions in an intercalating epithelium , the frog neuroepithelium [14] , no role has been documented for Cdc42 in the rearrangement of post-mitotic intercalating cells . To study Cdc42 in an intercalating epithelium , we employed the Caenorhabditis elegans dorsal epidermis . During dorsal intercalation , two rows of ten epidermal cells interdigitate into a single row of twenty [15] . Dorsal intercalation is accompanied by the appearance of highly protrusive medial tips; in contrast , lateral ( i . e . , rear ) cell borders are protrusively inactive [16] . Previous work made CDC-42 a good candidate for regulating polarity during dorsal intercalation . CDC-42 has roles in collective cell migration of ventral epidermal cells in the C . elegans embryo [17] , suggesting that it is active in the epidermis . Moreover , we recently discovered that wsp-1/WASP , a key Cdc42 downstream effector , and wve-1/WAVE function redundantly to promote medial protrusive activity during dorsal intercalation [16] . In this study , we use a combination of two genetic approaches—epidermal-specific dominant-negative mutant CDC-42 and late loss of CDC-42 function—along with in vivo CDC-42 biosensor data to establish a role for CDC-42 in forming and fine-tuning the orientation of the medial tips of dorsal epidermal cells as they extend during intercalation . Additionally , we leverage results from large-scale genetic synthetic lethal screens [18] to implicate VAB-1/Eph Receptor ( EphR ) as an upstream activator of CDC-42 in this context . Together , these results demonstrate the importance of CDC-42 as a key regulator of oriented cell migration and identify a genetic pathway involving highly conserved molecular components centered on CDC-42 that operates during epithelial cell intercalation .
Cdc42 is a Rho family GTPase that polarizes cells during migration [19] . Previously , examination of the role of CDC-42 in cellular migrations in intact C . elegans embryos has been hampered by early requirements during anterior-posterior axis formation [6 , 20] . To determine if CDC-42 function is required for the highly directed cell migrations that occur during dorsal intercalation ( about 3 hours after the first division of the zygote ) , we sought to bypass these early requirements . To do so , we generated an epidermal-specific , inducible dominant-negative CDC-42 ( T17N ) [21] ( hereafter cdc-42 ( DN ) ) using the Nonsense Mediated Decay ( NMD ) -mediated system that we described previously for CED-10/Rac [16] . Briefly , transgenes expressing cDNA under control of the epidermal-specific lin-26 promoter and an NMD-sensitive 3’UTR are generated in the temperature-sensitive smg-1 ( cc546ts ) ( NMD defective ) background; at the permissive temperature of 15°C , transcripts from these transgenes are degraded by the NMD RNA surveillance system . Conversely , at the restrictive temperature of 25°C the NMD system is inactivated , transcripts from the transgenes are stabilized , and protein is produced . In addition , we utilize a second , previously established transgene-mediated system to conditionally perturb CDC-42 and PAR-3/PAR-6 function . This system relies on the degradation of functional ZF1-tagged transgenes [22–24] in mutant backgrounds , which rescue the mutant phenotype in the early embryo , thus circumventing requirements [6 , 20] , but lead to loss of function during gastrulation and later in embryogenesis . Upon incubation at 25°C , cdc-42 ( DN ) embryos displayed two classes of intercalation defects , which were seen at significantly higher frequencies than in wild-type or in gfp expressing controls ( p≤0 . 01 , Fisher’s Exact Test ) ( Fig 1 ) . First , adjacent dorsal cells often migrated together across the dorsal array , instead of interdigitating with contralateral neighbors ( 40% , n = 16 ) , a phenotype we call “ipsilateral comigration” . This defect may be due to a mispolarization of the extending tip during intercalation . Second , medial edges of dorsal cells occasionally appeared blunt , rather than extending a pointed tip ( 7% , n = 16 ) , as if opposing cells mutually block migration of its contralateral partner . We have described this phenotype previously [16]; here we refer to it as “medial delay” . These defects may result from a weak requirement for CDC-42 activity in early tip formation . Both types of defects sometimes occurred within the same pair of cells ( i . e . , cells tips are blunt , then migrate in the wrong direction ) ; Fig 1B accordingly shows the pooled frequency of these two defects in cdc-42 loss of function embryos . To verify that these defects were due to reduction of CDC-42 function , we utilized a second approach to circumvent early requirements for CDC-42 . Transgenic CDC-42 tagged with the ZF1 degron is stable during initial zygotic divisions but sensitive to degradation by the E3 ubiquitin ligase ZIF-1 during gastrulation in somatic cells , leading to late maternal loss of function [22 , 23] . The ZF1::CDC-42-expressing transgene can be combined with the putative null allele , cdc-42 ( gk388 ) [25 , 26] to additionally mediate strong zygotic loss of CDC-42 function . The resulting ZF1::cdc-42; cdc-42 ( gk388 ) embryos ( hereafter ZF1::cdc-42 ) [23 , 27] circumvent early requirements for CDC-42 while depleting CDC-42 in the later embryo . Both ipsilateral comigration and medial delay occurred in ZF1::cdc-42 embryos ( 20% and 24% , respectively , n = 35; Fig 2 ) , and the penetrance of these defects was not significantly different from cdc-42 ( DN ) embryos ( p = 0 . 749 , Fisher’s Exact Test ) . Together , these data suggest that CDC-42 has roles in both tip extension and orientation during dorsal intercalation in C . elegans . We next sought to determine whether atypical cell morphology preceded the cell migration defects visible by DIC microscopy in cells with compromised CDC-42 . To do so , we analyzed wild-type and ZF1::cdc-42 cells expressing an epidermal cytoplasmic reporter ( Plbp-1::gfp ) [28] at a time prior to migration but when cells normally begin to take on a mediolaterally polarized morphology ( 45 min . after the terminal division; for comparison , the “0 min . ” time point on all DIC images is 60 min . after the terminal epidermal divisions ) . Our analysis uncovered two abnormalities . First , we found that the angular orientation of medial tips was significantly less uniform in ZF1::cdc-42 than in age-matched wild-type controls ( Mardia-Watson-Wheeler , p = 0 . 002; Fig 3A and 3B ) . In ZF1::cdc-42 cells , the tip was often oriented more anteriorly or posteriorly than in wild-type , suggesting that CDC-42 is involved in fine-tuning medial tip orientation . This initial misorientation also provides a potential explanation for ipsilateral comigration: if sufficiently misoriented , once cell migration ensues , then comigration could occur . In addition to medial tip orientation defects , our analysis revealed a second abnormality . The length/width ratio ( aspect ratio ) of intercalating cells was significantly lower in ZF1::cdc-42 than in age-matched wild-type controls ( Student’s T-test , p = 0 . 02; Fig 3C ) . ZF1::cdc-42 cells were often less elongated , suggesting that CDC-42 is required for cells to properly extend their medial tips during dorsal intercalation . Such extension defects may , in severe cases , result in the medial delay phenotype observed by DIC microscopy . Since we had shown previously that CED-10/Rac is important for tip extension during dorsal intercalation [16] , we next examined whether CED-10 and CDC-42 act together in this process . When we crossed the ced-10 ( n3246 ) missense mutation into the Plin-26::cdc-42 ( DN ) ::smgS; smg-1 ( cc546ts ) background , the resulting heat shocked embryos had fully penetrant intercalation defects ( S1 Fig ) . The penetrance of total defects was significantly higher ( Student’s T-test , p<0 . 01 ) than similarly heat-shocked smg-1 ( cc546ts ) , ced-10 ( n3246 ) ; smg-1 ( cc546ts ) or Plin-26::cdc-42 ( DN ) ::smgS; smg-1 ( cc546ts ) controls . Significantly , Plin-26::cdc-42 ( DN ) ::smgS; ced-10 ( n3246 ) ; smg-1 ( cc546ts ) loss-of-function embryos had a significantly greater penetrance of medial delay than controls ( ANOVA , p<0 . 02 ) , while the penetrance of ipsilateral comigration remained the same , suggesting that CED-10/Rac and CDC-42 normally cooperate specifically to extend medial tips . Taken together , these data support a minor role for CDC-42 in tip extension during dorsal intercalation . Cdc42 forms a complex with Par6 and Par3 to influence cell polarity [7] , so we next compared the phenotypes of cdc-42 and par loss of function during dorsal intercalation . We again utilized existing ZF1 transgenes to abrogate par-3 and par-6 function [8 , 9 , 24] . Late maternal loss of function in par-3::ZF1::gfp; par-3 ( it71 ) embryos led to defects closely resembling those in ZF1::cdc-42 and cdc-42 ( DN ) embryos , including ipsilateral comigration and medial delay ( 37% and 5% , respectively , n = 19 ) ( Fig 3 ) . Late maternal par-6 loss of function—through par-6::ZF1::gfp combined with zygotic loss of function , using the strong loss of function par-6 ( tm1425 ) allele—resulted in similar phenotypes ( 33% ipsilateral comigration and 17% medial delay , n = 18 ) ( Fig 3 ) . Together , these results suggest that CDC-42 and the PAR complex both act to orient and extend medial tips during dorsal intercalation . If CDC-42 is involved in medial tip formation , it might be expected that its activity would be enriched at sites of tip formation . As a Rho family GTPase , CDC-42 cycles between active ( GTP-bound ) and inactive ( GDP-bound ) states . We determined the localization of active CDC-42 using a validated CDC-42 biosensor ( wsp-1 ( G-protein binding domain ) ::gfp [29] ) , under the control of its endogenous promoter ( hereafter wsp-1 ( GBD ) ::gfp ) . WSP-1 ( GBD ) ::GFP localization was significantly enriched at the medial edge relative to a uniformly distributed membrane marker , PH::mCherry [30] ( Student’s T-test , p = 0 . 03 , Fig 4C ) . WSP-1 ( GBD ) ::GFP localization was polarized from the time of tip formation ( about 30–60 min . post-terminal division ) through tip extension ( about 90–120 min . post-terminal division; Fig 4A ) . The timing of this medial enrichment of active CDC-42 coincides with the timing of tip orientation and extension , and with the defects in intercalation we observed in cdc-42 loss of function backgrounds described above ( see Fig 2 ) . We next addressed the functional relationship between CDC-42 and the PAR proteins during dorsal intercalation . Construction of strains for standard epistasis testing proved technically challenging due to maternal requirements for CDC-42 , PAR-3 , and PAR-6 , and complexities in genotyping , and we were unable to recover transgenic lines following injection of DNA encoding the CDC-42 activity biosensor into par-3::ZF1::gfp; par-3 ( it71 ) worms . However , we were able to obtain one line of par-6::ZF1::gfp;par-6 ( tm1425 ) worms expressing wsp-1 ( GBD ) ::gfp . Based on the previous literature , there were two possible predicted outcomes from this experiment . Since PAR-6 is typically considered a downstream effector of Cdc42 [2] , loss of par-6 function might be predicted to have little effect on CDC-42 activity . However , a study in Drosophila neuroblasts [31] and results from the one-cell C . elegans zygote [29] ( reviewed in [32] ) suggested another possibility . These studies indicated positive feedback between CDC-42 and PAR-6 . In this case , loss of par-6 activity might be expected to lead to loss of CDC-42 activity at the midline due to disruption of the feedback loop . As Fig 4B and 4C indicates , par-6::ZF1::gfp;par-6 ( tm1425 ) embryos show loss of accumulation of WSP-1 ( GBD ) ::GFP at the dorsal midline , suggesting that CDC-42 and PAR-6 may engage in positive feedback to polarize intercalating cells in the dorsal epidermis . We next sought to identify additional components of the CDC-42 pathway that regulates dorsal intercalation . To do so , we mined the literature for genes that might be associated with activation of CDC-42 . We identified one gene expressed in the dorsal epidermis during intercalation , vab-1/EphR [33] , which had a genetic interaction with cdc-42 in a previous large-scale study [18] . Indeed , when we imaged GFP::WSP-1 ( GBD ) in vab-1 ( dx31 ) null embryos during intercalation , we observed a significant decrease in the medial enrichment of active CDC-42 ( Student’s T-test , p = 0 . 03; Fig 4B and 4C ) . These data suggest that VAB-1/EphR leads to activation of CDC-42 at the medial edge during dorsal intercalation . We next examined spatial localization of VAB-1 in dorsal epidermal cells using a rescuing , GFP-tagged vab-1 fosmid . VAB-1::GFP was enriched at medial edges in dorsal cells ( S2 Fig ) in a manner similar to GFP::WSP-1 ( GBD ) . If VAB-1/EphR is required for medial CDC-42 activity , loss of vab-1 function would be expected to lead to defects similar to those following loss of cdc-42 function . When we imaged vab-1 ( dx31 ) null mutants , we also observed the comigration and medial delay phenotypes ( Fig 5; Fisher’s Exact Test to wild-type , p = 0 . 038 ) seen in ZF1::cdc-42 and cdc-42 ( DN ) mutants . Given the wide array of mapped genetic lesions available within the vab-1 locus [34] , we were additionally able to ask whether certain domains were required for VAB-1 function . We found that animals homozygous for mutations in the kinase domain ( e118 and e2 ) , which prevent phosphorylation and activation of VAB-1 [35] , also displayed dorsal intercalation defects ( Fisher’s Exact Test , p≤0 . 007 ) . This suggests that , in contrast to some other VAB-1-dependent processes , such as hyp6 cell fusion [35] and amphid axon guidance [36] , dorsal intercalation requires VAB-1/EphR kinase activity . We next examined the potential roles of Eph ligands in dorsal intercalation . First , we examined embryos homozygous for a vab-1 mutation ( e699 ) , which causes an amino acid substitution ( T63I ) within the extracellular domain that binds to canonical ephrin ligands [34 , 35 , 37 , 38] . In contrast to putative kinase-dead mutants , vab-1 ( e699 ) homozygotes displayed intercalation defects at intermediate levels that were not significantly different from either wild-type or vab-1 ( dx31 ) null mutants ( Fisher’s Exact Test , p = 0 . 135 and p = 0 . 796 ) . Given the surprisingly mild defects in vab-1 ( e699 ) mutants , we next analyzed ephrin mutants directly . Surprisingly , neither triple ephrin mutants ( vab-2 ( ju1 ) , efn-2 ( ev658 ) ; efn-3 ( ev696 ) ) [39] nor putative null mutants of the more divergent ephrin , efn-4 ( bx80 ) [40] , displayed intercalation defects ( S3 Fig ) . Taken together , these data suggest that while kinase activity is important for VAB-1 function during intercalation , interaction with traditional ephrin ligands is dispensable . The phenocopy of cdc-42 loss of function in vab-1 mutants , medial enrichment of VAB-1 , and the requirement of VAB-1 function for CDC-42 medial enrichment suggests that these two genes lie in the same pathway to promote oriented cell migration during dorsal intercalation . To examine this possibility , we crossed the weak allele , vab-1 ( e2 ) , into the cdc-42 ( DN ) background . We predicted that at a semi-permissive temperature ( 20°C ) —when induction of transgenes using the NMD-sensitive system is weak ( 16 ) —the combination of weak vab-1 and cdc-42 loss of function would enhance intercalation defects to levels seen at the restrictive temperature ( 25°C ) for cdc-42 ( DN ) alone or in ZF1::cdc-42 embryos . Indeed , at 20°C , intercalation defects in cdc-42 ( DN ) were significantly less frequent than at 25°C ( Fisher’s Exact Test , p = 0 . 006 , Fig 6 ) . Furthermore , the penetrance of defects was significantly enhanced in the vab-1 ( e2 ) ; cdc-42 ( DN ) background relative to either cdc-42 ( DN ) or vab-1 ( e2 ) ; smg-1 ( cc546ts ) grown at 20°C ( Fisher’s Exact Test , p≤0 . 004 , Fig 6 and S4 Fig ) . Moreover , the frequency of enhanced intercalation defects was not significantly different from either cdc-42 ( DN ) at 25°C or ZF1::cdc-42 , suggesting that CDC-42 and VAB-1 function together during intercalation . Combined with the loss of medial enrichment of the CDC-42 biosensor in vab-1 ( dx31 ) , these results suggest that vab-1 and cdc-42 function co-linearly in the same genetic pathway during intercalation to promote proper cell guidance and cell migration .
Cell intercalation comprises a series of highly directed cell rearrangements . Given that Cdc42 is a highly conserved regulator of cell guidance , it is surprising that relatively little is known about the role of Cdc42 in this process . While Cdc42 has a known role in regulating adhesion of mesenchymal cells during convergent extension in Xenopus [10] , there is little evidence for a role for Cdc42 during the intercalation of other cell types , particularly epithelia . Here , using conditional loss of function experiments and an in vivo biosensor , we describe a role for CDC-42 at the medial tips of intercalating cells in the dorsal epidermis of C . elegans . Dorsal intercalation is an epithelial intercalation event that occurs via cell migration and protrusive activity rather than predominantly by spatially-restricted apical junctional rearrangement [15 , 16] . Our study suggests that CDC-42 functions with PAR-6 , PAR-3 , and VAB-1 during dorsal intercalation , particularly through formation and orientation of medial tips . Loss of cdc-42 function—either using tissue-specific expression of a dominant-negative ( NMD-sensitive system ) or degradation of a functional , early maternally-rescuing transgene ( ZF1 ) —can result in intercalating cells that fail to form a medial tip ( “medial delay” ) and/or that fail to interdigitate properly ( “ipsilateral comigration” ) . Detailed analysis of a cytoplasmic reporter in ZF1::cdc-42 cells uncovered defects in the orientation of medial tips and the extension of dorsal cells . These results suggest that CDC-42 has two roles during dorsal intercalation: 1 ) to help form and 2 ) to orient the medial tips of intercalating cells ( Fig 7 ) . When cell tips do not form efficiently , the medial edges of cells appear blunt , leading to the medial delay phenotype . These blunt medial edges often resolve eventually , albeit sometimes through comigration of adjacent cells across the dorsal array . When cells do appear to form tips efficiently , they nevertheless often become misoriented , which also leads to comigration of cells that appear otherwise normal . Since the roles of CDC-42 in tip formation and orientation are separable , we propose two separate roles for CDC-42 during intercalation . Previously , we uncovered a role for ced-10/Rac and mig-2/RhoG in directing tip formation through actin polymerization mediated by wve-1/WAVE and wsp-1/WASP , respectively [16] . We hypothesize that CDC-42 plays a minor role in this tip formation process through WASP , which is a well-documented downstream effector of active Cdc42 . This hypothesis is consistent with the synergistic effects of weak loss of ced-10 function combined with perturbation of cdc-42 function , which specifically affects tip extension . In addition to its minor role in supporting tip extension in intercalating cells , CDC-42 plays a role in orienting the tips of intercalating cells . We cannot rule out the possibility that subtle orientation defects occur in dorsal epidermal cells prior to the formation of protrusive tips , although as Fig 7 indicates , we favor roles for CDC-42 immediately prior to intercalation . What CDC-42 effector pathways lead to reliable alternation of contralateral cells during dorsal intercalation is an important area of future study . One potential mechanism by which comigration could occur is through abnormal cell adhesion; perhaps adjacent ipsilateral cells adhere too strongly or contralateral cells do not adhere strongly enough in cdc-42 loss of function embryos . This idea is supported by findings regarding the PAR complex in epithelial cells in C . elegans: while junctional proteins acquire their normal apicobasal localization in par-6::ZF1 and par-3::ZF1 embryos , they do not form continuous belt-like structures and cells in such embryos display subsequent defects in cell adhesion [8 , 9 , 24] . While the comigration phenotype has not been reported upon loss of components of the apical junction [41–43] , it is possible that inherent local asymmetries of adhesion are required for interdigitation , which would not be revealed by experiments involving simple loss of function . Indeed , the Drosophila E-cadherin homologue , Shotgun/DE-cadherin , is asymmetrically localized in the intercalating germband [44] , and local junctional disassembly is an important driver of tissue rearrangement [45] . Alternatively , the PAR complex may direct microtubule reorganization ( reviewed in [3 , 46] ) to either form or orient the medial tip . In the single reported drug study , microtubule depolymerization was reported to block intercalation [15] consistent with this possibility . Additional experiments will be required to address these potential mechanisms . In this work we also describe an upstream role for the sole C . elegans Eph receptor , VAB-1 , in CDC-42 regulation during dorsal intercalation . vab-1 mutants not only phenocopy cdc-42 loss of function , but the localization of a biosensor for active CDC-42 to the medial tip is lost in vab-1 mutants . Nevertheless , the phenotype of vab-1 null mutants is milder than that resulting from loss of cdc-42 function . This result suggests that other signaling pathways may impinge on CDC-42 in addition to VAB-1 , and warrants further investigation in the future . It is unclear by what mechanism VAB-1 and CDC-42 interact . One straightforward explanation is that VAB-1/EphR activates or localizes a guanine nucleotide exchange factor ( GEF ) that can activate CDC-42 . There are several vertebrate GEFs with such reported specificities: ephexin , intersectin , and Vav2 . ephx-1/ephexin interacts with EphR to direct Cdc42 activation in neurons in C . elegans [47 , 48] . However , homozygous null ephx-1 mutants intercalate normally ( S5 Fig ) . Intersectin is a Cdc42 GEF [49 , 50] that also binds EphR and WASP [51 , 52] . However , the GEF domain is not conserved in worm ITSN-1 [53] . Vav2 is a GEF that mediates endocytosis of Eph-ephrin complexes in neurons [54] . The sole Vav family member in C . elegans , VAV-1 , is required for rhythmic contractions in multiple cell types [55] . Additionally , VAV-1 functions in a pathway with VAB-1 during oocyte maturation [56] without changing VAB-1 localization [39] . A vav-1 transcriptional reporter [57] is present in the dorsal epidermis [16] . While vav-1 ( RNAi ) did not result in intercalation defects [16] , it is possible that vav-1 is functioning redundantly with another GEF during dorsal intercalation . Another open question regarding VAB-1/EphR is which of its ligands functions during dorsal intercalation . Surprisingly , we found that vab-1 ( e699 ) , which disrupts the ephrin binding domain , leads to intercalation defects at frequencies between wild-type and vab-1 ( dx31 ) ( Fig 5 ) . However , neither canonical ephrin ( vab-2 efn-2; efn-3 ) triple mutants nor a divergent ephrin ( efn-4 ) mutant had significant intercalation defects ( S3 Fig ) . There are two possible explanations for these results . First , all four ephrins could function redundantly during dorsal intercalation . Second , dorsal intercalation could be an ephrin-independent process and the e699 mutation disrupts a portion of the extracellular domain that also binds to other , non-canonical ligands . While the former possibility is difficult to test because EFN-4 has VAB-1 and EFN-1 independent roles that make vab-1; efn-4 and efn-1; efn-4 mutants synthetic lethal [40] , we sought to investigate the latter possibility . Recently , a noncanonical VAB-1/EphR ligand , VPR-1/VAPB , was identified . VPR-1/VAPB is a secreted protein; mutations in vpr-1 have pleiotropic effects [58–60] . The progeny of vpr-1 ( tm1411 ) null homozygotes are maternal effect lethal and display the comigration phenotype ( S6 Fig ) ; however , given the widespread disruption of epidermal cell positioning in such embryos we were not able to rule out earlier defects during cell specification and gastrulation as contributing factors . Future studies involving conditional loss of vpr-1 function will be needed to definitively determine whether vpr-1 acts in the same pathway as cdc-42 during dorsal intercalation . While roles for Cdc42 are well established for migrating cells in culture , roles for Cdc42 during morphogenesis are difficult to establish due to earlier Cdc42-dependent processes , such as gastrulation and polarized cell division . Our results implicate polarized Cdc42 activity in orienting an intercalating epithelium during morphogenesis . Dorsal intercalation is only one of many examples of epithelial intercalation that involve basolateral protrusion [1 , 61] . Given its ubiquitous role in establishing cell polarity and in regulating the actin cytoskeleton , it is likely that Cdc42 has widespread functions during these epithelial intercalation events .
Worms were maintained on Escherichia coli OP50 , as previously described [62] . The wild-type strain used in this study was Bristol N2 . Experiments were performed at 20°C unless otherwise specified . The following genetic lesions were utilized in this study . LGI: par-6 ( tm1425 ) , smg-1 ( cc546ts ) , vpr-1 ( tm1411 ) . LGII: cdc-42 ( gk388 ) , ephx-1 ( ok494 ) , vab-1 ( dx31 ) , vab-1 ( e699 ) , vab-1 ( e118 ) , vab-1 ( e2 ) . LGIII: par-3 ( it71 ) , unc-119 ( ed3 ) . LGIV: efn-2 ( ev658 ) , efn-4 ( bx80 ) , vab-2 ( ju1 ) . LGX: efn-3 ( ev696 ) . Additionally , the following transgenic arrays were made for or utilized in this study: jcEx200[Plin-26::cdc-42 ( T17N/DN ) ::smg sensitive 3'UTR , sur-5::mCherry] , ojEx99[Pcdc-42::gfp::wsp-1 ( G-protein binding domain ) ; unc-119 ( + ) ] based on the cdc-42 biosensor described in [29] , jcEx273[Pcdc-42::gfp::wsp-1 ( G-protein binding domain ) ; Pttx-3::dsRed] , jcEx215[Plbp-1::gfp; pRF4] , quEx531[vab-1::2xTY1::gfp::3xFLAG fosmid; odr-1::rfp] , ltIs44[Ppie-1::PHPLC1∂1::mCherry , unc-119 ( + ) ] [30] , reIs9[Plin-26::gfp::smg sensitive 3'UTR , rol-6 ( su1006 ) ] [16] , xnIs83[Pcdc-42::2xHA::ZF1::cdc-42; unc-119 ( + ) ] [22] , zuIs20[Ppar-3::par-3::ZF1::gfp; unc-119 ( + ) ] [24] , zuIs43[Ppie-1::gfp::par-6::ZF1; unc-119 ( + ) ] [9] . To make par-6 ( M/Z ) mutants , gfp::par-6::ZF1; par-6 ( tm1425 ) embryos were obtained through crossing , as described previously [9] . par-6 ( M/Z ) mutants die late in embryogenesis [9] , so only embryos that died at this stage were analyzed for intercalation defects . The vab-1 tagged fosmid was obtained through the C . elegans TransgeneOme project [63] . The vab-1 fosmid was injected into wild-type animals at 30ng/μL along with 50ng/μL odr-1:: rfp coinjection marker to obtain transgenic strain IC1403 quEx531[vab-1::2xTY1::gfp::3xFLAG fosmid; odr-1::rfp] . The quEx531 array was crossed into vab-1 ( dx31 ) to create strain IC1487 vab-1 ( dx31 ) ; quEx531 and tested for functionality . To incorporate the epidermally-enriched cytoplasmic reporter , Plbp-1::gfp , into ZF1::cdc-42 worms , we microinjected pSL500[Plbp-1::gfp::unc-54 3’UTR] [64] , directly into the gonads of ZF1::cdc-42 worms , as described previously [65] . Genetic crosses were used to incorporate the CDC-42 biosensor ( gfp::wsp-1 ( GBD ) ) into the vab-1 ( dx31 ) background . To analyze efn-4 ( bx80 ) , an outcross was performed to remove the him-5 ( e1490 ) marker from the background . Six outcrosses were performed to remove background mutations from ephx-1 ( ok494 ) ( allele generated in [66] ) . Additionally , crosses were performed to incorporate vab-1 ( e2 ) into the CDC-42 dominant-negative and constitutively active backgrounds . The plasmid backbone ( pCM1 . 3 ) for the epidermal-specific , NMD-sensitive expression system was described previously [16] . The wild-type cdc-42 cDNA was amplified with primers DJR491 , 5’ TTTTTTTTggccggcctggcATGCAGACGATCAAGTGCGTCGTCG 3’ , and DJR512 , 5’ TTTTTTcccgggTTAGAGAATATTGCACTTCTTCTTC 3’ , digested with FseI and XmaI , and cloned into pCM1 . 3 digested with FseI/XmaI to make pCM7 . 4 . Restriction sites in the primers are underlined . The dominant-negative cdc-42 , cdc-42 ( T17N/DN ) ( pEWS26 ) , was made with pCM7 . 4 ( cdc-42 ( + ) ) as a template for PCR site-directed mutagenesis , using the following primers: Forward 5’ ATTGTCTCCTGATCAGCTATACC 3’ Reverse 5’ TTTTACCGACAGCTCCATCTC 3’ . Underlined bases denote those mutated relative to wild type . As described previously [16] , gonads of wild-type animals were microinjected with these constructs at 40 ng/μL and a coinjection marker [65] . Resulting lines in the wild-type background were screened for defects using smg-1 feeding RNAi [67] . Representative lines were crossed into the smg-1 ( cc546ts ) background , which can be detected by PCR using Forward: 5’ CAGTCGTGAGCTTTGGATGCGTGC 3’ and Reverse: 5’ TCGGGGATACGCAGATTCTTTCCC 3’ followed by digestion specifically of wild-type product using MslI . At least three lines were analyzed per construct . The resulting lines were maintained at 15°C and heat shocked at 25°C for 24 hours to induce transgene expression prior to filming . Crosses were performed at 15°C . Filming was performed at 25°C . For vab-1 ( e2 ) enhancement and suppression experiments , a semi-permissive temperature of 20°C was used for heat shock and filming . Four dimensional DIC movies were gathered on either a Nikon Optiphot-2 connected to a QImaging camera or Olympus BX50 connected to a Scion camera . ImageJ plugins ( available at http://worms . zoology . wisc . edu/research/4d/4d . html ) were used to compress and view movies . Embryos scored “medial delay” appeared to have blunt medial edges for greater than five time points ( corresponding to 15 minutes ) . Embryos were scored “ipsilateral comigration” if at least one pair of two adjacent cells intercalated together across the dorsal array . Some embryos had both defects . For this reason , comigration and medial delay phenotypes are pooled into “intercalation defects” . Though nuclear migration defects were observed in some embryos , they were not considered “intercalation defects” as nuclear migration failure does not prevent successful intercalation [68] . For strains with extrachromosomal arrays—cdc-42 ( DN ) and cdc-42 ( CA ) —only embryos that inherited the arrays were analyzed , as determined by inheritance of a co-injected nuclear fluorescent protein ( SUR-5::mCherry ) , assayed by epifluorescence . For statistical analysis of intercalation defects among genotypes , Fisher’s Exact Test—a form of chi-squared specialized for low n values that has been used previously to compare vab-1 alleles [36]—was used . Freeze-cracking was used to permeabilize embryos [69] for antibody staining . Staining was performed as described previously [70] . Embryos were incubated with primary antibodies in PBST+dry milk overnight at 4°C . Embryos were incubated with secondary antibodies in PBST+dry milk for two hours at room temperature . The following primary antibodies were used: 1:1000 rabbit-anti-GFP ( Invitrogen ) , 1:200 mouse-anti-AJM-1 ( MH27 ) . The following secondary antibodies were used: 1:50 anti-mouse IgG Texas Red ( Jackson ImmunoResearch ) and 1:50 anti-rabbit FITC ( Jackson ImmunoResearch ) . Images of stained embryos were acquired as described below . Spinning-disk , confocal images were acquired with a Z-slice spacing of 0 . 4 μm using Micromanager software [71 , 72] and a Nikon Eclipse E600 microscope connected to a Yokogawa CSU10 spinning disk scanhead and a Hamamatsu ORCA-ER charge-coupled device ( CCD ) camera . | As embryos develop , tissues must change shape to establish an animal’s form . One key form-shaping movement , cell intercalation , often occurs when a tissue elongates in a preferred direction . How cells in epithelial sheets can intercalate while maintaining tissue integrity is not well understood . Here we use the dorsal epidermis in embryos of the nematode worm , C . elegans , to study cell intercalation . As cells begin to intercalate , they form highly polarized tips that lead their migration . While some mechanisms that polarize intercalating cells have been established in other systems , our work identifies a new role for CDC-42—a highly conserved , highly regulated protein that controls the actin cytoskeleton . We previously established that a related protein , Rac , is involved in tip extension during dorsal intercalation . CDC-42 also contributes to this process in addition to helping orient the extending tip . CDC-42 appears to work in conjunction with two other known cell polarity proteins , PAR-3 and PAR-6 , and the cell surface receptor , VAB-1 . Our work identifies a novel pathway involving proteins conserved from worms to humans that regulates a ubiquitous process during animal development . | [
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... | 2016 | CDC-42 Orients Cell Migration during Epithelial Intercalation in the Caenorhabditis elegans Epidermis |
Rapid and selective ion transport is essential for the generation and regulation of electrical signaling pathways in living organisms . Here , we use molecular dynamics ( MD ) simulations with an applied membrane potential to investigate the ion flux of bacterial sodium channel NaVMs . 5 . 9 µs simulations with 500 mM NaCl suggest different mechanisms for inward and outward flux . The predicted inward conductance rate of ∼27±3 pS , agrees with experiment . The estimated outward conductance rate is 15±3 pS , which is considerably lower . Comparing inward and outward flux , the mean ion dwell time in the selectivity filter ( SF ) is prolonged from 13 . 5±0 . 6 ns to 20 . 1±1 . 1 ns . Analysis of the Na+ distribution revealed distinct patterns for influx and efflux events . In 32 . 0±5 . 9% of the simulation time , the E53 side chains adopted a flipped conformation during outward conduction , whereas this conformational change was rarely observed ( 2 . 7±0 . 5% ) during influx . Further , simulations with dihedral restraints revealed that influx is less affected by the E53 conformational flexibility . In contrast , during outward conduction , our simulations indicate that the flipped E53 conformation provides direct coordination for Na+ . The free energy profile ( potential of mean force calculations ) indicates that this conformational change lowers the putative barriers between sites SCEN and SHFS during outward conduction . We hypothesize that during an action potential , the increased Na+ outward transition propensities at depolarizing potentials might increase the probability of E53 conformational changes in the SF . Subsequently , this might be a first step towards initiating slow inactivation .
Na+ flux through voltage gated sodium channels ( NaV ) is crucial for initiating action potentials in the membranes of electrically excitable cells . They mediate a variety of biological functions such as muscle contraction , propagation of nerve impulses , release of hormones and many more [1] . As a consequence , mutations in NaV channels lead to a variety of channelopathies , such as congenital epilepsy , cardiac arrhythmias or chronic pain [2] , [3] . Recently , homotetrameric crystal structures of several bacterial NaV channels were successfully resolved [4]–[10] , providing a tremendous opportunity to investigate the structure and function of these channels on the atomistic level . They are composed of four membrane spanning subunits and contain six transmembrane ( TM ) helices per subunit . Helices S1 to S4 form the voltage sensing module . Helices S5 , P1 segments , the selectivity filter ( SF ) region , P2 segments and S6 helices , lining the inner pore cavity , form the pore module . The SF of most bacterial channels contains the amino acid sequence TLESW . The four glutamic acid side chains [11] form a high field strength binding site ( HFS ) [12] which is essential for ion selectivity . In eukaryotic sodium channels , this site consists of the amino acids motif DEKA . The molecular mechanisms underlying ion conduction and selectivity in NaV are beginning to emerge . Computational methods , particularly molecular dynamics ( MD ) simulations are extensively adopted to address these questions [13]–[23] . As reviewed recently [24] , sodium ions were illustrated to spontaneously traverse the SF into the cavity with energy barriers between ∼2–5 kcal/mol . Compared to potassium coordination in KV channels , sodium ions partially or fully preserve their first hydration shells [13] , [15]–[17] , [20] , [22] . A loosely coupled knock-on mechanism with an average ion occupancy around two in the SF was predicted during ion conduction [14] , [15] , [21] , [22] . The incoming ion repulses the present ion out of the SF . The wide radius ( ≥9 Å ) of the SF enables double occupancy of ions at the same level [20] , [21] . Further , Na+ vs . K+ [15] , [16] , [20] , [20]–[22] and Na+ vs . Ca2+ [17] , [20] discrimination studies were carried out . These studies revealed higher energy barriers in the SF for K+ and Ca2+ compared to Na+ . Subsequently , non-equilibrium simulations were performed to investigate conduction under applied membrane potentials and to study kinetics [21] , [22] . The estimated inward conductance rate successfully reproduced electrophysiology data [22] . A recent study by Chakrabarti et al . [23] , suggested that conformational changes at the EEEE motif ( corresponding to E177 in NaVAb ) might play an important role in ion conduction . However , this observation was not reported in other simulations , except for simulations using Ca2+ as a charge carrier [17] . In K+ channels , subtle structural changes in the SF , involving rotations around a highly conserved glycine residue result in different non-conductive conformations [25] , [26] . This regulation of ion flow by conformational changes of the selectivity filter is termed C-type inactivation . It is not clear to which extend structural changes at the EEEE locus in Nav channels are crucial for conductance and inactivation in Nav channels . To investigate these issues , we conducted MD simulations using the open conformation of the bacterial sodium channel homologue NaVMs ( Magnetococcus sp . ( strain MC-1 ) ) [7] pore domain focusing on the structural changes of the SF during inward and outward conduction .
The four-fold symmetrical structure of NaVMs ( pdb identifier: 4F4L ) was generated using chain A ( splayed outward by 25° rotation about its Ψ-bond at position T84 ) , which creates an open pore with a diameter of ∼14 Å [7] . As described by Ulmschneider et al . [22] , a harmonic restraint was exerted on the S5 and S6 TM helices to keep the gate in the open conformation throughout simulations . This structure was then embedded into a POPC lipid patch and duplicated in the Z direction ( pore axis ) . A constant charge imbalance of four elementary charges ( 4 e ) across each lipid bilayer between the central electrolyte bath and the two outer ones was maintained during simulation ( supplementary movie S1 ) [27] . Four times 500 ns double-patch MD simulations with 500 mM NaCl were performed , with the first 100 ns treated as equilibration . Figure 1 shows the cumulative ion conducting events from MD simulations with depolarized and hyperpolarized membrane potentials of ΔV: 565±126 mV ( Figure 2A ) . The estimated sodium current in the inward direction is γ = 27±3 pS . This value agrees with previously observed single channel conductance measurements ( γ∼33 pS ) [22] . Our double bilayer simulations enabled us to further estimate outward conduction , which amounts to γ = 15±3 pS . Interestingly , this process is distinguishably slower than inward ion flux ( P<0 . 01 , N = 4 ) . To explore the underlying differences between inward and outward ion permeation , we plotted the ion probability density map across the pore region from all four simulations . Several favorable ion-interacting sites ( SEX , SHFS , SCEN and SIN ) from periplasm to cytoplasm were assigned as proposed previously by Payandeh et al . [4] . The ion-interacting sites across the SF were determined by measuring the axial distance ( Z axis ) along certain atoms from −5 . 00 to 10 . 25 Å as shown in Figure 3 . Side chain oxygens of S54 from all four chains were taken as the origin ( Z = 0 . 0 Å ) of the SF . Additionally , in two previous studies , a site with an energy barrier ( ∼2 kcal/mol ) distinguishing between site SHFS and SCEN was identified [17] , [22] . In this study , we refer to this site as “SBAR” , indicating this barrier ( 2 . 75≤Z<4 . 75 Å ) . During influx ( Figure 4A and B ) , short-lived Na+ binding at site SEX was observed ( 2 . 6±0 . 5 ns ) in an asymmetrical manner . SHFS is the dominant site with the highest ion density . At this site , ions tended to be directly coordinated with side chain oxygens of E53 and S54 in an off-axis manner . Additionally , a less densely populated configuration was observed in the center of this site consistent with previous studies [21] , [24] . Moving inward from site SHFS , ions further translocated transiently via site SBAR ( 1 . 5±0 . 3 ns ) to site SCEN ( 3 . 4±0 . 3 ns ) . Subsequently , ions reciprocally traversed between sites SCEN and SIN . These results are in good agreement with previous simulation studies [14] , [15] , [17] , [21] , [22] . Our simulations revealed a distinct ion distribution pattern for efflux compared to influx as shown in Figure 4C and D . After entering into site SIN from the cytosol , ions mainly populated sites SCEN and SBAR with extended dwell times compared to inward conduction , ( SCEN: 11 . 9±1 . 1 ns vs . 3 . 4±0 . 3 ns; SBAR: 8 . 2±0 . 9 ns vs . 1 . 5±0 . 3 ns ) suggesting a putative barrier for efflux between sites SBAR and SHFS . Additionally , during efflux , Na+ ions tended to traverse in an on-axis manner through the filter . Conformational isomerization of the E53 side chains has been reported previously [17] , [23] . Generally , the glutamic acid side chain might adopt two main conformations ( Figure 5A and B ) : inward-facing ( χ2 angle ∼60° , flipped ) and outward-facing ( χ2 angle ∼290° , non-flipped ) . In our simulations , during influx , flipping events were observed only in 2 . 7±0 . 5% of the simulation time , thus the E53 side chain mainly adopted a non-flipped conformation . In contrast , during efflux 32 . 0±5 . 9% flipping events were observed ( P value = 0 . 015 , see Figure 5C ) . A more detailed investigation of this flipping events revealed that 80% of these changes occurred in only one of the four glutamic acid side chains ( “one-flip” ) ( Figure 5D ) . To investigate the influence of the presence of Na+ ions on E53 side chain dynamics , three repeated simulations without ions in the SF ( “no salt” ) were performed . Irrespective of the directionality of the applied potentials , the flip probability is less than 0 . 6% in all simulations ( Figure 5C ) . This indicates , that a depolarizing potential per se does not significantly influence the number of E53 flipping events . This suggests that the combination of local positive charge carried by outward Na+ flux in the SF especially at sites SHFS and SBAR and the outward attracting membrane potential might collectively induce the rotation of the χ2 angle from ∼290° to ∼60° . A detailed investigation of the ionic binding modes and their relations to free energy profiles enabled us to describe mechanisms regarding different conducting directionalities ( Figure 6 and 7 ) . During inward conduction , the largest barrier in the SF occurs between sites SHFS and SBAR which amounts to 2 . 1 kcal/mol ( Figure 6B ) . At site SHFS , the probe ions ( yellow ) mainly distributed in an off-axis manner , the first coupling Na+ ions ( blue ) may occupy site SCEN ( IN ) , and there existed a second binding site for coupling ions at site SEX ( Figure 6A , II ) . Subsequently , the probe ions distributed in the middle of channel axis when traversing the short lived site SBAR , with the other two coupling ions populating sites SEX and SIN ( CAV ) respectively ( Figure 6A , III ) . The probe ions then occupied site SCEN in both on-axis and off-axis manners , other coupling ions in the SF were distributed mainly at sites SIN and SEX . Only a few coupled ions occupied sites SHFS and SBAR ( Figure 6A , IV ) . In these ionic binding modes , under hyperpolarized potential , the coupling ions in the SF generally demonstrated a loosely coupled knock-on mechanism with only a few of them present in the adjacent binding sites to the probe ions ( Figure 6A , II–IV ) . This is in agreement with a study by [21] , [22] , where it was shown that during inward conduction the ions displayed a combination of mono-ionic and multi-ionic mechanism with an overall occupancy of 1 . 8 ions in the pore region . The flipping probability analysis indicates that the conformational changes of the E53 side chains play a minor role for ion inward conduction as shown in Figure 6A , II′–IV′ . Compared to inward permeation , the maximum energy barrier during outward conduction amounted to 2 . 3 kcal/mol between sites SBAR and SHFS . It is interesting that the free energy difference between sites SCEN and SHFS is 2 . 2 kcal/mol , which is close to the largest energy barrier ( Figure 7B ) . In addition , the ionic binding modes demonstrate a distinct conduction mechanism compared to Na+ influx . Traversing outward from the cavity , ions at site SIN were tightly coupled with ions at site SBAR ( Figure 7A , III and V ) corresponding to two energy wells in Figure 7B , III and V ) . When probe ions located at site SCEN , the coupling ions distributed in the upper part of the cavity ( Figure 7A , IV ) which corresponds to the energy well at site SCEN ( Figure 7B , IV ) . Generally , the translocation of probe ions from the cytoplasm to site SBAR is readily stepwise by a tight knock-off mechanism without significant energy barriers . At all three energy wells ( Figure 7B III–V ) the E53 side chains maintained non-flipped conformations . When probe ions faced the energy barrier at site SBAR , a delicate tightly-coupled “knock-off” conducting mechanism occurred . Initially , E53 started to flip and one of the carboxyl oxygens started to coordinate the probe ions ( Figure 7A , III′ and S5 , B ) . Compared to ions located in the close energy wells ( Figure 7A , III ) , the probe ions were meanwhile expulsed by the outward movements of approaching coupling ions at site SIN ( Figure 7A , III′ ) . If this knock-off mechanism was successful , the probe ions would then migrate to site SHFS , as a result , the coupling ions would move outward to site SCEN simultaneous ( Figure 7A , II′ and IV′ ) . At this time , two carboxyl oxygens of the flipped E53 side chain tended to coordinate with the probe ions and coupling ions respectively ( Figure 7A , II′ and IV′ and S5 , B ) . Afterwards , ions left site SHFS promptly ( t = 2 . 6 ns ) into the periplasm via site SEX . If the attempt to overcome the barrier failed , the aforementioned mechanism was easily reversed , the probe ions and coupling ions occupied the two stable energy wells at sites SBAR with the coupling ions at site SIN ( Figure 7A , III and V ) and site SCEN with the coupling ions in the cavity ( Figure 7A , IV ) again . That is the reason why ions stayed longer in sites SBAR and SCEN . One the one hand , larger Pi values ( flip inducing probability of number of probe ions , see methods for details ) values of sites SBAR , SCEN and SHFS indicated the flipped conformations of E53 were crucial ( Pi>90% ) in overcoming the dual energy barriers between SCEN , SBAR and SHFS . On the other hand , smaller Pt values ( flipping time probability for all probe ions , see methods for details ) values indicated that the flipping events were easily reversible . Because of these flipping events , the major ion distribution for outward conduction is limited to the center of the channel axis during translocation within the SF . To further explore the correlation between flux directionality and E53 conformation , we performed two sets of inward and outward conduction simulations ( four times 300 ns ) with dihedral restraints to maintain “non-flip” and “one-flip” configurations during sampling . The influx rate was independent of the E53 conformations as shown in Figure 8A . This observation disagrees with recent data from Chakrabarti et al . [23] on the NaVAb channel . The outward conduction with “one-flip” simulation displayed an increased efflux rate compared to simulations without dihedral restraints on E53 , where the flipping events would be reversible when conducting ions ( Figures S1 , S2 , S3 , S4 ) . Interestingly , if E53 was restrained to a “non-flip” configuration , sodium ions translocation slowed down ( Figure 8B ) . These results suggest a clear influence of filter dynamics on the efflux rate . Comparison of the free energy profiles from outward simulations of these three types of configurations revealed that the largest energy barrier of the “non-flip” simulations is increased from 2 . 3 kcal/mol ( non-restraint ) to 3 . 4 kcal/mol ( Figure 9A and C ) . The lowest energy well at site SBAR was also replaced by site SCEN . The energy profile of “one-flip” simulations indicated that the energy barrier between sites SCEN and SHFS was diminished , although the original energy barrier increased slightly by 0 . 3 kcal/mol ( Figure 9A and B ) . Therefore , the outward conduction would be more straightforward without reversible backward translocation compared to the simulations without the dihedral restraints from a kinetic point of view .
The mechanism of ion conduction and selectivity of bacterial voltage gated sodium channels is gradually emerging . The inverted tepee shape architecture of the SF lined with TLESW sequence enables sodium influx at the diffusion rate . The glutamic acid side chains are responsible for recruiting Na+ ions from the outer vestibule . Ions will then translocate via sites SHFS , SBAR , SCEN and SIN spontaneously to complete a conduction event [21]–[23] . Details of conduction are only partially understood . Large conformational changes of the glutamic acid side chains were described recently [23] . However , its role for conduction is still under discussion [24] . To gain further insights into these questions , we performed MD simulations to compare the different binding patterns and characterize the structural dynamics of glutamic acids during ion permeation . Double bilayer simulations with the open NaVMs structure enabled us to investigate influx and efflux separately . The calculated inward conductance rate is in good agreement with a previously reported experiment and computational data [22] . The estimated outward conductance rate obtained from MD simulations is predicted to be markedly lower than inward permeation ( 15±3 pS vs 27±3 pS , Figure 1 ) . Ion translocation between sites SBAR and SHFS is substantially prolonged ( 8 . 2±0 . 9 ns vs 1 . 5±0 . 3 ns , Figure 4 ) during Na+ efflux . From the energetic point of view , this would imply a potential barrier . This agrees with previous two-ion free energy calculation studies , revealing a higher energy barrier in this region for outward current compared to inward conduction ( ΔG: 4 . 6 kcal/mol vs 0 . 4 kcal/mol , Stock et al . [21]; ΔG: 3 . 5±0 . 5 kcal/mol and 2 . 4±0 . 3 kcal/mol , Furini and Domene [15] ) . In our studies , this barrier is also higher for outward conduction ( ΔG: 2 . 3 kcal/mol vs . 2 . 1 kcal/mol ) . In agreement with previous studies [21] , [22] during inward conduction , our simulations revealed that ion translocations in the SF generally involve a loosely coupled knock on mechanism with an average ion occupancy of 1 . 8 ( Figure 6 ) . A possible outward conduction mechanism was described by Stock et al . , [21] using a “fully activated-open” NaVAb channel structure [28] . They have found a third ion denoted k , directly coupling with the probe ions triggering outward conduction by a “nudging” collision effect . Similar results were obtained in our study , which shows that the coupling ions directly couple with the probe ions by a tight “knock-off” mechanism . Moreover , our simulations further elucidated that this “knock-off” mechanism is highly dependent on the conformational isomerization of the glutamic acid side chains in the SF . In other words , to overcome the energy barriers of outward conduction , at least one of the glutamic acid side chains has to be flipped to an inward facing conformation ( Figure 7 ) . A recent simulation study under ∼0 mV membrane potential with a closed gate NaVAb structure suggested that Na+ in- and outward movement involves variable configurations of multiple glutamic acid side chains giving rise to non-simple degenerated ion binding modes [23] . Remarkably , detailed investigations of the structural dynamics of E53 in our study revealed distinct isomerization patterns between forward and backward translocations respectively . When the ion moved into the SF from the outer vestibule under hyperpolarized membrane potential , the E53 remained mostly in the non-flipped conformation . In contrast , during outward conduction , the flipping occurrence increased significantly with a typical “one-flip” configuration ( Figure 5C and D ) when coordinating ions occupied the SF ( Figure 7 ) . In our simulations , the depolarized and hyperpolarized membrane potentials of approximately ΔV: 565 mV enabled the detailed investigation of ion permeation directionalities . This was not possible in previous simulations at ∼0 mV [23] . The conductive , open gate structure used in this study may also reduce the repulsive effect which could have been induced by ions present in the cavity in previous simulations with a closed gate [23] . In addition , different forcefields used in these two studies may also play a substantial role for these discrepancies . As reported by Cordomi et al [29] ) , compared to the combination of OPLS-AA protein with Berger lipids parameters , combining Amber99sb protein and Berger lipids gives more accurate free energies of solvation in water and water to cyclohexane transfer with respect to experimental data for glutamic acid side chains . This may explain the reduced flexibility of the glutamic acid side chain dynamics observed in our study . Further , the force field discrepancies might explain the contrasting results for the “no salt” simulations in these two studies . In the study by Chacrabarti et al [23] , the E side chains are more favorable to form flipped conformations even in the “no salt” conformation . This is in contrast to our simulations , where flipping events occurred rarely ( Figure 5C ) in the “no salt” simulations . While the inward flow exhibited indistinguishable flux rates irrespective of the E53 conformation ( Figure 8A ) , efflux displayed different rates depending on the configurations of the E53 side chain . The highest efflux rate was observed in our “one-flip” simulations and the lowest rate with all four glutamic acid side chains restrained to an outward-facing conformation ( Figure 8B ) . PMF calculations further confirmed that the energy barrier for outward conduction increased from 2 . 3 kcal/mol to 3 . 4 kcal/mol when the flipping conformation is prohibited ( Figure 9C ) . That indicates that this flipping conformation provides direct coordination for Na+ ions , which lowers the energy barrier and aids outward conduction . A simulation study published [30] after the submission of this manuscript , indicates that the SF dynamics , especially the side chain conformational changes of the EEEE locus in the SF , may lead to the conformational changes of the cavity lining helix on the µs timescale , subsequently initiating slow inactivation in NaV channels . We hypothesize that the E53 dynamics under depolarizing potentials uncovered in this study provide further insights into slow inactivation , especially the fast slow inactivation for prokaryotic species during action potentials . When the membrane potential depolarizes , the probability of Na+ outward transitions increases . As a result , the inactivation probability of the channel is increased probably due to a series of conformational changes starting from the EEEE locus in the SF . A general limitation of current force fields is that the simulated linear current–voltage regime can only be achieved at higher membrane potentials compared to experimental conditions , resulting from the large electrostatic barriers in the transmembrane region [31] , [32] . It should be noted that the computational electrophysiology simulations in this study were not done at constant membrane potentials ( 565±126 mV ) . This may result from the movement of the ions inside the channels and the fluctuation of the ions in the aqueous compartments ( Figure 2A and B ) . However , a single ion permeation event under physiological conditions will also exist as a non-equilibrium process . Thus , to which extent , current simulation methods resemble ion channels' electrophysiology needs to be further validated . In addition , inaccuracies in the interaction parameters ( from the forcefield ) between ions and surrounding atoms could also influence the conduction rates [33] . Thus , further structural and computational studies ( including optimized strategies for ion interaction with surrounding atoms and polarizable force fields with different lipid species ) will be required to further investigate the conformational changes of the SF under different electrochemical drives and the influence of different protonation states of the EEEE locus . In addition , experimental validation is essential to further uncover the structural determinants and the importance of the protonation states of the EEEE locus on ion conductance and selectivity . Summarizing , our simulations , using applied membrane potentials , reveal different conduction mechanisms for ion inward and outward transitions respectively . An inward facing conformation ( flip ) of one glutamic acid side chain in the SF would reduce the energy barrier for ion outward transition by providing direct coordination with interacting Na+ ions . This local change can provide insights into the slow inactivation of NaV channels as suggested by Boiteux et al [30] during an action potential , when the membrane potential is depolarized .
The coordinates of NaVMs ( PDB Entry: 4F4L; Resolution: 3 . 49 Å ) [7] with a conductive pore gate were used . The symmetric tetrameric structure consists of residues 8 to 94 . All charged residues were treated keeping their charge states at physiological pH 7 . 4 . In order to investigate ion conductance under two opposite membrane potentials , we used the computational electrophysiology method developed by Kutzner et al . [27] with a double-bilayer scheme . Each bilayer leaflet consists of 242 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine ( POPC ) lipids encompassing the protein structure , solvated with 500 mM NaCl solution . The system was then duplicated in the Z direction ( pore axis ) . The virtual-site model was adopted for hydrogen atoms [34] . MD simulations were performed with Gromacs version 4 . 5 . 5-dev [35] , [36] . Simulations were carried out with the AMBER99sb [37] all atom force field , POPC lipids parameter were taken from Berger et al . [29] , [38] with the TIP3P water model [39] . All covalent bonds were constrained using the LINCS algorithm [40] , allowing for an integration time step of 4 fs with virtual sites . A 10 Å cutoff was adopted for calculating short-range electrostatic interactions and the Particle Mesh Ewald [41] summation was used for calculating long-range electrostatic interactions . The corrected monovalent ion Lennard-Jones parameters for the amber forcefield [42] were implemented in this study and the vdW interactions were calculated with a cutoff of 10 Å . The Nose-Hoover thermostat [43] , [44] and the semi-isotropic Parrinello-Rahman barostat algorithm [45] was used to maintain simulation temperature and pressure constantly at 300 K and 1 bar , respectively . Prior to MD simulations , 3000 conjugate gradient energy-minimization steps were performed , followed by 5 ns equilibration in order to fully solvate mobile water and lipids around the restrained protein with a force constant of 1000 kJ/mol/nm2 on all heavy atoms . Hereafter , an equal number of Na+ ions and a net difference of 4 e of Cl− across each lipid bilayer between the central electrolyte bath and the two outer ones were sustained during the simulation by a swapping mechanism [27] . In this scheme , a new form of Poisson equation [46] was adopted to derive the potential profile as a function of system length ( z ) . The well-defined transmembrane voltage across each lipid patch was directly assessed by twice integration of this sustained charge density differences between the central electrolyte bath and the two outer ones [27] . In our simulations , depolarized and hyperpolarized membrane potentials were calculated as ΔV = 565±126 mV ( Figure 2 ) . Harmonic restraints ( 1 kcal/mol/Å2 ) were exerted on the α-carbon atoms of the TM helices ( S5 and S6 ) throughout the simulations to maintain the open configuration in the absence of the voltage-sensing domain as suggested by Ulmschneider et al . [22] . Four times 500 ns MD simulations were performed; the first 100 ns were treated as equilibration . Simulation trajectories were saved every 100 ps; as a result , 4000 snapshots ( analyzing windows ) were recorded for analyzing data . Three repeated 500 ns simulations ( 400 ns were adopted for analysis ) with only ions neutralizing the system and no ions in the SF ( “no salt” ) were performed as control to investigate the influence of the membrane potential on the E53 side chain dynamics . In this setup , only ions used to generate the charge imbalance and neutralize the net charge were kept . Further , four repeated 300 ns simulation ( 200 ns were adopted for analysis ) for “non-flip” and “one-flip” configurations respectively were carried out , where the dihedral restraints were applied on the χ2 dihedral of E53 for all four subunits with a force constant of 500 kcal/mol/rad2 . This allows dynamic ranges of 56±10° for “one-flip” configuration and 288±10° for “non-flip” configuration of the χ2 angle . The total number of ions ( i ) which completed their conduction in the SF were analyzed ( i = 158 , from four inward simulations; and i = 79 from four outward simulations ) . All snapshots of the probe ions ( yellow ) and coupling ions in the SF ( blue ) were rendered for five different interaction sites ( SEX , SHFS , SBAR , SCEN and SIN ) . For sites SHFS , SBAR and SCEN , the side chain isomerization states were separated into flipped and non-flipped categories and analyzed . For flipped ones , all four protein chains and the relative ion positions were aligned to chain A to achieve a better representation of the ionic binding patterns . Two probability parameters Pi and Pt were calculated to characterize the influence of E53 dynamics on ion conduction for these three sites . Pi = ( Fi/i ) *100% , where Fi denotes the number of ions which generated at least one E53 flipping event during their permeation through each site . Pt = Ft/Tt*100% , where Ft denotes the number of snapshots where E53 flipped when the probe ions traversed each site and Tt denotes the total number of snapshots when the probe ions traversed each site . The 1-D potential of mean force profile of the ions under membrane potentials were calculated by taking the logarithm of the Na+ probability distribution along the channel axis ( z ) in the SF region , according to G ( z ) = −kBT ln [p ( Ri ) ] , where kB is the Boltzmann constant , T is the temperature , and p ( Ri ) is the probability distribution of the probe ions . 100 bins were used to achieve a bin width of 0 . 15 Å depicting the details of the profile . Error bars are S . E . M . from four different simulations . | Voltage gated sodium channels are essential components of living cell membranes . They regulate the cell potential by facilitating permeation of ions across the membrane . In the past decades , studies revealed that the bacterial selectivity filter ( SF ) exhibits a constricted architecture lined with electronegative carboxyl oxygens of four glutamic acid side chains ( EEEE motif ) , which repulse anions but attract Na+ ions . Crystal structures enable the investigation of structural dynamics with computational methods . Ion selectivity and conduction mechanisms between Na+ , K+ and Ca2+ are progressively elucidated by molecular dynamics simulations and free energy calculations . The structural dynamics of the protein , especially the flexibility of SF and its fundamental role in kinetics underpinning ion selectivity , conduction and channel gating are less well understood . To shed light on this question , we use computational simulations to simulate ion conduction with membrane potentials . Our results suggest different dynamical behaviors of the EEEE locus and distinct ion distribution patterns in the SF with respect to permeating directionalities . These findings indicate a novel mechanism in differentiating reciprocal transitions of ion flow , preventing large sodium efflux during action potential initiation and may further suggest that increased flipping propensities at depolarizing potentials , might initially trigger channel slow inactivation . | [
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] | 2014 | Different Inward and Outward Conduction Mechanisms in NaVMs Suggested by Molecular Dynamics Simulations |
Group B coxsackieviruses ( CVB ) are associated with viral-induced heart disease and are among the leading causes of aseptic meningitis worldwide . Here we show that CVB entry into polarized brain microvasculature and aortic endothelial cells triggers a depletion of intracellular calcium stores initiated through viral attachment to the apical attachment factor decay-accelerating factor . Calcium release was dependent upon a signaling cascade that required the activity of the Src family of tyrosine kinases , phospholipase C , and the inositol 1 , 4 , 5-trisphosphate receptor isoform 3 . CVB-mediated calcium release was required for the activation of calpain-2 , a calcium-dependent cysteine protease , which controlled the vesicular trafficking of internalized CVB particles . These data point to a specific role for calcium signaling in CVB entry into polarized endothelial monolayers and highlight the unique signaling mechanisms used by these viruses to cross endothelial barriers .
Coxsackievirus B ( CVB ) , a member of the enterovirus family , is associated with a number of diverse syndromes including aseptic meningitis , myocarditis , febrile illness , and diabetes [1] . CVBs are transmitted via the fecal-oral route and encounter the polarized epithelium lining the gastrointestinal tract early in infection . Following dissemination , CVBs likely access secondary sites of infection via transmission through an endothelial monolayer such as that of the blood-brain barrier ( BBB ) and/or venous endothelium . Thus , although both polarized epithelial and endothelial cells function to prevent pathogen access to the interstitium , CVBs have developed strategies to subvert these barriers in order to promote their entry and/or dissemination . We have shown that CVB entry into polarized intestinal epithelial cells requires the activation of specific intracellular signaling molecules to promote viral endocytosis [2] , [3] . However , it remains unclear if CVB also requires the initiation of host cell signaling to facilitate its entry ( a process involving both endocytosis and vesicular trafficking ) into the endothelium and whether the same signals are required between the epithelium and endothelium . The binding of viruses to receptors on host cells often initiates elaborate signaling pathways aimed at facilitating viral uptake . The coxsackievirus and adenovirus receptor ( CAR ) mediates attachment by all six CVB serotypes [4] , but is inaccessible to viruses on the luminal surface due to its localization within intercellular tight junctions [5] . For this reason , polarized cells are often resistant to infection by a number of CVB isolates [5] . Decay accelerating factor ( DAF ) is a glycosylphosphatidylinositol ( GPI ) -anchored membrane protein shown to bind several isolates of CVB ( −1 , −3 , and −5 ) [4] , [6] , [7] , [8] , [9] and promote their infection of polarized cells [5] . As DAF is a GPI-anchored protein , it is localized to the apical surface of polarized cells and is accessible to virus in the lumen . In addition to providing a convenient site for virus attachment , the GPI anchor of DAF also facilitates its association with cholesterol-enriched lipid microdomains [10] . Lipid rafts are enriched in a number of signaling molecules including receptor tyrosine kinases , the Src family of nonreceptor tyrosine kinases , small G proteins , and adenylyl cyclases ( ACs ) [11] . Although DAF is anchored to the outer leaflet of the plasma membrane via a GPI anchor ( and thus does not contain an intracellular domain ) , DAF and other GPI-anchored membrane proteins can be induced to form larger raft patches upon lateral crosslinking ( most commonly with antibodies ) [12] . We have shown previously that CVB-induced DAF clustering is essential for downstream signaling events required to facilitate virus entry into polarized intestinal epithelial cells [2] . Two tyrosine kinases ( Abl and Fyn ) are activated by DAF clustering and both are required for CVB entry into polarized epithelial cells [2] . Although clustering of GPI-anchored proteins is most commonly associated with the initiation of tyrosine kinase-based signaling cascades , the release of intracellular calcium ( Cai2+ ) following lateral crosslinking of these receptors has also been documented [13] . Antibody-mediated crosslinking of DAF has been linked to the release of Cai2+ [14] , [15] as a means to initiate monocyte activation [16] . Calcium is one of the most prominent second messengers in the cell . It is involved in many signaling cascades that have diverse outcomes depending on the spatiotemporal aspects of the calcium release . For this reason , intracellular calcium ( Cai2+ ) homeostasis is under tight regulation by the cell . The free cytoplasmic calcium concentration is maintained around 50–100 nM whereas intracellular stores such as the ER ( endoplasmic reticulum ) maintain much higher free concentrations ( µM amounts ) . Intracellular calcium levels rise upon a stimulus ( such as ligand-receptor interaction on the cell surface ) and often converge on phospholipase C ( PLC ) , an enzyme that mediates the hydrolysis of phosphatidylinositol-4 , 5-bisphophate ( PIP2 ) into diacylglycerol ( DAG ) and inositol 1 , 4 , 5-trisphosphate ( IP3 ) . IP3 diffuses through the cytoplasm and binds to IP3 receptors ( IP3R ) localized on the ER membrane . Cytoplasmic calcium levels are brought back down to basal concentrations by multiple calcium channels such as the plasma membrane Ca2+-ATPase ( PMCA ) , as well as the sarco-endoplasmic reticulum ATPase ( SERCA ) pump . As Cai2+ levels regulate a variety of cellular processes , it is not surprising that many viral pathogens have evolved strategies to exploit Ca2+-mediated signaling events to promote mechanisms required to facilitate viral entry , replication , and/or spread [17] . Our previous studies have highlighted the intracellular signals that regulate CVB entry into polarized epithelial cells [2] , [18] . In the present study , we have defined the role of Cai2+ in facilitating CVB entry into human brain microvascular endothelia cells ( HBMEC ) , an in vitro model of the blood-brain barrier . These studies have revealed that CVB-induced clustering of DAF induces an immediate depletion of Cai2+ stores . CVB-induced Cai2+ mobilization is regulated by several host cell factors including the Src family of tyrosine kinases , PLC , and is mediated specifically by the IP3R isoform 3 . We also show that the calpain family of Ca2+-activated proteases plays a role in mediating the trafficking of CVB-containing vesicles within the cell . Interestingly , we also find that Cai2+ release is involved in mediating CVB entry into primary human aortic endothelial cells , but is not required for CVB entry into polarized epithelial cells , suggesting that the intracellular signaling molecules hijacked by CVB to facilitate entry are distinct between the endothelium and epithelium .
Nonenveloped viruses gain entry into host cells by endocytic mechanisms that may include clathrin- or caveolar-mediated endocytosis , and macropinocytosis [19] . Some of these pathways are dependent upon the activity of dynamin , a GTPase required for vesicle fission . In previous studies , we found that CVB entry into polarized intestinal epithelial Caco-2 cells was independent of dynamin II [2] and occurred by a pathway that incorporates aspects of both caveolar-mediated endocytosis and macropinocytosis [3] . Because of the unique aspects of this pathway , we determined whether CVB entry into HBMEC occurrs via a similar mechanism . [Unless otherwise stated , all experiments were performed with CVB3-RD , a DAF-binding isolate of CVB] . First , we used three independent methods to alter dynamin II activity – ( 1 ) dynasore , a cell-permeable inhibitor of dynamin [20] , ( 2 ) a dominant-negative mutant of dynamin II ( dynamin II K44A ) [21] , and ( 3 ) siRNA-mediated depletion of dynamin II ( Supplemental Figure S5 ) –and determined the effects of this alteration on CVB infection of HBMEC and Caco-2 cells . Under all of these conditions , CVB infection of Caco-2 cells was unaffected ( Figure 1A ) while all methods significantly reduced infection of HBMEC by CVB ( Figure 1A ) . Moreover , using a fluorescence-based assay for viral internalization that discriminates between virus on the cell surface and that which has internalized [18] , we confirmed that dynasore specifically inhibited CVB entry into HBMEC ( Figure 1B , top ) while having no effect on its entry into Caco-2 cells ( Figure 1B , bottom ) . Interestingly , CVB infection of primary human aortic endothelial cells ( HAEC ) was also inhibited by dynasore treatment ( Figure 1A ) , suggesting that the route of entry of CVB into the aortic endothelium may be similar to that in the CNS microvasculature . We next determined the effect of dominant-negative mutants of various endocytic pathways for their effects on CVB infection of HBMEC . These studies revealed that CVB infection of HBMEC was significantly impaired when mutants of the caveolar pathway were expressed ( caveolin-1 and -3 ) , consistent with what we observed previously in Caco-2 cells [2] ( Figure 1C ) . Furthermore , immunofluorescence microscopy revealed colocalization of cytoplasmic CVB-containing vesicles with caveolin-1 and cholera toxin B ( a marker of the caveolar pathway ) ( Figure 1D ) . In contrast , infection was unaffected by expression of a mutant of the clathrin endocytic pathway ( Eps15 ) in HBMEC ( Figure 1C ) . These data indicate that the mechanism of CVB entry into the endothelium is clathrin-independent , and likely occurs via a dynamin- and caveolar-dependent pathway . In contrast , entry into the epithelium occurs via a clathrin-and dynamin-independent , but caveolin-dependent pathway [2] . Taken together , these findings point to a divergent mechanism of endocytosis between the endothelium and epithelium . We have shown that CVB entry into polarized epithelial Caco-2 cells requires the activation of intracellular signaling molecules to facilitate viral endocytosis [2] and are initiated by viral attachment to DAF on the apical cell surface . Because our current findings indicate that CVB entry occurs via disparate mechanisms between HBMEC and Caco-2 cells ( Figure 1A , 1B ) , we investigated the host cell signaling molecules involved in facilitating CVB entry into HBMEC and whether these molecules were unique between these cell types . As DAF signaling has been associated with the release of Cai2+[14] , [15] , we determined whether CVB infection of HBMEC was sensitive to manipulation of Cai2+ stores . We found that in cells pretreated with Bapta-AM ( a chelator of intracellular calcium ) , infection was significantly reduced compared to no inhibitor controls ( Figure 2A ) . Interestingly , Bapta-AM lost its inhibitory effect when added at a post-entry time point [2 hrs post infection ( p . i . ) , Figure 2A] , indicating that Ca2+ may be required for events occurring at or very close to the time of virus entry . Similar results were obtained in primary human aortic endothelial cells ( HAEC ) ( Figure 2A ) . We found that this effect was specific for CVB as Bapta-AM had no effect on vesicular stomatitis virus ( VSV ) infection of HBMEC ( Supplemental Figure S1A ) . In addition , Bapta-AM had no effect on CVB infection of intestinal epithelial Caco-2 cells either pre- or post-treatment ( Figure 2A ) , indicating that the role of Ca2+ in early events associated with CVB is specific to polarized endothelia . Because we observed that Ca2+ chelation inhibited CVB infection ( Figure 2A ) of HBMEC , we next determined the kinetics of CVB-mediated Cai2+ release in real-time . To do this , we used live-cell imaging of HBMEC loaded with the ratiometric Cai2+ indicator Fura-2 AM . This allowed for the tracking of individual cells to pinpoint the precise timeframe during which intracellular Ca2+ store depletion occurred . Images were captured every 5 sec at both excitation wavelengths for Fura ( 340/380 nm , emission 510 ) . Following a brief period to establish baseline levels of Cai2+ ( t = 50 sec ) , CVB ( MOI = 50 ) was added directly to monolayers . To prevent Ca2+ influx due to alterations in membrane permeability , monolayers were bathed in Ca2+-free HEPES-buffered saline . The addition of CVB resulted in an almost immediate release ( <20sec ) of Cai2+ [shown in still images spanning 1 min from virus addition ( Figure 2B , Supplemental Movie S1 ) ] . As quickly as 15 sec following the addition of CVB , the majority of cells have been almost completely depleted of Cai2+ [shown in the graphical representation ( Figure 2C ) ] . Of particular significance , this depletion occurred at a time point prior to viral uncoating ( Supplemental Figure S1D ) and the production of viral proteins ( Supplemental Figure S1B , S1C ) , which have previously been shown to induce Cai2+ release at late stages of virus replication [22] . We next tested whether primary human aortic endothelial cells ( HAEC ) were also depleted of Cai2+ in response to CVB exposure . In some cases , microvasculature and arterial endothelial cells differ in the degree of tight junction function and in their responsiveness to calcium ionophores [23] . Furthermore , myocarditis and dilated cardiomyopathy are often associated with CVB infection and CVB may infect aortic endothelial cells during cardiac infections [24] , [25] . Interestingly , we observed the depletion of Cai2+ in response to CVB exposure of HAEC similar to that observed in HBMEC ( Figure 2B , C ) . However , CVB-induced depletion of Cai2+ proceeded at a more gradual pace ( <120 sec ) in HAEC compared to HBMEC ( Supplemental Movie S2 ) . Consistent with our findings that Bapta-AM had no effect on CVB infection Caco-2 cells ( Figure 1A ) , we found that CVB entry had no effect on Cai2+ levels in these cells ( Figure 2B , 2C , Supplemental Movie S3 ) . These data indicate that the role of Cai2+ in mediating CVB entry is specific to the endothelium and suggest that there may be unique signaling molecules activated between the endothelium and epithelium . Although DAF is known to mediate CVB attachment to and infection of polarized epithelial cells [5] , little is known regarding its role in mediating infection of the polarized endothelium . Consistent with what has been observed in polarized intestinal monolayers [5] , we found that a non-DAF binding CVB isolate ( CVB-Nancy ) was incapable of infecting HBMEC from the apical surface ( Figure 3A ) and DAF siRNA ( Supplemental Figure S5 ) inhibited binding and infection by CVB ( Supplemental Figure S2A ) . This would indicate that DAF plays an essential role in facilitating CVB infection of the endothelium [likely because CAR is also sequestered in the tight junctions of HBMEC ( Supplemental Figure S2B ) and is not exposed to virus approaching from the apical domain] . To determine whether CVB-DAF interactions are involved in Cai2+ store depletion in HBMEC , we used a non-DAF binding isolate of CVB ( CVB4 ) and determined its effects on Cai2+ release . We found that CVB4 did not induce any noticeable Cai2+ release ( Figure 3B , C ) as CVB4-exposed cells retained their Cai2+levels throughout the entire 10 min time course ( Supplemental Movie S4 ) . To exclude any CAR-dependent signaling events upstream of CVB-induced Cai2+ release , we determined the extent of Cai2+ release in HBMEC transfected with CAR siRNA and exposed to DAF-binding CVB . We found that CAR siRNA ( which led to a >90% depletion of CAR expression , Supplemental Figure S5 ) had no effect on CVB-induced Cai2+ release in HBMEC ( Figure 3B , 3C , and Movie S5 ) . These data support a role for DAF , but not CAR , in the induction of Cai2+ release in response to CVB entry . Cai2+ mobilization is often initiated by ligand interaction with cell surface receptors which can lead to the activation of intracellular signaling molecules such as tyrosine kinases , and/or PLCs ( reviewed in [26] ) . These molecules can either act directly to increase IP3 levels ( i . e . PLCs ) or increase IP3R sensitivity to IP3 binding in the absence of the generation of new IP3 ( i . e . tyrosine kinases ) [27] , [28] , [29] . To determine whether CVB-induced Cai2+ release required the activation of PLC ( and the subsequent IP3R-mediated release of Cai2+ ) , we tested the effects of 2-APB ( an inhibitor of IP3R channels ) and U73122 ( a specific PLC inhibitor ) for their effects on CVB infection in HBMEC . We found that pre-treatment of cells with both 2-APB and U73122 led to a significant reduction in CVB infection ( Figure 4A ) . In contrast , exposure of cells to both inhibitors at a post-entry time point ( 2 hrs p . i . ) had no effect . We also found that U73122 inhibited Cai2+ release in response to CVB entry ( Figure 4B ) . Consistent with our findings that CVB entry into Caco-2 does not require Cai2+ ( Figure 1B ) , we found that 2-APB and U73122 had no effect on CVB infection in Caco-2 cells at either pre- or post-entry time points ( Figure 4A ) . Although we observed an inhibition of Cai2+release in cells treated with U73122 , this inhibitor targets a wide range of PLC isoforms . For this reason , we determined whether PLCγ1 ( PLCG1 ) , a known mediator of Cai2+ release , was specifically involved in CVB-induced Cai2+ release using siRNA-mediated knockdown . We found that depletion of PLCγ1 significantly inhibited CVB-mediated release of Cai2+ ( Figure 4 , Supplemental Figure S5 , Movie S6 ) . The majority of Cai2+ oscillations within cells occur via bursts , sparks , or waves produced by the activation of IP3R . Three IP3R have been identified in mammalian cells that differ in their affinity for IP3 , but whose specific functions remain uncertain ( reviewed in[26] ) . The expression pattern of the different IP3R subtypes between tissues is likely responsible for the variety of patterns associated with Cai2+ release between cell types ( and may ultimately determine the physiological outcomes of this release ) . Endothelial cells generally express all three IP3R isoforms to some degree [30]–[31] . We employed the use of siRNAs to specifically knockdown IP3R isoforms expressed in HBMEC– IP3R-1 , IP3R-2 , and IP3R-3 ( Supplemental Figure S5 ) . Whereas knockdown of IP3R-1 and IP3R-2 had modest effects on CVB-induced Cai2+ release ( Supplemental Figure S3 and Movie S7 and S8 ) , knockdown of IP3R-3 resulted in a complete inhibition of Cai2+ release upon exposure to CVB ( Figure 4C , supplemental Movie S9 ) . These data indicate that while IP3R-1 and IP3R-2 may play minor roles in mediating CVB-induced Cai2+ release , IP3R-3 is likely the critical IP3R isoform involved . We have shown that CVB exploits DAF-mediated tyrosine signaling pathways to surmount the epithelial barrier in order to gain entry into polarized epithelial cells [2] . Because we observed that CVB-induced Cai2+ release in HBMEC required DAF-binding ( Figure 3B ) , we tested whether tyrosine kinases might play a role upstream of Cai2+ release in HBMEC . We found that tyrosine kinase activity was required for CVB infection of HBMEC as treatment of cells with the non-specific tyrosine kinase inhibitor genistein reduced both CVB infection ( Figure 5A ) and entry ( Figure 5B ) . Because genistein targets a broad range of tyrosine kinases , we determined the effects of PP2 ( a specific Src tyrosine kinase inhibitor ) on CVB entry and infection . We found that PP2 significantly reduced CVB infection ( Figure 5A ) and entry ( Figure 5A ) , indicating that Src family kinase activity is required for CVB entry into HBMEC ( similar to our previous findings in Caco-2 cells ) . Because tyrosine kinases , including members of the Src kinase family [28] , [32] , have been shown to function upstream of Cai2+ release , we next determined whether tyrosine kinases and/or Src kinase activity was required to facilitate CVB-mediated Cai2+ release . To do this , we pre-treated HBMEC with either genistein or PP2 and measured CVB-induced Cai2+ release in real-time . We found that there was a profound inhibition of CVB-induced Cai2+ release by both genistein and PP2 compared to controls ( Figure 5C and D ) . We also found that genistein inhibited CVB-induced Cai2+ release in HAEC , indicating a similar mechanism of release may exist between the microvasculature and arterial endothelium ( Supplemental Figure S4 ) . These data point to a role for Src family tyrosine kinase signaling in CVB-induced Cai2+ release . We recently performed an RNAi screen for host factors involved in CVB infection of HBMEC and identified calpain-2 , a Cai2+-dependent cysteine protease , as being required for CVB infection of HBMEC ( CB Coyne and S Cherry , unpublished data ) . Members of the calpain family are activated by release of Cai2+ and can be categorized into two subfamilies–µ-calpains ( eg . , calpain-1 ) are activated by micromolar concentrations of Cai2+; and m-calpains ( eg . , calpain-2 ) are activated by millimolar concentrations of Cai2+ [reviewed in [33]] . We found that whereas siRNA-mediated knockdown of calpain-2 decreased CVB infection significantly , downregulation of calpain-1 had little effect ( Figure 6A , bottom , and Supplemental Figure S5 ) . In accordance with our findings that Cai2+ plays no role in CVB entry into Caco-2 cells ( Figure 2A , 2B , 4A ) , we found that reduction of calpain-2 expression had no effect on CVB infection of Caco-2 cells ( Figure 6A , bottom ) . To confirm the role of calpain-2 in mediating CVB infection of HBMEC , we treated cells with three known inhibitors of calpains–ALLN , calpeptin , and calpain inhibitor III—and found that they significantly reduced infection by CVB in HBMEC ( Figure 6B ) . Likewise , HAEC pre-treated with calpain inhibitor III also had a significant reduction in infection ( Supplemental Figure 6B ) . In contrast , calpain activity was not required for CVB infection in Caco-2 cells ( Supplemental Figure S6A ) . Although inhibition of calpain activity exhibited potent reduction in CVB infection when cells were pretreated with inhibitor , we found that this effect did not occur when calpain inhibitors were added at post-entry time points ( 2 hr p . i . ) ( Figure 6B and Supplemental Figure S6B ) . These findings suggest that calpain activity is required early in the life cycle of CVB ( possibly at or near the time of viral entry ) . Consistent with this , we found that calpains were activated by 30 min p . i . , ( Figure 6C ) , likely coincident with CVB entry and following the release of Cai2+ induced by CVB binding . To further define the mechanism by which calpain-2 facilitates CVB infection we used a fluorescence-based assay for viral internalization . Using this assay , we found that while calpain activity was not required for viral endocytosis into the cytoplasm , it was required for proper vesicular trafficking as we observed the appearance of large CVB-containing intracellular vesicles >500 nm in diameter ( much larger than the average size of endosomes ) when calpain activity was inhibited in HBMEC ( Figure 6D and Supplemental Figure S6C , D ) . These large structures remained in the cytoplasm for extended periods of time ( >5 hours , not shown ) whereas in untreated cells these vesicles traveled to a perinuclear compartment by 60–120 min ( where the release of viral RNA likely occurs ) . In contrast , inhibition of calpain activity had no effect on CVB entry or trafficking within Caco-2 cells ( Figure 6D ) . We found that these long-lived cytoplasmic virus-containing vesicles were heavily associated with calpain-2 ( Figure 6E ) and cholera toxin B ( Figure 6F ) . However , we did not observe any significant colocalization between internalized CVB particles and calpain-2 in control cells ( Figure 6E ) . Although calpain-2 has been shown to regulate endosomal trafficking [34] , [35] , it remains unclear if calpain associates with endosomal membranes for any significant length of time . Consistent with a potential transient interaction between calpain-2 and endosomal membrane protein components , we also did not observe any significant colocalization between calpain-2 and a component of early endosomes ( Rab5 GTPase ) ( Supplemental Figure S6F ) . Taken together , these data suggest that Cai2+ release results in the specific activation of calpain-2 that in turn facilitates the trafficking of virus-containing vesicles within the cytoplasm to a perinuclear location for uncoating and RNA replication to ensue . Furthermore , the role of calpain-2 is specific to the endothelium as inhibition of calpain activity had no effect on CVB infection of intestinal epithelial cells . Because both PLCγ1 and IP3R-3 appeared to play significant roles in mediating Cai2+ signaling in response to CVB entry , we next determined whether they were also involved in facilitating CVB entry and/or trafficking . Similar to our findings when calpain activity was inhibited , we found that knockdown of PLCγ1 and IP3R-3 also altered the ability of internalized CVB particles to properly traffic within the cytoplasm and led to the accumulation of long-lived CVB-containing vesicles within the cytoplasm ( Figure 6G and Supplemental Figure 6E ) . These data suggest that the PLCγ1- and IP3R-3-dependent Cai2+ release induced by CVB entry is required for the activation of calpain-2 to facilitate vesicular trafficking of internalized viral particles .
Many viral pathogens have developed strategies to subvert the barriers presented by epithelia and endothelia in order to infect the host or spread to secondary sites of infection . The CNS and heart are common sites of CVB secondary infection . In order to infect these tissues , circulating CVB would require passage through or infection of the endothelium in order to traffic from the circulatory system into the underlying tissue ( through a process that likely requires apical DAF engagement ) . Our previous studies have established that CVB enters polarized cells by endocytic mechanisms that require activation of specific intracellular signaling molecules including the tyrosine kinases Fyn and Abl [2] , [18] . Here we show how CVB specifically exploits Cai2+-mediated signaling events in order to facilitate its entry into polarized endothelial cells . We provide evidence that CVB-induced Cai2+ release is triggered by virus binding to DAF and involves the activity of the Src family of tyrosine kinases , PLCγ1 , and the expression of a specific IP3R isoform , IP3R-3 . The release of Cai2+ induced by CVB is required for the subsequent activation of calpain-2 , which facilitates CVB vesicular trafficking . We also show that the Cai2+-dependence of CVB entry is specific to the endothelium and is not involved in mediating CVB entry into the epithelium . The necessity for Cai2+ release in endothelia , but not epithelia , demonstrates that the entry of CVB ( and likely other viral pathogens ) is mediated by cell-type-specific intracellular signals that may differ between polarized cell types . Viral receptors often facilitate host cell signaling events required for virus entry . Our results show that CVB-induced Cai2+ release is triggered by CVB-DAF interactions and occurs even in the absence of CAR expression ( Figure 3B ) . This is not surprising given that DAF is located within lipid raft domains [2] and is in close proximity to signaling molecules such as receptor tyrosine kinases and PLCs [11] . CD59 , another GPI-anchored receptor , leads to the recruitment of tyrosine kinases , the heterotrimeric G protein Gαi2 , and PLCγ1 upon antibody-induced lateral crosslinking . This crosslinking leads to the activation of PLCγ1 and a subsequent burst in Cai2+ [13] . It is therefore likely that CVB exploits Cai2+-associated signaling events associated with DAF crosslinking in order to facilitate its entry and intracellular trafficking . Several viruses have been shown to manipulate host cell Cai2+ homeostasis in order to promote their entry and/or replication [22] , [27] , [36] , [37] , [38] , [39] , [40] , [41] . Herpes simplex virus ( HSV ) has been shown to utilize a transient increase in intracellular Cai2+ concentration triggered by receptor binding to promote its internalization [42] . Similar to our findings with CVB , HSV-induced Cai2+ release is mediated by the activation of PLC and subsequent activation of IP3R [43] . In addition , depletion of ER-derived Ca2+ stores inhibits infection of SV40 , suggesting that there may be modulation of Cai2+ homeostasis induced during its entry [44] . It is thus becoming clear that viruses from several unrelated families have developed strategies to target Cai2+ signaling in order to facilitate their entry . Tyrosine kinase signaling often functions upstream of Cai2+ release to activate PLCγ and/or directly phosphorylate IP3Rs . The role for Src family tyrosine kinases in the release of Cai2+ is clear–mice deficient in Fyn kinase are devoid of certain types of Cai2+ release [45] , c-Src-specific antibodies inhibit PLCγ-dependent Cai2+ release [46] , [47] , and Fyn directly phosphorylates IP3R to permit extended Cai2+ release upon IP3 binding [32] [28] . We found that Src kinases were critical for CVB-induced Cai2+ release . Interestingly , our previous work has shown that Src kinases , specifically Fyn , mediate the entry of CVB into intestinal Caco-2 cells [2] . However , here we show that Cai2+ plays no role in CVB entry into Caco-2 cells , indicating that although Src kinases facilitate CVB entry into both polarized epithelia and endothelia , they target divergent downstream targets to do so . We also show that inhibition of Src kinase activity prevents CVB entry into HBMEC ( Figure 5B ) . However , the point in the entry process that was inhibited by Src kinase inhibition ( e . g . cell surface ) was unique from what we observed by inhibiting calpains or PLCγ and IP3R-3 expression ( e . g . intracellular viral trafficking ) . As Src kinases function in many aspects of endocytosis [48]–[49] , these data indicate that they likely serve multiple functions in regulating CVB entry into HBMEC beyond that of Cai2+ release . Taken together , our findings indicate that Src kinases are pivotal regulators of CVB-induced signal propagation in the endothelium and epithelium , but likely target unique downstream effector molecules to facilitate CVB entry . Src kinases have been shown to directly phosphorylate IP3Rs in order to modulate their affinity for IP3s and/or alter their gating kinetics [32] [28] . There are three isoforms of the IP3R in mammalian cells , but the precise function and cellular requirement for each isoform remains uncertain . Although functional redundancy likely exists between isoforms , IP3R-specific localization , gating , and regulation by ligands/proteins for specific cell processes contributes to isoform-specific signaling . Our results indicate that Cai2+ release downstream of CVB-induced DAF clustering is mediated via activation of IP3R-3 , as siRNA targeting IP3R-3 inhibited this release ( Figure 4C ) . However , other Ca2+ channels may be involved as we cannot exclude the possibility that channels ( such as store-operated cation channels or Ca2+-release activated channels ) are activated via IP3R-3-mediated Ca2+ release to induce Ca2+ influx . Interestingly , caveolin-1 has been shown to directly bind IP3R-3 to regulate agonist-induced Cai2+ release [50] and the endothelium of mice deficient in caveolin-1 display alterations in Cai2+ fluxes ( despite equivalent levels of IP3 production ) [51] . As we found that CVB gains entry into HBMEC via a caveolar-dependent mechanism ( Figure 1 ) , it is conceivable that the activation of caveolar-mediated endocytosis induced by CVB entry alters the association between caveolin-1 and IP3R-3 to alter its gating properties and/or sensitivity to IP3 as a mechanism to promote Cai2+ release . We observed pronounced activation of calpain coincident with CVB entry ( Figure 6 ) and calpain activity was required to regulate the trafficking of CVB-containing vesicles within the cell cytoplasm . Calpains are Ca2+-dependent cysteine proteases , most of which are ubiquitously expressed , and function in many cellular processes , although the vast majority of these functions are still largely unclear ( reviewed in [33] ) . Calpain substrates can include cytoskeletal proteins , kinases and phosphatases , membrane-associated proteins including ion channels , and various transcription factors [33] . Several studies have linked calpains as important regulators of viral replication . Latently infected HIV-1 cells utilize Ca2+-dependent calpain activation in order to initiate viral replication [52] , hepatitis C virus utilizes calpain activity in the cleavage of viral nonstructural proteins [53] , and echovirus 1 requires calpains for an as-yet-unidentified facet of its replication [54] . In contrast to these other viruses , we find that calpain-2 is required at the time of CVB entry and has little role in post-entry events in the virus life cycle . The precise role for calpain-2 in regulating the trafficking of CVB-containing vesicles is uncertain . However , calpains have been implicated in endocytosis , particularly in the regulation of intracellular membrane fusion , and are associated with coated vesicles within the cytoplasm [34] , [35] , [55] . A role for calpain-2 in regulating vesicular fusion during CVB entry is supported by our observation that internalized CVB particles accumulate within enlarged cytoplasmic vesicles when calpain activity is inhibited . Additionally , calpains have also been associated with the remodeling of the actin cytoskeleton by targeting a variety of actin-associated components . Thus , calpains may facilitate CVB trafficking by modulating the actin cytoskeletal network for proper vesicular trafficking . Calpain-2 is activated by high levels of Cai2+ ( mM ) , consistent with the pronounced release of Cai2+ induced during CVB entry . Moreover , we also observed the appearance of enlarged CVB-positive cytoplasmic vesicles when the expression of PLCγ1 and IP3R-3 were depleted , supporting a role for PLCγ1- and IP3R3-dependent Cai2+ release upstream of calpain-2 activation . Although many viral pathogens target polarized cells , little is known regarding the mechanisms used by viruses to enter polarized monolayers or whether these mechanisms might differ between the epithelium and endothelium . CVB entry into polarized epithelial cells is a complex process that involves the activation of a variety of intracellular signaling molecules that regulate distinct aspects of the viral internalization process [2] , [3] . The results presented here show that CVB entry into polarized endothelial cells is regulated by a divergent intracellular signaling pathway than that in the epithelium–the mobilization of Cai2+ . Thus , CVB has evolved to hijack two distinct pathways in the endothelium and epithelium to bypass polarized cell barriers . These results provide an illustration of the complexities likely to be associated with viral internalization into polarized cells and may serve as a model for how other viral pathogens circumvent the barriers presented by polarized cell monolayers .
HBMEC were cultured in RPMI 1640 ( Hyclone , Logan , Utah ) with 10% FBS ( Gibco , Grand Island , New York ) , 10% NuSerum ( BD Biosciences , Bedford , MA ) , 100 U/ml of NEAA ( nonessential amino acids ) , MEM vitamins , and sodium pyruvate ( all Hyclone ) , 10 U/ml of PenStrep ( Gibco ) , and 30 µg/ml of Endothelial Cell Growth Supplement ( BD Biosciences ) and have been described previously [56] . Primary HAEC were obtained from Lonza-Clonetics ( Allendale , NJ ) and cultured in EGM-2 media per manufacturer's instructions . Caco-2 ( BBE clone ) were purchased from the ATCC and grown in DMEM-H supplemented with 10% FBS and 10 U/ml PenStrep . CVB3-RD and CVB4 were expanded by infecting HeLa cells , purified through centrifugation in a sucrose gradient , and tittered by plaque assays on HeLa cells as described previously [2] . Mouse anti-enterovirus VP1 ( Ncl-Entero ) was obtained from Novocastra Laboratories ( New Castle upon Tyne , UK ) . Goat polyclonal antibodies to calpain-2 ( N-19 ) was purchased from Santa Cruz Biotechnology ( Santa Cruz , CA ) . Alexa fluor-conjugated secondary antibodies and cholera toxin B were purchased from Invitrogen ( Carlsbad , CA ) . Genistein ( 20 µM ) , ALLN ( 50 µM ) , calpeptin ( 10 µM ) , dideoxyadnesoine ( 5 µM ) , PP2 ( 10 µM ) , and calpain inhibitor III ( 5 µM ) were purchased from Calbiochem ( Gibbstown , NJ ) ; U73122 ( 700 µM ) , Bapta-AM ( 10 µM ) , 2-APB ( 30 µM ) , and dynasore ( 100 µM ) were purchased from Sigma ( St . Louis , MO ) . Toxicity panels were performed to ensure inhibitors did not cause unwanted effects ( Supplemental Figure S7 ) . HBMEC monolayers grown in collagen-coated chamber slides ( BD Biosciences , San Jose , CA ) were exposed to CVB in binding buffer for 1 hour at 16°C then washed and placed at 37°C to initiate entry ( for entry experiments ) , or 14 hours at 37°C ( for infection experiments ) . For entry experiments the cells were washed and fixed with 4% paraformaldehyde ( PFA ) and then incubated with primary VP1 antibody for 1 hour . Each well was then washed and incubated with the appropriate Alexa Fluor-594-conjugated antibody for 30 min . After another washing the cells were fixed again with 4% PFA , washed , permeabilized with 0 . 1% Triton-X 100 in PBS , incubated again with VP1 primary antibody for 1 hour at room temperature , washed , and subsequently incubated with Alexa Fluor-488-conjugated secondary antibody for 30 min , washed , and then mounted with Vectashield ( Vector Laboratories , Burlingame CA ) . For infection experiments cells were exposed to virus at MOIs stated then washed and fixed with ice-cold methanol acetone ( 3∶1 ) . Monolayers were then incubated with primary VP1 antibody for 1 hour , washed , and incubated with secondary Alexa Fluor–488-conjugated antibody . Cells were imaged on an Olympus IX81 inverted microscope equipped with a motorized stage for obtaining Z stacks . For virus entry experiments , images were captured with an Olympus PlanApo 60x/1 . 42 NA oil objective with z stacks ( 0 . 25 µM slices ) and deconvolution performed by using the nearest neighbor function in Slidebook 5 . 0 . Infection images were captured with an Olympus UplanApo 10x/0 . 4 NA objective and quantified using ImageJ ( http://rsb . info . nih . gov/ij/ ) as a ratio of VP1+/DAPI+ . Immunoflorescence imaging for internalized viral particles was performed as described in detail previously [18] . Briefly , monolayers were exposed to CVB ( 50 particles/cell ) and at the indicated times fixed in 4% PFA , washed in PBS containing 50 mM NH4Cl for 5 min , and incubated with monoclonal anti-VP1 antibody ( NCL-ENTERO ) for 1 h at RT . Cells were then washed and incubated with Alexa Fluor ( AF ) 594-conjugated secondary antibody . Following washing , cells were fixed again in 4% PFA , incubated for 5 min in PBS containing 50 mM NH4Cl , and permeabilized with 0 . 1% Triton X-100 for 10 min . Permeabilized monolayers were re-incubated with anti-VP1 antibody , washed , and incubated with AF 488-conjugated secondary antibody . Cells were mounted with Vectashield containing DAPI and images captured as described above . Cells grown on collagen-coated glass bottom 35 mm dishes ( MatTek Corp . , Ashland , MA ) were loaded with Fura-2 AM ( 1 µM - Invitrogen ) for 30 min at 37°C . These culture conditions promoted the formation of polarized monolayers characterized by the asymmetric localization of apical and basolateral protein components ( Supplemental Figure S2B ) . Cells were rinsed 3 times with Ca2+- and Mg2+-free PBS , bathed in a final volume of 1 ml . Images were captured on an Olympus IX81 motorized inverted microscope equipped with a Hamamatsu Orca-R2 CCD camera , Sutter Lambda 10-3 High Speed filter wheel system , and an Olympus UApo/340 20x objective with an N . A . of 0 . 75 . Images were acquired using Slidebook 5 . 0 advanced imaging software . Selected cells were chosen ( 40 regions of interest ( ROI ) /dish ) and images captured at both excitation 340 nm and 380 nm every 5 seconds for 10 minutes ( experiments were performed a minimum of three times ) . Virus was added to dishes once baseline was established ( t = 55 sec ) at the specified MOIs . Intensity ratios for selected ROIs were calculated using Slidebook 5 . 0 , and replicates averaged and plotted as a function of time . Images were pseudocolored ( using Slidebook 5 . 0 ) in order to better visualize Cai2+ mobilization with blue = low Cai2+ and red = high Cai2+ . siRNAs were purchased from Dharmacon . HBMEC were transfected using HiPerFect ( Qiagen , Valencia , CA ) as described previously [3] . Reverse transfections were performed as follows– OptiMEM:HiPerfect complexes were incubated for 10 min with the indicated siRNAs and then added to cells in suspension ( harvested following trypsinization ) and incubated for 48–72 hours . In some cases , siRNAs were delivered by nucleofection [Nucleofector System ( Amaxa ) using Nucleofector solution V and program T023] . Total RNA was isolated with TRI Reagent Solution ( Applied Biosystems , Foster City , CA ) according to the manufacturer's protocol . For complementary DNA synthesis , 1 µg total RNA was used in a 20-µL reaction containing 1 mM deoxynucleotide triphosphates ( dNTPs ) , 2 . 5 mM oligo dT or random hexamers ( for CVB amplification ) , 1000 U/ml RNase inhibitor , 0 . 1 volume 10X buffer ( supplied by manufacturer ) , and 2500 U/ml murine leukemia virus reverse transcriptase ( Invitrogen , Carlsbad , CA ) . The reverse transcription ( RT ) reaction was carried out at 1 cycle in a thermal cycler at 42°C for 50 min , followed by 15 min incubation at 70°C . PCR for IP3R-2 was carried out with primers to the gene of interest ( primer sequences can be found in Supplemental Figure S5B ) and Taq DNA polymerase for 25 cycles . PCR products were separated on a 1% agarose gel containing ethidium bromide . Primer sequences are as follows: IP3R-2 ( sense 5′-CTTGAAGATCTGGGGGATCA-3′ and antisense 5′-GTGCCTTCTTTTGCCTCTTG-3′ ) ; IP3R-1 ( sense 5′-CAAGCGAGTTCCTGTTCTCC-3′ and antisense 5′-GTGGACTCCAGCTTCTCCTG-3′ ) ; GAPDH ( sense 5′-ACCACCAACTGCTTAGCA-3′ and antisense 5′-CCCTGTTGCTGTAGCCAA-3′ ) . CVB PCR was performed using a Maxim Biotech amplification kit for enteroviruses as per the manufacturer's instructions . Calpain activity was assessed in HBMEC exposed to CVB ( 100 PFU/cell ) at the indicated times using a fluorogenic calpain activity assay ( Calbiochem ) . Briefly , control or CVB-exposed cells ( at the indicated times ) were lysed in RIPA buffer ( without protease inhibitors ) and incubated with fluorogenic calpain substrate for 15 min at room temperature . Fluorescence intensity measurements were acquired using a fluorescence plate reader ( BioTek Synergy 4 , BioTek ) at an excitation wavelength of ∼360–380 nm and an emission wavelength of ∼440–460 nm . Readings were normalized to background ( RIPA alone ) controls and data presented as the fold change in calpain activity in CVB-exposed cells compared to no virus controls . ID numbers for proteins/genes mentioned in the text ( numbers were taken from GenBank at Pubmed ) : inositol 1 , 4 , 5-trisphosphate receptor 1 ( ITPR1 ) 3708; inositol 1 , 4 , 5-trisphosphate receptor 3 ( ITPR3 ) 3710; phospholipase C gamma-1 ( PLCG1 ) 5335; decay accelerating factor ( DAF or CD55 ) 1604; coxsackievirus and adenovirus receptor ( CXADR ) 1525; calpain-2 ( CAPN2 ) 824; calpain-1 ( CAPN1 ) 823; Tec kinase ( TEC ) 7006; dynamin ( DNM1 ) 1759; dynamin II ( DNM2 ) 1785; caveolin-1 ( CAV1 ) 857; caveolin-3 ( CAV3 ) 859; EPS15 2060 . | Enteroviruses are associated with a number of diverse syndromes such as myocarditis , febrile illness , and are the main causative agents of aseptic meningitis . No effective therapeutics exist to combat non-poliovirus enterovirus infections . A better understanding of the mechanisms by which these viruses infect host cells could lead to the design of effective therapeutic interventions . In this study , we found that intracellular calcium stores in polarized endothelial monolayers are depleted upon exposure to coxsackievirus B ( CVB ) and that this release is mediated by viral attachment to its receptor decay-accelerating factor . We also discovered that the calcium release requires the activation of signaling molecules involved in calcium signaling such as Src tyrosine kinases , phospholipase C , and the inositol 1 , 4 , 5-trisphosphate receptor isoform 3 on the ER membrane . Furthermore , we found that a calcium-activated cystein protease , calpain-2 , was activated and necessary for proper viral trafficking inside the cell . Interestingly , we found that this signaling cascade was critical for CVB internalization into the endothelium , but was not involved in CVB entry into the epithelium . This is an important advance in our understanding of how enteroviruses hijack host endothelial cell signaling mechanisms in order to facilitate their entry and eventual spread . | [
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] | 2010 | Release of Intracellular Calcium Stores Facilitates Coxsackievirus Entry into Polarized Endothelial Cells |
Carriage of the genetic combination encoding a high expression inhibitory Killer Immunoglobulin-like Receptor ( KIR ) 3DL1 with its ligand , HLA-B*57 ( *h/*y+B*57 ) is associated with slower time to AIDS and better HIV viral load control than being a Bw6 homozygote ( Bw6hmz ) . Natural Killer ( NK ) cells from *h/*y+B*57 carriers receive potent educational signals through HLA-B*57 KIR3DL1 ligation leading to high functional potential . NK cells from Bw6hmz are not educated through KIR3DL1 because Bw6 antigens do not interact with this inhibitory receptor . To better understand the impact of KIR/HLA combinations on NK cell mediated anti-viral activity we measured NK cell mediated inhibition of HIV replication in autologous infected CD4 ( iCD4 ) cells by assessing the frequency of p24 positive CD4 targets and supernatant levels of HIV p24 longitudinally in the presence versus absence of NK cells . Forty-seven HIV uninfected subjects were studied , including carriers of *h/*y+B*57 , a low expression KIR3DL1 genotype with HLA-B*57 termed *l/*x+B*57 , a genotype designated 3DS1+*80I and Bw6hmz . NK cells from *h/*y+B*57 carriers , like those from 3DS1+*80I subjects , inhibited HIV replication in autologous iCD4 cells better than those from Bw6hmz and *l/*x+B*57 carriers . Cell contact between NK and iCD4 cells activated NK cells to inhibit viral replication in a non-contact dependent fashion through secretion of CC-chemokines . iCD4 stimulated NK cells from *h/*y+B*57 and 3DS1+*80I carriers produced higher levels of CC-chemokines than those from Bw6hmz or *l/*x+B*57 carriers . Higher levels of CC-chemokines were produced by KIR3DL1+ than KIR3DL1− NK cells . We conclude that NK-mediated inhibition of viral replication in autologous iCD4 cells is partially due to a block at the level of HIV entry into new targets by secreted CC-chemokines .
NK cells function in innate immune responses to transformed and virally infected cells . They can exert their anti-viral effects soon after encountering infected targets without prior sensitization [1] . NK cell function is determined by signals from activating and inhibitory cell surface receptors , which include Killer Immunoglobulin-like Receptors ( KIR ) [2] . Among these are inhibitory KIR3DL1 ( 3DL1 ) and activating KIR3DS1 ( 3DS1 ) receptors , which are encoded by alleles at the same KIR3DL1/S1 locus [3] . 3DL1 receptors can be classified into those expressed on NK cell surfaces at high levels ( *h ) low levels ( *l ) or *004 , which is only transiently expressed [4]–[7] . 3DL1 homozygous genotypes can be dichotomized into *h/*y and *l/*x groups where *h/*y genotypes encode receptors expressed on the NK cell surface at higher levels than those encoded by *l/*x genotypes [6] . Epidemiological studies have found that several 3DL1 homozygous genotypes co-carried with a subset of HLA-B and –A alleles belonging to the HLA-Bw4 group are associated with slower time to AIDS and viral load ( VL ) control [7] . HLA-Bw4 antigens differ from the remaining HLA-Bw6 ( Bw6 ) antigens by amino acids at positions 77–83 [8] . The genotype combinations that confers the highest degree of protection in terms of time to AIDS and VL control is 3DL1*h/*y co-carried with HLA-B*57 ( *h/*y+B*57 ) [7] . Subjects with this combined genotype are more frequent among HIV Exposed Seronegative ( HESN ) than HIV susceptible individuals , implicating carriage of this genotype combination in reducing HIV infection risk [9] . NK cells from carriers of *h/*y+B*57 have more potent NK cell functional potential as defined by HLA-null cell induced secretion of IFN-γ and TNF-α and expression of CD107a , a marker for degranulation , than those from carriers of the receptor or ligand alone , including those from carriers of the *l/*x+B*57 KIR/HLA genotype and Bw6 homozygotes ( Bw6hmz ) [9] , [10] . Bw6 antigens do not interact with 3DL1 receptors and are thus unable to educate NK cells through this inhibitory NK receptor [11] , [12] . NK cell education is an ontological process that depends on the interaction of inhibitory NK receptors , such as 3DL1 , with their MHC class I ( MHC-1 ) ligands . The strength of educational signals received during NK cell development determines NK cell functional potential [11] , [13] , [14] . Thus , NK cells from *l/*x+B*57 carriers may be less functional than those from *h/*y+B*57 positive subjects since the former express less 3DL1 than the later and thus receive lower level educational signals upon interaction with the same ligand [6] , [10] , [15] . The KIR/HLA combination 3DS1 co-expressed with a Bw4 antigen having an isoleucine at position 80 of the HLA heavy chain ( 3DS1+*80I ) is also associated with slower time to AIDS and VL control [16] , [17] . NK cells from carriers of the 3DS1+*80I genotype inhibit viral replication in autologous HIV-infected CD4 ( iCD4 ) T cells more potently than those from individuals carrying the receptor or ligand alone , or neither [18] . Together , these functional studies suggest that the association of certain KIR/HLA genotypes with either protection from HIV infection in HESN subjects or slow time to AIDS and VL control in those who are HIV infected , is linked to NK cell function . How NK cells inhibit viral replication in autologous CD4 T cells is not completely understood . One possibility is through the secretion of the CC-chemokines CCL3 , CCL4 , and CCL5 upon activation following recognition of autologous HIV iCD4 cells . These chemokines can suppress HIV replication by competing with the virus for binding the CCR5 co-receptor and blocking HIV entry into CD4 cells [19] , [20] . In this report we investigated whether NK cells from individuals carrying *h/*y+B*57 inhibited HIV replication in autologous HIV iCD4 cells better than those from *l/*x+B*57 carriers and Bw6hmz . The cell contact requirement for inhibition of viral replication was assessed . We also measured the production of CC-chemokines by NK cells stimulated with autologous HIV iCD4 cells . We determined whether CC-chemokine secretion levels differed based on the KIR/HLA genotype and evaluated the effect of CC-chemokine neutralization on NK cell mediated inhibition of HIV replication .
CD4 cells from 17 individuals were infected with HIV and co-cultured with or without autologous NK cells at an NK∶iCD4 ratio of 10∶1 and 1∶1 . Fig . S1 shows that NK cells inhibited viral replication , at all times tested and at both NK∶iCD4 ratios . For the 10∶1 and 1∶1 NK∶iCD4 cell ratios there were no significant between time point differences in viral inhibition ( p = 0 . 15 and p = 0 . 42 , Friedman test ) . Viral inhibition was significantly higher at days 7 and 10 for the 10∶1 versus 1∶1 NK∶iCD4 ratio ( p = 0 . 51 , 0 . 002 and 0 . 008 for days 3 , 7 and 10 , respectively , Mann-Whitney test ) . The higher viral inhibition levels seen in wells containing NK and iCD4 cells at a 10∶1 ratio compared to a 1∶1 ratio or for iCD4 cells cultured alone could have been due to differences in the number of cells in these culture conditions . For example , it was possible that higher cell numbers limited cell survival and this is what led to inhibition of HIV replication . To rule out this possibility we compared the number of live CD4 cells present at days 7 and 10 of culture between conditions where NK and iCD4 were cultured at 10∶1 with those where iCD4 were cultured alone . No significant differences in CD4 numbers were found ( not shown ) . Based on the 10∶1 NK∶iCD4 T cell ratio showing more potent inhibition of viral replication than the 1∶1 ratio , we used the 10∶1 ratio for subsequent experiments . NK cell mediated inhibition of viral replication was assessed by measuring the frequency of intracellular HIV-Gag-p24 positive CD4 cells using anti-p24 specific KC57 monoclonal antibody ( mAb ) . Fig . S2 shows the gating strategy used to assess the percent of p24 positive CD4 cells . Fig . 1A depicts flow cytometry plots showing the frequency of p24 positive CD4 cells at day 7 for several culture conditions for a single individual . Fig . 1B shows longitudinal results for up to 12 subjects , 5 on day 3 , 10 on day 7 and 12 on day 10 . In the presence of NK cells ( NK+iCD4 ) the frequency of p24 positive CD4 cells was lower than that in cultures of iCD4 cells alone ( p = 0 . 18 , p = 0 . 002 and p<0 . 001 for day 3 , 7 and 10 , respectively , Wilcoxon matched-pairs test ) . When NK and iCD4 cells were cultured in different transwell chambers ( NK/iCD4 TW ) , which prevents NK and iCD4 cell contact , the frequency of p24 positive CD4 cells was significantly higher than in conditions where NK and iCD4 were cultured together either in regular wells or the same chamber of a transwell ( NK+iCD4 or NK+iCD4 TW versus NK/iCD4 TW , p<0 . 05 for all comparisons at days 7 and 10 , Wilcoxon ) . However , the frequency of p24+ CD4 cells in the NK/iCD4 TW condition remained below that observed in iCD4 cells ( p≤0 . 002 for comparisons at days 7 and 10 ) . These results implicate NK iCD4 cell contact as a contributing factor in suppression of virus spread . However , since abrogating NK and iCD4 contact does not return the percent of p24 positive CD4 cells to levels seen in iCD4 cells cultured alone , non-contact dependent mechanisms are also likely involved in NK cell mediated inhibition of HIV replication . If iCD4 cells and co-cultures of NK and iCD4 cells are incubated in upper and lower transwell chambers , respectively , the frequency of p24 positive CD4 cells in the upper chamber ( iCD4 TW ) is lower than that seen when only NK cells are present in the lower chamber ( NK/iCD4 TW ) ( p = 0 . 007 and p = 0 . 08 for days 7 and 10 , Wilcoxon ) . These results suggest that contact between NK and autologous HIV iCD4 cells produces soluble factors that can then suppress HIV spread in the same well or cross a transwell membrane to suppress the spread of HIV in iCD4 cells physically separated from NK cells . We questioned whether autologous iCD4 cells could activate NK cells to secrete CC-chemokines . We reasoned that if this were the case , these soluble factors could be responsible for inhibiting HIV replication under conditions where iCD4 are either co-cultured with NK cells or in a separate transwell chamber from NK+iCD4 cultures . We assessed CC-chemokine secretion under several conditions at days 1 , 2 and 3 of culture . Fig . 2A–C show that PHA stimulated HIV iCD4 cells co-cultured with NK cells and recombinant human IL-2 ( IL-2 ) ( NK+iCD4+IL-2 ) produced CCL3 , CCL4 and CCL5 at higher levels than do either NK cells alone with IL-2 ( NK+IL-2 ) , NK cells cultured with uninfected CD4 cells and IL-2 ( NK+CD4+IL-2 ) or iCD4 cells with IL-2 ( iCD4+IL-2 ) ( p<0 . 05 for all CC-chemokines on each day tested , Kruskal-Wallis test ) . All pair-wise comparisons between CC-chemokine levels secreted in the NK+iCD4+IL-2 condition and those in each of the other 3 conditions were statistically significant , except for those between NK+iCD4+IL-2 and NK+CD4+IL-2 for CCL5 at days 1 , 2 and 3 ( p = 0 . 07 , 0 . 25 and 0 . 34 , respectively , Dunn's post-test comparisons ) . NK cells cultured without IL-2 and CD4 cells , whether PHA stimulated or not , HIV infected or not and cultured with or without IL-2 produced low levels of the 3 CC-chemokines in the range of 200 pg/ml or lower ( data not shown ) . Thus , NK cells stimulated by autologous HIV iCD4 cells and IL-2 are a source of secreted CC-chemokines and produce more CC-chemokines than NK cells or iCD4 cells alone culture media containing IL-2 . To confirm that CC-chemokines contribute to inhibition of HIV replication , neutralizing Abs to each CC-chemokine were added to iCD4 cells at the same time as NK cells . As seen in Fig . 2D for percent inhibition of viral replication compared to iCD4 cells alone , the addition of neutralizing Abs to individual CC-chemokines had no effect on percent inhibition of HIV replication mediated by NK cells ( p>0 . 05 for all comparisons , Wilcoxon matched pairs test ) . Addition of Abs to all 3 chemokines reduced NK-mediated HIV suppression . Comparisons of percent inhibition of HIV replication between NK+iCD4+neutralizing Abs to all 3 CC-chemokines and NK+iCD4 with either no Abs or antibodies to single CC-chemokines were significant for all comparisons except one at days 3 and 7 ( p<0 . 05 , Wilcoxon ) . The exception was the comparison of percent inhibition between NK+iCD4+neutralizing Abs to the 3 CC-chemokines and NK+iCD4 with no Abs ( p = 0 . 23 , Wilcoxon ) . None of the comparisons for percent inhibition at day 10 achieved statistical significance . These results indicate that iCD4 stimulated NK cell secretion of CC-chemokines contributes to inhibition on HIV replication . We next questioned whether NK cells from carriers of *h/*y+B*57 , a genotype combination that confers protection from HIV disease progression , VL control and lowered infection risk , inhibits viral replication better than NK cells from Bw6hmz [7] , [9] . Fig . 3 shows results for inhibition of HIV replication by NK cells from subjects positive for *h/*y+B*57 ( n = 7 ) , 3DS1+*80I ( n = 9 ) , *l/*x+B*57 ( n = 4 ) and Bw6hmz ( n = 11 ) . In this experiment NK cells from 3DS1+*80I carriers are used as a positive control since Alter et al . had previously shown their capacity to inhibit HIV replication in autologous iCD4 cells [18] . NK cells from *h/*y+B*57 carriers inhibited HIV replication better than those from Bw6hmz and this was significant at all times tested ( p = 0 . 01 , 0 . 007 , and 0 . 05 for days 3 , 7 , and 10 , respectively , Mann-Whitney test ) . They also inhibited HIV replication better than those from *l/*x+B*57 carriers ( p<0 . 05 for days 7 and 10 ) . We confirmed that NK cells from 3DS1+*80I carriers inhibit HIV replication better than those from Bw6hmz and *l/*x+B*57 carriers ( p<0 . 05 for all comparisons at days 7 and 10 ) . NK cells from carriers of 3DS1+*80I and *h/*y+B*57 inhibit viral replication in autologous iCD4 cells with a similar potency at the times tested . We verified that these results are not due to a differential ability of HIV to replicate in CD4 cells from subjects carrying these 4 genotypes ( Fig . S3 ) . HIV p24 levels in culture supernatants of iCD4 cells from carriers of the 4 genotypes was equivalent at all times tested ( p>0 . 05 , Kruskal-Wallis test ) . Together these results show that NK cells from carriers of the *h/*y+B*57 genotype inhibit HIV replication in autologous CD4 cells better than those from Bw6hmz or carriers of the *l/*x+B*57 genotype . We next asked whether NK cells from individuals carrying protective KIR/HLA genotype combinations and Bw6hmz differed from each other in the amount of CC-chemokines they secreted upon stimulation with autologous iCD4 cells . We assessed the amount of CC-chemokines secreted over 3 days by NK cells from 7 *h/*y+B*57 , 12 3DS1+*80I and 5 *l/*x+B*57 carriers and 10 Bw6hmz . Stimulated NK cells from *h/*y+B*57 and 3DS1 +*80I carriers secreted similar levels of CCL3 , CCL4 and CCL5 to each other and more than those from Bw6hmz ( Figs . 4 , S4 , S5 and Table S1 ) . CC-chemokine secretion by stimulated NK cells from *l/*x+B*57 carriers was similar to that from Bw6hmz for CCL3 and CCL5 and higher than that from Bw6hmz for CCL4 ( Fig . S5 and Table S1 ) . In general , iCD4 stimulated NK cells from *h/*y+B*57 and 3DS1+*80I carriers secreted higher CC-chemokine levels compared to those from *l/*x+B*57 carriers , though several of these comparisons did not achieve statistical significance ( Fig . S5 and Table S1 ) . We also stimulated NK cells overnight with autologous 7 day iCD4 and assessed intracellular CCL3 , CCL4 , IFN-γ and CD107a expression by total NK cells as well as by 3DL1+ and 3DL1− NK cell subsets using the gating strategy shown in Fig . S6 . Fig . 5 shows for CCL3 in the upper and CCL4 in the lower panels that a higher frequency of NK cells from *h/*y+B*57 carriers secrete these chemokines upon stimulations with autologous iCD4 than those from Bw6hmz . A similar but non-significant trend is when 3DL1+ NK cells are gated on that is absent in the 3DL1− population ( Fig . 5 ) . We also compared the frequency of 3DL1+ and 3DL1− cells within individuals secreting CCL3 , CCL4 and IFN-γ and expressing CD107a ( Figs . S7 , S8 , S9 , S10 ) . In general , a higher frequency of functional 3DL1+ than 3DL1− NK cells was observed in *h/*y+B*57 carriers ( p = 0 . 15 , 0 . 02 , 0 . 05 for CCL3 , CCL4 and IFN-γ secretion , respectively ) , but not in *l/*x+B*57 carriers and Bw6hmz . It would have been desirable to compare the frequency of intracellular CCL3 , CCL4 and IFN-γ positive cell in 3DL1+ *h versus *l allele expressing NK cell subsets following iCD4 stimulation of *l/*x+B*57 NK cells . Unfortunately , only 2 *l/*x+B*57 subjects carried both an *h and *l allele . The others were either homozygous for *l alleles or carried an *l and an *004 allele . The composition of the *l/*x+B*57 group precluded making firm conclusions regarding CC-chemokine or IFN-γ expression in these 3DL1+ NK subsets . Together , the intracellular cytokine staining results show that KIR/HLA genotype is a determinant of iCD4 stimulated NK cell functionality with regard to CC-chemokine secretion . The higher functionality of 3DL1+ NK cells in *h/*y+B*57 compared to *l/*x+B*57 carriers and Bw6hmz implicates this KIR/HLA combination in potent NK cell licensing for functional potential .
In this report we showed that NK cells cultured with autologous iCD4 cells limit the spread of HIV resulting in a lower frequency of HIV iCD4 cells and lower levels of viral replication compared to iCD4 cells cultured alone . Contact between NK and iCD4 cells stimulates NK cells to produce soluble factors , which suppress HIV replication in a non-contact dependent fashion . NK cells activated by autologous iCD4 cells in the presence of IL-2 secrete CC-chemokines at higher levels than when only IL-2 is present . CC-chemokine secretion is responsible , at least in part , for the inhibitory effect of NK cells on viral replication . KIR/HLA genotype influences the potency of inhibition of viral replication . We showed that NK cells from *h/*y+B*57 and 3DS1+*80I carriers , genotypes associated slower time to AIDS and VL control , inhibited HIV replication more potently than did those from Bw6hmz and carriers of the *l/*x+B*57genotype . NK cells , and in particular the 3DL1+ subset of NK cells , from carriers of the *h/*y+B*57 genotype secrete higher levels of CC-chemokines than those from Bw6hmz and *l/*x+B*57 subjects . The superior control of HIV replication in autologous iCD4 cells by NK cells from carriers of *h/*y+B*57 versus those from *l/*x+B*57 and Bw6hmz subjects implicates NK cell education as a determinant of this anti-viral NK function . NK cell education is important for the development of self-tolerant NK cells and for endowing NK cells with the capacity to mediate cytokine/chemokine secretion and cytolysis upon encountering cells with reduced MHC-I cell surface expression such as occurs in the context of HIV infected targets [11] , [21] , [22] . The ligation of inhibitory NK receptors such as 3DL1 is required for NK education but the process is tuned by the set of signals received from all the NK cell surface activating and inhibitory receptors interacting with their ligands on neighboring target cells [23]–[25] . The stronger the inhibitory signals received during NK cell education the broader and more potent the effector functions that NK cells will have against appropriate targets [23] . The *h/*y+B*57 KIR/HLA combination appears to be a particularly potent one for NK cell education , since NK cells from *h/*y+B*57 carriers showed higher functionality when stimulated with HLA-null cells than those from carriers of 3DL1*h/*y genotypes co-carried with other Bw4 or *80I alleles , 3DL1*l/*x genotypes co-carried with B*57 or those from Bw6hmz [10] , [15] . The difference in functional potential between NK cells from carriers of *h/*y+B*57 versus those from 3DL1hmz who carry other Bw4 alleles may reflect differences in the impact of HLA-B*57 versus other Bw4 antigens in providing educational signals to NK cells during development . Transgenic mice expressing single MHC-I alleles have been used to show that MHC-I antigens differ in their impact on NK cell education [24] . The strength of the inhibitory input during education , as determined by the strength of the interaction between inhibitory NK receptors and their ligands , is directly related to the functional responsiveness of individual NK cells [23] , [24] . Thus , it appears that B*57 differs from most other Bw4 molecules in the strength with which it interacts with 3DL1 to educate NK cells . NK cells from 3DL1*h/*y positive subjects express higher levels of 3DL1 inhibitory receptors than those from 3DL1*l/*x positive individuals [6] The observation that NK cells from *l/*x+B*57 carriers secrete less CC-chemokines and inhibit HIV replication more poorly than those from *h/*y+B*57 carriers may be related to less potent NK education due to lower levels of cell surface 3DL1 mediating lower inhibitory signals for NK cell education , even in the presence of the potent B*57 3DL1 ligand . A caveat to this interpretation is that while there is experimental evidence that B*57 binds 3DL1 it has not been demonstrated that the affinity of the interaction between these 2 molecules is greater than that between 3DL1 and other Bw4 molecules because different peptides influence 3DL1 Bw4 binding [26] , [27] . In the presence of the same epitope and 3DL1 receptor HLA-Bw4*80T variants bind with about 60% of the affinity of B*57 [27] . The impact of *h/*y+B*57 on NK cell education and the relationship between NK education and NK cell responsiveness may underlie epidemiological findings that carriers of this genotype have a lower risk of HIV infection and in those who become infected have a slower time to AIDS and lower VL than carriers of other 3DL1 hmz Bw4 genotypes , including *l/*x+B*57 carriers [7] , [9] . The influence of *h/*y+B*57 on NK cell education may also play a role in the superior ability of NK cells from carriers of this KIR/HLA genotype to inhibit viral replication in autologous HIV infected cells compared to those from Bw6hmz . It is notable that the frequency of p24 positive CD4 cells in conditions where NK and iCD4 cells are in separate transwells is lower than that of iCD4 cells cultured alone but higher than that of iCD4 cells and NK cells cultured together . This implies that NK-CD4 cell contact contributes to NK cell activation and secretion of soluble factors that can inhibit HIV replication in a non-contact dependent manner . IL-2 by itself can also activate NK cells to secrete soluble factors such as CC-chemokines , though at lower levels than when iCD4 cells are also present . This may be why the percent of p24+ CD4 cells in conditions where iCD4 and NK cells are in separate transwell chambers is not as high as when iCD4 are cultured alone . It is not known whether these soluble factors are limited to CC-chemokines . Simultaneous neutralization of the CCL3 , CCL4 and CCL5 restored HIV replication measured at 3 and 7 days of culture to levels that were significantly higher than when NK and iCD4 cells were co-cultured in the absence of CC-chemokine neutralization . Neutralization of all 3 CC-chemokines was not sufficient to reduce NK cell mediated inhibition of HIV replication at day 10 of culture . The reason for this is unclear but may be due to the continued production of chemokines over and above the amounts that anti-CC-chemokine Abs are able to neutralize . High inter-subject variability precludes making a clear determination as to whether CC-chemokine neutralization is sufficient to reverse NK cell mediated inhibition . It is possible that iCD4 stimulate NK cells to inhibit HIV replication by other mechanisms in addition to CC-chemokine secretion . These activities could target other stages of the HIV replication cycle and may or may not be dependent on contact between NK and iCD4 cells . Previous studies have shown that NK cells secrete CC-chemokines following stimulation through CD16 cross-linking and co-culture with iCD4 cells in the presence of IL-2 [20] . Here we report for the first time that a KIR/HLA genotype combination that influences the potency of NK cell education also determines the level of CC-chemokines that NK cells secrete in response to autologous iCD4 cells . CC-chemokines can bind CCR5 , the HIV co-receptor , and prevent HIV from interacting with this receptor thus reducing HIV entry [19] , [20] . Transwell experiments implicate cell contact as a factor in NK cell stimulation leading to CC-chemokine secretion . Pelak et al . reported that in carriers of 3DS1+*80I , the copy number of 3DL1 alleles influenced NK cell mediated inhibition of HIV replication in autologous iCD4 T cells [28] . Copy number variation ( CNV ) is common at the 3DL1/S1 locus . Screening for CNV at this locus revealed no duplications or deletions at this locus among subjects having the 4 genotypes focused on in this study . Therefore , CNV at the 3DL1/S1 locus can be excluded as a factor influencing the experimental findings reported here . In summary , we show that NK cells from carriers of *h/*y+B*57 inhibit HIV viral replication in autologous iCD4 cells more effectively than those from *l/*x+B*57 carriers and Bw6hmz . The level of anti-viral function of NK cells from carriers of this genotype is likely related to NK cell education arising from B*57 interactions with high expression inhibitory 3DL1 receptors . Anti-viral function is mediated at least in part by CC-chemokine secretion levels able to block HIV entry into CD4 cell targets . The higher level of CC-chemokine secretion by NK cells from carriers of protective versus non-protective KIR/HLA genotypes may underlie their superior ability to inhibit HIV replication in infected targets .
This study was conducted according to the principles expressed in the Declaration of Helsinki . It was approved by the Institutional Review Boards of the Comité d'Éthique de la Recheche du Centre Hospitalier de l'Université de Montréal and the Research Ethics Committee of the McGill University Health Centre - Montreal General Hospital . All subjects provided written informed consent for the collection of samples and subsequent analysis . We studied 47 HIV seronegative individuals , including 7 who were positive for *h/*y+B*57 , 12 for 3DS1+*80I , 11 who were 3DL1hmz and Bw6hmz , 4 who were *l/*x+B*57 positive and 13 with other KIR/HLA genotypes ( Table 1 ) . Informed consent was obtained from all study subjects , and the research conformed to all ethical guidelines of all the authors' institutions . All subjects were typed for MHC-I alleles by sequence based typing using kits from Atria Genetics , Inc . ( South San Francisco , CA ) . Assign 3 . 5+ software was used to interpret sequence information for allele assignment ( Conexio Genetics , Perth , Australia ) . KIR3DL1/S1 generic genotyping was performed by PCR using 2 pairs of primers specific for either 3DL1 or 3DS1 alleles as previously described [29] . 3DL1 allotyping was done by sequencing 3DL1 exons as previously described [9] . Single nucleotide polymorphisms ( SNP ) corresponding to the 3DL1 alleles were identified by aligning the sequenced DNA to a reference consensus sequence consisting of 3DL1 cDNA sequences . The *h/*y genotype refers to a 3DL1 homozygous genotype with no *l alleles . Bw6hmz lacked Bw4 alleles at the HLA-A and -B loci . Peripheral blood mononuclear cells ( PBMC ) were isolated from blood by density gradient centrifugation ( Ficoll-Paque; Pharmacia , Uppsala , Sweden ) and cryopreserved in 10% dimethyl sulfoxide ( DMSO; Sigma-Aldrich , St . Louis , MO ) with 90% fetal bovine serum ( FBS; Wisent , Inc . St . Bruno , QC , Canada ) . CD4 T cells were isolated from thawed PBMC by positive selection using immunomagnetic beads ( STEMCELL Technologies , Inc . Vancouver , BC , Canada ) . The purity of the CD4 cell population was verified by flow cytometry ( average 95 . 3% ) . NK cells were isolated from thawed PBMC by negative selection ( STEMCELL Technologies , Inc . ) and yielded an average purity of 97 . 2% . Purified CD4 cells ( 106/ml ) were stimulated with 1 ug/ml PHA-P ( MP Biomedicals , Santa Ana , CA ) and 100 international units ( IU ) /ml of IL-2 ( Chiron Corp . , Emeryville , CA ) overnight in RPMI medium containing 2 mM L-glutamine , 100 IU/ml Penicillin , 100 µg/ml Streptomycin ( cRPMI ) ( all from Wisent ) supplemented with 10% FBS , ( Wisent , [R10] ) at 37°C in a 5% CO2 humidified incubator . Stimulated CD4 cells were then washed three times with cRPMI supplemented with 2% FBS ( R2 ) , and cultured in R10 with 100 IU IL-2 for 3 days . On day 4 , CD4 cells were infected at a multiplicity of infection of 0 . 01 with HIV-1JR CSF in R10 for 4 hrs and washed three times with R2 . Equal numbers ( 3 . 0 to 4 . 0×104 ) of these iCD4 cells were plated at NK∶iCD4 ratios of 10∶1 , 1∶1 or alone for 10 days in 300 ul of R10; 100 IU/ml IL-2 . Supernatants were collected by removing supernatants and replenishing wells with 300 ul of fresh R10; 100 IU/ml IL-2 on days 3 , 7 and 10 for assessment of p24 levels and on days 1 , 2 and 3 for assessment of CC-chemokine levels . For some experiments CD4 cells were collected on days 3 , 7 and 10 for intracellular Gag p24 staining . Cells were stained with an Aqua amine reactive fluorescent dye ( Invitrogen , Burlington , ON , Canada ) to identify viable cells . Cell surface staining with anti-CD3 APC-eFluor 780 ( eBioscience , San Diego , CA ) and anti-CD4 PE ( BD Biosciences , Mississauga , ON , Canada ) were used to detect CD4 T cells . After fixation and permeabilization intracellular HIV Gag p24 positive cells were detected using the mAb KC57 ( Beckman-Coulter , Mississauga , ON , Canada ) . Acquisition was done on a BD FACSCanto II flow cytometer ( BD Biosciences , San Jose , CA ) . Between 50 , 000 and 200 , 000 events were acquired per sample . Flow cytometry results were analyzed with Flowjo software Mac 9 . 4 ( Treestar , Ashland , OR ) . The gating strategy used for intracellular p24 positive cells is shown in Fig . S2 . To ascertain the requirement for NK-CD4 cell contact for NK cell–mediated inhibition of HIV replication , autologous NK cells were physically separated from iCD4 cells in transwell plates ( Corning , Tewksbury MA ) . iCD4 cells ( 105/well ) were cultured in the upper chamber with either 106 NK cells alone or 106 NK cells with 105 iCD4 T cells in the lower chamber . Cells in wells containing iCD4 cells were collected on days 3 , 7 and 10 to quantitate the frequency of HIV Gag p24 positive CD4 cells . | Natural Killer ( NK ) cells function in anti-tumor and anti-viral defenses , including those directed against HIV . HIV infected cells can activate NK cells , which , once activated , inhibit HIV replication in infected targets . NK cell activation levels depend on the interaction of cell surface receptors on NK cells with the molecules ( or ligands ) they recognize on neighboring target cells . One receptor-ligand combination has been identified to have a strong effect on slowing time to AIDS , HIV viral load control and NK cell activation potential . We compared anti-HIV NK cell responses in individuals with this NK receptor-ligand combination to those from subjects having NK receptor-ligand combinations associated a neutral effect on time to AIDS . NK cells inhibited HIV replication in autologous infected cells more potently when they came from individuals with NK receptor-ligand ( KIR/HLA ) gene combinations associated with slow versus typical time to AIDS . Inhibition of HIV replication was due to secretion of factors ( chemokines ) that bind and block the co-receptor HIV uses to enter susceptible target cells . NK cells from subjects with KIR/HLA combinations associated with potent NK cell anti-HIV activity secreted more chemokines than those from subjects with KIR/HLA combinations associated with weak anti-HIV NK cell activity . | [
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"sexually",
"transmitte... | 2014 | HIV Protective KIR3DL1/S1-HLA-B Genotypes Influence NK Cell-Mediated Inhibition of HIV Replication in Autologous CD4 Targets |
A universal feature of metazoan sexual development is the generation of oocyte P granules that withhold certain mRNA species from translation to provide coding potential for proteins during early post-fertilization development . Stabilisation of translationally quiescent mRNA pools in female Plasmodium gametocytes depends on the RNA helicase DOZI , but the molecular machinery involved in the silencing of transcripts in these protozoans is unknown . Using affinity purification coupled with mass-spectrometric analysis we identify a messenger ribonucleoprotein ( mRNP ) from Plasmodium berghei gametocytes defined by DOZI and the Sm-like factor CITH ( homolog of worm CAR-I and fly Trailer Hitch ) . This mRNP includes 16 major factors , including proteins with homologies to components of metazoan P granules and archaeal proteins . Containing translationally silent transcripts , this mRNP integrates eIF4E and poly ( A ) -binding protein but excludes P body RNA degradation factors and translation-initiation promoting eIF4G . Gene deletion mutants of 2 core components of this mRNP ( DOZI and CITH ) are fertilization-competent , but zygotes fail to develop into ookinetes in a female gametocyte-mutant fashion . Through RNA-immunoprecipitation and global expression profiling of CITH-KO mutants we highlight CITH as a crucial repressor of maternally supplied mRNAs . Our data define Plasmodium P granules as an ancient mRNP whose protein core has remained evolutionarily conserved from single-cell organisms to germ cells of multi-cellular animals and stores translationally silent mRNAs that are critical for early post-fertilization development during the initial stages of mosquito infection . Therefore , translational repression may offer avenues as a target for the generation of transmission blocking strategies and contribute to limiting the spread of malaria .
Early post-fertilization development in multi-cellular organisms relies on mRNAs supplied in the oocyte in translationally silent P body related storage particles known as P granules . Translation of these maternal mRNA pools depends on fertilization and occurs prior to maternal to zygote transition when transcription from the zygotic genome is initiated [1] , [2] . Many P granule components are known [3]–[7] but there is a long-standing question to what constitutes the evolutionarily conserved and essential protein core that controls related events in unicellular eukaryotes during sexual reproduction . In the protozoan Plasmodium , formation of a diploid zygote during sexual development coincides with , and is essential for parasite transmission from the human to the mosquito host . Plasmodium are haploid throughout most of their life cycle and sexual development in malaria parasites is initiated with the generation of sexual precursor cells , or gametocytes , in the blood of the mammalian host . These mature , haploid male or female forms present distinct proteomic profiles [8] in the absence of sex chromosomes . In the mosquito midgut fertilization yields a diploid zygote that undergoes meiosis without cell division resulting in a tetraploid cell that within 18 hours transforms into the motile ookinete able to truly infect the mosquito . Zygote to ookinete transformation relies on the translational activation of stored , silent mRNAs probably deposited in mRNPs of unknown composition in the female gametocyte [9] . Translationally quiescent mRNAs are found in the cytoplasm of female gametocytes [9]–[12] , where long-term maintenance and stabilisation depends on the conserved DEAD-box RNA helicase DOZI [9] , a homolog of Saccharomyces cerevisiae ( yeast ) Dhh1p , Drosophila melanogaster ( fly ) Me31b , Caenorhabditis elegans ( worm ) CGH-1 and vertebrate members Xenopus laevis P54 and human RCK/P54 . In the absence of DOZI , Plasmodium berghei zygotes fail to develop into ookinetes , most likely due to a failure to form mRNPs that store and stabilise silenced transcripts . Collectively these destabilized mRNAs encode proteins that are essential for zygote to ookinete transformation during the initial phase of mosquito infection and include adhesins and factors known to be necessary for ookinete motility and traversal through mosquito midgut cells [9] . Translational silencing of certain mRNA species is mediated by a U-rich RNA motif present in the 5′ or 3′ untranslated regions of the implicated mRNAs [13] which also have been shown to specifically silence transgene expression [14] . We provide here the most in-depth characterisation of the protein composition of a P granule to date and demonstrate that the Plasmodium particle has a protein core with widespread phylogenetic conservation containing proteins known to form equivalent particles in metazoan oocytes . In addition novel protein components are demonstrated that , although highly conserved , to our knowledge have not been associated with mRNP formation . Functional characterisation of two of the conserved core components revealed distinct phenotypes implying that functionally distinct sub-populations of silenced mRNPs exist .
The construction and characterization of a recombinant parasite line that expresses DOZI::GFP from a modified dozi allele has been previously reported [9] . Through immunoprecipitation ( IP ) of DOZI::GFP followed by RNA analysis of IP eluates by Northern and RT-PCR analysis we have previously shown a clear physical association of this DEAD-box RNA helicase with mRNAs known to be translationally silenced in mature , female gametocytes [9] . To define the molecular nature of this putative complex we sought to identify proteins that co-operate with DOZI in the assembly and maintenance of translationally repressed mRNAs . In two independent IP experiments targeting DOZI::GFP a complex from Plasmodium berghei gametocytes was purified and analyzed by LC-MS/MS yielding a group of DOZI interaction partners ( Figure 1A-B , D; Table S1 ) ; one of the co-eluted proteins , PB000768 . 03 . 0 , showed strong homology with worm CAR-I and fly Trailer Hitch but also Xenopus Rap55; these proteins co-localize with their respective DOZI homologues CGH-1 and Me31b to germ cell and P granules [3]–[7] – the Plasmodium protein contains both the conserved LSM14 domain and the extended FDF motif ( Figures 1D , S1 ) known to compete with the enhancer of mRNA decapping EDC3 for binding to DDX6 helicases [15] , and is therefore designated CITH ( CAR-I/Trailer Hitch Homolog; Figure S1 ) . To corroborate the DOZI pull down results , a reciprocal IP ( Figure 1C ) was performed using lysates from gametocytes of a transgenic P . berghei line expressing only C-terminally GFP-tagged CITH ( Figure S2 ) . Mass-spectrometric analysis of the CITH::GFP pull down resulted in the identification of the same 16 core factors ( Figures 1D and Table S1 ) . A linear regression analysis revealed no bias towards high molecular weight or abundant proteins ( Figure S3 ) ; 7 of the 16 proteins were previously found to be sexual stage specific in P . berghei ( PB000695 . 03 . 0 , PB000120 . 01 . 0 , PB001107 . 03 . 0 , PB000768 . 03 . 0 , PB000603 . 01 . 0 , PB000647 . 02 . 0 , PB000124 . 01 . 0 ) [8] . The analysis of the DOZI and CITH pull down eluates gives an unprecedented depth of characterisation of the protein component of a P granule ( Figure 1D ) . Among the DOZI and CITH-associated proteins identified with a high level of confidence are the Plasmodium homologs of the 5′ cap binding protein eIF4E ( PB000857 . 02 . 0 , Figure S4 ) and poly ( A ) binding protein ( PABP; PB001286 . 00 . 0 , Figure S5 ) . Both are commonly found in mammalian stress granules [16] and PABP protects mRNAs from de-adenylation and degradation . In addition we identified orthologs of proteins that function as translational regulators in metazoans; one protein with strong homology to the ELAV/BRUNO-family and a second with weak homology to Musashi: the Plasmodium proteins are Homolog of Drosophila BRUNO ( HoBo , PB001285 . 00 . 0 , Figure S6 ) , and Homolog of Musashi with two RNA recognition motifs ( HoMu , PB000805 . 02 . 0 Figure S7 ) . Drosophila BRUNO targets mRNAs such as oskar containing the 3′ UTR BRUNO response element for silencing [17] , while Musashi is a translational regulator found to compete with eIF4G for PABP-binding in neural stem cells [18] . For the first time we identify in maternal mRNPs Alba domain proteins ( Acetylation Lowers Binding Affinity ) ; the entire complement of P . berghei Alba domain proteins ( Alba-1 , PB000862 . 00 . 0; Alba-2 , PB000812 . 02 . 0 and Alba-3 , PB000878 . 02 . 0 ) ( Figure 1D ) co-IPs with DOZI and CITH . These proteins are small , with predicted molecular weights of 27 , 23 and 12 kDa , respectively with a single , N-terminal Alba domain ( Figure S8 ) . Alba-1 contains multiple RGG-box RNA binding domains at the C-terminus , a characteristic of plant and protozoan proteins . Phylogenetic analyses places Alba-1 and Alba-2 into the MDP2/Rpp25 superfamily , whereas Alba-3 belongs to the POP7/Rpp20 group ( Figures 2 and S8 ) [19] . Interestingly , within the Apicomplexa only the genus Plasmodium appears to have 2 members within the MDP2 group . Two enzymes potentially associated with glycolysis were identified , i . e . a member of the phosphoglycerate mutase ( PGAM ) family ( PB001107 . 03 . 0 , Figure S9 ) and enolase ( PB000456 . 03 . 0 , Figure S10 ) . Lastly , 5 abundant proteins show no or little homology to proteins outside Plasmodium spp . ; they are PB000695 . 03 . 0 , PB000120 . 01 . 0 , PB000647 . 02 . 0 , PB000124 . 01 . 0 and PB000642 . 01 . 0 . Consistent with the similarities in protein content of the DOZI and CITH IPs , the same silenced mRNA species associated with DOZI [9] were also found to co-elute with CITH by Northern analysis and RT-PCR ( Figures 3A and B ) but not transcripts known to be translated in gametocytes . The RNA-IP experiments indicate that CITH together with DOZI resides in a stable , translationally quiescent P body-like structure . The bulk of DOZI and CITH protein is present in female gametocytes as shown by immunofluorescence and proteome analysis of asexual stage and purified gametocytes [8] and expression of both proteins persists throughout ookinete development [8] , [13] . The P . falciparum DOZI ortholog ( PFC0915w ) has also been detected in sporozoites [20] . We consistently observe large CITH::GFP granules in live gametocyte preparations ( Figure 3C ) ; in addition the protein overlaps partially with two of the best characterised maternally silenced mRNAs , p25 and p28 , in cytoplasmic foci with a speckled appearance typical for such mRNPs ( Figure 3D ) . The similarities of mRNA and major protein contents of DOZI and CITH IPs indicate that they are largely a component of the same mRNP responsible for post-transcriptional regulation of gene expression at the level of translation initially defined by DOZI . As zygotes lacking DOZI fail to progress through meiosis and are unable to transform into ookinetes [9] we wanted to identify any possible effects on zygote to ookinete transformation in the absence of CITH . Mutant parasite lines that lack pbcith ( Δpbcith ) ( Figure S11 ) showed normal asexual blood stage development and wild type production of gametocytes and gametes but failed to generate ookinetes ( data not shown ) . To analyse in greater detail possible fertilization and meiosis defects we generated Δpbcith and Δpbdozi lines [934cl1 ( Figure S12 ) and line 927cl1 ( Figure S13 ) ] , respectively] in a reporter line with red fluorescent protein ( RFP ) expression exclusive to female gametocytes , that persists throughout ookinete development ( Figures 4A-C and S14; see also Protocol S1 and www . pberghei . eu ) . In addition to RFP under the control of the female-specific promoter of gene pb000504 . 02 . 0 , GFP is driven by the male-specific promoter of gene pb000791 . 03 . 0 . Both transgenes are stably introduced into the 230p locus on chromosome 2 . Therefore , stage specific RFP expression permits identification of female gametes and zygotes after fertilization for FACS-analysis of their DNA contents by Hoechst staining . Such analyses made 4 hours after activation , when the zygote normally has completed meiosis , are able to reveal cell ploidy and are therefore a quantitative indicator of fertilization success and zygote development from the diploid to the tetraploid state ( Figures 4D and E ) . These studies confirmed that the Δpbdozi line fertilises normally when compared to wild type; the male and female nuclei fuse but fail to complete meiotic replication and remain diploid ( Figure 4F ) . Surprisingly , Δpbcith mutants present a different phenotype where they also fertilise normally yet progress through meiotic DNA replication to establish tetraploidy ( Figure 4G ) . However , further development of the spherical zygote into the motile , banana-shaped ookinete is aborted soon after zygote stage I/II , before gross morphological changes become apparent [21] . Consequently , neither gene deletion mutants are able to transform into ookinetes ( Table 1A ) . Standard cross-fertilization assays [8] in which gametes of Δpbcith were crossed with either fertile male ( parasite line 137 . 1 , Δp47 ) or female gametes ( parasite line 370 . 1 , Δp48/45 ) demonstrated that male gametes are unaffected by the absence of CITH – the block in development of the zygote is due to the absence of the protein provided by female gametes resulting in sterility ( Table 1B ) . Therefore , despite the clear similarities in proteins associated with DOZI and CITH , their maternal origin and essential role in zygote to ookinete transformation , the specific effects on early zygote development are different . In Δpbdozi gametocytes the expression levels of 370 transcripts ( 6% of all Plasmodium genes ) were more than 2-fold reduced when compared to wild type gametocytes [9] . In order to identify if similar molecular effects contribute to the observed developmental defect in the pbcith mutant parasite , we performed a small Northern survey of abundant but translationally repressed mRNAs , among them the hallmark gene p28 . Using RNA isolated from gametocytes , p28 together with 3 additional transcripts appeared less abundant in the CITH KO parasites , indicating a destabilising effect on these mRNAs in the absence of CITH ( Figure 5A ) , thus prompting us to perform a global transcriptome profiling of gametocyte RNA and identify whether mRNA destabilisation is a global phenomenon . Microarray hybridisation of Δpbcith mutants revealed that the expression levels of 232 transcripts were significantly changed , with 183 mRNAs more than 2-fold down regulated ( DR ) representing 50% of the Δpbdozi number ( Table S2 ) . As in Δpbdozi , several transcripts ( 46 ) were unexpectedly up-regulated ( UR ) in the absence of CITH . In total , 82% of the protein products of all differentially expressed transcripts are absent from the gametocyte proteome [8] indicating that these transcripts are stabilised and silenced in a CITH dependent manner . 127 mRNAs were common to the DOZI and CITH data sets ( Figure 5B and Table S3 ) although neither the degree of a given individual transcript nor the rank order was consistent between the two mutants ( R2 = 0 . 25; Pearson r = 0 . 50 , Figure S15A ) . 117 are DR in both KOs , 3 were UR , whereas 7 transcripts are inversely modulated . Gene Ontology ( GO ) enrichment analysis ( Figure S15B ) revealed no bias most likely due to incomplete and therefore high number of hypothetical annotations ( 89 genes ) . However , 21 proteins are predicted to contain a signal peptide and 24 contain one or more trans-membrane domains suggesting cell surface localisation; among those are known adhesins – factors that function in host-cell receptor interactions and promote successful invasion of the midgut epithelium resulting in infection of the mosquito – and include p25 , p28 , warp , p36 , and members of the pb-fam-5/cpw-wpc and lap families including ccp2 and lap5 . In addition 5 alveolins ( membrane sac proteins ) , inner membrane complex 1b protein , gliding motility associated protein gap45 , 3 protein kinases , a member of the ap2/erf family of transcription factors ( api-o ) that initiates transcription of ookinete-specific genes [22] and rad51 are DR; finally , so are the 9 mRNAs previously shown to share a cis-acting RNA motif that confers silencing in female gametocytes [13] , [14] . In total , only 8% of the common differentially expressed genes are present in the Plasmodium gametocyte proteome [8] suggesting that CITH and DOZI co-operate in the protection from degradation of translationally quiescent , maternally supplied mRNAs . While a large number of genes are co-regulated by DOZI and CITH , the differences in the repertoire of DR mRNAs between Δpbdozi and Δpbcith gametocytes may indicate the presence of mRNPs with distinct mRNA content that is reflected in the observed developmental defects . A number of meiosis-associated transcripts were exclusively depleted in the Δpbdozi mutant which arrests before completion of meiosis ( Table S4 ) ; these include the RNA-binding protein mei2 ( pb001281 . 02 . 0 ) and the chromosome segregation myosin-ATPase ( pb300220 . 00 . 0 ) . Three additional AP2/ERF transcription factors ( pb00974 . 00 . 0 , pb001077 . 01 . 0 , pb300561 . 00 . 0 ) [23] , [24] as well as 6 mRNAs encoding Zn-finger domain proteins are significantly destabilized , and their protein products are likely to play a role in the activation of the zygotic genome . p28 is one of the first and best characterized translationally repressed mRNAs . Its protein product , which is displayed on the surface of the ookinete , plays an important role during mosquito midgut invasion making it a promising candidate for transmission blocking intervention [25] . DOZI and CITH gene deletion mutants fail to stabilize p28 and this failure could potentially lead to the precocious translation of P28 protein in blood stage gametocytes . Therefore we wanted to know the fate of p28 mRNA in DOZI and CITH gene deletion mutants . As shown in Figure 5C , absence of either factor does not result in P28 protein translation indicating that the mRNA is most likely degraded when not stored , and unable to resume translation .
The LC-MS/MS analysis of the DOZI and CITH-associated proteins revealed 16 common , major protein factors . They could be grouped into a number of different classes based on predicted activity: 1 . Proteins with homology to constituents of metazoan P granules; these proteins ( DOZI , CITH , eIF4E , HoBo/BRUNO , and HoMu/Musashi ) have been demonstrated to be present in mRNPs from various organisms although never in a single mRNP as presented in this study . The presence of PABP in Plasmodium P granules and metazoan germ cell granules [4] , [7] may indicate an intrinsic readiness of the particle to present repressed transcripts to the ribosome in response to the identified need for speed at the same time protecting mRNAs from degradation . 2 . Alba domain containing proteins have not been identified in association with mRNPs before . In Archaea they are known to bind RNA , principally ribosomal RNA [27] , and eukaryotic POP7 and Rpp25-related proteins take part in tRNA and rRNA processing , while ciliate MDP2 is a factor in macronuclear development [28] , [29] . In the Archaea Alba regulates transcription through chromatin organisation where DNA binding affinity is controlled by the sirtuin SIR2 and Pat [30] , [31] . In Plasmodium SIR2 regulates the expression of sub-telomerically located genes of multigene families encoding variant antigens [32]–[37] . However , the association of Alba proteins with factors that regulate translational repression ( TR ) might indicate that sirtuin de-acetylases and their counterpart acetylases also have a post-transcriptional role in the control of gene expression in Plasmodium . 3 . Two proteins associated with glycolysis were identified independently in DOZI and CITH IP-eluates , enolase and a member of the PGAM family . The role in TR implied by their association with the DOZI/CITH mRNP found in Plasmodium and possible moonlighting functions are currently obscure and requires further attention . However , it is well established that enolase has roles in biological processes besides its role in glycolysis , including transcription , heat shock , autoimmunity and it may also serve as a plasminogen receptor and function in the bacterial degradasome [38] . PGAM family members are known to participate in complexes including those which repress gene expression at the level of transcription [39] . CGH-1/Me31b/Dhh1 are present in diverse , functionally distinct P body families; these include maternal P [5]–[7] and stress granules [16] , but also co-localisation with RNA de-capping factors such as DCP1/2 [40] and presence in a miRNA-induced silencing complex [41] has been shown . It is significant that in this study and in contrast to the identification of proteins that are present in P bodies and stress granules , no factors were identified that constitute the core of P bodies during RNA degradation [42] and that interact with DOZI . Plasmodium homologs of most proteins with exonuclease activity and involved in mRNA de-adenylation and decapping ( e . g . XRN1 , Lsm1-7 , DCP1 and 2 , UPF1-3 ) are readily identified in the annotated genome ( Table S5 ) . Yet they were absent from the IP eluates , confirming that gametocyte mRNPs defined by DOZI and CITH contain stable , translationally repressed transcripts awaiting re-activation and translation following fertilization . This emphasizes that in the context of the gametocyte the activity of DOZI is predominantly one of mRNA storage and not degradation . It is intriguing that specific and overlapping , but also non-identical mRNA populations are destabilized in the gametocyte in the absence of DOZI and/or CITH . Our data support the existence of different forms of P granules that are defined by the destabilized mRNA populations in the CITH and DOZI depleted mutants and the observed different developmental defects of these mutants . Our experiments detailing the destabilising effect on a substantial mRNA population of the gametocyte show that silencing influences diverse processes during zygote to ookinete formation . For example , the newly formed zygote is provided with coding potential for proteins known and likely to be involved in ookinete development , for instance in the activation of the zygotic genome; the presence of AP2/ERF transcription factors ( TF ) and DNA Zinc-finger binding domains in the down regulated set of genes indicate that these factors are already supplied in the female gametocyte . One of these TF ( API-O ) promotes transcription of genes during ookinete development and is present in DOZI-defined mRNPs [22] . Secondly , 25% of the commonly down regulated mRNAs encode proteins with known and predicted surface localisation . In the case of P25 and P28 it is well established that they facilitate the escape of the parasite from the hostile mosquito midgut milieu [43]; transcription of these mRNAs in blood stage gametocytes and subsequent retention in silent mRNPs provides rapid access of these transcripts to ribosomes and therefore the production of these essential proteins . However , storage in P granules may also contribute to immune evasion mechanisms in the mammalian host . Antibodies to P25 and P28 are promising transmission blocking vaccines – their presence in a mosquito blood meal substantially reduces the ability of the parasite to infect the mosquito vector [44] , [45] . We have shown here and previously [9] that prevention of complex formation in gametocytes in CITH and DOZI KO mutants induces degradation and not translation of p25 and p28 mRNAs , while female gametocytes are fully translation competent as shown in numerous GFP transgene experiments [8] , [46] , [47] . In addition gfp translation can be abrogated when tethered to the 3′ UTR of p28 [46] . It is therefore tempting to speculate that the parasite has evolved a fail-safe mechanism that results in the degradation of mRNAs meant to be silenced in case the transcript fails to be stored in P granules . Our experiments corroborate that protozoans , like female germ cells of higher eukaryotes , rely on the storage of mRNA in the female gamete during sexual reproduction , specifically during early post-fertilization development . DOZI and CITH in Plasmodium are bona fide translational repressors that contribute to successful ookinete development in and infection of the mosquito vector by storing a substantial mRNA population in pre-fertilization , female gametocytes . In worm oocytes CGH-1 granules associate with roughly 6% of all known expressed genes [4] compared with approximately 7% down regulated mRNAs in Plasmodium gametocytes . Although the protected mRNA species are not conserved in the 2 organisms , the fundamental DOZI/CGH-1-dependent protection of transcripts is . The normal generation of ookinetes from crossings of CITH and DOZI-KO male gametes wild type female gametes also show that the observed sterile developmental phenotype is entirely a maternal effect previously identified for Drosophila Trailer hitch where mutant female flies are sterile and present defects in egg laying [7] , [48] . Translational repression ( TR ) is an important mechanism of post-transcriptional gene regulation that in metazoan germ-line but also somatic cells generates spatial and temporal protein diversity that is independent from transcriptional control and protein targeting signals . Our data demonstrate that such mRNPs in the protozoan Plasmodium rely on an evolutionarily conserved and ancient protein core that secures mRNP integrity and future translatability of stored mRNAs in a DOZI and CITH-dependent manner . The relatively tractable nature of the P . berghei malaria model will allow a detailed dissection of the role of conserved and species-specific proteins in TR . Furthermore , the novel involvement of Alba proteins in TR and the coupling of post-transcriptional modifications to signalling as an effector of TR may yet prove to be informative of control of TR in general .
All studies in which animals are involved were performed according to the regulations of the Dutch “Experiments on Animals Act” and European regulations ( EU directive no . 86/609 regarding the Protection of Animals used for Experimental and Other Scientific Purposes ) and approved by the Animal Experiments Committee of the LUMC ( ADEC; established under section 18 of the “Experiments on Animals Act” and registered at the Dutch Inspectorate for Health Protection and Veterinary Public Health , which is part of the Ministry of Health , Welfare and Sport ) . The mutant parasite line that expresses a C-terminally GFP-tagged version of DOZI ( 683cl4 ) has been described [9] . CITH::GFP parasites [line 909cl1 ( Figure S2 ) ] were generated in the parent reference line of the ANKA strain cl15cy1 with a GFP-tagging vector pL1200 containing a single genomic targeting region for single cross-over homologous recombination generated by PCR with primers 2831-EcoRI and 2832-NotI . Mutant parasites express only the GFP-tagged gene . Targeting regions , primers used and genotype analysis are shown in Table S6 . Please also refer to www . pberghei . eu and Table S7 for mutant P . berghei parasites lines used in this study . IP of DOZI::GFP and CITH::GFP complexes was performed on whole cell lysates from purified gametocytes as described in Supplementary Online Material of reference [9] using monoclonal anti-GFP antibodies ( Roche ) and control anti-cmyc antibodies ( SIGMA ) . Processing of eluates and mass-spectrometric analysis by LTQ-FT are described in Protocol S1 . Total RNA from IP eluates was extracted with TRIzol and used in Northern blot analysis and RT-PCR . Primers used are shown in Table S6 . Western blot analysis of IP eluates was performed using monoclonal anti-GFP antibodies ( Roche ) and anti-P47 [49] as described [9] . pbcith ( pb000768 . 03 . 0 ) was targeted for genetic disruption by standard double-crossover homologous recombination with vectors containing the Toxoplasma gondii ( tg ) dhfr/ts selection cassette flanked by targeting sequences of the corresponding ORF ( Figure S11 ) . Targeting regions were generated by PCR with primers 2773-Asp718I and 2774-HindIII , and 2775-EcoRI and 2776-NotI . Transfection and selection of mutant parasites was performed using genetic modification technology developed for P . berghei [50] . Correct integration of plasmids and disruption of the genes was verified by Southern analysis of separated chromosomes and diagnostic PCRs , and Northern analysis . Targeting regions , primers used and gels are shown in Figure S12 . pbcith was disrupted in three independent experiments ( 856 , 893 , 934 ) ; lines 856 and 893 were generated in a wild type reference line of the ANKA strain ( 507cl1 ) that constitutively expresses a gfp transgene under the control of the eef1a promoter , stably integrated into the pb230p locus without use of a drug-selectable marker [50] . Line 934 was generated in a second wild type reference line ( line 820cl1m1cl1; Figure S12 ) which contains gfp and rfp transgenes under the control of male ( pb000791 . 03 . 0 ) and female ( pb000504 . 02 . 0 ) specific promoters , respectively , stably integrated into the pb230p locus without the use of a drug selectable marker ( see Protocol S1 Generation of a reporter P . berghei line that expresses RFP in female gametocytes , gametes and zygotes for further details of the generation of this line ) . Cloned parasite lines of transfection 856 and 934 were obtained by limiting dilution and used for further analysis of the phenotype ( Figure S12 ) . Parasite lines in which pbdozi has been disrupted in the ANKA strain have been described . In addition , we disrupted for this study pbdozi in the reference line 820cl1m1cl1 ( 927cl1; Figure S13 ) using the same DNA construct as described [9] . Generation of a reporter P . berghei line ( 820cl1m1cl1 ) is described in detail in Protocol S1 and Figure S14 . The fertility of wild type and mutant gamete populations was analysed by standard in vitro fertilization and ookinete maturation assays [8] , [49] from highly pure gametocyte populations [51] . Fertility ( ookinete conversion ) of gametes is defined as the percentage of female gametes that develop into mature ookinetes determined by counting female gametes and mature ookinetes in Giemsa stained blood smears 16–18 hours after gametocyte activation . Fertility of individual sexes ( macro- and micro-gametes ) was determined by in vitro cross-fertilization studies in which gametes are cross-fertilised with gametes of lines that produce only fertile male ( 270cl1 ) or only fertile female gametes ( 137cl1 ) [8] , [9] , [49] . All assays were done in triplicate on multiple occasions in independent experiments . Fertilization and meiosis in wild type and mutant lines was inferred from their DNA content ( or ploidy ) determined by FACS measurement of fluorescence intensity of cells stained with the DNA-specific dye Hoechst-33258 . For these experiments we used the mutant lines generated in the parent line 820cl1m1cl ( see Protocol S1 ) that expresses RFP in the female gametocyte/gamete and continues into the zygote and ookinete . Stage specific RFP expression allows selection of female gametes and zygote stages in the process of FACS-analysis of the DNA content of cells . Activation of gametocytes was performed in in vitro ( cross ) fertilization and ookinete maturation assays as described above . At 4 hours post activation ( hpa ) cells were stained for one hour at room temperature with Hoechst-33258 ( 10 µM ) and analysed at room temperature by FACS using a LSR-II flow cytometer ( Becton Dickinson ) with the following filters and settings: UB 440/40 ( Hoechst ) |400 ( parameter ) |5000 ( threshold ) ; BE 575/26 ( RFP ) |500|5000; BF 530/30 ( GFP ) |500|5000; FSC|250|2000; SSC|200|5000 . Cells for Hoechst analysis were selected on size by gating on FSC and SSC . Per sample 10 . 000–500 . 000 cells were analyzed ( medium flow speed , sample pressure: medium ) and all measurements were performed on triplicate cultures . Female cells were selected for Hoechst-33258 fluorescence intensity based on their RFP expression ( Figure 4 ) . To determine the Hoechst-fluorescence intensity from the populations of unfertilized female gametes and zygotes gates were set as shown in the Figures . Data processing and analysis was performed using the program FlowJo ( www . flowjo . com ) . Hybridisation with total RNA from wild type and CITH KO mutants were done in biological triplicates on glass slides from Agilent Technologies ( www . agilent . com ) containing sixty-mer oligonucleotides for the 5283 predicted P . berghei transcripts as described in Supplementary Online Material of reference [9] . Transcripts were tested for differential abundance through competitive hybridization of WT vs . CITH-KO labeled RNAs . Significance of expression was determined using TIGR MIDAS and MeV software and a LOWESS normalization method ( p value < . 05 ) . Genes found differentially expressed in both wild type vs . DOZI-KO and wild type vs . CITH-KO and with a fold change cut-off of 2 , were clustered using a Euclidean distance matrix of log2 ratio of genes for each condition . The heat map was drawn using the gplots package of R/Bioconductor [52] with up-regulated genes in the wild type parasites in red and down regulated genes in the wild type parasites in blue . For primers used in the generation of plasmid vectors , templates for probes for Northern and Southern blots , RT-PCR , please refer to Table S6 . Please refer to Table S7 and www . pberghei . eu . | Transmission of malaria relies on ingestion of male and female sexual precursor cells ( gametocytes ) from the human host by the mosquito vector . Fertilization results in the formation of a diploid zygote that transforms into the ookinete , the motile form of the parasite that is capable of escaping the hostile mosquito midgut environment and truly infecting the mosquito vector . The developmental program of the Plasmodium zygote depends on the availability of mRNA pools transcribed and stored , but not translated , in the female gametocyte . Here we identify the core protein factors that co-operate in the assembly of mRNAs into a translationally silent ribonucleoprotein complex . In the absence of either DOZI or CITH—two key molecules within this complex—gametocytes suffer large scale mRNA de-stabilization that does not affect fertilization but culminates in the abortion of ookinete development soon after zygote formation . We characterize large scale , evolutionarily ancient translational silencing as a principal regulatory element during Plasmodium sexual development . | [
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"... | 2010 | Universal Features of Post-Transcriptional Gene Regulation Are Critical for Plasmodium Zygote Development |
The transcriptome in a cell is finely regulated by a large number of molecular mechanisms able to control the balance between mRNA production and degradation . Recent experimental findings have evidenced that fine and specific regulation of degradation is needed for proper orchestration of a global cell response to environmental conditions . We developed a computational technique based on stochastic modeling , to infer condition-specific individual mRNA half-lives directly from gene expression time-courses . Predictions from our method were validated by experimentally measured mRNA decay rates during the intraerythrocytic developmental cycle of Plasmodium falciparum . We then applied our methodology to publicly available data on the reproductive and metabolic cycle of budding yeast . Strikingly , our analysis revealed , in all cases , the presence of periodic changes in decay rates of sequentially induced genes and co-ordination strategies between transcription and degradation , thus suggesting a general principle for the proper coordination of transcription and degradation machinery in response to internal and/or external stimuli .
Experimental evidence suggest that the majority of mRNAs are degraded with a first-order decay rate [2] , [21] . This allows to characterize mRNA disappearance time profiles by a first-order rate equation ( 1 ) where is the decay rate ( or half-life , with ) , is the mRNA concentration and is the promoter activity ( the rate of production of new mRNAs ) . It is worth noting that , the degradation rate cannot be estimated from the concentration time profile for a single gene , since the term is not usually available in the typical time-course microarray experiment . The measurement of the promoter activity time profile would require additional experiments ( such as those described in [15] ) but , in this paper , we will assume that only mRNA abundance time-series data are available . At steady-state and are constant so that and , consequently , we obtain ( 2 ) From the above equation , it is clear that at steady-state an increase ( decrease ) in mRNA concentration can be produced either by an increase ( decrease ) of transcription or by a decreased ( increased ) value of the decay rate: the two regulatory strategies have therefore an equivalent outcome . As a result , from steady-states measurements , it is hopeless to reveal the relative contribution of transcription and degradation and , most importantly , their co-ordinated activity as well . By contrast , the whole kinetics of induction and relaxation , as measured by time-courses experiments , depends on the degradation and production rate in different ways: increasing ( decreasing ) the production rate results in a proportionally increased ( decreased ) mRNA abundance , whereas the rise time ( i . e . the time required for the response to rise from to of its final value ) is not affected . Increasing the decay rate results in a faster rise time both in the induction and relaxation phases , whereas a decrease results in slower rise time [10] . This key point is illustrated in Figure 2A and in Supplementary Figures S2A and S2B . Another important consequence of half-life specificity is the regulation of the timing of gene induction , as pointed out by Elkon et al . [22] . In fact , an expression wave , i . e . the sequential activation of genes , is usually interpreted as resulting from the corresponding activation of a multi-step transcription factors cascade ( as illustrated in Figure 2B ) . Whereas such mechanism is certainly very important , there is also an alternative way to obtain an expression wave by means of a “stability gradient” . As illustrated in Figure 2C , a single transcription factor may initiate transcription of a set of target genes and their peak of induction can be modulated by a stability “gradient” , i . e . by specifically adjusted decay rates . More precisely , early induced genes would have short half-lives and late responding genes would have long half-lives . Clearly , both mechanism may well act in cells , thus generating a wide spectrum of responses . Time-courses are a very common design for microarray analysis , which allows researchers to follow the dynamics of the cellular response to perturbations [22] . Such data are available for a very large number of experimental conditions and organisms: only the Stanford Microarray Database includes to date 1545 time course data sets . Among the examples later illustrated in the paper , it is worth mentioning the genome-wide gene expression time-series obtained during the reproductive cell cycle [18] , [19] , the metabolic cycle [20] and the P . falciparum IDC [16] . The time-series datasets used in this paper are summarized in Table 2 . The goal of the DRAGON methodology is to derive a robust estimate of each mRNA species half-life starting from all available gene expression pairs . The rationale for the algorithm mainly draws on properties of pairs exhibiting a certain degree of common promoter activity ( as in [23] ) . Besides , DRAGON infers common promoter activity using a statistical model that simulates both gene-specific and common effects . The rate of change of mRNA concentration for a generic pair of genes , say gene and gene , is: ( 3 ) where the symbols and represent the mRNA time profiles of the gene pair and , and are the promoters activity , and and are the degradation rate of mRNA of gene and , respectively . The terms , are not known since we considered the case in which only mRNA abundance is measured . We modeled promoter activities as the sum of two terms , the first one common to the pair and the other one specific for each gene: ( 4 ) where is the common part , scaled by constants and , whereas and are gene specific independent stochastic processes with zero mean , that is , . Equations ( 3 ) – ( 4 ) encompass the case of: Equations ( 3 ) – ( 4 ) can therefore be written for all available gene pairs; thus , for a set of genes , we have pairs to analyze . For each gene pair DRAGON provides an estimate of the time profile of , of all the parameters , , , , , and the covariance matrix of the stochastic processes . For each gene we therefore have estimates of the decay rate , one for each pair containing that gene . Notice that equations ( 3 ) – ( 4 ) yield a couple of linear stochastic differential equations . Since measurements of mRNA concentrations are available only at given time points , it is necessary to transform ( 3 ) – ( 4 ) in a couple of discrete stochastic equations . The exact discretization of ( 3 ) – ( 4 ) is possible since they are linear [24] . The Kalman filter [25] is used on the resulting discrete equations and a maximum likelihood algorithm is exploited to generate the best possible estimate of the parameters . A complete description of the mathematical model and of the discretization and parameters estimation procedure is given in the paragraph Stochastic modeling of expression kinetics and Kalman filtering of the Materials and Methods section .
The IDC is characterized by four morphologic stages: ring , trophozoite , schizont and late schizont . The cycle begins with the red cells invasion by merozoites followed by a remodeling of the host cell in the ring stage [16] . The merozoites then develop into trophozoites . During the schizont stage , after a period of growth , the trophozoite undergoes an asexual dividing process and the parasite is ready for the next round of invasion by new merozoites ( late schizont phase ) . Bozdech et al . [16] , using microarrays , measured genome-wide mRNA abundance profiles across 48 h during one cycle of P . falciparum IDC , collecting one sample per hour . Later on , Shock et al . [17] measured mRNA half-lives of 2774 transcripts of the IDC using chemical inhibitors to reach transcriptional shut-off . The simultaneous availability of gene expression and decay data during the same biological process ( IDC ) represents a natural test bed for the validation of the DRAGON algorithm . Therefore , we applied DRAGON on Bozdech et al . dataset to obtain mRNA stability estimations ( provided in Supplementary Table S6 ) to be compared with Shock et al . measurements for performance evaluation . The resulting Pearson correlation between in vitro and in silico measures is ( P value ) , and the first principal component accounts for of the variability , thus showing a good performance for DRAGON algorithm ( see Figure 3 ) . However , since gene expression and decay data have been measured by different groups , we can speculate that of unexplained variability may be partly due to inherent biological variability and to transcriptional inhibition stress . As further analysis , we computed average mRNA half-lives in both studies for functional categories ( see Supplementary Figure S3 ) . We found that the two studies are in better accordance when half-lives are averaged for all genes within any given functional category ( Pearson correlation ) . Remarkably , Shock et al . in [17] , found progressive stage-dependent average increases in mRNA stability and suggested such phenomenon to be a major determinant of mRNA accumulation ( see Figure 4A ) . The same feature is also found using DRAGON estimated half-lives ( see Figure 4B ) . To investigate in further detail the behavior of average half-life of genes sequentially induced during IDC , we computed for each gene the time point corresponding to its peak of expression ( see the Data processing paragraph of the Materials and Methods section for details ) and selected groups of genes having peak of expression at each hourly time points over the hours monitored by Shock et al . For each gene group we computed half-lives mean and standard deviation and found a high correlation with the corresponding curve obtained using experimental data ( Pearson correlation , P value ; see Figure 4C ) . Early responding genes are characterized by high instability , whereas late responders are more stable , as also reported by Elkon et al . in [22] when studying mammalian cells . A possible explanation for the presence of stable mRNAs at the schizont stage , suggested by Shock et al . , is that it may be important for the merozoite to receive a carefully regulated “starting package” , that would allow rapid activation of the IDC following the next round of invasion [17] . By contrast , the initial low mRNA stability values may be an indication of the fast dynamic remodeling after merozoite invasion [17] . To evaluate the probability of obtaining such behavior by chance , we randomized the gene expression matrix and used DRAGON to estimate half-lives ( see Figure 4D ) . Consistently , the estimation of half-lives using random data does not produce any correlation with experimental data ( Pearson correlation ) . Gene expression during yeast cell cycle has been recently measured by Pramila et al . [18] using alpha-factor synchronization and by Orlando et al . [19] using centrifugal elutriation for synchronization . We obtained a high consistency of DRAGON estimations using data for 569 transcripts over replicate datasets ( Pearson correlation for Pramila et al . dataset and Pearson correlation for the Orlando et al . dataset; see Figure 5A–B ) . The larger variability in half-lives estimations may be explained by the inconsistencies between replicate time-series in the Pramila et al . dataset with respect to the Orlando et al . dataset ( see Figure 5C ) . All half-lives estimations obtained with the DRAGON algorithm are provided in Supplementary Tables S1 and S2 ( Pramila ) and in Supplementary Tables S3 and S4 ( Orlando ) . Notwithstanding significant differences in synchronization procedures , we also found a high correlation of DRAGON half-lives estimations over the two datasets ( Pearson correlation , P value ; see Figure 5D ) where the first principal component accounts for of the overall variability . We can speculate that of unexplained variability may be partly due to the different synchronization methods used . In fact , Orlando et al . obtained a cell cycle duration of about 2 hours , 8 samples per cycle [19] , whereas Pramila et al . obtained a cell cycle duration of about 1 hour , 12 samples per cycle . Consistently , most of the transcripts during the slower cycle display higher half-lives when compared to the fastest cycle ( see Figure 5D ) . In this paragraph we briefly discuss functional annotations ( done using GOrilla software [26] ) of novel predicted half-lives provided by DRAGON algorithm using yeast reproductive and metabolic cycle time series . For the yeast cell cycle we normalized the half-life log-distribution ( Z-score ) , for each dataset , and then computed the geometric mean to obtain a single half-life value for each gene . Notably , the averaging has the effect of reducing the impact of the different synchronization stress response . The list of half-lives normalized values ( geometric mean value equal to 1 ) for common genes to all datasets is provided as Supplementary Table S7 in the Half-life estimation paragraph of the Materials and Methods section . Unstable genes are enriched with replication fork complex ( p-value ) and stable genes ( histones HA1-2 , HB1-2 ) are enriched with nucleosome ( p-value ) . This is consistent with the need of producing a large number of histones during DNA replication process so that stable histone mRNAs contribute to a higher translation efficiency . Moreover , DNA replication timing requires first the formation of the replication fork , then the production of the needed histones for chromatin assembling: such temporal sequence of events is consistent with a rapid turnover of the replication complex genes and a slow turnover of the histone genes ( see Supplementary Figure S4 ) . Among unstable genes we also found the G1/S transition cyclins and among stable ones we found G2/M transition cyclins ( see Supplementary Figure S5 ) . In this case , the temporal sequence of events is the progression of the cell cycle from DNA replication to mitosis . For the yeast metabolic cycle ( half-lives estimations using DRAGON algorithm are provided in Supplementary Table S5 ) we found many stable mRNA species involved in the organic acid and arginine metabolism and protein catabolic processes . Among unstable messengers we found genes involved in DNA repair ( p-value ) , DNA metabolism ( p-value ) and chromatin silencing ( p-value ) .
The increasing pattern of average half-life found during P . falciparum IDC ( shown in Figure 4A ) motivated us to investigate whether a periodicity could be found also in other cyclical biological processes . We focused on the reproductive cell cycle and the metabolic cycle in Saccharomyces cerevisiae , for which high resolution time series measurements are available on public repositories ( see Table 2 ) . To study if a periodic pattern of average half-life of sequentially induced genes exists along the cell cycle progression , for each gene we computed the time points at which maximal expression is attained ( see the Data processing paragraph of the Materials and Methods section for details ) . Thus , we obtained , for each time point , the list of genes having expression peak value at that time and computed the corresponding mean and variance of DRAGON estimated half-lives values . Indeed , we found a cyclic behavior along sequentially induced genes in both datasets ( see Figure 6A for the Pramila et al . dataset and Figure 6B for the Orlando et al . dataset ) . Synchronization methods , cell cycle duration and number of samples are different between the two cited studies , but , reassuringly , the phases of the cell cycle at which mean half-life is minimal or maximal is consistent . In fact , for both datasets we observed a cyclical increase of mean half-life from G1 phase to M phase and a subsequent decrease back to G1 . The figure clarifies that the minimal mean half-life is reached at the G1/S transition , whereas the maximal value correspond to the M/G2 phase for both cycles and datasets . The latter is consistent with the observation that , in higher eukaryotes , mitosis is accompanied by global repression of nuclear RNA synthesis [27] , indicating that mRNAs must be stable to be inherited from daughter cells . The yeast metabolic cycle has been recently studied by Tu et al . [20] using a continuous culture system , after a brief starvation period , the culture spontaneously began persistent respiratory cycles of about 5 hours . In the same study , a genome-wide microarray gene expression measurement was performed . Samples were taken every 25 minutes for 3 consecutive cycles . Using DRAGON algorithm we estimated half-lives using data of 1043 transcripts . Surprisingly , also in this case we found a cyclical pattern for mean half-life of sequentially induced genes . The maximum peak is located at the RC phase and the minimum peak located at RB phase ( see Figure 6C ) . Recently , the appearance of a number of studies has revealed the fundamental role of stability regulation in shaping appropriate cell response [1] . A key point has been recently addressed by Shalem et al . [10] , who have shown the dynamic co-ordinated interplay between transcription and degradation . They have found in yeast two basic regulatory strategies in response to stress . More precisely , they measured changes of mRNA abundance and decay rates in a yeast population subjected to oxidative and DNA damage stress . By grouping genes according to the time point at which the maximal ( minimal ) fold change is attained and combining normalized ( mean and variance ) mRNA abundance and decay rate data , they constructed a “stability versus folding” ( SF ) diagram where change in mRNA stability relative to a reference state ( mean value in our case ) is plotted against the maximal fold change . Using yeast expression time-course data obtained in response to an oxidative stress and a DNA damage , they were able to reveal two different strategies: a ) a “counteracting regulation” strategy ( see Figure 7A ) , characterized by genes in which an increase ( decrease ) in degradation rates counteracts a increase ( decrease ) in mRNA abundance , i . e . repressed genes are stabilized and induced genes are destabilized; b ) a “synergistic regulation” strategy ( see Figure 7B ) , characterized by genes in which an increase ( decrease ) in degradation rates is associated with an decrease ( increase ) in mRNA abundance , i . e . induced genes are stabilized and repressed genes are destabilized . Shalem et al . also found that , progressing from early time points forward , the negative correlation ( counteracting ) was replaced with a positive correlation ( synergistic ) . Such co-ordination strategy may permit crosstalk between different steps of mRNA biogenesis , providing a mechanism to control the order and timing of events [28] . The work of Shalem et al . has shown the importance of combining expression data with decay rates under the same experimental condition to reveal the underlying strategy of co-ordination of the two “regulatory arms” , namely transcription and degradation . Uncovering such relationships is certainly a fundamental task , since the underlying reciprocal influences between mRNA production and degradation are largely unexplored [10] . The DRAGON algorithm , by estimating half-lives directly from gene expression data under specific conditions , allows the computational integration of mRNA abundance and decay rates data , making this powerful combined analysis possible when experimentally measured half-lives are not available . We computed SF diagrams for P . falciparum IDC , yeast cell cycle ( Pramila et al . dataset ) and metabolic cycle ( shown in Figure 8 ) . In panels A , C and E each blue dot corresponds to a Pearson correlation of the SF diagram at the peak time point indicated on the x-axis , for the three datasets . In panels B , D and F the SF diagrams corresponding to the correlation values indicated by the arrows in panels A , C and E are displayed . The arrows point to maximal negative ( red dots in panels B , D , F and red arrows in panels A , C , E ) and maximal positive correlation values ( green dots in panels B , D , F and green arrows in panels A , C , E ) . Strikingly , in all cases we reached the same conclusions of Shalem et al . , namely we found that early induced genes show counteracting regulation , whereas late induced genes show a synergistic regulation . The main advantage of the DRAGON algorithm consists in the estimation of the mRNA half-lives directly from gene expression time-course during condition-specific experiments . Moreover it estimates the correlation among promoter activities between pairs of genes . Another advantage of the algorithm lies in its robustness . Specifically , we observed that even if the accuracy of the absolute values of the estimated half-lives can be influenced by many factors ( such as the number of points in the time series , the accuracy of the measurements , the time interval between samples , the choice of the thresholds for the outliers , etc . ) , the ranking of half-lives is insensitive to the factors mentioned above . The main disadvantages are the following: DRAGON can work only with time-series under the same experimental condition and cannot handle steady-state values under different conditions . As a general rule a reliable estimate requires at least 10–12 time samples , i . e . a number significantly larger than the number of parameters to be estimated ( this rule is not obviously always applicable as the required number of points depends strongly on the signal to noise ratio ) and a sampling time not larger than the expected average half-life . If no information is available about the correlation of promoter activities , as a rule of thumb , a set of at least 50–100 time series must be processed together in order to have reliable half-lives estimates . One basic hypothesis is that the half-life of a transcript is approximately constant during the time course of the experiment , thus a substantial change of its value would yield an unreliable estimate . These problems can be handled by performing more measurements using a shorter sample time , or by considering moving time windows . The computational overhead can be significant: for a sample of 1000 time series there are pairs to analyze , requiring a computation of about 150 hours on a medium-speed single-processor machine capable of analyzing 2 pairs per second . Our analysis supports and strengthens Shalem et al . conclusions about the coordination of transcriptional and mRNA degradation in the cell in response to stress . We have demonstrated that during periodic processes , such as the P . falciparum IDC , the reproductive cell cycle and the metabolic cycle , the alternative interplays between changes in mRNA stability and changes in mRNA abundance are activated by periodically switching from a counteracting to a synergistic regulation . In light of these results , the classical vision of periodic processes as the result of serial transcription factor sequential activation , should be re-considered from a broader point of view by including post-transcriptional regulation and coordination .
We defined as the time profile of the expression of gene at time . The underlying conservation equation simply stems from the observation that the rate of change of with time , i . e . its time derivative , must equal the difference between the production and degradation term . Based on experimental evidence [2] , the degradation is well described by a first order term . The dynamics of the -th transcript is therefore described by ( 5 ) where is the mRNA decay rate of -th messenger . This value is linked to the half-life of the transcript by the relation . is the -th gene promoter activity regulated by transcription factors . Such regulation occurs by triggering or suppressing the transcription of the -th gene , thus we have . Moreover , the observed measure is also a noisy time-series , thus we have ( 6 ) where is the standard deviation of measurements white noise ( see supplementary material Text S1 for an example of the identification procedure ) . We considered a generic pair of expression time profiles characterized by the presence of two terms: a stochastically correlated promoter activity and a gene-specific term . We then considered the case: ( 7 ) where is a scaling factor accounting for the relative contribution to the overall promoter activity regarding gene . The term models the part of the promoter activity which is not common to the pair . We model this part by means of a noise term , which is assumed to be a white noise . The common part is modeled as a Wiener process: ( 8 ) where is white noise . Thus . The complete mathematical dynamic model for two transcripts and , together with their respective measurement equations , is ( 9 ) We can rewrite the linear dynamic system ( 9 ) using a compact matrix notation ( 10 ) whereandSince the dynamic system ( 10 ) is linear , it can be exactly discretized ( see [24] ) for a given time interval , corresponding to the time interval between two consecutive measurements . The -th measurements corresponds to , thus in the discretized system we can use in place of , to keep the notation simple . The solution of the linear dynamic system ( 10 ) is ( 11 ) and its discretized form is ( 12 ) whereand is the covariance matrix defined by ( 13 ) The unknown parameters of the model to be estimated are , , , with , and . The state variables of the system are , and . For each given choice of the parameters we used the Kalman filter [25] to estimate of the state variables . The Kalman filter equation uses a feedback control strategy . It contains a prediction term for projecting forward ( in time ) the current state to obtain the a priori estimate , and a correcting term for incorporating a new measurement into the a priori estimate to obtain an improved a posteriori estimate ( 14 ) where is the prediction Kalman gain that depends on the parameters of the stochastic equation . For each choice of we run the Kalman filter . A probability value is associated to the resulting estimation . These values measures the probability that the current parametrization of the model generates the measured time series . Denoting by the innovation of the stochastic process , is a sequence of independent gaussian random variables with covariance . The optimal set of parameters if therefore chosen according to a maximum likelihood criterion as the choice corresponding to the maximum of the a priori probability density of the innovation sequence . This corresponds to the minimum of the likelihood functionwhere is the number of samples . We are interested in the half life of the -th messenger . To use all the available information and make the method robust with respect to measurement and estimation errors , we have designed the following algorithm ( see Supplementary Figure S6 ) : Public experimental data used throughout the paper are described in Table 1 ( experimental half-lives measurements ) and in Table 2 ( gene expression time series ) . Pramila et al . in [18] and Orlando et al . in [19] experimentally measured genome-wide gene expression data during the reproductive cell cycle . We considered the ranking provided by the combined test developed by de Lichtenberg et al . [29] for each replicate for the two datasets and , among the list of 1000 genes with highest ranking , we selected those common to all datasets . We ended up with a list of 569 genes that we used for half-life estimation . Tu et al . in [20] experimentally measured genome-wide gene expression data during the metabolic cell cycle . We selected 1000 genes with the best periodicity score according to [20] . Of the 1000 genes , DRAGON estimated half-lives are 939 . To estimate peak timing , for a given noisy gene expression time profile , we preliminary performed the smoothing algorithm presented by Bar-Joseph et al . in [30] . The algorithm employs two parameters: grid ( number of spline curves ) and classes ( number of classes to use for clustering ) . In particular , for Pramila datasets we used and , for Orlando datasets we used and , for Tu dataset we used and , for Malaria dataset we used and . DRAGON estimated half-lives are provided as supplementary materials Tables S1 , S2 , S3 , S4 , S5 , S6 , S7 , described Supporting Information section . Matlab code will be provided upon request . Additional data and information can be found at web site http://www . dis . uniroma1 . it/~farina/dragon . | The amount of a given transcript in a cell is determined by a fine tuned balance of production and degradation in a complex regulatory network . Regulation of transcription controls when transcription occurs and how much mRNA is created , whereas regulation of degradation controls the rate at which messengers are destroyed . The latter mechanism has recently gained attention due to the increasing evidence of its key role in the overall co-ordination of gene expression . A long lifetime of mRNA enables a cell to produce more proteins from that mRNA . By contrast , a short lifetime rapidly alters protein levels in response to changing needs . Measuring mRNA stability is a complex and expensive experiment and , given the condition-specific response of the degradation pathway , it would be desirable to take advantage of the large variety of expression experiments stored in public databases . To this end , we developed a stochastic model to infer each specific mRNA lifetime from gene expression data . Predictions were validated using malaria data . We then applied our methodology to the reproductive and metabolic cycle of budding yeast and found , in all cases , the presence of a general principle for the proper coordination of transcription and degradation machinery . | [
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] | 2012 | Stochastic Modeling of Expression Kinetics Identifies Messenger Half-Lives and Reveals Sequential Waves of Co-ordinated Transcription and Decay |
Early warning systems ( EWS ) are management tools to predict the occurrence of epidemics of infectious diseases . While climate-based EWS have been developed for malaria , no standard protocol to evaluate and compare EWS has been proposed . Additionally , there are several neglected tropical diseases whose transmission is sensitive to environmental conditions , for which no EWS have been proposed , though they represent a large burden for the affected populations . In the present paper , an overview of the available linear and non-linear tools to predict seasonal time series of diseases is presented . Also , a general methodology to compare and evaluate models for prediction is presented and illustrated using American cutaneous leishmaniasis , a neglected tropical disease , as an example . The comparison of the different models using the predictive R2 for forecasts of “out-of-fit” data ( data that has not been used to fit the models ) shows that for the several linear and non-linear models tested , the best results were obtained for seasonal autoregressive ( SAR ) models that incorporate climatic covariates . An additional bootstrapping experiment shows that the relationship of the disease time series with the climatic covariates is strong and consistent for the SAR modeling approach . While the autoregressive part of the model is not significant , the exogenous forcing due to climate is always statistically significant . Prediction accuracy can vary from 50% to over 80% for disease burden at time scales of one year or shorter . This study illustrates a protocol for the development of EWS that includes three main steps: ( i ) the fitting of different models using several methodologies , ( ii ) the comparison of models based on the predictability of “out-of-fit” data , and ( iii ) the assessment of the robustness of the relationship between the disease and the variables in the model selected as best with an objective criterion .
One of the best documented patterns in the dynamics of vector-transmitted diseases is their periodicity at seasonal and interannual temporal scales [1–7] . These periodicities are the basis for the proposal that early warning systems ( EWS ) are feasible and useful tools for planning and decision making [2] . EWS are alert systems whose objective is to predict either epidemic outbreaks in regions where disease transmission is unstable or large outbreaks where the disease is endemic . From the early 1910s , when Captain S . R . Christophers of the British army developed a system to predict malaria in India using climatic and socioeconomic data [8 , 9] , to present times when systems are based on indoor resting densities of vectors [10] , climate , land use , and satellite imagery [11] , EWS have been regarded as useful tools to help the development of poor and disease-stricken nations [2 , 11] . The early experience by Christophers was highly successful , and his system was in use until the 1940s , when the importance of malaria as a public health issue in the Indian subcontinent diminished [9 , 11] . However , recent results have demonstrated that the blind use of EWS can lead to unreliable forecasts , especially when models are used in regions where the connection between climate and disease is not well understood [12] . Despite the possible caveats of climate-based EWS , especially because of the complexity of human diseases for which social components can be as important as natural forces [13–15] , there are successful examples of prediction of “out-of-fit” data based on the known association between climate and disease [6] . Although most of the effort in developing EWS has been focused on malaria [1 , 2] , [16] , similar efforts would be valuable for neglected tropical diseases , which represent a large burden for developing countries and whose transmission is sensitive to climate variability [6 , 17] . The leishmaniases in particular represent the fourth most important neglected tropical disease , with a burden of at least 2 . 1 million infected people per year , second to malaria in terms of the number of people affected by a protozoan vector-transmitted disease [17 , 18] . Like many other diseases , the infections are caused by protozoa , belonging to any of several different species of Leishmania spp . ( Kinetoplastida: Trypanosomatidae ) , transmitted by sand flies ( Diptera: Psychodidae ) . The clinical manifestation encompasses visceral and cutaneous/mucocutaneous cases , and is associated with a certain parasite species [6] . Our previous results indicate that American cutaneous leishmaniasis ( ACL ) is a good candidate for the use of climate-based EWS , because predictions with seasonal autoregressive ( SAR ) models can have an accuracy of over 75% [6] . Our objective in this paper is to illustrate a protocol for the development of EWS , including the evaluation of different linear and non-linear techniques for time series modeling and prediction , as well as assessment of the robustness of the relationship between the disease and climate that is the basis for building EWS .
Once the best modeling approach was selected , the robustness of the association between the cases and the exogenous forces T and MEI was assessed with a non-parametric bootstrap approach based on 10 , 000 randomizations . The idea of the non-parametric bootstrap is to reconstruct an experimental dataset based on the fitted values of a model plus the residuals sampled with replacement from such a model [24] . To generate the bootstrap samples , the model with the highest predictive R2 was used . The bootstrap was initially used to see the frequency ( % ) of times that the model from which we generated the bootstrap samples was actually selected as the best model , using the Akaike Information criterion [25] , [26] . Then , using the sub-sample of models selected as best that also have the highest probabilities in the above bootstrap test , we constructed confidence intervals for the parameters . We further refitted the model without the last 24 points to make forecasts and get the predictive R2 confidence intervals .
Figure 2 shows the square root transformed cases plotted against their lagged values ( 1 , 12 and 13 months ) and the lagged covariates T ( 4 months ) and MEI ( 13 months ) . In all cases , no obvious non-linearity is apparent in the relationship among the three variables . As expected , all models but FNN were most successful for predictions of 1 month ahead . However , for prediction steps larger than one month only NLF , SAR and GAM models with environmental covariates , MEI and T ( 4 months lag ) , did better than predictions based on the average of the time series ( Table 1] ) . The models with the worst performance were FNNs , followed by BSM and the null SAR ( i . e . , without covariates ) . For NLF , the best results were found with E = 2 and E = 3 , with the latter embedding dimension providing slightly better results for a 12 months ahead prediction . The predictive R2 was highest for the SAR model with T ( 4 months lag ) and MEI ( 13 months lag ) as covariates . Thus , the fitted values and residuals used for the bootstrap were those of the model in the first equation of ( 1 ) in Protocol S1 . The bootstrap results show that the best model is the one used to generate the data ( for 67 . 40% of the simulated time series , the model was selected as best ) . The confidence intervals for this model show that the parameters for T and MEI are statistically significant , a result that holds even if the intervals are constructed using the values for this parameter when the model was not selected as best ( Figure 3A ) . The autoregressive terms , however , are not significant as the confidence intervals include 0 . The variance of the residuals obtained from the real data is significantly shorter than the one from the simulations , probably because of the destruction of the autoregressive structure by the re-sampling of residuals [25] . Finally , the results also show ( Figure 3B ) that the maximum forecasting ability for these models is 80% , and can be as low as 50% probably because of the sensitivity of the models to a lack of a well-defined SAR structure .
EWS are a feasible ecological application for neglected tropical diseases , as illustrated for ACL . Available models have good levels of predictability up to one year ahead for the number of cases . Predictability strongly depends on the use of an appropriate structure for the different components of the model , including seasonality and exogenous drivers such as climatic variables . Depending on the model , predictability can range from poor , with approximately 50% accuracy , to high , with 80% accuracy , significantly better than that of seasonal averages ( about 65% ) . Forecasts can be useful in planning services for the populations affected , allowing estimates of approximate number of hospital beds , vaccine shots , drug doses and vector control measures . If EWS need to incorporate the spatial spread of the disease , they should do so dynamically and in relation to different landscapes , such as the geopolitical unit of this study or regions with similar climatic patterns [53]; otherwise , predictions are likely to fail , as illustrated by [12] . While there is no unique early warning system for a given disease , there should be a general approach for the development of EWS . Our work illustrates three key components of such an approach for vector-borne diseases: ( i ) the evaluation of predictability with “out-of-fit” data and not simply goodness of fit [6 , 40 , 41]; ( ii ) the comparison of a suite of possible models in terms of predictability [55 , 56] , and ( iii ) the robustness of the relationship with covariates in the selected model . Here , robustness is used following [55] , to identify covariates that are useful to predict disease numbers even when the skeleton of the model changes . Finally , none of these efforts are possible without the invaluable role of sustained surveillance and monitoring efforts . A historical retrospective reinforces this point: the success of Christophers was possible because of data availability and a deep knowledge of malaria biology , from parasites to mosquitoes and humans , realizing the influence of factors as diverse as weather and wheat prices in rendering the epidemics of malaria predictable [8] . Time series sufficiently long for developing and evaluating forecasting models around the world are countable; their number pales by comparison to the data available for weather forecasting . It is imperative that ongoing efforts are sustained and new ones are initiated whose long-term planning includes EWS as a specific goal . | Early Warning Systems ( EWS ) are management tools to predict the occurrence of epidemics . They are based on the dependence of a given infectious disease on environmental variables . Although several neglected tropical diseases are sensitive to the effect of climate , our ability to predict their dynamics has been barely studied . In this paper , we use several models to determine if the relationship between cases and climatic variability is robust—that is , not simply an artifact of model choice . We propose that EWS should be based on results from several models that are to be compared in terms of their ability to predict future number of cases . We use a specific metric for this comparison known as the predictive R2 , which measures the accuracy of the predictions . For example , an R2 of 1 indicates perfect accuracy for predictions that perfectly match observed cases . For cutaneous leishmaniasis , R2 values range from 72% to77% , well above predictions using mean seasonal values ( 64% ) . We emphasize that predictability should be evaluated with observations that have not been used to fit the model . Finally , we argue that EWS should incorporate climatic variables that are known to have a consistent relationship with the number of observed cases . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"ecology/theoretical",
"ecology",
"ecology/population",
"ecology",
"science",
"policy",
"infectious",
"diseases/epidemiology",
"and",
"control",
"of",
"infectious",
"diseases"
] | 2007 | Comparing Models for Early Warning Systems of Neglected Tropical Diseases |
Almost every animal lineage is characterized by unique sex-specific traits , implying that such traits are gained and lost frequently in evolution . However , the genetic mechanisms responsible for these changes are not understood . In Drosophila , the activity of the sex determination pathway is restricted to sexually dimorphic tissues , suggesting that spatial regulation of this pathway may contribute to the evolution of sex-specific traits . We examine the regulation and function of doublesex ( dsx ) , the main transcriptional effector of the sex determination pathway , in the development and evolution of Drosophila sex combs . Sex combs are a recent evolutionary innovation and show dramatic diversity in the relatively few Drosophila species that have them . We show that dsx expression in the presumptive sex comb region is activated by the HOX gene Sex combs reduced ( Scr ) , and that the male isoform of dsx up-regulates Scr so that both genes become expressed at high levels in this region in males but not in females . Precise spatial regulation of dsx is essential for defining sex comb position and morphology . Comparative analysis of Scr and dsx expression reveals a tight correlation between sex comb morphology and the expression patterns of both genes . In species that primitively lack sex combs , no dsx expression is observed in the homologous region , suggesting that the origin and diversification of this structure were linked to the gain of a new dsx expression domain . Two other , distantly related fly lineages that independently evolved novel male-specific structures show evolutionary gains of dsx expression in the corresponding tissues , where dsx may also be controlled by Scr . These findings suggest that changes in the spatial regulation of sex-determining genes are a key mechanism that enables the evolution of new sex-specific traits , contributing to some of the most dramatic examples of phenotypic diversification in nature .
Sexual dimorphism is a common feature of animal morphology . Most lineages display unique sets of sex-specific traits , indicating that new sexual characters are gained and old ones are lost , frequently in evolution . At the genetic level , the origin of sex-specific structures from sexually monomorphic precursors implies the evolution of new , sexually dimorphic regulatory pathways . One way in which this could occur is through the emergence of novel interactions between the sex determination pathway and an ancestrally monomorphic genetic network that controls pattern formation and morphogenesis in the evolving tissue . The nature and origin of such interactions can best be understood by characterizing the development of recently evolved sex-specific traits that have sexually monomorphic homologs in closely related species [1]–[5] . One such trait is the Drosophila sex comb , a male-specific structure that develops on the first pair of legs ( T1 ) from stereotypically arranged mechanosensory bristles . The sex comb is a recent evolutionary innovation , present in a relatively small subset of Drosophila species including the melanogaster and obscura species groups [6]–[8] . Following their origin , sex combs have undergone dramatic morphological diversification with many examples of rapid divergence between closely related species and convergent evolution in distantly related ones [9] , [10] . This pattern may be caused by sexual selection , since sex combs are used by males for grasping and stimulating females during mating [11]–[14] . Sex combs can develop by different cellular mechanisms , including a coordinated rotation of the surrounding epithelium [15]–[18] . The presence of sex combs in the model species D . melanogaster , their diversity among close relatives of this species , and the existence of more distant Drosophila lineages that primitively lack sex combs make this structure an excellent model for investigating the developmental mechanisms responsible for the origin and diversification of novel sex-specific traits . In Drosophila , sexual differentiation of most somatic tissues is controlled by the sex-specific transcription factors encoded by doublesex ( dsx ) , an effector of the sex determination pathway that is regulated by alternative pre-mRNA splicing [19]–[21] . The male isoform ( dsxM ) promotes the development of male-specific structures , including the sex comb , and represses female-specific structures; the female isoform ( dsxF ) promotes female-specific and represses male-specific traits [22]–[25] . DsxM and DsxF proteins share a common N-terminal DNA-binding domain , but have different C-terminal domains that have different effects on target gene expression [2] , [26]–[29] . Recent studies have shown that dsx is not only regulated at the level of sex-specific splicing , but is also expressed in precisely defined spatial patterns in the gonad , CNS , and other tissues [30]–[35] . These observations suggest that sexual identity is only interpreted by the subset of cells that undergo sex-specific differentiation [35] , [36] . However , the regulatory mechanisms that control the spatial pattern of dsx expression , and the importance of spatial regulation of dsx in the development and evolution of sexually dimorphic structures , are not understood . In this report , we show that precise spatial regulation of dsx is essential for sex comb development and has played a key role in the origin and evolution of this structure . The sex comb of D . melanogaster develops from a transverse bristle row that is present in both sexes but undergoes male-specific morphogenesis including a 90 degree rotation and a strong modification of the individual bristle shafts ( “teeth” ) [15] , [16] , [18] . Sex comb development requires both dsx and the HOX gene Sex combs reduced ( Scr ) , suggesting that dsx and Scr cooperate to induce sex- and segment-specific downstream targets [24] , [37] . We now show that Scr acts in part by activating localized dsx expression in the presumptive sex comb region . dsx and Scr then establish an autoregulatory loop that drives sexually dimorphic differentiation of the sex comb and surrounding epidermal cells . In Drosophila species that primitively lack sex combs , dsx is not expressed in the homologous region of the T1 leg , while in species that do have sex combs the spatial patterns of dsx and Scr reflect sex comb position and morphology . Our results suggest that the origin of a new dsx expression domain , and the evolution of the dsx-Scr feedback loop , have led to the emergence and diversification of this novel sex-specific structure . We propose that similar mechanisms based on the spatial regulation of sex-determining genes may contribute to the origin of new sex-specific traits in other animals . Consistent with this hypothesis , dsx shows specific , derived expression patterns in two other Drosophilid lineages that independently evolved different male-specific structures on their legs .
In D . melanogaster , the anterior-ventral side of the first tarsal segment ( ta1 ) is covered with tightly packed transverse bristle rows ( TBRs ) in both sexes . In the male , the most distal TBR is modified into the sex comb that rotates 90 degrees to align along the proximo-distal leg axis ( Figure 1P , Q ) [15] , [16] , [18] , [38] . In T1 leg imaginal discs of third instar larvae ( L3 ) , Scr is strongly expressed in the anterior-ventral region of the presumptive distal tibia ( Ti ) and ta1 corresponding to the future location of TBRs and the sex comb , and at a lower level in the rest of the disc [10] , [39] , [40] . dsx is expressed in an apparently more restricted domain in the T1 imaginal disc , as indicated by a dsx-Gal4 reporter [35] . To characterize the expression pattern of dsx during sex comb development in greater detail , we co-stained T1 legs at different stages with antibodies against Scr [39] and the common domain of Dsx ( DsxC ) , which is shared by the male- and female-specific protein isoforms [33] . In late non-wandering L3 T1 leg discs , high levels of Scr are already detectable in the Ti and ta1 region ( Figure 1A ) . In contrast , no Dsx expression is observed at this time in the leg discs of either sex ( Figure 1A ) . By the wandering L3 and white prepupal stages , Dsx expression is apparent in both male and female T1 discs in an anterior-ventral crescent that overlaps the distal but not the proximal part of the high Scr expression domain ( Figure 1B , C ) . In some males , Dsx expression also extends more distally and posteriorly into the region of low Scr expression ( Figure 1B ) ; this variability may reflect subtle temporal differences . Dsx expression was not detected in T2 or T3 leg discs ( Figure 1D and unpublished data ) . In the prepupal legs at 5 h after pupariation ( 5 h AP ) , Dsx expression is clearly seen in the distal ta1 in both male and female T1 legs ( Figure 1E–H ) . However , the overlap with high Scr expression , which extends more proximally , is more extensive in males than in females ( Figure 1G , H ) . In males , but not in females , Dsx expression is also seen in clusters of cells in the more distal tarsal segments ( ta2–ta5 ) ( Figure 1F , G ) . Thus , Dsx expression becomes sexually dimorphic at the prepupal stage , before the future sex comb bristles are determined . At 16 h AP , when the sex comb begins its rotation , DsxC expression in the distal ta1 is obviously dimorphic . In males , Dsx is expressed strongly in and around the presumptive sex comb , while female expression is consistently lower ( Figure 1I–K ) . Male-specific expression of Dsx in ta2–ta5 disappears by this time ( Figure 1I and unpublished data ) . By 24 h AP , when sex comb rotation is complete , Dsx and Scr develop roughly complementary expression patterns in the male leg ( Figure 1L , M ) . Dsx is expressed at a high level in sex comb teeth and surrounding epidermal cells , whereas Scr expression is low or absent in sex comb teeth but highest in the adjacent epidermal cells ( Figure 1L , M ) . This pattern is maintained at later stages ( Figure 1O ) . In females , Dsx expression becomes very low or undetectable , and Scr expression in the distal ta1 is much weaker than in males , by 24 h AP ( Figure 1N ) . These observations show that both Dsx and Scr are expressed in tightly restricted and sex-specific patterns in the sex comb at the critical time in its development . The similarities between Dsx protein ( Figure 1B , C ) and dsx-Gal4 [35] expression patterns suggest that the spatially restricted expression of dsx in the T1 leg is due to transcriptional regulation . To confirm this , we used an RNA probe directed against the male-specific dsx exon to examine dsxM expression by in situ hybridization . In wandering L3 and white prepupal leg discs , dsxM transcript is present in the same pattern as the Dsx protein ( Figure 2A , Figure 1B ) . This transcript is undetectable either in the male T2 and T3 discs or in the female T1 ( Figure 2B ) . At 24 h AP , dsxM transcript in the male T1 leg is confined to the presumptive sex comb region ( Figure 2C ) , similar to the protein distribution ( Figure 1M ) . To confirm that the restriction of dsxM to the sex comb region is not due to post-transcriptional regulation , we drove ectopic UAS-dsxM expression in both males and females using the rn-Gal4 driver , which is expressed around the entire circumference of the pupal leg from distal ta1 to ta4 [10] . The UAS-dsxM construct [30] contains most of the male-specific 3′UTR , including a predicted recognition site for the bantam miRNA . Both in situ hybridization with a dsxM-specific probe and immunostaining with the antibody specific to DsxM [31] revealed ectopic dsxM expression throughout the tarsus in all three legs , with no detectable difference between males and females ( Figure 2D , E ) . Thus , we find no evidence for a post-transcriptional mechanism confining dsx expression to the sex comb region . Finally , quantitative rt-PCR with primers flanking male-specific and female-specific exon junctions did not reveal any differences in dsx splicing between T1 and T2 legs ( unpublished data ) . We conclude that the spatially restricted expression of dsx in the sex comb region is caused by its precise transcriptional regulation . To determine the significance of the spatial regulation of dsx in sex comb development , we examined the effects of loss and ectopic expression of the male-specific dsx isoform ( dsxM ) in different cell types . Knocking down dsx in both bristle precursors and epidermal cells in rn-Gal4/UAS-dsxRNAi males resulted in an intersex phenotype with small , partially rotated sex combs composed of bristles that were intermediate in morphology between normal sex comb teeth and female TBR bristles ( Figure 3C ) . A similar intersex phenotype was observed in females ( not shown ) . In both sexes , the number of bristles in the partially formed sex comb was intermediate between a wild-type sex comb and the distal-most female TBR . These phenotypes are very similar to the dsx null phenotype , confirming that dsxM promotes sex comb development in males while dsxF actively blocks it in females [22] , [35] . Expression of dsxM in all leg bristles in tub-Gal80ts; neur-Gal4/UAS-dsxM flies that were shifted to the restrictive temperature as late L3 larvae resulted in the transformation of all or most TBR bristles into sex comb teeth , as indicated by thicker and blunter shafts and dark pigmentation ( Figure 3B , F ) . This phenotype was observed in both males and females . However , none of the bristles outside of the TBRs showed any signs of transformation . In males , the transformation was strongest toward the distal end of ta1 , while in females this region showed the weakest transformation ( Figure 3B , F ) . These results suggest that DsxM expression in bristle precursor cells is sufficient to induce sex comb tooth development , but only in regions that express high levels of Scr . The number of bristles in the distal-most female TBR was unchanged despite the changes in bristle morphology . Interestingly , the ventral-posterior region of ta1 in the T3 leg , which also carries TBRs , also developed bristles with sex comb-like morphology; in contrast , no changes were observed in T2 legs ( not shown ) . The T3 TBRs are specified by the HOX gene Ultrabithorax [40] , while epidermal cells in the T2 leg do not express any HOX genes at the late larval and pupal stages . Thus , it appears that Ubx or its downstream targets can substitute for Scr in cooperating with dsxM to induce sex comb tooth development . In contrast to our results , ectopic expression of dsxM under the control of a heat shock promoter ( hs:dsxM ) can induce tooth-like bristles in all three legs and in regions outside of the high Scr domain in T1 [24] . This difference may be explained by the fact that the hs:dsxM constructs were expressed throughout development in both bristle and epithelial cells . Despite the changes in bristle shaft morphology , the proximal TBRs showed little or no rotation in tub-Gal80ts; neur-Gal4/UAS-dsxM flies . We next drove ectopic dsxM expression in both the bristles and the epidermal cells in the distal ta1–ta4 in tub-Gal80ts; rn-Gal4/UAS-dsxM flies . In the female , this treatment transformed two to four distal TBRs into small sex combs that underwent complete or partial rotation ( Figure 3G ) . However , the number of bristles per TBR was unchanged . A similar phenotype was observed in males ( not shown ) . No effects were observed in the more distal tarsal segments ( Figure 3G ) . These results confirm that sex comb rotation is driven by the surrounding epidermal cells [16] , [18] and that these cells require high levels of both Dsx and Scr . In summary , ectopic expression experiments indicate that dsxM acts in bristle precursor cells to specify sex comb tooth morphology and in the surrounding epidermal cells to promote sex comb rotation , and that precise spatial regulation of dsx is essential for determining the location and size of this structure . Next , we investigated cell type-specific requirements for Scr in sex comb development . As previously reported [10] , uniform Scr expression in the distal tarsus in tub-Gal80ts; rn-Gal4/UAS-Scr flies results in the formation of ectopic , non-rotating sex combs in ta2–ta4 in the male T1 leg ( Figure 3I ) . Knocking down Scr in tub-Gal80ts; rn-Gal4/UAS-ScrRNAi flies results in the complete loss of the sex comb and TBRs in the distal ta1 , indicating a homeotic transformation to the T2 identity ( Figure 3J ) . However , when Scr function was knocked down specifically in bristle precursor cells in tub-Gal80ts; neur-Gal4/UAS-ScrRNAi flies , the number of sex comb teeth was reduced to ∼50% of normal , but tooth morphology and rotation were not affected ( Figure 3K ) . This phenotype was not significantly enhanced by the addition of UAS-Gal4 ( not shown ) . Since Scr expression in ta1 is sexually dimorphic and the sex comb contains more bristles than the homologous female TBR , it is possible that Scr levels determine the number of bristle precursors during larval or prepupal stages . Scr may also be required in epidermal cells for sex comb rotation , but is dispensable for the male-specific differentiation of sex comb teeth , at later stages . This is consistent with the observation that Scr protein disappears from the sex comb precursor bristles by 16 h AP ( Figure 1LI , J ) . Thus , many functions of Scr in sex comb development may be mediated by the activation of dsx expression ( see below ) . Based on the observations that Dsx is expressed only in the T1 leg disc overlapping the high Scr domain and that Scr expression precedes that of Dsx ( Figure 1A–C ) , we hypothesized that Scr positively regulates dsx expression . To test this hypothesis , we first performed an RNAi knockdown of Scr in tub-Gal80ts; rn-Gal4/UAS-ScrRNAi and tub-Gal80ts/UAS-Gal4; rn-Gal4/UAS-ScrRNAi flies . In both male and female T1 leg discs , DsxC expression was strongly reduced in the former genotype and undetectable in the latter after a 24-h shift to the restrictive temperature ( Figure 3L ) , indicating that Scr is necessary for Dsx expression . In a reciprocal experiment , we expressed Scr around the entire circumference of distal ta1–ta4 in all three legs in tub-Gal80ts; rn-Gal4/UAS-Scr flies . This resulted in the ectopic expression of Dsx in the same pattern as the ectopic Scr in all three pairs of leg discs ( Figure 3M and unpublished data ) , indicating that Scr is sufficient to activate Dsx in the tarsus . These observations suggest that a major role of Scr in sex comb development is to initiate a sex-specific developmental program by turning on dsx expression . Consistent with this notion , co-expression of Scr and dsxM in tub-Gal80ts; rn-Gal4/UAS-Scr UAS-dsxM flies produces the same phenotype as ectopic expression of Scr alone ( not shown ) . Scr expression in the T1 leg is sexually dimorphic in D . melanogaster and other species with rotated sex combs ( Figure 1L–P ) [10] . To test whether dsx is responsible for the sex-specific regulation of Scr , we first examined the effects of dsx knockdown in rn-Gal4/UAS-dsxRNAi males . At 24 h AP , Scr expression in the distal ta1 was reduced , becoming intermediate between wild-type male and wild-type female ( Figure 3D , compare to Figure 1L–N ) . In a reciprocal experiment , we looked at the effect of dsxM expression in rn-Gal4/UAS-dsxM females . In this genotype , Scr expression was induced in the distal ta1 in a pattern identical to the rn-Gal4/UAS-dsxRNAi males ( Figure 3H ) . These results are consistent with the effects of dsx on adult morphology: in the absence of either dsxM or dsxF , or in the presence of both isoforms , the distal-most TBR assumes a morphology intermediate between a sex comb and a female TBR in both XX and XY flies ( Figure 3C ) [22] . We conclude that in the absence of dsx , Scr is expressed at an intermediate level and that this level is sufficient to induce partial sex comb development in D . melanogaster . DsxM up-regulates and DsxF down-regulates Scr relative to this default level , so that both isoforms are actively involved in sexually dimorphic development . The size and location of sex combs in the melanogaster and obscura species groups correlate with the domain of high Scr expression [10] , [41] . Moreover , Scr expression is sexually dimorphic in species with rotated sex combs , but not in species in which sex comb teeth remain organized into TBRs [10] . We used DsxC and DsxM antibodies to examine dsx expression in the presumptive sex comb region in melanogaster group species with diverse sex comb morphologies ( Figure 4 ) . Importantly , these species represent several independent phylogenetic contrasts , since distantly related species have evolved similar sex combs independently ( Figure 4H ) [10] . In all species , Dsx expression is strongest in sex comb teeth and is also present in the adjacent epithelial cells , while Scr expression is low or absent in sex comb teeth but highest in the surrounding cells ( Figure 4 ) . In D . ficusphila and D . kikkawai , which independently evolved large sex combs spanning the entire ta1 and ta2 , Scr and Dsx are expressed throughout the anterior-ventral surface of these segments ( Figure 4A , E ) . In D . bipectinata and D . biarmipes , which independently evolved rotated sex combs derived from two separate TBRs , Dsx and Scr are expressed in and around both rows of teeth ( Figure 4B , F ) . In the closest relatives of these species that have transverse sex combs ( D . malerkotliana and D . takahashii , respectively ) , Dsx expression in epithelial cells is lower than in the species with rotated sex combs , and is only seen in a few cells immediately adjacent to the sex comb ( Figure 4C , G ) . In D . nikananu , whose sex comb is secondarily reduced from a D . kikkawai-like ancestral state , Dsx expression is also confined to a smaller domain that resembles the D . melanogaster pattern ( Figure 4D ) . Thus , the spatial correlation of Dsx and Scr expression is maintained in all species and reflects sex comb morphology rather than phylogenetic history . This pervasive pattern of convergent evolution suggests that the cross-regulatory relationship between Dsx and Scr is conserved throughout the melanogaster species group and may contribute to the rapid evolution of sex comb morphology . The sex comb is a recent evolutionary innovation that is absent in most Drosophila species . In the ancestral condition , the pattern of mechanosensory bristles is similar in males and females . To understand the role of dsx regulation in the origin of sex combs , we examined Dsx expression in several distantly related species of Drosophila and related genera ( Figures 5 , 6 ) . The melanogaster and obscura species groups form a monophyletic lineage characterized by the presence of sex combs ( Figure 5A ) . In D . pseudoobscura , a representative of the obscura group , Dsx is expressed in the presumptive sex comb region ( Figure 5D , E ) , suggesting that this expression domain was already present in the last common ancestor of both species groups . In Scaptodrosophila lebanonensis and Drosophila ( Dorsilopha ) busckii , which are among the most distant outgroups in our analysis , no Dsx expression is seen in the L3 leg discs ( Figure 5B , C ) . In D . hydei and D . virilis , which represent different species groups in the subgenus Drosophila and also primitively lack sex combs , Dsx is expressed in the T1 tarsus in two clusters per segment during the larval and prepupal stages ( Figure 5N , P and unpublished data ) . These clusters , which are seen in both males and females but only in the T1 leg ( Figure 5O ) , are not homologous to the presumptive sex comb region , resembling instead the transient expression in the distal tarsal segments of male D . melanogaster ( Figure 1F ) . Dsx expression in D . hydei and D . virilis is also transient: by the time of leg extension in the early pupa , when bristles begin to develop , no Dsx expression is detected in either males or females ( Figure 5Q ) . The closest well-studied relatives of the melanogaster and obscura species groups that lack sex combs are the Neotropical Sophophora including the willistoni and saltans species groups ( Figure 5A ) [42] , [43] . However , the Neotropical Sophophora have recently been shown to be the sister group of the genus Lordiphosa , some but not all representatives of which have sex combs [8] , [44]–[46] . Thus , it is not clear whether the willistoni and saltans species groups lack sex combs primitively or have lost them secondarily . In D . willistoni and D . saltans imaginal discs , the DsxC antibody shows expression around the entire circumference of the tarsus in all three pairs of legs in both sexes , as well as in a more proximal crescent that is only seen in the male T1 disc ( Figure 5G–I ) . Surprisingly , the ring pattern is seen with both DsxC and DsxM antibodies in both males and females , while the male T1 crescent is only detected with the DsxC antibody . Although the DsxC antibody reveals a typical dsx expression pattern in the adult brain of D . willistoni ( Figure 5J ) , the DsxM antibody shows a different pattern , suggesting that it may not be specific to Dsx . Thus , it is not clear whether the ring seen in larval leg discs reflects dsx expression . At 5 h AP , this ring can be seen to extend from ta2 to ta4; the crescent pattern can no longer be detected at this stage ( Figure 5K ) . By the time of leg extension in the early pupa ( 24–27 h AP ) , the ring pattern also disappears from the T1 legs of both sexes ( Figure 5L ) . Thus , in contrast to the melanogaster and obscura species groups , dsx expression is not maintained at the developmental stage when bristle differentiation begins . The morphology and chaetotaxy of T1 and other legs in D . willistoni and D . saltans are sexually monomorphic , lacking even the male-specific chemosensory bristles that are present in most other Drosophila lineages ( Figure 5F , not shown ) . This suggests that dsx is not directing sex-specific morphological differentiation in the legs of these species . Overall , our results show that dsx is expressed in temporally dynamic , rapidly evolving , and segment-specific patterns in Drosophila legs . However , dsx expression in the presumptive sex comb region appears to be an evolutionary innovation that coincides with the origin of the sex comb . Sex combs are only one example of sex-specific structures that decorate the legs of many Drosophilidae and other Diptera [47] . For example , T1 TBRs show strong sexual dimorphism in the immigrans species group , a member of the Drosophila subgenus that is distantly related to the melanogaster and obscura groups and other Sophophora ( Figure 5A ) . The females of D . immigrans have the same arrangement of TBRs as other Drosophila species , while in males the anterior-ventral surface of ta1 and ta2 is covered with smaller but much more numerous and densely packed bristles ( Figure 6A , B ) . The corresponding region of the L3 imaginal disc shows Dsx expression in both males and females ( Figure 6C , not shown ) ; in contrast , no expression is seen in T2 and T3 legs ( Figure 6D ) . By 5 h AP , this expression remains strong in males but begins to fade in females ( Figure 6E , not shown ) . In extended pupal legs , when bristles begin to differentiate , all of the densely packed bristles are expressing high levels of Dsx in males , whereas no expression is seen in the homologous region in females ( Figure 6F , G ) . In most species of the genus Zaprionus , TBRs on the distal ta1 of the T1 leg are replaced with much thinner and more numerous bristles that form a densely packed brush [48] , [49] . This structure is only observed in males , while females retain standard T1 leg morphology ( Figure 6H–J ) . As in the melanogaster and immigrans species groups , we find that this sex-specific pattern is prefigured by Dsx expression in the corresponding region of the T1 leg ( Figure 6K , L ) , but no expression is seen in the T2 and T3 legs ( not shown ) . Phylogenetic analysis suggests that male-specific morphological structures originated independently in the immigrans species group , Zaprionus , and the melanogaster+obscura clade ( Figure 5A ) . In each case , these morphological innovations correlate with newly evolved , T1-specific patterns of dsx expression . These observations suggest that the evolutionary gain of new dsx expression domains through a regulatory link between Scr and dsx has been a key step in the origin of novel sexually dimorphic structures .
Traditional models of sexually dimorphic development in Drosophila have assumed that the sex determination pathway functions ubiquitously , and emphasized the joint regulation of target genes by dsx and the genes that establish positional information [19] . Indeed , co-regulation of downstream targets by dsx and spatial selector genes and signaling pathways plays a key role in the development of sex-specific morphological structures including genitalia [50]–[52] , posterior abdominal segments [1] , [2] , and oenocytes [53] . However , recent work has shown that dsx is expressed in tightly restricted spatial patterns [30]–[35] , suggesting that sexually dimorphic development may also be regulated through localized deployment of dsx . Here , we show that localized transcriptional activation of dsx in the T1 leg initiates the development of a sex-specific structure , and that the spatial pattern of dsx defines the position and morphology of this structure . For the first time , we also identify an upstream regulator of dsx transcription , the HOX gene Scr . Our results indicate that Scr is responsible for activating dsx expression in the T1 leg , and thus for restricting sexually dimorphic chaetotaxy to a single thoracic segment . Since Dsx expression is more restricted than that of Scr , we suspect that dsx is also regulated by one or more of the transcription factors that establish the proximo-distal leg axis . In turn , dsx up-regulates Scr in males in the presumptive sex comb region prior to and during sex comb rotation . Thus , the HOX and sex determination genes establish a positive autoregulatory loop ( Figure 7 ) . The mutual up-regulation of Scr and dsxM may explain why Dsx levels become much higher in males than in females as sex comb development progresses . The loss of Dsx expression in the homologous region in females is caused by the gradual reduction of protein levels in both epithelial and bristle cells; we do not observe large amounts of cell death in this region at the pupal stage . In contrast , Dsx-expressing domains in the central nervous system ( CNS ) become sexually dimorphic through programmed cell death and cell division . In one set of Dsx-expressing neurons , DsxF directs cell death in females , while in another DsxM contributes to an increase in cell division in males [33] . In the embryonic gonad , sex differences in the number of Dsx-expressing cells also result from the activation of cell death by DsxF [54] . Taken together , these results demonstrate that differences in dsx transcription , functional differences between Dsx isoforms , and the cellular context in which these isoforms are expressed can lead to sex-specific differentiation through a variety of cellular processes . The molecular mechanisms responsible for the Scr-dsx feedback loop may be different at different stages . The initial activation of dsx by Scr in the late L3 leg disc may be direct , since the two proteins accumulate in the same cells . However , once the bristle precursor and epithelial cells are segregated at the pupal stage , Scr and Dsx domains become complementary and cell type-specific: Dsx expression is highest in the sex comb teeth while Scr is excluded from the bristle cells but is strongly up-regulated in the epithelial cells immediately adjacent to the sex comb . These patterns suggest that the cross-regulation between Dsx and Scr at this stage may be mediated by cell-cell signaling . The regulation of dsx and Scr in precise spatial and cell type-specific patterns casts the roles of HOX and sex determination genes in development in a new light . Instead of modulating the output of a patterning network from the outside as “master regulators , ” both dsx and Scr are intimately integrated into the middle of this network ( Figure 7 ) . Akam [55] has suggested that HOX genes may act more as “micromanagers” than master regulators in many developmental contexts . It now appears that the main determinant of sex-specific development may have to be demoted to a similar position . New sex-specific traits may arise in two different ways . If the sex determination pathway is already active in the relevant tissue , the origin of a novel trait requires only the acquisition of new joint downstream targets by the sex determination and spatial patterning genes . This may happen either through evolution of Dsx binding sites in a previously sexually monomorphic enhancer or through the co-option of a pre-existing dimorphic enhancer into a new tissue [2] , [53] . In contrast , a tissue that shows no sexual dimorphism in the ancestral condition may not express dsx at all . In this case , a new sex-specific trait cannot arise without the evolution of a new dsx expression domain . To our knowledge , the sex comb is the first example of an evolutionary change of this kind . We suggest that in the common ancestor of the melanogaster and obscura species groups , dsx was recruited into a previously sexually monomorphic developmental pathway , resulting in the gain of a novel expression domain in the presumptive sex comb region ( Figure 7 ) . This cooption may have been facilitated by the fact that dsx is already expressed in segment-specific , and presumably Scr-regulated patterns in some species that primitively lack sex combs . In parallel , Scr and dsx must have acquired new joint downstream targets that mediate different aspects of sex comb morphogenesis including bristle patterning , tissue rotation , and modification of bristle shafts . Subsequent changes in the spatial regulation and cross-regulation of dsx and Scr , as well as gains and losses of downstream targets , have likely contributed to the dramatic evolutionary diversification of sex combs . The positive feedback loop between dsx and Scr may play a major role in generating sex comb diversity across species . Regardless of the exact molecular mechanism , our results suggest that any alteration in Scr expression expands or contracts the dsx domain , and vice versa . One can imagine that any mutation that increases Scr expression , for example a cis-regulatory mutation in the Scr leg enhancer , would increase the expression of dsx , which in turn would further up-regulate Scr in the male , and so on; the effects of any mutation that increases the expression of dsx would be similarly amplified . Conversely , mutations that reduce either Scr or dsx expression would also have their effects on both Scr and dsx magnified by the autoregulatory loop . This positive-feedback amplification would allow sex comb morphology to respond rapidly to selection for increased or decreased sex comb size . Comparative and experimental analyses show that male secondary sexual traits are lost ( or reduced ) as frequently as acquired ( or exaggerated ) , and that this pattern may be due to rapidly shifting female preferences [56] , [57] . It is possible that positive feedback loops similar to the Scr-dsx circuit are involved in the rapid gain , diversification , and loss of other exaggerated display characters and sexually selected traits . The spatial regulation of dsx in Drosophila raises an intriguing question about the evolution of sex-specific traits in general . Sexual selection leads not only to the rapid evolution of existing characters , but also to the frequent origin of novel morphological structures , behaviors , and other phenotypes [58] , [59] . Almost every lineage of animals has invented its own sex-specific ( often , but not always , male-limited ) organs . In Diptera , different families and genera have evolved a variety of sex-specific structures and modifications on all three pairs of legs , on the eyes , mouthparts , and the head capsule , on the thorax , abdomen , and generally on every body part imaginable [47] , [60] , [61] . Some of these structures reach truly bizarre appearance and proportions , such as the branched and malformed legs of some Dolichopodidae and Platypezidae or the eye stalks that exceed body length in Diopsidae and Platystomatidae , yet they have no clear homologues outside of the lineages that possess them . At the same time , the loss of sex-specific characters occurs at roughly the same rate as the origin of new ones [56]—in other words , there is a constant turnover of sex-specific traits . Is it possible that the proximate cause of this turnover of sex-specific traits lies in the acquisition and loss of new spatial expression domains of dsx ? This model is supported by our observation that different male-specific structures that independently evolved in the immigrans species group and in the genus Zaprionus are , like the sex comb , associated with the origin of new dsx expression patterns . Male-specific reduction of wing size in Nasonia wasps , which is associated with genetic changes near the dsx locus , may represent another example [62] . The modular organization of transcriptional control allows gene expression in different tissues to be decoupled both functionally and evolutionarily through the use of modular , tissue-specific enhancers [63] , [64] , making the gain and loss of discrete expression domains entirely possible . A dsx enhancer responsible for sex comb development in Drosophila was gained and underwent rapid diversification within the genus , raising the possibility that other novel enhancers and expression domains have originated in other lineages on similarly short timescales .
The following strains were used: rn-Gal44–5 [65] , neur-Gal4A101 [66] , UAS-dsxM ( Lee et al . 2002 ) [30] , UAS-ScrM15 [67] , UAS-dsxRNAi , UAS-ScrRNAi [68] , and tub-Gal80ts20 [69] . Expression of the UAS constructs was activated at the wandering third instar or white prepupal stage by shifting tub-Gal80ts20; Gal4/UAS flies from 18°C to 30°C . Animals were reared , processed for immunocytochemistry , and imaged as described [16] , [33] . The primary antibodies used were rat anti-DsxCommon , 1∶50 [33] , rat anti-DsxM , 1∶500 [31] , and mouse anti-Scr 6H4 . 1 , 1∶10 [39] . The secondary antibodies were AlexaFluor 488 and 594 used at 1∶200 ( Invitrogen , Carlsbad , CA ) . In D . melanogaster , both Dsx antibodies showed identical expression patterns in larval leg discs and pupal legs . In species distantly related to D . melanogaster , cross-reactivity of the Dsx antibodies was confirmed by staining adult male and female brains . The DsxC antibody identified neuronal clusters that were similar in size and position to those seen in D . melanogaster ( Figure 5 ) , while the DsxM antibody showed variable staining in different species suggesting that it may not be fully specific . With the exception of D . melanogaster , all Dsx expression patterns shown in the figures were determined using the DsxC antibody . In situ hybridization on pupal legs and imaginal discs was performed as described [37] using RNA probes directed against the male-specific exon of dsx . Probe template was amplified from genomic DNA by PCR using primers dsxM-Fwd ( AATCGCACTGTAGCCCAGATC ) and dsxM-Rev ( CTGGAGTCGGTGGACAAATC ) . | Most animals are sexually dimorphic , yet each species has a different set of sex-specific traits . Much of evolutionary biology since Darwin has focused on explaining these differences . In contrast to the well-developed theories of sexual selection ( how and why males compete for females ) we are still far from understanding the molecular mechanisms underlying the rapid gain and loss of sexually dimorphic phenotypes . In Drosophila melanogaster , the development of most sex-specific traits is controlled by the doublesex transcription factor . One of these traits is the sex comb , a group of modified bristles that develops on the front legs of males , which they use during mating to grasp the female's abdomen and genitalia . Sex combs are a recent innovation that evolved within the genus Drosophila but show dramatic diversity in the relatively few species that have them . In this study , we show that the origin and diversification of sex combs were associated with an evolutionary gain of a new doublesex expression domain and novel regulatory interactions between doublesex and the HOX gene Sex combs reduced , best known for its role in the specification of the labial and first thoracic segments . We find that other sex-specific structures that evolved in separate Drosophila lineages are also linked to new doublesex expression domains , suggesting that changes in the spatial regulation of doublesex may be a general mechanism enabling the evolutionary turnover of sex-specific traits . | [
"Abstract",
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] | 2011 | Evolution of Sex-Specific Traits through Changes in HOX-Dependent doublesex Expression |
The rymv1-2 and rymv1-3 alleles of the RYMV1 resistance to Rice yellow mottle virus ( RYMV ) , coded by an eIF ( iso ) 4G1 gene , occur in a few cultivars of the Asiatic ( Oryza sativa ) and African ( O . glaberrima ) rice species , respectively . The most salient feature of the resistance breaking ( RB ) process is the converse genetic barrier to rymv1-2 and rymv1-3 resistance breakdown . This specificity is modulated by the amino acid ( glutamic acid vs . threonine ) at codon 49 of the Viral Protein genome-linked ( VPg ) , a position which is adjacent to the virulence codons 48 and 52 . Isolates with a glutamic acid ( E ) do not overcome rymv1-3 whereas those with a threonine ( T ) rarely overcome rymv1-2 . We found that isolates with T49 had a strong selective advantage over isolates with E49 in O . glaberrima susceptible cultivars . This explains the fixation of the mutation T49 during RYMV evolution and accounts for the diversifying selection estimated at codon 49 . Better adapted to O . glaberrima , isolates with T49 are also more prone than isolates with E49 to fix rymv1-3 RB mutations at codon 52 in resistant O . glaberrima cultivars . However , subsequent genetic constraints impaired the ability of isolates with T49 to fix rymv1-2 RB mutations at codons 48 and 52 in resistant O . sativa cultivars . The origin and role of the amino acid at codon 49 of the VPg exemplifies the importance of historical contingencies in the ability of RYMV to overcome RYMV1 resistance .
Rice yellow mottle virus , of the genus Sobemovirus , causes a major disease in Africa [1] . The virus has a narrow host range restricted to the two cultivated rice species , Oryza sativa and O . glaberrima , and a few wild grasses [2] . The Asiatic rice O . sativa , which was introduced to East Africa and Madagascar in the 11th century and to West Africa in the 15th century , is now grown throughout Africa . In contrast , the African rice O . glaberrima , which was domesticated in the Niger Interior Delta ca . 3000 years ago , has been grown in West Africa only [3]–[6] . A few rice cultivars exhibit a high resistance to RYMV characterized by an absence of symptoms and no viral detection by ELISA [7] . The inheritance of this high resistance is recessive . The resistance gene RYMV1 was identified as an eIF ( iso ) 4G gene [8] . Four rymv1 resistance alleles have been characterized , one in O . sativa ( rymv1-2 ) and three in O . glaberrima cultivars ( rymv1-3 , rymv1-4 , rymv1-5 ) [9] . The genetic determinants of the ability to break resistance alleles rymv1-2 , rymv1-3 and rymv1-4 were investigated in this study . The genetic determinants of RYMV1 resistance are located in the central domain of eIF ( iso ) 4G1 ( named MIF4G domain ) [8] , [10] . The resistance allele rymv1-2 of the two O . sativa cultivars Gigante and Bekarosaka is due to a point mutation leading to the replacement of a glutamic acid by a lysine at codon 309 ( Figure 1a ) . The resistance allele rymv1-3 of the cultivar Tog5681 and of a few other O . glaberrima cultivars [9] , [11] is caused by a deletion of codons 322-324 [8] . The rymv1-4 resistance allele of the cultivar Tog5672 and of a few other O . glaberrima cultivars is due to a point mutation resulting in the replacement of a glutamic acid by a lysine at codon 321 [8] . Interestingly , there is a single amino acid-specific difference between O . glaberrima and O . sativa at position 303 of the MIF4G domain , close to the genetic determinants of RYMV1 resistance . An alanine occurs in O . sativa whereas an aspartic acid exists in O . glaberrima ( Figure 1a ) . This result was first established on a few sequences [8] , [9] and recently confirmed on a larger data set ( C . Lirette and E . Hébrard , unpublished results ) . Earlier studies established the features of the RYMV1 resistance breakdown ( RB ) process [12]–[15] . In most instances , the inoculation of resistant plants does not cause infection . Occasionally however , fully systemic symptoms are expressed , the virus content reaches that of susceptible plants , and the virus is readily transmitted from plant-to-plant: i . e . the resistance is overcome . Subsequently , wild-type ( WT ) isolates are displaced in the resistant plants by newly-adapted mutants with higher multiplication rates . Resistance-breaking process involves the Viral Protein genome linked ( VPg ) , which is an intrinsically disordered protein [16] . The RB mutations occur in codons - referred to as virulence codons - in the central domain of the VPg between positions 37 and 52 . The rymv1-2 resistance is most frequently overcome by the substitution of arginine ( R ) by glycine ( G ) at codon 48 and then of glycine by glutamic acid ( E ) at the same codon [13] ( Figure 1b ) . Minor RB mutations occur at codons 48 ( R48I and R48W ) , 42 ( N42Y ) , 43 ( T43A ) and 52 ( H52Y ) . The rymv1-3 resistance breakdown of the O . glaberrima cv . Tog5681 involves the replacement of serine ( S ) by proline ( P ) or alanine ( A ) at codon 41 , and/or the replacement of histidine ( H ) by tyrosine ( Y ) at codon 52 [15] . Interestingly , H52Y is the only RB mutation that overcomes both rymv1-2 and rymv1-3 resistance alleles . Recently , the direct interaction between the MIF4G domain and the VPg was shown by co-purification and two-hybrid assays [17] . Mutations mimicking the resistance alleles in MIF4G strongly diminished the interaction with the VPg . Virulence mutations that become fixed in the VPg partially or fully restored the interaction . One of the most salient feature of RYMV1 RB process is the converse genetic barrier to rymv1-2 and rymv1-3 resistance breakdown . Isolates that can break rymv1-2 never overcome rymv1-3 whereas isolates that can break rymv1-3 rarely overcome rymv1-2 . This specificity is associated with the amino acid at codon 49 of the VPg , a position which is adjacent to the main virulence codons 48 and 52 . Isolates with a glutamic acid ( E ) do not overcome rymv1-3 [15] whereas those with a threonine ( T ) rarely overcome rymv1-2 [13] . The changes at codon 49 are coordinated with those at the adjacent codons 48 and 50 ( Figure 1c ) . Mutations R ( AGA ) 48R ( AGG ) , E ( GAG ) 49T ( ACG ) and R ( AGG ) 50K ( AAG ) are generally associated , R ( AGA ) 48E49R50 being the ancestral motif and R ( AGG ) 48T49K50 being the derived motif [13] . There are , however , a few isolates with the REK motif [13] . This stretch of four coordinated changes ( one synonymous and three non-synonymous ) at codons 48-49-50 is a notable case of epistasis [18] . In this study , the identity , role and origin of the genetic determinants of the ability to break RYMV1 resistance alleles were investigated . First , we tested whether the amino acid at codon 49 was a key genetic determinant and not just a molecular ‘signature’ of the ability to overcome the RYMV1 resistance alleles . Second , we examined the effects of the amino acid at codon 49 on the fixation of RB mutations at the adjacent virulence codons 48 and 52 . Third , we studied whether the threonine and glutamic acid at codon 49 resulted from past adaptation to O . glaberrima and O . sativa , respectively . This hypothesis arose from the matching geographical distribution of the amino acid polymorphism at codon 49 of the VPg of the virus and that of the two cultivated rice species in Africa . O . glaberrima cultivars and RYMV isolates with T49 occur in West Africa only , whereas O . sativa cultivars and RYMV isolates with E49 occur everywhere in Africa [3] , [15] . Moreover , codon 49 is under strong diversifying selection [13] , which may reflect past adaptation to rice species . In conclusion , we found that RYMV-rice relationships do not fit a simple co-evolutionary arms race model . An alternative evolutionary scenario is proposed that emphasizes the role of historical contingencies in virus adaptation to a new host .
The mutant CIa*49E was obtained from the infectious clone of the isolate CIa ( T49 ) to establish the causal role of the amino acid at codon 49 in the ability to break RYMV1 resistance alleles . The RB ability of the isolate CIa ( T49 ) and of the mutant CIa*49E were then compared . The isolate CIa and the mutant CIa*49E were inoculated at the same concentration to the rymv1-2 resistant O . sativa indica cv . Gigante and to the rymv1-3 resistant O . glaberrima cv . Tog5681 . Forty-two days after the inoculation of the isolate CIa ( T49 ) , ca . 96% of the rymv1-3 resistant plants but only ca . 5% of the rymv1-2 resistant plants showed severe symptoms and reached a high virus content ( Table 1 ) . The high ability of the isolate CIa ( T49 ) to overcome rymv1-3 and its low ability to overcome rymv1-2 are typical of isolates with T49 [13] , [15] . The substitution of threonine by a glutamic acid at codon 49 of the mutant CIa*49E reversed these trends . The mutant CIa*49E fully lost its ability to overcome rymv1-3 whereas its ability to overcome rymv1-2 increased dramatically from 5 to 40% . Consequently , the resistance breaking pattern of the mutant CIa*49E became similar to that of isolates with E49 . The link between the amino acid at codon 49 and the ability to break the rymv1-2 and rymv1-3 resistance alleles was highly significant ( χ2 = 17 . 5 , P<0 . 001 and χ2 = 95 . 3 , P<0 . 001 for rymv1-2 and rymv1-3 , respectively ) . Not only did the substitution of threonine by glutamic acid at codon 49 in the mutant CIa*49E reverse the RYMV1 RB ability of isolate CIa ( T49 ) , but it also modified the mutational pathways . Sequencing the viral population after rymv1-2 resistance breakdown by the mutant CIa*49E revealed the presence of the *48G mutation ( i . e . the first step of the major mutational pathway toward rymv1-2 resistance breakdown ) . Furthermore , minor rymv1-2 RB mutations *42Y and *43A were observed . These mutations are sometimes detected after rymv1-2 resistance breakdown by isolates with E49 but never by isolates with T49 [13] , [15] . Moreover , the rymv1-2 RB mutation *48W which sometimes emerged after the inoculation of the isolates with T49 [13] , [15] was not detected after the inoculation of the mutant CIa*49E . Considering both its resistance breaking ability and the kind of RB mutations fixed , the mutant CIa*49E responded like the isolates with E49 , and no longer like the isolate CIa ( T49 ) or any other isolates with T49 . Overall , these results show that the amino acid at codon 49 is a key genetic determinant of the ability to overcome rymv1-2 and rymv1-3 resistance alleles . It has been established that directed mutagenesis resulting in the replacement of arginine at codon 48 of the VPg by glycine within the infectious clone CIa ( T49 ) was lethal to the virus [13] . It was then postulated that it was the cause of the inability of isolates with T49 to follow the main rymv1-2 RB mutational pathway R>G>E at codon 48 . To test this hypothesis , the double mutant CIa*48G*49E was constructed by directed mutagenesis , and its infectivity was compared with that of the single mutant CIa*48G ( T49 ) . The viral RNAs obtained by in vitro transcription were inoculated to the susceptible O . sativa indica cv . IR64 and to the rymv1-2 resistant cv . Bekarosaka . Thirty days after inoculation , the single mutant CIa*48G ( T49 ) was not detected by DAS-ELISA either in the susceptible or the resistant cultivar ( Figure 2 ) , confirming that the combination of amino acids G48-T49 was unfit . By contrast , all plants - either resistant or susceptible - inoculated with the double mutant CIa*48G*49E expressed symptoms in non-inoculated leaves and reached a high virus content . The infection of the rymv1-2 resistant plants by the mutant CIa*49E ( R48 ) was followed . As mentioned above , the *48G mutation was fixed after the inoculation of the mutant CIa*49E ( R48 ) to the rymv1-2 resistant plants . At 90 days post-inoculation ( dpi ) , the mutant CIa*48G*49E was itself displaced by CIa*48E*49E ( Figure 3 ) . Interestingly , synonymous differences at codon 48 R ( AGG or AGA ) , G ( GGG or GGA ) and E ( GAG or GAA ) did not interfere with the ability of the mutant CIa*49E to follow the mutational pathway R>G>E . Reversion from CIa*48E*49E to CIa*48G*49E in susceptible rice cultivars has been reported earlier [14] . In the present experiment , reversion from CIa*48G*49E to CIa*48R*49E was observed in susceptible cultivars . Altogether , the substitution of threonine by glutamic acid at codon 49 of the VPg of isolate CIa restored the viability of the mutant CIa*48G*49E and also its ability to adapt to rymv1-2 resistant cultivars or to revert to the avirulent form in susceptible hosts . The mutant CIa*49E responded like the isolates with E49 and no longer like the isolate CIa ( T49 ) or any other isolates with T49 . These results validated the claim that lack of fitness of genotypes with the combination G48-T49 is the cause of the inability of isolates with T49 to overcome rymv1-2 by the main mutational pathway R>G>E at codon 48 . Collectively , these results demonstrate the critical effect of the amino acid at codon 49 on the ability to fix rymv1-2 RB mutations at the virulence codon 48 . Mutation H52Y is unique in that it breaks both rymv1-2 and rymv1-3 resistance alleles . However , the fixation of mutation *52Y depends upon the amino acid at codon 49 . In rymv1-2 resistance-breakdown , the mutation *52Y is only fixed after inoculation of isolates with E49 [13] whereas for the rymv1-3 resistant cv . Tog5681 , the mutation *52Y is only fixed after the inoculation of isolates with T49 [15] . Several isolates with *52Y and T49 ( CIa , Ma105 and Ma203 ) or E49 ( Mg16 ) obtained earlier during passage experiments [13] , [15] were inoculated to rymv1-2 resistant O . sativa indica cvs . Gigante and Bekarosaka and to rymv1-3 resistant O . glaberrima cv . Tog5681 . The results depended on the amino acid at codon 49 . The isolates with T49 and *52Y were infectious in both rymv1-2 and rymv1-3 resistant plants , whereas the isolates with E49 and *52Y were infectious in rymv1-2 but not in rymv1-3 resistant plants ( Table 2 ) . The single mutant CIa*52Y ( T49 ) and the double mutant CIa*49E*52Y were constructed to establish the role of the amino acid at codon 49 in this contrasted infectivity . The mutants were then inoculated at the same concentration to the rymv1-2 resistant cv . Bekarosaka and to the rymv1-3 resistant cv . Tog5681 . The single mutant CIa*52Y ( T49 ) was detected at a high concentration in both resistant cultivars . In contrast , the double mutant CIa*49E*52Y was infectious in cv . Bekarosaka but not in cv . Tog5681 ( Table 2 ) . Two nearly isogenic lines ( NILs ) were tested to understand the role of the rice genetic background on the relationship between codons 49 and 52 during the RYMV1 RB process . The viral accumulation of the single mutant CIa*52Y ( T49 ) and that of the double mutant CIa*49E*52Y in each NIL and in the parental O . glaberrima resistant cv . Tog5681 was compared . At 30 dpi , the single mutant CIa*52Y ( T49 ) genotype was detected at high levels in both rymv1-2 and rymv1-3 resistant plants ( Figure 4a ) . In contrast , the double mutant CIa*49E*52Y was infectious in the rymv1-2 NIL , but not in the resistant O . glaberrima cv . Tog5681 or in the rymv1-3 NIL . Similarly , the isolate Mg16*52Y , an RB isolate with E49 of a different strain , was not infectious in rymv1-3 resistant cultivar cv . Tog5681 and NILs ( Figure 4b ) . Thus , the presence of a threonine at codon 49 is a prerequisite for fixation of RB mutation *52Y in rymv1-3 resistant plants . Conversely , isolates with E49 are unable to fix the rymv1-3 RB mutation *52Y . It was earlier established that rymv1-2 RB mutants with E48 or Y52 were not able to overcome rymv1-4 although a similar substitution - glutamic acid into a lysine - causes the rymv1-2 and rymv1-4 resistance at positions 309 and 321 , respectively [12] . In contrast , the response to RYMV inoculation of rymv1-3 and rymv1-4 resistance alleles , which both occur in O . glaberrima cultivars , showed marked similarities: ( i ) the single mutant CIa*52Y ( T49 ) was infectious in cv . Tog5672 , although at a lower rate than in the rymv1-3 resistant cv . Tog5681; ( ii ) only isolates with T49 were able to break the rymv1-4 resistance; ( iii ) after inoculation of isolate Ng25 ( T49 ) , the RB mutation S41P was fixed to rymv1-3 resistant cv . Tog5681 and rymv1-4 resistant cv . Tog5672 ( data not shown ) . These similarities in the rymv1-4 and rymv1-3 in the RB process suggest that the breakdown of RYMV1 resistance is host-species dependent . Comparison of the response of the parental lines and NILs indicates that the origin of the contrasted responses between rymv1-2 and rymv1-3 occurs in the MIF4G domain rather than elsewhere in the genome . Position 303 is the most likely candidate as it is the only position that distinguishes O . sativa from O . glaberrima and it is located close to the genetic determinants of resistance . Yeast double-hybrid assays were used to investigate the effect of the amino acid at codon 49 on the VPg/MIF4G interaction . The strength of the interaction between the MIF4G domain with the VPg of wild-type isolates ( R48-H52 ) , and of RB mutants *48I , *48G , *48E and *52Y with T49 or E49 was assessed . The mutated MIF4G domain mimicking the rymv1-2 resistance substantially reduced the interaction with the wild-type VPg , whereas the VPg mimicking RB mutations partly or fully restored the interaction ( Figure 5 ) . These findings extended previous results with genotypes with T49 and RB mutations at codon 48 [17] to genotypes with E49 and RB mutations at codon 52 . Overall , there was a marked effect of the amino acid at codon 49 on the strength of the interaction between the mutated MIF4G domain and the VPg of WT isolates and RB mutants . The interaction with the rymv1-2 mutated MIF4G domain was significantly lower when VPg was carrying T49 than that observed in VPg with *49E . This was true both for the WT isolate ( R48 ) and for RB mutants *48I , *48G and *52Y ( P<0 . 001 , P<0 . 001 , P<0 . 025 , respectively ) . The interaction was also lower in VPg with *48E-T49 than with *48E-*49E , although the difference was not statistically significant . Lower affinity to the rymv1- 2-MIF4G domain may contribute to the low propensity of isolates with T49 to overcome rymv1-2 resistance . In particular , it may explain the inability of isolates with T49 to fix the rymv1-2 RB mutation *52Y . Interaction between the VPg and the mutated MIF4G domain mimicking the rymv1-3 resistance was different . There was no significant influence of the amino-acid at codon 49 on the strength of the interaction between the WT or *52Y mutated VPg and the rymv1-3 mutated MIF4G ( data not shown ) . We tested whether the amino acid at codon 49 of the VPg in wild-type isolates conferred a selective advantage in the infection of susceptible O . glaberrima , O . sativa indica and O . sativa japonica cultivars . Isolates representative of the diversity of RYMV were selected ( cf . Materials and Methods ) . Preliminary experiments showed that single accumulation of each isolate , evaluated by qRT-PCR , was not significantly different between susceptible O . sativa and O . glaberrima cultivars ( data not shown ) . Several pairs of isolates with E49 and T49 were then co-inoculated at the same concentration to O . glaberrima , O . sativa indica and O . sativa japonica cultivars . The outcome of the competition was assessed by sequencing the VPg of the viral population at successive dates after inoculation . Each co-inoculation was replicated at least twice . Altogether , the results of 92 competitions were sequenced during this study . The outcome of the co-inoculation of T49 and E49 isolates in susceptible cultivars depended upon the rice species ( Table 3 ) . In O . glaberrima cultivars , the results were always clear-cut . Whatever the cultivar tested or the pair of isolates inoculated , the isolates with E49 were rapidly excluded ( i . e . within 28 days ) and only the isolates with T49 were fixed . The relationship between the amino acid at codon 49 and the result of the competition was highly significant in O . glaberrima ( χ2 = 36 , P<0 . 001 ) . In O . sativa indica cultivars , the exclusion of one of the two genotypes also occurred but more slowly ( i . e . within 60 days ) . Either isolates with E49 or T49 were fixed and there was no significant relationship between the amino acid at codon 49 and the outcome of the competitions . In O . sativa japonica cultivars , isolates with E49 or T49 were fixed , but there was a preferential fixation of isolates with T49 ( χ2 = 9 . 8 , P = 0 . 02 ) . However , coexistence of the two genotypes was sometimes observed until 100 dpi . The isolate CIa ( T49 ) and the mutant CIa*49E were inoculated at the same concentration to susceptible O . glaberrima , O . sativa indica and O . sativa japonica cultivars to further assess the effect of the amino acid at codon 49 on the outcome of the competitions . Preliminary experiments showed there was no significant difference in virus accumulation after single inoculations ( evaluated by DAS-ELISA and qRT-PCR ) between the O . sativa indica cv . IR64 and the O . glaberrima cv . Tog5673 ( Figure S1 ) . The results of the competitions were consistent among the six replicates . In the O . glaberrima susceptible cv . Tog5673 , the isolate CIa ( T49 ) always displaced the single mutant CIa*49E . In the O . sativa indica and the O . sativa japonica susceptible cultivars , the single mutant CIa*49E displaced the isolate CIa ( T49 ) .
The first objective of this study was to establish that the amino acid at codon 49 of the VPg was not only a molecular ‘signature’ but was a key genetic determinant of the ability to overcome rymv1-2 and rymv1-3 resistance alleles . This was shown by directed mutagenesis of an infectious clone . The replacement of threonine by glutamic acid at codon 49 reversed the ability of isolate CIa to overcome RYMV1 resistance alleles and modified the mutational pathways which were followed . Considering both its resistance breaking ability and the kind of RB mutations fixed , the mutant CIa*49E responded like the isolates with E49 , and no longer like the isolate CIa ( T49 ) or any other isolates with T49 . This result validates the claim that the amino acid at codon 49 modulates the ability of RYMV isolates to overcome rymv1-2 and rymv1-3 resistances either by promoting or by restraining the fixation of RB mutations at virulence codons . Consistently , the virulence spectrum of Potato virus Y ( PVY ) on the pvr2 resistance in pepper is dependent on epistatic interactions between nearby codons of the VPg [19] . Indeed , there is increasing phylogenetic and experimental evidence that epistatic relationships play a critical role in the adaptation of viruses ( reviewed in [20] ) . This justifies our attempts to investigate the modes of action of the amino acid at codon 49 on the ability to break RYMV1 resistance . The second objective of this study was to determine the effects of the amino acid at codon 49 in the main rymv1-2 and rymv1-3 virulence pathways . The lack of fixation of the rymv1-2 RB mutation *48G in genotypes with T49 , and of the rymv1-3 RB mutation *52Y in genotypes with E49 involved different processes . Genotypes with E49-*52Y were not infectious in rymv1-3 resistant cultivars but were fit in susceptible and rymv1-2 resistant cultivars . In contrast , genotypes with *48G-T49 were unfit in both susceptible and resistant cultivars . Combination *48W-T49 [15] in the clone CIa was also unfit in both susceptible and resistant cultivars . Actually , the range of possible changes within this motif is restricted , as indicated by the coordinated changes associated with the E49T substitution at codons 48-49-50 during RYMV evolution . Double-hybrid tests revealed that a threonine at codon 49 reduced the affinity of the RB mutants with the MIF4G domain of rymv1-2 . The lower affinity to the MIF4G domain of rymv1-2 possibly explain why mutation *52Y was not fixed after inoculation of isolates with T49 . Altogether , the effects of the amino acid at codon 49 on RYMV1 resistance breakdown found in this study are complex , critical , and numerous , and yet unlikely to be exhaustive . Similarities in the response of rymv1-3 and rymv1-4 O . glaberrima resistant cultivars to RYMV despite the differences of their genetic determinants ( deletion vs . point mutation ) suggested that RYMV1 resistance breakdown was host-species dependent . Codon 49 of the VPg and codon 303 of MIF4G epitomized on the virus and plant sides , respectively , the effect of the genetic background on the outcome of the infection of RYMV1 resistant cultivars . This was exemplified by mutation *52Y which was fixed in isolates with E49 in rymv1-2 resistant O . sativa cultivars with A303 . In constrast , mutation *52Y was fixed in isolates with T49 in rymv1-3 resistant O . glaberrima cultivars with D303 . Consistently , mutation *52Y was also fixed after inoculation of isolate with T49 to the rymv1-4 resistant O . glaberrima cv . Tog5672 ( A . Nicaise and A . Pinel-Galzi , unpublished results ) . It was earlier stressed that the strategy of a RNA plant virus such as RYMV to gain virulence against host resistance showed striking parallels with HIV resistance against antiviral treatments [13] . The present finding supports this parallel . Epistatis within the 48-49-50 motif of the VPg is a major constraint to RYMV evolution by preventing key adaptive mutational pathways . Recently , it was also established that a major consequence of epistatis for HIV evolution is that many of the minimum-length mutational trajectories between the wild type and the mutant with highest fitness are selectively inaccessible [21] . The third objective of this study was to determine the origin of the amino acid polymorphism at codon 49 . Altogether , a threonine at codon 49 conferred a strong selective advantage over glutamic acid in the O . glaberrima cultivars . Independent fixation events of T49 account for the diversifying selection estimated at codon 49 [13] , [15] . The selective advantage of isolates with T49 in O . glaberrima over isolates with E49 is a likely explanation of these fixations . This is one of the first experimental validation of the adaptive role of a codon predicted to be under positive selection in the host range expansion of a pathogen in a natural environment [22] . The selective advantage of isolates with T49 in O . glaberrima also provides an explanation of the link between the threonine at codon 49 and the ability to overcome rymv1-3 resistance in O . glaberrima . Isolates with T49 , which are better adapted to O . glaberrima than isolates with E49 , would be more prone to break O . glaberrima rymv1-3 resistant cultivars . In contrast , isolates with E49 , which are less fit in O . glaberrima , would not be able to overcome rymv1-3 resistance . Consistently , the frequency of breakdown of an eIF4E resistance in pepper by mutations in the VPg of PVY was high in a susceptible genetic background and low when the same gene was introgressed in a partially resistant genetic background [23] . Results of the competitions between isolates with E49 and T49 in O . sativa were more variable than in O . glaberrima . The narrow genetic diversity of O . glaberrima [24]–[26] may explain the homogenous outcome of the competitions whereas the wide genetic diversity in O . sativa may account for the diversity in responses . It has been proposed that a direct interaction between the eIF4E of pepper and the VPg of PVY drives the co-evolution between resistance and pathogenicity , leading to the diversifying selection of both genes [27] , [28] . This is considered to be the best example of plant-virus co-evolution [29] . It was of interest to see whether this evolutionary scenario was relevant to RYMV in rice . Superficially , the evolution of eIF ( iso ) 4G in rice and of the VPg in RYMV presents features similar to such an “arms race” co-evolution model . Under a co-evolution model similar to that between pepper and PVY , the resistance mutations in rice should have been fixed under the selection pressure imposed by RYMV . However , this hypothesis does not match what is known about the epidemiology of the virus . There is evidence that RYMV diversified only two centuries ago and that epidemics developed only in recent decades [30] , [31] . This short time-scale is inconsistent with the hypothesis of rice adaptation to RYMV . More likely , the RYMV1 resistance alleles resulted from past selection pressure imposed under a wider time-frame [30] , possibly that of RYMV-related sobemoviruses [9] . RYMV variation does not fit a simple rice-RYMV co-evolution scenario either . With PVY , the codons of the VPg under diversifying selection are the virulence codons involved in eIF4E resistance breakdown [32] , [33] . According to a co-evolution model , the virulence mutations were fixed under the selection pressure imposed by the resistance genes . With RYMV however , virulence codons of the VPg of wild type isolates are not variable , possibly because resistant cultivars have not yet been deployed widely in the fields . Most importantly , codon 49 - which is a under strong diversifying selection [13] - is not a virulence codon itself but is adjacent to virulence codons 48 and 52 . Its indirect mode of action on resistance breakdown suggests an evolutionary scenario different from co-evolution . In conclusion , our results – although superficially consistent with a co-evolution scenario – suggest a different historical relationship between RYMV and rice . On the host plant side , the RYMV1 resistance alleles were presumably not fixed under the selection pressure of RYMV . On the virus side , the current ability to overcome resistances in the two rice species is modulated by past host adaptations and subsequent genetics constraints which are epitomized by a single amino acid of the VPg . This genetic determinant of RYMV1 resistance breakdown exemplifies the importance of historical contingencies in the adaptability of a virus to a new host .
Twelve rice cultivars were used in the experiments: three susceptible O . sativa indica cvs . ( IR64 , Bouaké189 , BG90-2 ) , three susceptible O . glaberrima cvs . ( Tog5673 , CG14 , G39 ) , two O . sativa japonica cvs . ( Nipponbare , Azucena ) , two rymv1-2 resistant O . sativa indica cvs . ( Bekarosaka , Gigante ) , the rymv1-3 resistant O . glaberrima cv . Tog5681 and the rymv1-4 resistant O . glaberrima cv . Tog5672 . In addition , two NILs were constructed by introgression of the rymv1-3 and rymv1-2 resistance alleles into the susceptible O . sativa indica cv . IR64 . In the rymv1-3 NIL , the estimated percentage of O . glaberrima genes introgressed in the O . sativa indica cultivar was below 10% . Moreover , mapping this isogenic line showed that O . glaberrima genes potentially involved in the viral cycle such as translation initiation factor genes were not introgressed in the susceptible O . sativa indica cultivar ( data not shown ) . Then , differences between the rymv1-2 and rymv1-3 NILs were mostly restricted to the RYMV1 gene . The plants were kept in a growth chamber under 12 hours illumination at 120µE-2s-1 at 28°C and 90% humidity . Our experiments followed a two-step approach: ( i ) directed mutagenesis of the VPg of the infectious clone CIa ( T49 ) , ( ii ) comparison of a range of field isolates with E49 and T49 . The mutants CIa*49E , CIa*48G , CIa*48G*49E , CIa*52Y ( T49 ) and CIa*49E*52Y were constructed by directed mutagenesis of the infectious clone CIa ( T49 ) with QuikChange Site-Directed Mutagenesis Kit ( Stratagene ) [13] , [15] . Transcription of mutated clones and inoculation in planta of viral RNAs was done as described elsewhere [34] . None of the results obtained suggested a loss of competitive fitness of the mutated infectious clone CIa*49E . Nine wild-type isolates , including isolates of the three clades with T49 , were selected as representative of the diversity of the virus to be used in the experiments ( Table S1 ) . These isolates were from Burkina-Faso ( BF1 , BF5 ) , Côte d'Ivoire ( CIa , CI4 ) , Madagascar ( Mg16 ) , Mali ( Ma10 ) , Nigeria ( Ni1 ) and Tanzania ( Tz8 , Tz209 ) . Isolates CIa ( strain S2-S3 ) , BF1 ( strain S2-S3 ) , BF5 ( strain S1-wa ) and Ni1 ( strain S1-c ) had a threonine at codon 49 of the VPg . Isolates CI4 ( S1-wa ) , Ma10 ( strain S1-wa ) , Mg16 ( strain S4-mg ) , Tz8 ( strain S4-lm ) and Tz209 ( strain S6 ) had a glutamic acid at codon 49 . The genotype Mg16*52Y ( E49 ) was obtained after the inoculation of the rymv1-2 resistant plants [13] , [15] . The RB genotypes Ma105*52Y ( T49 ) and Ma203*Y52 ( T49 ) were obtained after the inoculation of the rymv1-3 resistant plants . The isolates were multiplied in the O . sativa susceptible cv . IR64 and were stored in a -20°C deep freezer before use in the experiments . The VPg of the 11 field isolates was sequenced as described in [13] . There were few differences between the amino acid sequences and none of them correlated with the polymorphism at position 49 ( Figure S2 ) . Interestingly , the amino-acid sequence of the VPg of the mutant CIa*49E fully matched that of the field isolate Tz209 . No mutation of T49 towards E49 ( or vice versa ) in the VPg was observed at the multiplication stages or after inoculation of susceptible or resistant O . sativa or O . glaberrima cultivars . Inoculum was prepared by grinding infected frozen leaves in 0 . 1 M phosphate buffer ( 0 . 1 g/ml , pH 7 . 2 ) and was rubbed on leaves of 14-day-old rice seedlings . Viral quantification was performed by q-RT-PCR as described previously [14] . For competition experiments , ca . 1012 copies of each isolate were mixed and inoculated to the different cultivars . In parallel , the same amount of inoculum of each isolate was inoculated singly to each cultivar . To test the RB ability of CIa and CIa*49E genotypes , ca . 1013 copies of each genotype were inoculated to resistant hosts . The accumulation of mutants CIa*48G , CIa*48G*49E , CIa*52Y and CIa*49E*52Y was compared after the inoculation of ca . 1012 copies of each genotype per plant . All genotype sequences were checked before and after inoculation ( see below ) . For each competition experiment in susceptible plants , expanded leaves of different plants were collected at 14 , 21 , 28 , 56 and 100 dpi . The VPg was then sequenced by analyzing RT-PCR fragments amplified from total RNA purified by RNeasy Plant Mini Kit ( QIagen ) as previously described [35] . Dual peaks in the sequencing electropherograms at the same position were interpreted as reflecting the co-existence of the two genotypes in the viral population [13] , [36] . In constrast , a single peak at a site polymorphic between the two competing genotypes suggested that one genotype predominated . The ELISA tests were performed on the leaf samples as described previously [37] . Two hybrid assays were conducted with the Matchmaker GAL4 Two-Hybrid System 3 ( Clontech ) . The yeast strain AH109 was co-transformed with either pGAD::MIF4G ( rymv1-1 ) or pGAD::MIF4G* ( mimicking the rymv1-2 allele ) and either pGBK::VPg ( wild-type with R48-H52 ) or pGBK::VPg* ( mimicking RB mutants *48I , *48G , *48E and *52Y ) constructs . Double-transformation of yeast cells and spotting on control or selective medium plates was performed as previously reported [17] . Yeast cells were grown at 30°C for 4 days . Growth intensity was monitored with ImageJ software [38] . The data were normalized to positive and negative controls and expressed as a percentage after quantifying spot intensities , assigning 100% efficiency to the susceptible rymv1-1 MIF4G/wild-type VPg interaction . At least four independent experiments were conducted . The results of the virus accumulation estimated by DAS-ELISA , and of the interaction efficiency evaluated in yeast two hybrid systems were analysed by ANOVA ( Statistica software version 6 . 0 ) . | A fundamental question in virus evolution is whether emergence into a new host species/cultivar requires adaptation of the virus during the early stages of infection or whether it is largely via a chance transmission of a viral strain with the requisite characteristics . Our studies of the breakdown of RYMV1 resistance to Rice yellow mottle virus ( RYMV ) in rice show that emergence of a virus into a new host is a two-step process , where adaptive mutations can be fixed only by isolates with the necessary genetic determinants . We found the identity , effects and origin of these genetic determinants . Past host adaptation and subsequent genetic constraints , epitomized in a single amino acid , modulate the current ability of RYMV to overcome RYMV1 resistance through epistatic relationships . The importance of epistatic interactions in virus adaptation is increasingly acknowledged , but has seldom been studied within an historical context . The origin and role of the genetic determinants in RYMV1 resistance breakdown exemplify the importance of historical contingencies in the adaptability of a virus to a new host , a scenario at variance with the widely accepted co-evolution arm race model between pathogens and their hosts . | [
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] | 2012 | Historical Contingencies Modulate the Adaptability of Rice Yellow Mottle Virus |
Echinococcosis is a complex zoonosis that has domestic and sylvatic lifecycles , and a range of different intermediate and definitive host species . The complexities of its transmission and the sparse evidence on the effectiveness of control strategies in diverse settings provide significant challenges for the design of effective public health policy against this disease . Mathematical modelling is a useful tool for simulating control packages under locally specific transmission conditions to inform optimal timing and frequency of phased interventions for cost-effective control of echinococcosis . The aims of this review of 30 years of Echinococcus modelling were to discern the epidemiological mechanisms underpinning models of Echinococcus granulosus and E . multilocularis transmission and to establish the need to include a human transmission component in such models . A search was conducted of all relevant articles published up until July 2012 , identified from the PubMED , Web of Knowledge and Medline databases and review of bibliographies of selected papers . Papers eligible for inclusion were those describing the design of a new model , or modification of an existing mathematical model of E . granulosus or E . multilocularis transmission . A total of 13 eligible papers were identified , five of which described mathematical models of E . granulosus and eight that described E . multilocularis transmission . These models varied primarily on the basis of six key mechanisms that all have the capacity to modulate model dynamics , qualitatively affecting projections . These are: 1 ) the inclusion of a ‘latent’ class and/or time delay from host exposure to infectiousness; 2 ) an age structure for animal hosts; 3 ) the presence of density-dependent constraints; 4 ) accounting for seasonality; 5 ) stochastic parameters; and 6 ) inclusion of spatial and risk structures . This review discusses the conditions under which these mechanisms may be important for inclusion in models of Echinococcus transmission and proposes recommendations for the design of dynamic human models of transmission . Accounting for the dynamic behaviour of the Echinococcus parasites in humans will be key to predicting changes in the disease burden over time and to simulate control strategies that optimise public health impact .
Echinococcosis is a parasitic disease caused by the larvae of fox and dog cestode worms of the genus Echinococcus . It is a complex zoonosis that has domestic and sylvatic lifecycles , and a range of different intermediate and definitive host species . The two most clinically relevant species are E . granulosus and E . multilocularis , which cause cystic and alveolar echinococcosis respectively . Transmission of both is influenced by climate change and anthropogenic environmental factors , mediated by changes in animal population dynamics , spatial overlap of competent hosts and the creation of favourable weather conditions for egg survival 1–4 . Humans are incidental hosts and , in most cases , do not contribute to continuance of the parasite life cycle , except under unique circumstances [5] . However , they bear the burden of serious morbidity and mortality as well as social and economic consequences [6]–[8] . There is an effective vaccine for use in sheep against E . granulosus [9] , but there is currently no human vaccine , and the disease is not readily detected until it is at an advanced stage without expensive public health screening comprising imaging studies ( e . g . ultrasound ) [10] . There is a lack of evidence for effective and sustainable control strategies for E . granulosus or E . multilocularis across regions that vary in endemicity and transmission conditions . Lessons learned from previous infectious disease elimination campaigns indicate that complex diseases cannot be successfully eliminated using a one-size-fits-all approach , but rather , that control strategies should be tailored to local contexts [11] , [12] . The complexities of echinococcosis , the diverse environmental conditions that support its transmission , and the sparse evidence on the effectiveness of control strategies in diverse settings , provide significant challenges for policy makers attempting to make informed control decisions . Such issues have given rise to the popularity of mathematical modelling to simulate control packages under locally specific transmission conditions . Importantly , modelling negates the expense of trialling scenarios in the field and provides evidence for optimal timing and frequency of phased control interventions . Model output can also be integrated with economic analyses to determine and compare the cost-effectiveness of different control and elimination interventions , alone and as part of an integrated approach . Early models of E . granulosus and E . multilocularis [13] , [14] described the basics of transmission and these have since been adapted based on advances in epidemiological understanding arising from field data from Australia , New Zealand , Europe , the Middle East and central Asia [15] . Models can vary from simple representations of the system to detailed epidemiological frameworks with large numbers of parameters [16] . To date , Echinococcus transmission models have focussed primarily on the life cycle in animal definitive and intermediate hosts and have not included the transmission pathway to humans . Although humans rarely contribute to transmission [5] they are indeed a host and valuable insight into the impact of interventions targeting both definitive and intermediate hosts can be gained by their inclusion into Echinococcus transmission models . While the risk of echinococcosis in humans and the impact of control interventions ( targeting definitive hosts ) on this risk have indeed been discussed in a number of papers detailing animal models of E . multilocularis – it is noteworthy that this has not been done for E . granulosus – this risk is based on the assumption that the number of human cases is proportional to the quantity of parasite eggs deposited in the environment [17]–[19] . The assumption that human risk increases linearly with increased prevalence of infected foxes is acknowledged to be an over simplification [18] , although this is still an important indicator of risk . These E . multilocularis risk models also do not account for heterogeneous human exposure arising from varying spatial overlap of hosts , or socioeconomic and environmental conditions affecting subpopulations of humans in endemic areas . Furthermore , they are unable to simulate preventive interventions targeting humans and hence the impact of these on infection and subsequent morbidity and mortality . Developing echinococcosis transmission models incorporating both animal and human hosts will be important for exploring the dynamics of transmission to humans [20] , for predicting changes in the human disease burden over time , and will be essential for public health planning of control strategies . Much progress has been made over the last 30 years in modelling the lifecycle of Echinococcus spp . in animal hosts . The aims of this review were to discern the epidemiological mechanisms underpinning models of E . granulosus and E . multilocularis transmission and to propose recommendations for the future design of dynamic models of E . granulosus and E . multilocularis transmission that incorporate the human host .
A search was conducted of all relevant articles published up until July 2012 , identified from the PubMED and Web of Knowledge databases . Key terms used in the search strategy included: ‘mathematical model OR models OR computer model OR decision support system OR decision tree’ AND ‘echinococcus OR echinococcosis OR E . granulosus OR E . multilocularis . ’ The search was limited to English language publications . Review of bibliographies of papers was also carried out to ensure completeness of inclusion of all relevant mathematical models . Papers eligible for inclusion were those describing the design of a new model , or modification of an existing mathematical model of E . granulosus or E . multilocularis transmission . Papers were excluded if they described: statistical risk modelling rather than dynamic , mechanistic modelling of Echinococcus spp . lifecycles; processes at a microbiological level with focus on an individual component of the life cycle; generic mathematical models of parasitic disease transmission; or if they described the implementation of an existing model without recommendations for modification of the model . In addition , review papers of models described elsewhere were excluded . The process of study selection is summarised in Figure 1 . Appendices S1 and S2 provide summaries of E . granulosus and E . multilocularis models included in this review and their specific assumptions .
Maturation of E . granulosus and E . multilocularis worms in the definitive host is thought to take approximately 6 weeks [8] . Maturation of cysts in intermediate hosts can differ not only between the two species , but also between different intermediate host species , particularly for E . granulosus . For example , maturation time for E . multilocularis cysts in small mammals is estimated at 2–4 months , while for E . granulosus cyst maturation can take 8–9 months in wallabies but 2–6+ years in sheep [8] , [23] , [24] . Time delays in parasite lifecycles tend to attenuate transmission potential because they allow for the possibility of host death between infection and infectiousness [25] . Time delays for parasite maturation are usually incorporated into compartmental models by the inclusion of a ‘latent’ class ( i . e . an exposed but not yet infectious class , also referred to as an ‘E’ class ) . This ‘latent’ class was present in four of the eight E . multilocularis models for both definitive and intermediate hosts [13] , [18] , [26] , [27] . Inclusion of a ‘latent’ class , however , does not always contribute qualitatively to the dynamics of a model [25] . For example , a study that resulted in the modification of the original E . multilocularis model of Roberts and Aubert ( 1995 ) found that exclusion of the ‘E’ class did not alter their conclusions and hence it was omitted and a simpler Susceptible – Infectious ( S-I ) model used [28] . Therefore , inclusion of a latent class may be more relevant for E . granulosus models , particularly those involving the intermediate sheep host where it takes years to reach cyst maturity and hence infectiousness . As such , the importance of the inclusion of a ‘latent’ class is dependent on the life expectancies of the hosts relative to the latent period [25] . Not including the appropriate time delay in the ‘latent’ period when it is warranted ( e . g . time to cyst maturation in sheep ) could result in an over-estimation of the proportion of infectious hosts in the natural system at any given time [25] . This would lead to inaccurate predictions of the impact of control measures or a failure to accurately estimate the time to disease elimination when simulating control strategies . Age stratification of hosts was incorporated into the design of the very first E . granulosus model [14] and remained an important component of all subsequent models . The intermediate host is universally assumed to remain infected for life and , in the absence of acquired immunity , subsequent exposure to parasite eggs results in the accumulation of cysts in the host , producing a linear relationship between age and the numbers of hydatid cysts [14] , [29]–[32] . While the inclusion of an age structure might be assumed to be less relevant for short-lived intermediate hosts of E . multilocularis ( e . g . the average lifespan of a vole is 7–8 months [33] ) , in reality , the maturation of cysts occurs relatively quickly ( 2–4 months ) compared with E . granulosus ( where growth of cysts is slow and variable ) [8] . Once an E . multilocularis cyst is established , a small mammal such as a vole , remains infected for life , and hence subsequent infections accumulate with increasing age [34] . Evidence of this was found in Arvicola terrestris in Switzerland , where increasing prevalence of E . multilocularis was observed over several age classes of voles trapped during the study period [34] . Therefore , the age structure of voles and other small mammals may be an important element for inclusion in models of E . multilocularis . However , including an intermediate host age structure in E . multilocularis models may mask detection of seasonal variation in infection pressure as the age distribution of small mammals can vary considerably between seasons [34] . The use of absolute age estimates has been suggested as a method for overcoming this limitation [34] . This involves determining the date of birth of each small mammal based on its age and trapping day , which is then used to assign mean day temperatures and precipitation ( which influence egg survival in the external environment ) to each day of life for each animal and to simulate seasonal variation in infection pressure [34] . Age stratification in the definitive host population has also been a characteristic of some E . granulosus models [31] , [32] . Age-related differences in parasite intensity or prevalence in naturally infected populations of dogs have been reported and are suggested to be related to the acquisition of temporary immunity ( discussed in the following section ) rather than to any age-related difference in infection pressure [31] , [32] . Age stratification of the definitive host is thought to be particularly important when there is likely to be a high turnover in the dog population as this will result in increases in the numbers of younger , more susceptible dogs which may increase infection pressure on human hosts [31] . However , this is dependent on the level of endemicity as classic age-prevalence curves of E . granulosus indicate that very young dogs may not survive long enough to become infectious [35] . The inclusion of an age structure in the definitive ( fox ) host when modelling E . multilocularis occurred as a result of field data showing higher worm burdens in juvenile foxes compared with adult foxes in Hokkaido , Japan [27] and is also thought to allow the model to more realistically reflect population dynamics by assigning different death rates to hosts of varying age [17] , [27] . Density-dependent constraints are factors that regulate population growth [36] , and have been shown to be critical in simulating the population biology and control of parasites [37] . The absence of expression of density-dependent constraints in a mathematical model of Echinococcus spp . makes elimination of parasite species theoretically easy . However , it has been acknowledged that this may not be the case in a natural setting [13] , [20] , [38] . In the models included in this review , the density-dependent constraints discussed are related to host demography ( i . e . the population density of definitive and intermediate hosts ) and natural immunity ( which regulates parasite abundance ) . Decisions regarding the inclusion or exclusion of such structural assumptions may have a marked effect on disease projections and the impact and cost-effectiveness of control strategies [39] . Accounting for stochasticity in parameter values is particularly important when modelling small populations or low disease prevalence where such an effect could produce local extinction or ‘fadeout’ of a disease [25] , [57] . In addition , modelling stochasticity allows predictions to capture variability in the epidemic profile in order to better understand the potential for disease persistence and the likely accuracy of the forecasts made , so as to better inform control and elimination strategies [57] . Two of the five E . granulosus models and three of the eight E . multilocularis models considered in this review incorporated stochasticity in their parameter values [17] , [26] , [27] , [30] , [46] . The authors of these models reported that parameter variability was captured in instances where there was: an absence of evidence for specific parameter values , unexplained variability in parameter values from surveillance data or reported in the scientific literature , and when there was uncertainty regarding the capture rate of intermediate and definitive hosts ( i . e . capture rate is calculated using an estimate of the total size of the host population ) [17] , [30] . In the reviewed E . multilocularis models , some specific parameters that were modelled stochastically included: fox population dynamics , worm burden in foxes , average number of eggs excreted per day by infected foxes , number of infected small mammals harbouring fertile cysts , and the basic infectious contact rate [17] , [26] , [27] . In the E . granulosus models , some specific parameters for which values could only be estimated from data or that displayed wide variability included: overall or age stratified infection pressure to both intermediate and definitive hosts , life expectancy of the parasite in dogs , time to maturity of cysts in sheep , age of feeding of sheep to dogs and the acquisition and loss of immunity in dogs [30] , [32] . In addition , there can be considerable uncertainty in baseline dog surveillance data obtained to inform parameter values for the definitive host model due to the absence of accurate dog population figures and hence uncertain capture rate of dogs [30] . In such circumstances , Monte-Carlo simulation allows this uncertainty to be quantified by modelling the variability and predicting best- and worst-case scenarios [30] . Spatial aggregation and heterogeneous exposure risk are two characteristics of E . granulosus and E . multilocularis transmission that are not frequently accounted for in the mathematical modelling of echinococcosis . Spatial aggregation can occur as a result of over-dispersion of the parasite in host populations , where a small proportion of animals harbour most of the parasite population , and there is heterogeneous distribution of Echinococcus eggs in the environment , both of which influence exposure risk to animal and human hosts [58] . Exposure risk can also be influenced by the spatial overlap of hosts . Explicit inclusion of spatial and contact structures can improve predictions of Echinococcus transmission at the population level as well as in the generation of risk mapping in order to target interventions . The inclusion of explicit spatial and contact structures is best achieved by more sophisticated simulation models that are able to assign a constrained set of exposure conditions to each individual in a host population [59] . Explicit inclusion of risk structure has only been partially realised in one of the five E . granulosus models , where the authors assigned a random contact rate to each individual sheep at birth and hence the model reflects heterogeneous infection of sheep in the population at any given time [29] . In addition , one of the eight E . multilocularis models assigned spatially explicit conditions to each fox in the population and modelled them individually to explore factors that contribute to the heterogeneous distribution of infected foxes and to explain the rapid resurgence of the disease following cessation of control measures [26] . Empirical evidence for effective and sustainable strategies for the control of E . granulosus and E . multilocularis transmission is sparse despite the serious health , social and economic consequences of echinococcosis [6]–[8] . The diverse conditions that support transmission provide a challenge for the design of cost effective control strategies across diverse settings . While mathematical models are useful tools in such situations , current Echinococcus models do not specifically include the human transmission pathway , nor do they allow for the simulation of interventions ( targeting both animal definitive and intermediate hosts and the human host ) to assess the impact on human infection . In addition , they do not account for heterogeneous exposure risk in humans that arises from variable spatial overlap of hosts and local environmental conditions that influence transmission . Therefore , in order to design optimal public health strategies to control and eliminate echinococcosis , inclusion of a human transmission component to E . granulosus and E . multilocularis will be essential . The following recommendations are proposed for modelling transmission in general and for those that also incorporate the human transmission pathway: While model complexity does not necessarily equate to realistic predictions , particularly in the absence of reliable parameter data [69] , precision in replication of the fundamental natural mechanisms of disease transmission in specific contexts and with the inclusion of transmission to humans , will allow Echinococcus spp . models to become useful public health tools for informing the development of targeted , cost-effective control strategies . | Echinococcosis is a complex zoonosis for which there is sparse evidence on the effectiveness of control strategies in diverse settings . This presents significant challenges for the design of effective public health policy against this disease . Mathematical modelling is a useful tool for simulating control packages under locally specific transmission conditions to inform optimal timing and frequency of phased interventions for cost-effective control of echinococcosis . This systematic review of 30 years of Echinococcus modelling discusses the importance of six key epidemiological mechanisms underpinning models of Echinococcus granulosus and E . multilocularis transmission and establishes the need to include a human transmission component . Accounting for the dynamic behaviour of the Echinococcus parasites in humans will be key to predicting changes in the disease burden over time and to simulate control strategies that optimise public health impact . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"and",
"Discussion"
] | [
"population",
"modeling",
"infectious",
"disease",
"modeling",
"biology",
"computational",
"biology"
] | 2013 | Synthesising 30 Years of Mathematical Modelling of Echinococcus Transmission |
Influenza A virus ( IAV ) polymerase complexes function in the nucleus of infected cells , generating mRNAs that bear 5′ caps and poly ( A ) tails , and which are exported to the cytoplasm and translated by host machinery . Host antiviral defences include mechanisms that detect the stress of virus infection and arrest cap-dependent mRNA translation , which normally results in the formation of cytoplasmic aggregates of translationally stalled mRNA-protein complexes known as stress granules ( SGs ) . It remains unclear how IAV ensures preferential translation of viral gene products while evading stress-induced translation arrest . Here , we demonstrate that at early stages of infection both viral and host mRNAs are sensitive to drug-induced translation arrest and SG formation . By contrast , at later stages of infection , IAV becomes partially resistant to stress-induced translation arrest , thereby maintaining ongoing translation of viral gene products . To this end , the virus deploys multiple proteins that block stress-induced SG formation: 1 ) non-structural protein 1 ( NS1 ) inactivates the antiviral double-stranded RNA ( dsRNA ) -activated kinase PKR , thereby preventing eIF2α phosphorylation and SG formation; 2 ) nucleoprotein ( NP ) inhibits SG formation without affecting eIF2α phosphorylation; 3 ) host-shutoff protein polymerase-acidic protein-X ( PA-X ) strongly inhibits SG formation concomitant with dramatic depletion of cytoplasmic poly ( A ) RNA and nuclear accumulation of poly ( A ) -binding protein . Recombinant viruses with disrupted PA-X host shutoff function fail to effectively inhibit stress-induced SG formation . The existence of three distinct mechanisms of IAV-mediated SG blockade reveals the magnitude of the threat of stress-induced translation arrest during viral replication .
Transcription of Influenza A virus ( IAV ) genes is performed by a viral polymerase that generates 5′-capped and polyadenylated ( poly[A] ) messenger RNAs ( mRNAs ) structurally similar to host mRNAs [1] . Despite this similarity , IAV transcripts gain preferential access to cellular translation machinery through a host shutoff mechanism executed by the viral non-structural protein 1 ( NS1 ) [2] , [3] and the recently discovered viral PA-X protein [4] . The reliance on cap-dependent translation initiation makes viral mRNAs susceptible to host-cell stress-induced translation inhibition mechanisms . This inhibition results from phosphorylation of eukaryotic translation initiation factor-2α ( eIF2α ) by any of four kinases activated by distinct types of stress [5] . Heme-regulated translation inhibitor ( HRI ) kinase is activated in response to oxidative stress , GCN2 senses nutrient deprivation and ultraviolet damage , double-stranded RNA ( dsRNA ) -dependent protein kinase R ( PKR ) is activated in response to viral infections , and the PKR-like endoplasmic reticulum kinase ( PERK ) signals in response to endoplasmic reticulum stress . Inhibition of translation initiation leads to runoff of elongating ribosomes from mRNA and the accumulation of stalled translation preinitiation complexes . Translationally inactive messenger ribonucleoproteins ( mRNPs ) recruit RNA-binding proteins with self-aggregating properties , including the T-cell intracellular antigen 1 ( TIA-1 ) , TIA-1-related protein ( TIAR ) , and ras GTPase-activating protein-binding protein 1 ( G3BP1 ) , which nucleate the formation of large cytoplasmic mRNP foci known as stress granules ( SGs; [6] ) . Many viruses have evolved specific mechanisms that modulate SG responses ( reviewed in [7] ) . Previously we demonstrated that SGs do not form at any point during IAV infection [8] . Importantly , complete inhibition of SG formation is dependent on NS1 . In cells infected with NS1-mutant viruses , SG formation is triggered by PKR activation . However , more than 50% of cells infected with NS1-mutant viruses remained SG-free and allowed IAV replication cycle progression , suggesting the existence of additional NS1-independent mechanisms of SG suppression . In this work , by analyzing SG formation in IAV-infected cells in response to a variety of stresses , we report a robust mechanism of SG inhibition that becomes engaged at later times post-infection and acts despite strong eIF2α phosphorylation . Maximal SG inhibition coincided with a striking depletion of cytoplasmic poly ( A ) mRNA and the nuclear re-localization of poly ( A ) -binding protein 1 ( PABP1 ) at later stages of viral replication , effects reminiscent of host shutoff mechanisms observed in other viral systems [9]–[11] . Screening of known IAV ORFs derived from A/PuertoRico/8/34 ( H1N1 ) strain ( PR8 ) revealed the identities of two additional viral SG inhibitors , nucleoprotein ( NP ) and polymerase-acidic protein-X ( PA-X ) , which both act independently of eIF2α phosphorylation . Furthermore , we provide evidence that in the early stages of infection , before the accumulation of sufficient quantities of SG-inhibiting proteins , viral replication is vulnerable to translation-inhibiting drugs that induce SGs and block viral replication .
Previously we have observed SG formation triggered by infection with recombinant influenza viruses with amino acid substitutions in NS1 that compromise its ability to counteract PKR [8] . For two different NS1 mutant viruses , SG formation peaked at 18 hpi; however , a majority of infected cells remained SG-free throughout a 24 h time course , suggesting that either these cells are not competent to form SGs , or that IAV has additional mechanisms that block PKR activity and/or SG formation . To test whether these infected cells are competent to form SGs , we treated cells infected with either WT or NS1 mutant viruses with sodium arsenite , a potent inducer of SGs that activates the oxidative stress-responsive eIF2α kinase HRI [12] . By 18 hpi , sodium arsenite had induced the formation of large , well-defined SGs in the cytoplasm of all uninfected cells , while most of the cells infected with either WT or NS1 dsRNA binding mutant viruses failed to form SGs ( Fig . 1A ) . We reinforced these observations through use of an additional NS1-mutant virus whose NS1 protein retains the functional dsRNA-binding domain but completely lacks the C-terminal effector domain ( PR8 NS1 N80; Fig . 1B upper panel ) , and a mutant virus completely lacking functional NS1 ( PR8 NS1 N15 ) . The latter virus triggered formation of PKR-mediated SGs , but was still able to partially inhibit arsenite-induced SG formation at 18 hpi ( Fig . 1B lower panel , and Fig . 1D ) . We observed striking inhibition of arsenite-induced SGs in WT PR8-infected cells from 12 hpi onwards , peaking at 18 hpi ( Fig . 1C ) . For this reason , we evaluated a panel of NS1-mutant viruses at this time point ( Fig . 1E ) . These NS1 mutant viruses , while triggering SG formation to varying degrees in infected cells , all inhibited arsenite-induced SGs compared to mock-infected cells . Furthermore , this effect was not cell-line specific , as we observed the same magnitude of SG inhibition by PR8 in U2OS osteosarcoma cells ( Fig . S1A–M ) . Importantly , SG inhibition did not result from blockade of arsenite-induced eIF-2α phosphorylation ( Fig . S1N ) , and the levels of SG constituent proteins TIA-1 , G3BP , PABP1 and eIF4A did not change throughout a 24 h infectious cycle ( Fig . S1O ) . What is the significance of SG inhibition at later times post-infection ? Using pulse-labeling experiments , we queried the rate of viral protein synthesis and sensitivity to sodium arsenite treatment . We observed a high rate of viral protein synthesis , which was maintained at later times post-infection ( Fig . 1F lanes 1–4 ) . Sodium arsenite treatment inhibited the production of viral proteins at 6 hpi and 16 hpi; however , the remaining translation rate was significantly higher at 16 hpi ( Fig . 1F , G ) , coincident with SG inhibition . Collectively , our data suggest that at later times post-infection the virus establishes an environment in which accumulation of viral proteins becomes less sensitive to stress-induced translation arrest . At later times post-infection , IAV vRNPs are exported from the nucleus to the cytoplasm and transit to the cell surface for assembly and egress . We observe inhibition of SG formation late in infection , coincident with the cytoplasmic export of vRNPs . To test whether cytoplasmic accumulation of vRNPs is necessary for SG inhibition , we treated mock- and IAV-infected cells with leptomycin B ( LMB ) , an inhibitor of CRM1-dependent nuclear export . Consistent with previous reports [13] , LMB treatment prevented cytoplasmic accumulation of vRNPs ( Fig . 2A ) . Importantly , in our system LMB treatment did not affect SG inhibition in virus-infected cells , and did not alter SG formation in mock-infected cells . Thus , cytoplasmic localization of vRNPs is dispensable for SG inhibition . Because SGs are comprised of stalled poly ( A ) mRNAs and certain translation initiation factors , we determined their intracellular location at late times post-infection . We saw a remarkable relocalization of PABP1 to the nucleus of infected cells ( Fig . 2B ) . PABP1 is nucleocytoplasmic shuttling protein that binds newly polyadenylated mRNAs in the nucleus [14] . Thus , the nuclear accumulation of PABP1 strongly suggested that the normal processing and transport of nascent mRNAs is impaired in infected cells , and/or cytoplasmic mRNA pools are severely depleted . Using fluorescence in situ hybridization ( FISH ) , we analyzed the nucleocytoplasmic localization of poly ( A ) mRNA at early and late times post-infection . Subcellular distribution of poly ( A ) RNA was comparable in mock- and IAV-infected cells at early times post-infection ( Fig . 2C and S2 ) . By contrast , at later stages post-infection , we observed striking loss of poly ( A ) RNA signal from the cytoplasm , and a noticeable increase in the nuclear poly ( A ) signal ( Fig . S2 ) . Importantly , upon arsenite treatment of mock- and IAV-infected cells at early times post-infection , bright cytoplasmic poly ( A ) foci were observed , consistent with the accretion of mRNAs into SGs . By contrast , no cytoplasmic foci were observed in cells that displayed nuclear accumulation of poly ( A ) RNA . Taken together , these data suggest that IAV SG inhibition coincides with bulk depletion of cytoplasmic poly ( A ) mRNA and the nuclear accumulation of PABP1 . In eukaryotes , eIF2α integrates signals from four stress-activated kinases , and we have established that IAV inhibits SG formation in response to either HRI- or PKR-mediated eIF2α phosphorylation . To determine whether the virus acts downstream of eIF2α phosphorylation , we assessed SG formation triggered by thapsigargin and UV light , which activate the two remaining eIF2α kinases , PERK and GCN2 , respectively . As a control , we also tested pateamine A ( PatA ) , which has been shown to induce SGs by translation inhibition but without eIF2α phosphorylation [15] ( Fig . 3A–C ) . In mock-infected cells , these treatments induced varying degrees of SG formation . Nevertheless , consistent with our sodium arsenite data , IAV inhibited SG formation in response to all three treatments without affecting eIF2α phosphorylation ( Fig . 3A–C ) . Most notably , IAV inhibited SG formation in response to PatA treatment , which did not induce eIF2α phosphorylation . Together , these findings establish that IAV can block SG formation in a manner independent of eIF2α phosphorylation . To identify additional IAV gene products that could function in this NS1-independent mechanism of SG inhibition , we analyzed arsenite-induced SG formation in cells ectopically expressing myc epitope-tagged constructs for each of the known IAV ORFs ( Fig . 4A ) . Among 11 ORFs analyzed , only PA and NP significantly inhibited arsenite-induced SG formation . PA-myc was predominantly localized to the cytoplasm , consistent with requirement of the PB1 viral subunit for its nuclear import [16] . In cells in which PA failed to suppress SG formation , PA-myc maintained a diffuse cytoplasmic localization and was not recruited to SGs ( Fig . 4A ) . By contrast , NP was found predominantly in the nucleus of transfected cells , with only a fraction of the total signal coming from the cytoplasm . NP-mediated inhibition of SG formation was dependent on NP expression level ( Fig . S3 ) , and many cells with low levels of NP formed SGs upon arsenite treatment . In cells in which NP failed to inhibit SG formation , NP was recruited to SGs , consistent with previous reports [17] . Finally , NS1 and the viral protein M1 were similarly recruited to arsenite-induced SGs , but did not interfere with their formation ( Fig . 4B ) . The distinct subcellular distribution and SG-recruitment characteristics of PA and NP suggested distinct mechanisms of SG suppression . Importantly , in cells expressing high levels of PA , SG inhibition coincided with striking relocalization of PABP1 to the nucleus ( Fig . 4B ) , similar to what we observed in IAV-infected cells at late times post-infection ( Fig . 2B ) . Importantly , in both PA-transfected and IAV-infected cells nuclear PABP accumulation invariably coincided with complete inhibition of SG formation in all our experiments . To confirm that both PA and NP interfere with SG formation regardless of eIF2α phosphorylation , we tested PatA-induced SG formation in cells expressing these IAV ORFs ( Fig . 4C , D ) . Both proteins were able to block SG formation in response to PatA . NS1 , as expected for this PKR-specific SG inhibitor , did not interfere with arsenite- and PatA-induced SG formation ( Fig . 4A–D ) . NP associates with IAV genomic and anti-genomic segments and is essential for vRNP synthesis , genome trafficking and virus assembly [18] . Extensive mutational analysis and atomic-resolution structural studies have identified important structural features of NP , including an amino-terminal nuclear-localization signal ( NLS ) and a carboxy-terminal oligomerization motif [19]–[21] , both critical for NP interaction with nascent vRNPs in the nucleus ( Fig . 5C ) . Mutation of arginine 8 to an alanine residue ( R8A ) prevented nuclear localization of NP , but did not impair its ability to suppress arsenite-induced SG formation , suggesting that NP nuclear accumulation is not required for SG inhibition ( Fig . 5A , B ) . NP oligomerization was experimentally disrupted by substitution of key resides in the tail loop ( F412A , R416A ) required for the formation of intermolecular salt bridges [19] , [20] . Disruption of NP oligomerization completely restored arsenite-induced SG formation in transfected cells ( Fig . 5A , B ) . During infection , NP oligomerization is essential for polymerase activity and for the stability and transport of vRNPs , but our experiments clearly demonstrate that NP-mediated inhibition of SG formation is independent of virion RNAs or polymerase subunits . NP has not been shown to interact with host RNAs , but a number of high-affinity interactions of IAV ribonucleoproteins with host factors have been identified [22] , many of which could plausibly be dependent on the ability of NP to oligomerize . Influenza virus infection results in the rapid decline of host protein synthesis , a process referred to as shutoff . The viral PA protein has been implicated in this host shutoff , but the mechanism remained obscure until the recent discovery of an additional protein , PA-X , produced from the PA gene by ribosomal frameshifting [4] ( Fig . 6F ) . PA-X was shown to inhibit cellular gene expression and modulate viral virulence and the global host response [4] , [23] . The PA-myc plasmid shown above to inhibit arsenite-induced SG formation and cause nuclear accumulation of PABP1 is predicted to also encode PA-X ( Fig . 4 ) . To determine whether the full-length PA protein , or the alternative PA-X protein product , is responsible for SG inhibition and PABP1 relocalization , we transiently transfected HeLa Tet-Off cells with a carboxy-terminal epitope-tagged PA expression vector , or the same construct with optimized codons for F191 and R192 that would greatly reduce the probability of frameshifting and PA-X production ( PA ( fs ) -myc ) , or a frameshift mutant constructed to express PA-X exclusively ( PA-X , Fig . 6F ) . Results with these plasmids strongly indicate that the PA-X protein produced as a result of ribosomal frameshifting is responsible for the SG inhibition and PABP1 relocalization observed in PA-myc transfected cells . In cells transfected with the PA-X construct , we observed the strongest nuclear accumulation of PABP1 , which was completely concordant with SG inhibition . By contrast , in PA ( fs ) -myc transfected cells , we could not detect any relocalization of PABP1 , and arsenite-induced SG formation was unperturbed . As expected , the PA-myc construct displayed an intermediate phenotype , consistent with a lower expected level of PA-X production ( Fig . 6B ) . To directly confirm that PA-X ORF is expressed in cells showing nuclear PABP relocalisation , we created an additional expression construct with a C-terminally tagged PA-X ORF . Due to high toxicity of PA-X , only cells expressing low levels of this fusion protein remained viable at 24 h post-transfection . Nevertheless , these levels were sufficient to cause nuclear relocalisation of PABP1 and complete blockade of SG formation in response to arsenite treatment ( Fig . S4 ) . In the study by Jagger et al . ( 2012 ) , PA-X expression was linked to host gene expression shutoff , which required an endonuclease function of the N-terminal domain shared by the full-length PA and PA-X proteins ( Fig . 6F ) . It was proposed that this endonuclease activity of PA-X is responsible for depletion of host cytoplasmic mRNA pools . To determine whether PA-X endonuclease activity is responsible for depletion of cytoplasmic PABP1 ( Fig . 2B ) and poly ( A ) mRNA ( Fig . 2C ) late in IAV infection , we disrupted the endonuclease activity of PA and PA-X by a single amino acid substitution at D108 ( PA ( 108A ) -myc ) , and analyzed SG formation and subcellular distribution of poly ( A ) RNA using FISH ( Fig . 6A , D ) . Similar to PA ( fs ) -myc transfected cells , cells transfected with PA ( 108A ) -myc displayed normal cytoplasmic levels of poly ( A ) RNA and formed SGs in response to sodium arsenite . To examine the contribution of PA-X to the SG-inhibition phenotype at late times post-infection , we created a recombinant IAV with a codon-optimized PA ORF , termed PR8 PA ( fs ) , expected to have an attenuated host-shutoff phenotype [4] . At 18 hpi , the PR8 PA ( fs ) infected cells showed dramatically decreased nuclear PABP1 relocalization and attenuated SG inhibition in response to sodium arsenite ( Fig . 6C , E ) . Notably , a fraction of cells infected with PR8 PA ( fs ) remained refractory to arsenite-induced SG formation , perhaps due to inhibition of SG formation by NP protein . We have established that at later stages of its replication cycle IAV efficiently blocks SG formation downstream of eIF2α phosphorylation , while early in infection IAV is particularly sensitive to treatments that induce translation arrest and SG formation ( Fig . 1C , F ) . This early part of the infectious cycle may therefore provide a window of opportunity for pharmacological induction of viral translation arrest and SG formation . Among the treatments that we have tested , we observed strongest induction of SG formation in response to sodium arsenite ( Fig . 1 ) and PatA ( Fig . 3 ) . To test the effects of these drugs on viral replication , we pulse-treated IAV-infected cells at 4 hpi , with subsequent removal of drug after 1 h . This 1 h treatment with PatA was sufficient to completely block IAV replication , as evidenced by lack of accumulation of viral proteins at later times post-infection ( Fig . 7A ) . Interestingly , sodium arsenite had minimal effects on IAV protein accumulation . Despite the high toxicity of sodium arsenite , upon its removal cells are reported to rapidly dephosphorylate eIF2α , dissolve SGs , and resume robust translation [24] . By contrast , PatA binds and inhibits the function of eIF4A helicase irreversibly [25] . These properties could explain the differential effect of these SG-inducing agents on viral replication . To test this , we compared translation rates in mock- and PR8-infected cells immediately post-treatment with sodium arsenite or PatA , and at regular intervals post-drug withdrawal . As shown in Fig . 7B , D , soon after removal of sodium arsenite , production of IAV proteins was largely restored . By contrast , translation rates remained low long after PatA was removed ( Fig . 7C , D ) . This sustained inhibition is in agreement with lack of viral protein accumulation up to 14 hours after removal of PatA-containing medium ( Fig . 7A ) , and is marked by the accumulation and persistence of SGs , both in mock- and PR8-infected cells ( Fig . 7F ) . Importantly , persistence of SGs prevented the accumulation of progeny vRNPs in the cytoplasm ( Fig . 7F ) , viral genomic RNA accumulation ( Fig . 7G ) , and infectious virus production ( Fig . 7E ) . By contrast , PatA treatment did not prevent viral mRNA accumulation , and even increased its abundance at later times post-infection ( Fig . 7H ) . This finding is consistent with the model in which viral replication is dependent on de novo protein synthesis [26] , and earlier observations that treatment with translation inhibitors prevents viral polymerase from initiating genome replication while still allowing viral mRNA synthesis [27] . Given the potency of PatA for IAV inhibition , we tested the effects of low-dose PatA treatment on multi-round IAV infection and observed an inhibitory effect at 2 and 0 . 4 nM ( Fig . 7I ) . It is notable that we observed no adverse effects of PatA on cell viability at these low doses , consistent with published observations in other model systems [28] .
Despite being generated by a viral RNA-dependent RNA polymerase complex , IAV mRNAs bear structural similarity to the products of host RNA polymerase II , with 5′ caps and 3′ poly ( A ) modifications that ensure efficient translation by host machinery [1] . These features render IAV mRNAs susceptible to negative regulation by eIF2α kinases that have evolved to sense a variety of environmental stresses , including viral infection [5] . Triggering of eIF2α phosphorylation during IAV infection would therefore be expected to arrest translation of a large fraction of viral mRNAs as well as host mRNAs , and entrap them in SGs . Remarkably , we observed that SGs do not form at any point during the IAV replication cycle and efficient translation of viral gene products is maintained in the later stages of infection , when a variety of eIF2α-kinase activating signals might be anticipated . Here we have identified three IAV proteins capable of inhibiting SG formation: NS1 , NP and PA-X ( Fig . 8 ) . NS1 binds to viral dsRNA and prevents PKR activation and consequent eIF2α phosphorylation . High levels of oligomeric NP block SG formation in an eIF2α-independent manner , through a yet to be identified mechanism . By contrast , inhibition of SG formation by PA-X , the alternative product of PA mRNA that is made as a result of translational frameshifting , is dependent on its endoribonuclease activity , and coincides with depletion of a large fraction of cytoplasmic poly ( A ) RNA . During PA mRNA translation , less than 2% of the ribosomes frame-shift at the Phe-191 , downstream of the endoribonuclease domain that is shared between PA and PA-X proteins [4] . This low rate of frame-shift ensures slow accumulation of PA-X , the potent shutoff factor that acts at later times post-infection . Indeed , mutations that inhibit frame-shifting and PA-X production by IAV disrupt the viral inhibition of host gene expression [4] . Our finding that PA-X inhibits SG formation led us to explore PA-X host shutoff function , and our findings support the recently proposed model; we find that PA-X causes striking depletion of poly ( A ) RNA from the cytoplasm of cells transfected with a PA-X expression vector , or in IAV-infected cells late in infection , but not in cells infected with a mutant virus with defects in PA-X protein production . Notably , in addition to the depletion of cytoplasmic poly ( A ) RNA and nuclear relocalization of PABP1 , we also observe a remarkable accumulation of poly ( A ) RNA in the nuclei of cells expressing PA-X and in virus-infected cells at later times post-infection . While this phenomenon has not been demonstrated previously for IAV infections , a similar accumulation of poly ( A ) RNA and PABP1 has been reported in other viral infections , and in each case the described perturbations of PABP1 has been linked to host shutoff [9]–[11] . For example , host shutoff in cells infected by Kaposi's sarcoma-associated herpesvirus ( KSHV ) is enforced by the shut-off exonuclease protein SOX , which accumulates during infection and degrades host cytoplasmic mRNAs [29] . SOX expression leads to strong nuclear accumulation of PABP1 and hyper-adenylated mRNAs . It was demonstrated that relocalization of PABP1 to the nucleus results in stimulation of poly ( A ) polymerase and synthesis of extended poly ( A ) tails [30] . Given the general inhibition of host mRNA synthesis by IAV , it is plausible that an increase in nuclear poly ( A ) RNA signal is due to hyperadenylation rather than a blockade of nascent mRNA export , similar to the hyperadenylation observed in cells ectopically expressing KSHV SOX protein or a mutant PABP1 that is restricted to the nucleus [31] . PABP1 localizes to SGs , but is not required for SG formation [6] , [32] . Therefore , it remains to be determined whether PA-X directly affects nucleocytoplasmic shuttling of PABP1 , or whether PABP1 nuclear accumulation simply results from bulk depletion of cytoplasmic poly ( A ) RNA . In either case , we have demonstrated here that the endonuclease activity of PA-X is important for SG inhibition , forging the first links between host shutoff and SG dynamics . Host shutoff is an important feature of influenza virus infection . In human-adapted strains of IAV , host shutoff is mediated at least in part by the NS1 proteins that are able to bind and inactivate the cellular cleavage and polyadenylation specificity factor 30 ( CPSF30 ) . This inactivation prevents polyadenylation and nuclear export of host pre-mRNAs [2] . The host shutoff mechanisms employed by IAV strains with NS1 proteins that lack CPSF30-binding activity remain poorly characterized , but the recent discovery of the PA-X protein significantly advances our understanding of host shutoff by these viruses . Curiously , our study reveals that PA-X expression causes nuclear accumulation of poly ( A ) RNA , which would require polyadenylation of at least some RNA species in the nucleus . At present , we cannot predict the cumulative effects on sub-cellular poly ( A ) RNA distribution of CPSF30 inactivation by NS1 and nuclear PABP1 relocalization by PA-X , because the NS1 of PR8 strain employed in this study does not bind CPSF30 . Interestingly , the PA segment of WSN/33 strain of IAV , which is competent for CPSF inactivation , was less effective at inhibiting host protein synthesis than the PA segment from A/California/04/2009 strain [23] . This difference suggests that the host-shut-off function of PA-X , similar to that of NS1 , may vary significantly between strains of IAV . NP is a highly conserved RNA-binding protein that plays essential roles in IAV transcription , genome replication , and genome packaging into nascent virions . NP binds to RNA with high affinity ( Kd approx . 20 nM ) , but there is little evidence for sequence specificity , and no links have yet been made to the control of mRNA translation [33] , [34] . Our data demonstrate that ectopic expression of NP alone , in the absence of viral RNA or other viral proteins , is sufficient to inhibit arsenite-induced SG formation . Structural studies have shown that NP forms oligomers in physiological ionic conditions [19] . The formation of these oligomers depends on intermolecular salt bridges , and we demonstrate that experimental disruption of these salt bridges by substitution of key residues in the tail loop ( F412A , R416A ) disabled the SG-inhibition function of NP . We are unable to assess the impact of these mutations on NP function during viral infection because they prevent IAV transcription and genome replication [35] . Proteomic studies have identified a number of host proteins bound by viral ribonucleoproteins [22] , several of which play roles in mRNA processing , stability and translation . In addition , a number of proteins recruited to SGs and involved in their formation have been shown to bind NP [36] , [37] , [38] . NP interacts with nuclear factor 90 ( NF90 ) that participates in virus-induced PKR activation and SG formation [38] . Another host antiviral factor that is recruited to SGs and interacts with both NP and NS1 is the RNA-associated protein 55 ( RAP55 ) [36] . When unable to block SG formation ( e . g . at early times post-infection ) NS1 and NP can be sequestered in SGs by RAP55 where their normal functions are likely to be inhibited . Thus , it is possible that IAV NS1 and NP can be both the antagonists and the targets of host antiviral SG responses . Recently , RNA-dependent interaction of IAV NP with Fragile X mental retardation protein ( FMRP ) was demonstrated and FMRP was shown to stimulate viral ribonucleoprotein assembly [37] . Unlike the antiviral factors NF90 or RAP55 , FMRP contributes to efficient IAV replication , and may be sequestered through recruitment to SGs . This would further impair viral replication and contribute to the antiviral effect of SGs . In our ongoing studies , we plan to employ WT and mutant NP constructs to screen for interactions with these and other host factors , and determine their relative impact on SG dynamics . We have established that early in infection , before significant accumulation of NP and PA-X proteins , IAV is particularly sensitive to treatments that induce translation arrest and SG formation . This may provide a window of opportunity for pharmacological induction of viral translation arrest and SG formation . Among the treatments tested , we observed strongest induction of SG formation in response to arsenite and to PatA . Until now , PatA and related structural analogs have been evaluated for their antitumor properties , and found to represent promising and selective anti-tumor agents [39] , [40] . Like tumor cells , IAV-infected cells require high levels of translation to support their metabolic needs . Moreover , at the concentrations used in our studies , PatA inhibits the translation of only a fraction of cellular RNAs , specifically ones important for tumor cell growth and metastasis [39] . Ongoing in vitro studies will address the mechanism of action of pateamine A and other translation inhibitor drugs in IAV restriction . In summary , we report that translation of IAV proteins continues uninterrupted until late in infection , long after the viral polymerase complex has been re-tasked to viral genome replication . We report the remarkable discovery of three functionally distinct IAV-encoded inhibitors of SG formation , which together strongly influence the translational landscape of infected cells . The existence of three distinct mechanisms of IAV-mediated SG inhibition reveals the magnitude of the threat ( to the virus ) of stress-induced translation arrest during viral replication , and hints that other viruses , large and small , may also encode multiple SG-regulatory proteins . Investigation of the mechanisms of action of these viral SG inhibitors may reveal important clues about how these viruses enforce host shutoff and ensure preferential production of viral gene products .
Unless specifically indicated , all chemical reagents were purchased from Sigma . Pateamine A was a kind gift from Dr . Jerry Pelletier ( McGill University , Montreal , QC , Canada ) . HeLa-TetOff cells were purchased from Clontech . A549 , U2OS , Vero , and HeLa-TetOff cells were maintained in Dulbecco's modified Eagle's medium ( DMEM; HiClone ) supplemented with 10% fetal bovine serum ( FBS , Life Technologies ) and 100 U/ml penicillin+100 µg/ml streptomycin+20 µM L-glutamine ( Pen/Strep/Gln; Wisent ) at 37°C in 5% CO2 atmosphere . The A/PuertoRico/8/34/ ( H1N1 ) virus ( PR8 ) and the recombinant mutant viruses PR8 NS1 38A , 41A and PR8 NS1 96 , 97A are described in [8] . To generate NS1 deletion mutant viruses PR8 NS1 N80 and PR8 NS1 N15 , the plasmid encoding segment 8 of PR8 [41] was subjected to Phusion PCR mutagenesis ( Finnzymes/Thermo Scientific ) to introduce termination codons at amino acid positions 81 and 16 , respectively , in the NS1 open reading frame . Resulting constructs were combined with 7 wild-type PR8 plasmids for rescue of the mutant viruses as described in [41] , producing PR8 mutants expressing either just the dsRNA binding domain of NS1 ( PR8 NS1 N80 ) or completely lacking functional NS1 and expressing only 15 N-terminal amino acids shared between NS1 and NEP upstream of the unaltered transcript splice site ( PR8 NS1 N15 ) . Recombinant virus PR8 PA ( fs ) was generated using the general approach described above . In the segment 3 rescue plasmid , sequence TTTCGT encoding Phe-191 and Arg-192 of the PA ORF was substituted with the optimized codon sequence TTCCGC to generate the recombinant virus with strongly attenuated +1 ribosomal frame shifting during PA mRNA translation . Mutagenesis primer sequences are available upon request . Virus stocks used for experiments were produced and titrated by plaque assays as described [8] . Genomic RNA segments 3 and 8 of each virus stock were verified by sequencing . For virus infection experiments , unless specified otherwise , infections were done at multiplicity of infection ( MOI ) of 0 . 5 . After inoculation , cells received infection medium containing 0 . 5% bovine serum albumin ( BSA ) in DMEM and incubated at 37°C in 5% CO2 atmosphere . When indicated , mock and virus-infected cells were treated with 0 . 75 mM sodium arsenite , 10 nM pateamine A ( except when other concentrations are given ) , or 1 µM Thapsigargin . For UV treatment , cells were washed briefly with PBS , exposed to 10 , 000 µJ/cm2 of 254 nm light in HL-2000 Hybrilinker chamber ( UVP ) and promptly returned to 37°C incubator . Rescue plasmids encoding 8 segments of PR8 virus ( pHW191-PB2 to pHW198-NS ) were kindly provided by Dr . Richard Webby ( St . Jude Children's Research Hospital , Memphis , TN , USA ) . pCR3 . 1-Myc and pCR3 . 1-NS1-myc expression vectors are described in [8] . Remaining expression vectors for c-terminally tagged PR8 ORF library ( with the exception of M2 ) were constructed by inserting PCR-amplified coding sequences from the rescue vectors between KpnI and Mlu sites of pCR3 . 1-Myc vector ( flanking restriction sites were introduced by PCR ) . M2 expression vector was generated by removal of the intervening intron sequence from pHW197-M plasmid using Phusion PCR mutagenesis protocol . Untagged NP expression vector was generated by inserting the PCR-amplified coding sequences from the pHW195-NP plasmid into pCR3 . 1 vector ( Invitrogen/Life Technologies ) between EcoRI and XhoI sites . Subsequent substitutions in the NP ORF were done by PCR mutagenesis to produce pCR3 . 1-NP ( 8A ) , pCR3 . 1-NP ( 412 , 416A ) and pCR3 . 1-NP ( 8 , 412 , 416A ) vectors . pCR3 . 1-PA ( fs ) -myc , pCR3 . 1-PA ( 108A ) -myc and pCR3 . 1-PA-X were generated by PCR mutagenesis of the pCR3 . 1-PA-myc vector from the PR8 ORF library . Cloning primer sequences are available upon request . HeLa- TetOff cells were seeded at a density of 100 , 000 cells/well of a 12-well culture plate and transfected the next day with 0 . 5 µg total DNA/well using FuGene HD reagent ( Promega ) according to manufacturer's protocol . At 24 h post-transfection , cells were treated as described and analysed by immunofluorescent staining . Cells grown on glass coverslips were fixed and immunostained according to the protocol in [6] using mouse monoclonal antibodies to IAV nucleoprotein ( AAH5; Abcam ) , G3BP ( clone 23 , BD Transduction Labs ) , and PABP1 ( sc-32318 , Santa Cruz Biotechnology ) ; goat polyclonal antibody to TIA-1 ( sc-1751 , Santa Cruz Biotechnology ) , influenza virus ( ab20841 , Abcam ) , rabbit antibody to TIAR ( clone D32D3 , Cell Signaling ) , YB-1 ( ab12148 , Abcam ) and myc-tag ( 9B11; Cell Signaling ) at manufacturer-recommended dilutions . AlexaFluor-conjugated secondary antibodies ( Molecular Probes ) were used at 1∶1000 dilution . Images were captured using Zeiss Axioplan II microscope or Zeiss LSM 510 laser scanning microscope . Quantification of stress granules was done in at least 3 random fields of view with greater than 200 cells analysed on each slide . Cells were considered stress granule-positive if two or more stress granule marker foci were present in the cytoplasm . For western blot analysis , whole cell lysates were resolved on denaturing 10% polyacrylamide gels and analyzed using primary antibodies described above and the antibodies to phospho-Ser-51- eIF2α ( rabbit , D9G8 , Cell Signaling ) , eIF4A ( goat , sc-14211 , Santa Cruz Biotechnology ) , and actin ( rabbit , 4968; Cell Signaling ) . As described in [8] . Cells grown on glass coverslips were washed briefly with PBS , fixed with 4% paraformaldehyde in PBS for 15 min at room temperature , and permeabilized using 0 . 1% Triton X-100 in PBS for 10 min . Coverslips were equilibrated in wash buffer ( 2× SSC , 20% formamide , 0 . 02% BSA ) for 15 min and then incubated overnight with 100 µl of hybridization mix ( wash buffer with 10% dextran sulphate , 0 . 4 mg/ml S . cerevisiae tRNA , and 100 nM Fluorescein-conjugated oligo-dT-40 probe ) at 42°C . Then , coverslips were washed in wash buffer for 15 min at 42°C , for 15 min at room temperature , and finally with PBS for 15 min prior to either mounting on slides or blocking for subsequent immunostaining . Total RNA was isolated using the RNeasy mini-prep kit ( Qiagen ) according to the manufacturer's instructions , and the cDNA synthesis was performed using the Super Script III Reverse Transcriptase Kit ( Life Technologies ) . Quantitative PCR analysis was performed using MX6000P unit ( Agilent Technologies ) and GoTaq PCR master mix ( Promega ) . For analysis of NS1 mRNA reverse transcription was performed using oligo-dT ( 20 ) primer . For reverse transcription of segment 8 vRNA the following primer was used: 5′-CAA ACA CTG TGT CAA GCT TTC AG . Same primer set was used for the mRNA and vRNA-derived cDNA amplification: NS1-Left 5′-CTG TGT CAA GCT TTC AGG TAG A and NS1-Right 5′- GGT ACA GAG GCC ATG GTC AT . Relative initial template quantities were determined using the standard curve method . For each biological replicate , reactions were done in duplicate and mean cycle threshold ( Ct ) values were used . All error bars represent standard deviations calculated from values obtained in at least 3 independent biological replicates . Analysis of variance ( ANOVA ) single-factor algorithm was applied to the select datasets using Microsoft Excel data analysis module ( Microsoft ) to calculate p values . Where indicated , asterisks denote p values lower than 0 . 05 ( * ) or 0 . 005 ( ** ) . | Like all viruses , Influenza A virus ( IAV ) is absolutely dependent on host-cell protein synthesis machinery . This dependence makes the virus vulnerable to the innate ability of cells to inhibit protein synthesis in response to various types of stress . This inhibition , termed translation arrest , helps cells survive adverse conditions by re-dedicating their energy to stress responses . When cells arrest translation , they form stress granules: depots of untranslated mRNAs and associated proteins . Translation arrest and formation of stress granules can be induced pharmacologically , and in this work we sought to determine whether stress granule induction would be effective in blocking IAV replication . Here we demonstrate that treatment of cells with inducers of stress granules at early times after infection resulted in blockade of viral protein synthesis and stopped viral replication . At later times post-infection , by contrast , IAV proteins prevented pharmacological induction of stress granules . We identified three viral proteins – more than in any virus to date – that work in concert to prevent stress granule formation . Taken together , our studies reveal a multipronged approach for viral suppression of translation arrest , and identify a window of opportunity early in infection when pharmacological induction of stress granules has a strong antiviral effect . | [
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] | 2014 | Influenza A Virus Host Shutoff Disables Antiviral Stress-Induced Translation Arrest |
Mitochondrial DNA ( mtDNA ) encodes for proteins required for oxidative phosphorylation , and mutations affecting the genome have been linked to a number of diseases as well as the natural ageing process in mammals . Human mtDNA is replicated by a molecular machinery that is distinct from the nuclear replisome , but there is still no consensus on the exact mode of mtDNA replication . We here demonstrate that the mitochondrial single-stranded DNA binding protein ( mtSSB ) directs origin specific initiation of mtDNA replication . MtSSB covers the parental heavy strand , which is displaced during mtDNA replication . MtSSB blocks primer synthesis on the displaced strand and restricts initiation of light-strand mtDNA synthesis to the specific origin of light-strand DNA synthesis ( OriL ) . The in vivo occupancy profile of mtSSB displays a distinct pattern , with the highest levels of mtSSB close to the mitochondrial control region and with a gradual decline towards OriL . The pattern correlates with the replication products expected for the strand displacement mode of mtDNA synthesis , lending strong in vivo support for this debated model for mitochondrial DNA replication .
In their inner membrane , mitochondria harbor the oxidative phosphorylation ( OXPHOS ) system , which generates ATP needed to drive energetically unfavorable cellular reactions . Most OXPHOS components are encoded in the nuclear genome , but genes for 13 essential subunits are encoded by a separate mitochondrial DNA genome ( mtDNA ) . Mutations , deletions and depletion of mtDNA result in defective energy production , which in turn causes a wide variety of disease symptoms [1] . Human mtDNA is a relatively small ( ∼16 , 6 Kb ) circular double-stranded molecule that is organized into nucleoprotein complexes , denoted nucleoids [2] , [3] , [4] . The two strands of the mitochondrial genome are known as the heavy strand ( H-strand ) and light strand ( L-strand ) , owing to a strand bias in guanine and thymine base content . The genome is replicated by a molecular machinery that is distinct from the nuclear replication apparatus . The core components of this machinery are related to their phage T7 counterparts , including the catalytic subunit of DNA polymerase γ ( POLγA ) , the DNA helicase TWINKLE , and the mitochondrial RNA polymerase ( POLRMT ) , which synthesizes RNA primers during initiation of DNA synthesis [5] , [6] . RNase H1 is also required for mtDNA maintenance and likely plays a role for primer removal during mtDNA maturation [7] . The mitochondrial single-stranded DNA binding ( mtSSB ) protein does not have a phage ancestry , but instead resembles Escherichia coli SSB [8] . MtSSB binds to single-stranded DNA ( ssDNA ) as a tetramer composed of four 16 kDa subunits [9] . The protein stimulates synthesis of mtDNA [10] by facilitating POLγ primer recognition [11] and enhancing POLγ processivity [12] . MtSSB also stimulates the dsDNA unwinding activity of TWINKLE [13] . Currently there is no consensus regarding the mechanism by which mammalian mtDNA is replicated . Early work reported that mtDNA replication occurs through a strand-displacement mode ( SDM ) [14] . According to this model , mtDNA synthesis is continuous on both strands . DNA synthesis is first initiated at the origin of heavy-strand DNA replication ( OriH ) and nascent H-strand DNA synthesis continues to displace the parental H-strand . During the first stage of DNA synthesis , there is no simultaneous light-strand ( L-strand ) DNA synthesis . When two-thirds of the H-strand has been synthesized , the replication machinery reaches the origin of light-strand DNA replication ( OriL ) . At this point , the H-strand of OriL is exposed in its single-stranded conformation and it folds into a stem-loop structure . The folded origin is recognized by POLRMT , which initiates primer synthesis from a short T-stretch in the single-stranded loop region of the activated origin . After about 25 nts , POLRMT is replaced by POLγ at the 3′-end of the RNA primer and lagging-strand DNA synthesis is initiated . After initiation , H- and L-strand DNA synthesis proceeds continuously until each strand is completely replicated and two daughter molecules are formed . SDM replication intermediates have been identified and characterized by various biochemical techniques ( for a detailed review , please see [15] ) . In recent years , key steps in the process have also been reconstituted in vitro with purified proteins [6] , [16] , [17] . The SDM is also supported by evolutionary analysis that revealed an asymmetric skew in bases frequencies at the third codon position in mitogenomes . This mutational gradient correlates with the time the H-strand would be in its more mutagenic single-stranded conformation according to SDM [18] . A number of studies have questioned the in vivo relevance of SDM and suggested the existence of two other , alternative models for mtDNA replication . The so-called ribonucleotide incorporation throughout the lagging strand ( RITOLS ) model is primarily based on data obtained by neutral two-dimensional agarose gel electrophoresis ( 2D-AGE ) [19] , [20] , [21] . The authors of these studies concluded that the mtDNA replication intermediates observed in 2D-AGE cannot be explained by SDM replication and they have also criticized the SDM for limited in vivo evidence [22] . RITOLS and SDM replication are very similar , with the exception of one striking difference , the requirement of mtSSB . According to the SDM , the displaced H-strand is single-stranded , and therefore coated with mtSSB , which is subsequently removed during L-strand synthesis . In contrast , the RITOLS replication model states that RNA covers the displaced lagging-strand during replication [20] . According to the RITOLS model , the RNA intermediates associated with mtDNA replication are processed transcripts ( including tRNAs ) , which are successively treaded onto the exposed lagging-strand template after passage of the replication fork [21] . A weakness of the RITOLS model is that the enzymes required for this process , i . e . hybridization of RNA intermediates , have not been defined . Whereas both the SDM and RITOLS models imply strand asynchronous mtDNA replication , there is also a third model , which suggests strand coupled ( SC ) replication of the mtDNA [19] . The SC model is based on the identification of fully duplex DNA replication intermediates in 2D-AGE analysis , which were seen as evidence for coupled leading and lagging strand DNA replication . These products are generally only a minor fraction of all replication intermediates , but they can be detected under specific experimental conditions [21] . We reasoned that a detailed characterization of mtSSB occupancy and its function during mtDNA replication could help to elucidate the mechanism for mtDNA replication . We here demonstrate that mtSSB exclusively covers the H-strand during mtDNA replication and helps to direct primer synthesis to OriL . Our findings reveal the molecular basis for origin specific initiation of light-strand mtDNA synthesis and provide strong experimental support for the in vivo relevance of the SDM replication model .
Both the SDM and RITOLS models imply that the parental H-strand will be single-stranded for a long period of time during mtDNA replication . This conformation presents a problem , since POLRMT has limited sequence requirements and it may readily initiate primer synthesis outside the OriL region . POLRMT uses ATP as the initiating nucleotide and only requires a single T ( thymine base ) in the template strand to start primer synthesis ( Fig . 1A , lane 2 ) . The reaction is further stimulated on templates containing a poly-T stretch ( Fig . 1A , compare lane 2–7 ) . Given the limited sequence requirement of POLRMT on ssDNA , it is essential to protect the displaced H-strand in order to prevent unrestricted initiation of L-strand DNA synthesis . We decided to investigate if mtSSB ( as expected for SDM ) or processed transcripts ( as predicted for RITOLS ) was responsible for protecting the parental H-strand . To this end , we first analyzed the in vivo levels of mtSSB to establish if the protein was sufficiently abundant to coat the parental H-strand during mtDNA synthesis . We isolated mitochondria from HeLa cells and performed quantitative immunoblot analysis with antibodies against mtSSB . The protein concentration was determined by comparison with known amounts of recombinant mtSSB protein ( Fig . 1B ) . In parallel , we determined mtDNA copy number by quantitative real-time PCR , which allowed us to calculate a ratio of about 2 , 100 molecules of mtSSB per mtDNA molecule ( Table 1 ) . Our values agreed nicely with previous estimates of 3 , 000 molecules of mtSSB per mtDNA molecule [23] . MtSSB binds to single-stranded DNA as a tetramer , corresponding to at least 500 mtSSB tetramers available per mtDNA molecule in the cell . Each tetramer of mtSSB has been reported to bind 59 nts [24] , which implies that there is more than enough mtSSB to coat the entire parental H-strand during mtDNA replication . We next investigated if mtSSB's ability to prevent random primer synthesis [16] could direct OriL specific initiation of lagging-strand synthesis in vitro . To this end , we combined POLγ , the mitochondrial helicase TWINKLE and mtSSB , and studied DNA synthesis on a double stranded template with a preformed replication fork [25] . In the presence of POLRMT , which acts as a primase for initiation of light-strand DNA synthesis at OriL , the system can catalyze the formation of dsDNA products in vitro ( Fig . 2 A and B lower panel ) [6] . When mtSSB was omitted or added at low levels to the rolling circle replication assay , we observed a smear of shorter products suggesting that primer synthesis and initiation of lagging-strand DNA synthesis could take place at multiple sites on the DNA template . Under these conditions , it was difficult to observe OriL specific initiation events ( Fig . 2A , lane 1–2 and Fig 2B , upper panel ) . Please note that we are using saturating levels of TWINKLE and POLγ , which explain why mtSSB only has a minor stimulatory effect on leading-strand DNA synthesis [25] . When we added increasing levels of mtSSB , we began to observe a specific product corresponding to the expected size for OriL-dependent lagging-strand initiation . At higher mtSSB concentrations the smear of shorter products completely disappeared and only the OriL-specific band remained intact ( Fig 2A lane 7–9 and Fig . 2B lower panel ) . Our findings demonstrated that mtSSB restricts initiation of lagging-strand DNA synthesis in vitro to OriL . To further verify our interpretations , we performed similar experiments , but analyzed the replication products with Southern blotting , using leading- and lagging-strand specific probes . As a negative control , we used a rolling-circle template lacking the OriL sequence ( OriL del ) . We observed efficient leading-strand DNA synthesis with both the wt and OriL del template ( Fig . 2C left panel ) . The leading-strand specific probes detected replication products of 3 , 900 nts ( input template ) and longer . Using the lagging-strand specific probes and in the absence of mtSSB , we detected multiple , shorter DNA synthesis products on both the wt and OriL del template ( Fig 2C right panel lanes 17–18 and 25–26 ) . When we increased the mtSSB concentrations , a product with a size corresponding to that expected for replication products initiated at OriL replaced the smear of shorter products produced with the wt template ( Fig . 2C lane 19–22 ) . The band was not observed when the OriL del was used as template ( Fig . 2C lane 27–30 ) We could thus conclude that mtSSB restricts initiation of lagging-strand DNA synthesis to OriL in our reconstituted in vitro system . We have previously suggested that the stem-loop structure of activated OriL prevents mtSSB binding , thereby making the T-stretch in the single-stranded loop region available for initiation of primer synthesis by POLRMT ( Figure S1 and [6] ) . To directly test this idea , we used gel shift analyses to monitor binding of mtSSB to OriL containing DNA substrates and mutant derivatives thereof ( Fig . 3 ) . MtSSB formed a complex and no free template was observed when mtSSB was added at a molar ratio of 1∶1 or higher relative to a single-stranded DNA oligonucleotide without any predicted secondary structure potential ( Fig . 3 , lanes 1–6 ) . MtSSB binding to an OriL containing oligonucleotide was less efficient ( Fig 3 , lane 7–12 ) . The effect was even more pronounced , when the stem region was stabilized by a 6-bp extension , which almost abolished mtSSB binding ( Fig . 3 , lane 13–18 ) . If correct , SDM would imply that mtSSB binds throughout the H-strand during mtDNA replication to block unspecific initiation of mtDNA synthesis at regions outside of OriL . To address if this was indeed the case in vivo , we investigated the binding pattern of mtSSB along the mtDNA genome using chromatin immunoprecipitation followed by real-time quantitative PCR . To this end , we designed primers that were specific for the two DNA strands , i . e . which could detect mtSSB binding to the H- or L-strand , respectively . Eight evenly distributed mtDNA regions were chosen for our analysis ( Fig . 4B , short black bars and the primers used are listed in Table S1 ) . First , strand-specific primers with distinct 5′ tag sequences were used to copy the H- or the L-strand . Next , the synthesized tagged-DNA was amplified by PCR with a primer corresponding to the tag sequence and a primer complementary to the target DNA . Our analysis revealed a striking strand bias in mtSSB binding ( Fig . 4A ) . There were only back-ground levels of mtSSB binding to the L-strand , whereas we observed a strong signal from the H-strand . The findings were thus in nice agreement with our model based on in vitro experiments , which stated that mtSSB should protect the parental H-strand . Interestingly , mtSSB binding to the H-strand was not uniform , but displayed a distinct pattern . The highest mtSSB occupancy was observed in CYTB , just downstream of the D-loop region and after this point we observed a progressive decrease with a minima of mtSSB occupancy in COX1 , just before OriL . A second smaller peak of mtSSB binding was observed just after OriL ( in ND2 ) , followed by a gradual decrease towards the control region ( ND1 and RNR2 ) ( Fig 4A , upper panel ) . The observed pattern therefore correlated with what would be expected for strand-displacement mtDNA replication ( Fig 4C , upper panel ) . According to this model , the region just downstream of the control region ( CYTB ) would be present in its single-stranded conformation for a much longer time than the region next to OriL ( COX1 ) . Similarly , the region just downstream of OriL ( ND2 ) would remain single-stranded longer than the regions just upstream of the control region ( ND1 and RNR2 ) . As a control , we also investigated the binding pattern of TFAM . A previous report has demonstrating that this dsDNA-binding protein is evenly distributed over the genome [26] . Using our strand-specific primers , we found that TFAM interacted with both the H- and L-strands to a similar extent ( Compare Fig . 4A , upper and lower panel ) . We could thus conclude that the observed strand-bias was specific for mtSSB . In order to obtain a more detailed profile of mtSSB occupancy , we again employed strand-specific ChIP analysis , but analyzed the precipitated material by strand-specific next generation sequencing . We excluded sequences that mapped to the nuclear genome and reads without a perfect match to the mtDNA from further analysis . In total , we could map 17 . 4% of the mtSSB ChIP reads to the mitochondrial genome , which was about 85-fold higher than the mapped reads ( 0 . 2% ) in the input samples , consistent with the fact that mtSSB is exclusively localized to the mitochondria . We could map 92 . 8% of the mtSSB ChIP reads to the H-strand ( blue color ) , while 7 . 2% reads mapped to the L-strand ( red color ) . Our sequencing data also revealed a distinct mtSSB-binding pattern , which correlated nicely with our strand specific ChIP-qPCR results . Outside the control region , mtSSB associated only with the H-strand and there was a negative gradient towards OriL . A second , smaller peak of mtSSB was observed just after OriL ( Fig . 4B ) . These results support the idea that mtSSB stabilizes the H-strand during mtDNA replication and the pattern of occupancy is in agreement with the strand-displacement model for mtDNA replication . In the control region , we noticed binding to both the H- and L-strands . The reason for this pattern is not clear to us , but our findings could support a role of mtSSB in 7S DNA turnover , as previously suggested [23] , [27] . Our data demonstrated that mtSSB protects the single-stranded H-strand in vivo . This finding was at odds with the RITOLS model , in which the parental H-strand is covered with processed mitochondrial transcripts . The RITOLS model is primarily based on the observation of long RNA/DNA hybrids using neutral two-dimensional agarose gel electrophoresis ( 2D-AGE ) [21] , [28] . We decided to repeat the published 2D-AGE analysis of mtDNA replication products in an attempt to better understand these contradictory results . To this end we purified mitochondria from HeLa cells and isolated mtDNA following protocols previously used to identify RITOLS [29] . Agarose analysis revealed that the mtDNA preparations prepared this way contained large amounts RNA , which were much more abundant than the DNA in these samples ( Fig . 5A , lanes 2 and 3 ) . We confirmed that the observed RNA contained processed mitochondrial transcripts by Northern blotting of ND4 , CYTB and COX3 transcripts ( Figure S2 ) . We next digested the isolated mtDNA with Hinc II and analyzed the replication products by 2D-AGE analysis ( Fig . 5A , lane 2 ) . For comparison , we also analysed samples in which we had removed RNA by treatment with RNase A and RNase H ( Fig 5A , lane 4 ) . For detection of replication intermediates , we performed Southern blotting using a radioactively labeled probe , which detected a Hinc II fragment spanning position 13 , 636 to 1 , 006 in mtDNA . When we analyzed the mtDNA preparations containing RNA , we detected two prominent structures , which also have been described in previous reports; an Y arc and a bubble arc . The Y-arc is unrelated to the RITOLS model [19] , [30] and how this structure may be formed will be addressed in the discussion ( see below ) . Bubble arcs are typically seen during theta type DNA replication and in the case of the RITOLS model , this structure is indicative of a replication bubble with an RNA patch on the parental H-strand ( Fig . 5 B , upper panel , and C ) . In agreement with this notion , removal of RNA by RNase A and RNase H treatment abolished the bubble arc ( Fig . 5 B , middle panel ) . Our 2D-AGE analysis was thus in agreement with previous reports supporting the RITOLS model . However , we hypothesized that the bubble was not a bona fide replication product , but instead is created during mtDNA preparation . The presence of excess amounts of mature mitochondrial transcripts presented a problem for the 2D-AGE analysis , since the mtDNA purification protocol included a proteinase K treatment step [29] , [31] . Proteolytic removal of mtSSB would expose the parental H-strand in its single-stranded conformation and the large excess of RNA present could then hybridize to the H-strand and obscure the subsequent 2D-AGE analysis . The RNA-DNA hybrids formed in this way could resemble the replications products seen as evidence for the RITOLS model . To address this possibility , we investigated if it was possible to recreate the bubble arc in vitro by simply mixing purified mtDNA ( RNase A and Rnase H treated ) with purified , processed transcripts ( DNase I treated ) ( mixing the samples depicted in Fig . 5 A , lanes 3 and 4 ) . As demonstrated in Fig . 5B , lower panel , the mixing reaction recreated a bubble arc , which was clearly visible in 2D-AGE analysis . Our analysis therefore demonstrated that a simple reannealing reaction during the preparation of mtDNA can explain the observed replication bubble intermediate seen as evidence for the RITOLS model .
The SDM of mtDNA replication was proposed already in 1972 based on density gradient ultracentrifugation and electron microscopy studies of replicative intermediates [32] . Later biochemical characterization have verified the principles of this model and explained the molecular mechanisms of the individual steps [6] , [17] , [33] . However , SDM is still not generally accepted and proponents of alternative models , RITOLS and strand-coupled DNA replication , have pointed to the lack of sufficient in vivo data supporting SDM [21] , [22] . The mtSSB protein is essential for mtDNA maintenance , but its functional role in vivo is not completely understood [27] , [34] , [35] . Others have demonstrated that mtSSB is actively recruited to nucleoids during mtDNA replication in vivo [34] . In the present work , we have used ChIP to analyze mtSSB distribution in vivo . In agreement with early electron microscopy studies of rat mtDNA , our data demonstrate that mtSSB covers displaced single strands of replicative intermediates [36] . With the exception of the triple-stranded D-loop region , we find that mtSSB associates exclusively with the H-strand . The protein is not evenly distributed over the genome . Instead , we observe high levels of mtSSB just downstream of the D-loop region , at the 3′-end of 7S DNA , which are followed by a gradual decrease of mtSSB occupancy towards OriL . A second , small peak of mtSSB occurs just after OriL , but it fades away as towards the D-loop region . The mtSSB profile is therefore in excellent agreement with the SDM . According to this model , the parental H-strand close to the D-loop region will remain single-stranded for a much longer time than regions closer to OriL , explaining the higher levels of mtSSB . Similarly , the region just after OriL will remain single stranded for a longer time than regions closer to OriH ( Fig . 4C , upper panel ) . Our findings strongly argue against the RITOLS mode of mtDNA replication . According to this model , we would not expect to find mtSSB on the parental H-strand , since processed RNA species should associate with this strand , leaving no space for mtSSB to bind ( Fig . 4 C , lower panel ) [22] . Our data also argue against strand-coupled replication , which would predict that mtSSB is evenly distributed over the mtDNA molecule . The lagging strand is synthesized in short pieces ( Okazaki fragment ) and mtSSB should therefore remain associated with each fragment for about the same length of time ( Fig . 4 C , middle panel ) . Our findings demonstrate that mtSSB helps to restrict initiation of lagging strand DNA synthesis to OriL . POLRMT utilizes ATP as the priming nucleotide and can initiate primer synthesis from single-stranded DNA that contains at least one T-residue . Given its low sequence specificity , POLRMT could potentially initiate primer synthesis at multiple sites on the displaced parental H-strand . The mtSSB may not only stabilize the parental H-strand in its single-stranded conformation , but also prevent random priming events and unregulated initiation of L-strand synthesis . The mtSSB protein is abundant ( 2 , 100 mtSSB per mtDNA ) and thus present in sufficient quantities to completely cover the parental H-strand and block unspecific primer synthesis during mtDNA replication ( Figs . 1B and 2A ) . An interesting possibility that we will address in the future is that mtSSB may not only act to restrict POLRMT synthesis at non-specific DNA sequences , but also actively recruits POLRMT to OriL in order to initiate L-strand mtDNA replication . Bubble type replication intermediates with associated RNA have been used as an in vivo proof for RITOLS . As demonstrated here , these intermediates can be recreated by a simple annealing reaction in vitro . During purification of mtDNA , mtSSB is removed by proteinase K treatment , exposing the parental H-strand in its single-stranded conformation . Under these conditions , the vast excess of mitochondrial transcripts present in mtDNA preparations may anneal to their complementary regions in the parental , single-stranded H-strand and form structures , which can easily be misinterpreted as replications intermediates . Strictly taken , our 2D-AGE experiments do not exclude the presence of RNA on the lagging strand in vivo , but we clearly demonstrate that the methods used in previous studies [21] , [28] , [31] can readily lead to hybridization of matured transcripts during DNA isolation due to the removal of mtSSB by proteinase K treatment . Combined with our mtSSB in vivo occupancy data , our findings strongly argue against the presence of the proposed RITOLS mode of replication in human cells . To demonstrate the in vivo relevance of RITOLS , others have cross-linked RNA to DNA before cell lysis and isolation of mtDNA [21] . The rationale behind this approach is that if RNA forms a hybrid with DNA in vivo , cross-linking should protect it from digestion by RNase H . In contrast , if the RITOLS-type intermediates are formed after cell lysis and crosslinking , they should remain RNase H sensitive . Indeed , the authors of this previous study could observe a small difference in RNase H sensitivity for some of the replication intermediates . However , the identification of hybridized , crosslinked RNA molecules does not provide evidence for them playing a role in mtDNA replication , but could simply be due to ongoing transcription . Transcript processing in mitochondria is limited to 3′-end cleavage and polyadenylation , both co-transcriptional events that may take place even if some part of the transcript is crosslinked to the DNA template . In agreement with published data from other groups , our 2D-AGE analysis of the OriH region revealed a Y-arc [19] , [30] . This 2D-AGE structure corresponds to a double-stranded fork structure , which for example is observed during coupled leading- and lagging-strand DNA synthesis . This observation led to the suggestion that mtDNA replication is performed by a strand-coupled mechanism . As noted above , our mtSSB binding data does not support the existence of the SC mode of mtDNA replication in vivo . Could there be alternative explanation for the Y-arc at OriH ? Clayton and colleagues have suggested a slightly modified version of the SDM of replication , according to which OriL is the major initiation site for initiation of L-strand synthesis , but other minor initiation sites also exists [30] . For example , it is possible that mtSSB fails to completely protect the displaced parental H-strand . Since the only requirement for POLRMT activity is the presence of a single dT , this could lead to initiation of lagging-strand DNA synthesis from sites outside OriL and the creation of a Y-arc structure in the OriH region . Another possible explanation is that the DNA replication rate might differ between H-strand and L-strand synthesis . Whereas H-strand DNA replication uses a double-stranded DNA template and relies on TWINKLE , which is a relatively slow DNA helicase , L-strand DNA replication takes place on a single-stranded DNA template and in this case TWINKLE is not required . L-strand DNA synthesis initiated from OriL may therefore reach the control region well before H-strand DNA synthesis is completed . Since OriH has been suggested to act as a pausing element for mtDNA replication , an asymmetry in replication speed will lead to the formation of a double-stranded fork structure and appearance of a Y-arc in 2D-AGE analysis . In agreement with this notion , we have previously reported that TWINKLE is a relatively slow helicase and rate limiting for DNA replication fork progression in vitro [25] . Future work may address how the mitochondrial replication machinery and its speed vary between the two strands in vivo .
Human TWINKLE , mtSSB , POLγA , POLγB , POLRMT , and TFAM were expressed and purified as described previously [16] , [25] , [37] . The reaction mixture contained 100 fmol ssDNA oligonucleotide , 10 mM Tris-HCl [pH 8 . 0] , 25 mM MgCl2 , 1 mM DTT , 100 µg/ml BSA , 400 µM ATP , 150 µM CTP , 10 µM GTP , 150 µM UTP , ( α-32P ) 2 µci GTP , 4 units RNase inhibitor ( Amersham Biosciences ) , 500 fmol of POLRMT and 1 pmol of mtSSB ( if added ) . After 30 min incubation at 32 °C , 12 units of RNase free DNase I ( Qiagen ) was added . Reactions were processed as previously described for in vitro transcription reactions [37] and analyzed on a 25% polyacrylamide gel containing 3 M urea in 1× TBE . The primers used were 0-dT , 1-dT , 2-dT , 3-dT , 4-dT , 5-dT , and 6-dT . The primer sequences are listed in Table S1 . For rolling-circle template formation , we cloned a DNA fragment corresponding to nts 5275–6203 of the mitochondrial human genome between the HindIII and EcoRI sites in the pBluescript SK ( + ) vector ( Agilent Technologies; La Jolla , CA ) . The pBluescript SK ( + ) OriL construct was used as a template in site-directed PCR mutagenesis reactions to generate the mutant variant OriL-del . Constructs were confirmed by sequencing and used for ssDNA isolation following the manufacturer's protocol ( Stratagene ) . To produce the rolling-circle DNA replication template , we annealed a 70-mer oligonucleotide ( 20 pmol ) ( 5′-42[T]-ATC TCA GCG ATC TGT CTA TTT CGT TCA T-3′ ) to the pBluescript SK ( + ) OriL ssDNA ( 2 pmol ) and the second strand was synthesized in a PCR reaction using KOD Hot Start DNA polymerase ( Novagen ) . The samples were purified using the QIAquick PCR Purification Kit ( QIAGEN ) . Reactions were carried out as described previously [6] . The reaction mixtures ( 20 µl ) contained 10 fmol of indicated dsDNA template , TWINKLE ( 100 fmol ) , POLγA ( 250 fmol ) , POLγB ( 375 fmol , calculated as dimer ) , POLRMT ( 250 fmol ) , and increasing amount of mtSSB as indicated in the figure legends . Rolling circle reactions and Southern blot analysis was carried out as described previously [6] . For detection we used a mixture of 50-nts long radioactively probes that detected various regions in the H-strand ( oligonucleotides H1 , H2 , H-out , H-out1 , H-out2 , H-out3 , H-out4 , H-out5 , and H-out6 ) or L-strand ( oligonucleotides H1 , H2 , H-out , H-out1 , H-out2 , H-out3 , H-out4 , H-out5 , and H-out6 ) . The sequences of these oligonucleotides are listed in the Table S1 . The OriL stem-loop conformation was analyzed with a DNA binding assay on ssDNA oligonucleotides with increasing concentrations of mtSSB ( 0 , 5 , 10 , 25 , 50 , 100 fmol ) . Reactions ( 15 µl ) contained 20 mM Tris-HCl [pH 8 . 0] , 10 mM MgCl2 , 100 µg/ml BSA , 1 mM DTT , 2 mM ATP , 10% Glycerol and 25 fmol of the indicated 5′-end [γ-32P] labeled ssDNA oligo . Reactions were incubated for 15 min at room temperature before separation on a 10% polyacrylamide native gel in 1 × TBE for 50 min at 150 V . The oligonucleotides used in the experiments were 6-dT , OriL-WT , and OriL+6 . The sequences of these oligonucleotides are listed in Table S1 . Mitochondria were isolated from HeLa cells as described in [28] . The isolated mitochondria were washed once with ice-cold PBS , and incubated in 1% formaldehyde in PBS for 10 min at room temperature . The crosslinking reaction was quenched by addition of glycine to a final concentration of 125 mM , followed by incubation for 5 additional minutes . After washing twice in ice-cold PBS , mitochondria were lysed in 25 mM HEPES-KOH ( pH 7 . 6 ) , 10% glycerol , 5 mM MgCl2 , 0 . 5 mM EDTA , 0 . 5% tween-20 , 0 . 15 M KCl , 1 mM phenylmethlsulfonylfluoride , 2 mM pepstatin A , 0 . 6 mM leupeptin , and 2 mM benzamidine . The mitochondrial lysates were sonicated in a Bioruptor UCD 200TM ( Diagenode ) for 3×10 min at high output , with intervals of 30 seconds on and 30 seconds off , and centrifuged for 5 min at 14 , 000×g . An aliquot of the supernatant ( 50 µl ) was taken out as input control , whereas 400 µl were incubated with either 20 µl of human TFAM polyclonal antibody ( Agrisera ) , 20 µl of human mtSSB polyclonal antibody ( 12212-1-AP , Proteintech ) , or 5 . 5 µl of rabbit IgG ( ab37415 , Abcam ) overnight on a rotator at 4°C . Fifty µl of protein A beads ( GE Healthcare ) were added to the samples and incubated for 1 hour at 4°C . After wash and elution , eluted DNA samples were incubated overnight at 65°C to reverse crosslinking . RNA contamination was removed by incubation with 100 ng/ml RNaseA for 15 min at 37°C . Proteins were removed by addition of 20 µg proteinase K and incubation for 2 hrs at 56°C . DNA was purified by phenol/chloroform extraction , and ethanol precipitation . The purified DNA was used for strand-specific PCR analysis or sequencing ( see below ) . Three independent biological replicates were carried out for ChIP analysis . Average and standard deviation were calculated and plotted in Microsoft Excel . Strand-specific qPCR was used to monitor the levels of H- and L-strand in the ChIP material . Three primers were used in each reaction: a Tagging primer , a Tag primer , and a Reverse primer for each region analyzed . The primers used and their sequences are listed in Table S1 . Primers with “H” were used for amplification of the heavy strand sequence , while primers with “L” for light strand . Reactions were initiated by annealing of the Tagging primer , which contains 11–13 nts of locus-specific sequence at its 3′-end and a 19-nts Tag sequence in its 5′-end . During the first cycle the annealed Tagging primer was extended to the end of the single-strand DNA . In subsequent cycles the generated hybrid sequence was amplified by qPCR with the Tag and Reverse primers . The reaction contains 400 nM of Tag primer , 400 nM of Reverse primer , and 8 nM Tagging primer in a reaction volume of 25 µl . The PCR cycles were as follows: 95°C for 3 min; 40°C for 5 min; increase to 72°C at a rate of 2°C/min; 94°C for 4 min; 94°C for 15 s and 64°C for 1 min ( 40 cycles ) . Quantification was performed using real time PCR Software ( Bio-Rad ) and Excel ( Microsoft ) ; ratios of IP/input are depicted in the figures after subtracting ratios obtained from rabbit IgG control . Standard curve was produced by different dilutions of Input , the quantity of ssDNA is calculated relative to the standards [38] . For strand-specific ChIP sequencing , the DNA was further prepared using standard protocols provided by Illumina and deep-sequenced by using Illumina's Solexa sequencer ( Beijing Genomics Institute ) . Quality control statistics were generated with FastQC ( http://www . bioinformatics . bbsrc . ac . uk/projects/fastqc ) . The concentration of recombinant and human mtSSB were determined by OD280 and Bradford measurements with Bovine Serum Albumin ( Sigma-Aldrich ) as standard . HeLa cell numbers were determined by a Vi-CELL XR system ( Beckman-Coulter ) . For PAGE analyses with Criterion Tris-HCl Gels ( Bio-Rad ) , HeLa cell pellets were resuspended in a buffer containing 100 mM Tris-Cl , pH 6 . 8 , 4% SDS , 20% glycerol ( Sigma-Aldrich ) and Complete Protease Inhibitor Mixture ( Roche Diagnostics ) , vortexed , incubated at 95°C for 5 min , and sonicated for 5 cycles at 30 s . Before PAGE , 200 mM DTT and 0 . 2% bromophenol blue ( Sigma-Aldrich ) were added . Western Blot analyses were performed with polyclonal antibodies against human mtSSB ( Proteintech ) . Anti-rabbit IgG horseradish peroxidase linked secondary antibodies were visualized by Amersham ECL Western Blotting Detection Reagents ( GE Healthcare ) . The quantification of mtSSB was performed by interpolating the intensity of the bands in the immunoblot in a calibration curve made with known amounts of recombinant proteins using the Quantity One 4 . 6 software ( Bio-Rad ) . HeLa cell numbers were determined as described above and lysed in QuantiLyse buffer and quantified as described previously [39] . HeLa cells were routinely grown in Dulbecco's modified Eagle's medium ( DMEM ) ( Life Technologies ) with 10% fetal bovine serum . After centrifugation , cell pellets were resuspended in nine volumes of homogenization buffer ( 225 mM Mannitol , 75 mM sucrose , 10 mM Hepes-NaOH [pH 7 . 4] , 10 mM EDTA ) containing 1 mg/ml BSA and 1 mM DTT , and cells were broken in a 7-ml Wheaton homogenizer with a tight-fitting glass pestle . Mitochondria were purified and lysed , protease treated and as described previously [29] . Mitochondrial nucleic acid was precipitated with isopropanol and stored in aliquots at −20°C . When indicated purified mtDNA ( 15 ug ) was further treated with 15 U of RNase H and 50 U of RNase A for 1 hr at 37°C or with 50 U DNase I for 1 hr at 4°C . HincII digestion was performed under conditions recommended by the manufacturer ( New England Biolabs ) for 3 hrs using 2 µg mtDNA . Neutral 2D-AGE was performed following published protocols [40] , [41] . Southern blot analysis of 2D gels were hybridized to a specific region of human mtDNA ( mtDNA 13636-1006 ) by 2 hrs incubation at 65°C in hybridizing buffer ( Amersham rapid-hyb buffer ) under conditions recommended by the manufacturer ( GE Healthcare ) . Posthybridization washes were 2× SSC , 0 . 1% SDS , twice for 10 min followed by 0 . 2× SSC , 0 . 1% SDS , twice for 15 min at 65°C . Filters were exposed to X-ray film . Radiolabeled probe against the region ( mtDNA 13636-1006 ) was generated using the Prime-it II kit ( Agilent Tech ) and a gel purified PCR fragment ( spanning a region of 14641-15590 in mtDNA ) as a template . Purified mtDNA treated with DNase I ( Fig . 5A , lane 3 ) was separated on a 1% formaldehyde agarose gel . The RNA was transferred onto a nylon Hybond-N+ membrane ( GE Healthcare ) using capillary blotting with 10× SSC buffer with 0 . 5× TBE buffer . After transfer of the RNA , the membrane was cross-linked using UV radiation . Strand specific DNA oligonucleotides against ND4 , CYTB and COX2 were radioactively labeled and then hybridized to the membrane . Data have been deposited in the NCBI Sequence Read Archive ( SRA ) under accession number SRX481175 . | Mitochondria are cytoplasmatic organelles that produce most of the adenosine triphosphate ( ATP ) used by the cell as a source of chemical energy . A subset of proteins required for ATP production is encoded by a distinct mitochondrial DNA genome ( mtDNA ) . Proper maintenance of mtDNA is essential , since mutations or depletion of this circular molecule may lead to a number of different diseases and also contribute to normal ageing . We are interested in the molecular mechanisms that ensure correct replication and propagation of mtDNA . Even if many of the responsible enzymes have been identified , there is still a debate within our scientific field regarding the exact mode of mtDNA replication . We have here used a combination of in vitro biochemistry and in vivo protein-DNA interaction characterization to address this question . Our findings demonstrate that the mitochondrial single-stranded DNA-binding protein ( mtSSB ) restricts initiation of mtDNA replication to a specific origin of replication . By characterizing how mtSSB interacts with the two strands of mtDNA in vivo , we are able to directly demonstrate the relevance of one proposed mode of mitochondrial DNA replication and at the same time seriously question the validity of other , alternative modes that have been proposed over the years . | [
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] | 2014 | In Vivo Occupancy of Mitochondrial Single-Stranded DNA Binding Protein Supports the Strand Displacement Mode of DNA Replication |
The organismal roles of the ubiquitously expressed class I PI3K isoform p110β remain largely unknown . Using a new kinase-dead knockin mouse model that mimics constitutive pharmacological inactivation of p110β , we document that full inactivation of p110β leads to embryonic lethality in a substantial fraction of mice . Interestingly , the homozygous p110β kinase-dead mice that survive into adulthood ( maximum ~26% on a mixed genetic background ) have no apparent phenotypes , other than subfertility in females and complete infertility in males . Systemic inhibition of p110β results in a highly specific blockade in the maturation of spermatogonia to spermatocytes . p110β was previously suggested to signal downstream of the c-kit tyrosine kinase receptor in germ cells to regulate their proliferation and survival . We now report that p110β also plays a germ cell-extrinsic role in the Sertoli cells ( SCs ) that support the developing sperm , with p110β inactivation dampening expression of the SC-specific Androgen Receptor ( AR ) target gene Rhox5 , a homeobox gene critical for spermatogenesis . All extragonadal androgen-dependent functions remain unaffected by global p110β inactivation . In line with a crucial role for p110β in SCs , selective inactivation of p110β in these cells results in male infertility . Our study is the first documentation of the involvement of a signalling enzyme , PI3K , in the regulation of AR activity during spermatogenesis . This developmental pathway may become active in prostate cancer where p110β and AR have previously been reported to functionally interact .
Upon stimulation of cells with extracellular ligands , class I phosphoinositide 3-kinases ( PI3Ks ) generate lipids that modulate the function of a range of signalling proteins , including protein kinases ( such as Akt/PKB ) , regulators of small GTPases and adaptor proteins . These PI3K effectors regulate an array of cellular outputs , including cell cycle progression , cell survival , metabolism , translation , transcription and cell motility . Class I PI3Ks have been implicated in cancer , immunity and metabolism , and are the subject of active drug development efforts [1–4] . Mammals have four class I PI3K catalytic isoforms ( called p110s ) that occur in a heterodimeric complex with a regulatory subunit . Class IA catalytic subunits ( p110α , β and δ ) are bound to an SH2 domain-containing p85 regulatory subunit , that binds to Tyr phosphorylated membrane-associated proteins , whereas the p84 and p101 regulatory subunits lack SH2 domains and link the single class IB PI3K , p110γ , to G protein-coupled receptors ( GPCRs ) . Tyrosine kinases activate p110α , β and δ , whereas GPCRs regulate p110β and γ . While p110α and β are ubiquitously expressed , p110γ and δ are mainly found in leukocytes but can also be expressed at lower levels in other cell types [5] . Studies using PI3K mutant mice and pharmacological PI3K inhibitors have largely focused on p110α , γ and δ and revealed isoform-selective signalling functions for the class I PI3Ks [1 , 6 , 7] . Comparatively less is known about p110β . Several genetic mouse models of p110β inactivation have been created , including mice with full [8] or partial [9 , 10] deletion of the p110β gene , and mice that produce a hybrid mouse/human inactive p110β protein [11 , 12] . Conflicting data have been obtained using these different mouse models: whereas one p110β gene deletion model [8] displays a fully penetrant , very early embryonic lethality ( at embryonic day E3 . 5 ) , p110β gene deletion using another strategy [9 , 10] , or its replacement with a cDNA encoding for an inactive p110β enzyme [11 , 12] , show only partial embryonic lethality that occurs at later stages of development . Mice that survived full p110β inactivation were apparently normal [11] but showed a mild growth retardation that normalized after 6 months of age , at which stage these mice became mildly insulin-resistant , with increased blood glucose levels [11] . Homozygous inactivation of p110β with this strategy had no impact on female fertility but led to male sterility [12] . The PI3K signalling pathway has previously been implicated in germ cell-intrinsic regulation of fertility . This was documented through conditional inactivation of PTEN in the female and male germlines [13–15] or by inactivation of PDK1 in the male germline [15] . Organismal inactivation of Akt1 or Akt2 has also been reported to lead to reduced testis size , reduced male fertility and increased apoptosis in male germ cells [16–18] . Mice in which the endogenous c-kit tyrosine kinase receptor no longer binds class IA PI3Ks further revealed a role for this group of PI3Ks in male [19 , 20] and female fertility [19] . Subsequent studies using mice with systemic inactivation of p110β [11] suggested that this kinase provides PI3K activity downstream of c-kit in male germ cells [12] , although a germ-cell-intrinsic role of p110β remains to be formally proven . In this study , we have investigated the organismal role of p110β by inactivating it in mice using a gene targeting strategy that we previously applied to p110α and p110δ [21–24] . We have created a knockin mouse line in which a point mutation in the kinase domain renders the endogenous p110β inactive but preserves its expression levels , thus mimicking the action of a kinase inhibitor . Such systemic inactivation of p110β in mice resulted in a substantial , although not fully penetrant , embryonic lethality , with the surviving mice showing defects in fertility , especially in males . In addition to the previously suggested role for p110β in the germ cell compartment [12] , we find that p110β also has a germ cell-extrinsic role in the regulation of fertility , namely by regulating androgen receptor ( AR ) gene expression in SCs , which is known to be critical for the proper development of the male germ cells . We also present evidence of a role for p110α , the other ubiquitously expressed class I PI3K , in male and female fertility .
Using a strategy previously applied to p110α and p110δ [22 , 24] , we created a mouse line in which endogenous p110β is converted to a kinase-dead protein . This was achieved by introducing a germline point mutation in Pik3cb , the gene encoding p110β , which converts the critical ATP-binding DFG motif in the p110β kinase domain to AFG ( S1 Fig and S1 Text ) . Experiments using mice homozygous for this mutant p110β ( referred to as p110βD931A/D931A mice ) showed that the p110βD931A protein lacked catalytic activity ( S2A Fig ) but was expressed at the same level as wild-type ( WT ) p110β ( S2B Fig ) . In addition , it did not affect expression of p85 ( S2B Fig ) nor the expression ( S2B Fig ) or activity ( S2A Fig ) of p110α . In heterozygous p110βD931A/WT mice , p110β lipid kinase activity was reduced by approximately 50% , with the remaining activity being significantly sensitive to the p110β-selective inhibitor TGX-221 ( S2C Fig ) . Loss of p110β activity did not decrease total PI3K activity present in phosphoTyr-peptide precipitates ( which pull down all p85 subunits; S2A Fig ) nor the basal phosphorylation of Akt/PKB on S473 in the lungs and testes ( S2B Fig ) . Taken together , these data show that germline conversion of p110β to the p110βD931A form inactivates this kinase without affecting the expression or activity of other , non-targeted , PI3K isoforms . Intercrosses of p110βD931A/WT mice yielded a significantly lower than expected fraction of homozygous p110βD931A/D931A mice born , based on a normal Mendelian distribution , both on a mixed C57BL/6 x 129S2/Sv or on a pure C57BL/6 background ( 10% and 1% versus 25% expected , respectively ) ( S3A Fig ) . The reason for the lethality of p110βD931A/D931A embryos is unknown at the moment . Indeed , it was not possible to identify a specific time point of embryonic lethality , as embryos were found to die at different stages of embryonic development ( S3A Fig ) . This is in stark contrast to the fully penetrant embryonic lethality of homozygous p110α kinase-dead mice that all die at E10 . 5 [22] . p110βD931A/D931A embryos ( S3B Fig ) and 4-week-old male mice ( S3C Fig ) showed a mild growth delay . However , no weight differences were seen in male or female adult mice ( S3D Fig ) . Necropsy and comprehensive histological analysis ( see S1 Table for a list of organs analyzed ) of ~6-month-old p110βD931A/D931A mice did not reveal any detectable alterations or pathology , apart from reduced size ( S4 Fig ) and altered histology ( see below ) of the testes ( Fig 1A shows the organ weights of 12-week-old mice ) . p110βD931A/D931A males , on both pure and mixed genetic backgrounds , were found to be sterile upon mating with WT females ( Fig 1B ) , suggesting oligo- or azoospermia . p110βD931A/WT males , when mated with WT females , also showed a 20% reduction in litter frequency compared to WT males ( Fig 1B ) , although the litter size was unaltered ( Fig 1C ) . Female p110βD931A/D931A mice also showed a substantial reduction in fertility . Indeed , p110βD931A/D931A females , when crossed with WT males , had a reduction of 70% in their capacity to have recurrent litters ( 0 . 34 litters born per month versus 1 . 20 in intercrosses of WT mice; Fig 1B ) , a reduced litter size when crossed with p110βD931A/WT males ( Fig 1C ) and a 24%-reduction in the percentage and absolute number of ovulated oocytes that made it to E13 . 5 embryos ( Fig 2A ) . p110βD931A/D931A females showed normal follicle maturation ( S5A Fig ) and oestrus cycles ( S5B Fig ) and generated the same number of 2-cell embryos upon superovulation and mating with WT males ( S5C Fig ) , suggesting normal ovulation in these mice . However , 2-cell p110βD931A/D931A embryos recovered from p110βD931A/D931A females had a decreased in vitro ability to develop into morula and blastocysts and to survive ex vivo , compared to p110βD931A/D931A embryos generated by p110βD931A/WT females ( Fig 2B; representative ex vivo cultures and genotyping results are shown in S5D and S5E Fig ) . Taken together , these data indicate that the lack of embryonic p110β activity is not , per se , detrimental to preimplantation development to blastocyst if the female is heterozygous for p110β inactivation . Thus , a maternal pool of p110β , provided by the oocyte cytoplasm and/or the host environment , likely participates in healthy embryonic development to blastocyst . We next analyzed the p110βD931A/D931A male sterility phenotype in more detail . At 12 weeks of age , the testes were the only organs reduced in weight ( by more than 70% compared to WT testes ) in the p110βD931A/D931A males ( Figs 1A and 3A ) . Testes of p110βD931A/D931A males had descended normally into the scrotum ( S6 Fig ) , indicating that the first intrauterine/early postnatal peaks of testosterone production had led to correct perinatal testicular development [25] . Serum levels of testosterone and luteinizing hormone ( LH ) were not significantly altered ( S7 Fig ) , in accordance with the unaffected organ weight of other androgen target tissues , including prostate , seminal vesicles and epididymal and retroperitoneal fat ( Figs 1A and 3A ) . However , compared to WT controls , p110βD931A/D931A males had a 37% increase in the serum levels of follicle-stimulating hormone ( FSH; S7 Fig ) , a phenomenon known to occur upon spermatogenic arrest [25] . The appearance and size of the interstitial tissue , that surrounds the germ cell-containing seminiferous tubules ( Fig 3B ) and is composed of the testosterone-producing Leydig cells ( labelled L in Fig 3C and S8 Fig ) , were analysed in WT and p110βD931A/D931A males . p110β inactivation did not lead to significant changes in the mRNA expression levels of the Leydig cell marker Hsd3b6 ( 3-β-hydroxysteroid dehydrogenase 6 ) , the localisation of the HSD3B protein ( Fig 3D and S8 Fig ) or the number of HSD3B-positive cells ( S8E Fig ) . However , a tendency for an increase in Leydig cell numbers was observed in aged p110βD931A/D931A mice . Homozygous inactivation of p110β did also not affect the presence of SCs in the testes ( Fig 3C ) . In addition , no differences were seen in the expression of the SC-specific mRNA claudin 11 in 12-week-old mice ( Fig 3D ) , or in the number of SC-marker SOX9-positive cells during testes development and aging ( S8E Fig ) . In addition , the localization of the adhesion molecule JAM-A was not affected in p110βD931A/D931A SCs ( Fig 3E ) , indicative of an intact blood-testis barrier of the seminiferous tubules . During spermatogenesis , specialized stem cells , called spermatogonia , undergo meiosis and cell differentiation into , sequentially , spermatocytes , spermatids and spermatozoa ( Fig 3B ) [26] . Spermatogonia reside on the basement membrane of the seminiferous tubules and are surrounded by supporting SCs . To understand the spermatogenic defect caused by p110β inactivation in more detail , we analysed the differentiation of spermatogonia . The first round of meiosis occurs between post-natal stage P10 to P15 . The spermatogenic cell pool was altered in p110βD931A/D931A males at P10 , the known time point of onset of meiosis , as evidenced by staining of the germ cell marker DDX4 ( S8A Fig ) . At P15 , the H&E staining and DDX4 staining of p110βD931A/D931A testes revealed a clear depletion of germ cells , coinciding with the known time point of primary spermatocyte appearance , and further observed at P35 ( Fig 3F and S8B Fig ) . At 12 weeks of age , the seminiferous tubules of p110βD931A/D931A testes contained only a few spermatogenic cells , many of which were detached ( Fig 3C and S8 Fig , DDX4 marker ) , and no JAM-C positive cells ( Fig 3E ) , indicative of a massive loss of spermatids . However , a low concentration of isolated round and elongating spermatids and few mature spermatozoa were present in the cauda epididymis of testes of 12-week-old p110βD931A/D931A mice ( Fig 3C ) . At 37 weeks of life , most of the tubular cross-sections of p110βD931A/D931A testes displayed a ‘SC only’ appearance ( Fig 3C ) . We next analysed the gene expression of selected differentiation markers ( A-Myb , PABP , TP1 and protamine1 ) in the testes . At P15 , the expression of A-Myb , a marker of the early meiotic prophase , was not significantly altered in p110βD931A/D931A testes ( Fig 3G ) whereas expression of PABP , a marker for late stage spermatocytes , was severely reduced ( Fig 3G ) , suggesting an altered progression in germ cell differentiation . The expression of all these markers was undetectable at 12 weeks of age ( Fig 3G ) . In heterozygous p110βD931A/WT males , the expression of both A-Myb and PABP was similar to that in WT mice at P15 ( Fig 3G ) . In adult heterozygous p110βD931A/WT males that have a modestly reduced fertility ( Fig 1B ) and a 35% reduction in testis size compared to WT mice ( Fig 3A ) , the diameter and lumen size of the seminiferous tubules were severely reduced at 12 weeks of age ( Fig 3C , bottom panel ) . Accordingly , the expression of markers of late primary spermatocyte ( PABP ) and spermatid ( TP1 and protamine1 ) stages was strongly reduced in these mice , while the expression of A-Myb was unaffected ( Fig 3G ) , indicative of unaffected spermatogenesis in young p110βD931A/WT males but a progressive depletion of spermatogenic germ cells upon aging . In summary , our data reveal an essential role for p110β in early meiosis ( Fig 3B ) . The presence of small numbers of post-meiotic germ cells in the epididymis of 12-week-old p110βD931A/D931A males suggests that meiosis is possible in the absence of p110β activity but that it occurs with a possible altered efficiency , loss-of-contact and sloughing off of the germinal lineage from SCs before timely spermiogenesis , resulting in severely impaired sperm production . The detachment of the germinal cell lineage and inefficient primary spermatocyte formation , observed in p110βD931A/D931A testes at P15 , are both phenomena that are known to be controlled by SCs [25 , 27] . This suggests that p110β activity could be important in SCs , despite the lack of obvious histological differences in p110βD931A/D931A SCs ( Fig 3C ) and the unaltered staining for the SC-specific marker SOX9 ( S8 Fig ) . To assess the possible role of p110β expressed in SCs ( Fig 4A ) , we crossed mice with a conditional inactivating allele of p110β ( p110βflox [9] ) with mice expressing the Cre recombinase under the control of the SC-specific AMH promoter ( AMH-Cre mice [28] ) . 12-week-old mice with recombined Pik3cb loci in AMH-Cre-expressing SCs ( referred to as SCβ-DEL; Fig 4B ) had a decrease in the weight of the testes ( 48% ) and epididymis ( 25% ) with no alterations in the weight of the prostate , seminal vesicles or spleen ( Fig 4C ) . The diameter of the seminiferous tubules was also reduced in SCβ-DEL testes ( Fig 4D ) , but , in contrast to p110βD931A/D931A , the germ cell composition of the testes was unaltered ( Fig 4D ) . In line with this , the expression of markers of each germ cell stage was unchanged in SCβ-DEL testes at 12 weeks of age , with the exception of the primordial germ cell marker Trap1a ( Fig 4E ) . Importantly , however , none of the SCβ-DEL mice gave rise to offspring when crossed with WT females ( Fig 4F ) . These data show that , like systemic organismal inactivation , SC-specific inactivation of p110β leads to male sterility but likely through a different mechanism than primary spermatocyte formation . These data suggest that , in addition to its key role in SCs , p110β may also regulate spermatogenesis in a germ cell-intrinsic manner . We next searched for the mechanism by which p110β activity regulates SC function . The AR has a key role in controlling spermatogenesis . As germ cells do not express the AR , androgen regulates fertility indirectly through regulating gene expression in SCs , which influences germ cell maturation . Testosterone binding to the AR in SCs is a key signal in the regulation of the first round of meiosis in male gametogenesis during early postnatal development [25 , 29] . Other roles of testosterone in SCs include the attachment of developing spermatids to the SCs [30 , 31] and lumen formation of seminiferous tubules [32] , both of which were found to be affected by full inactivation of p110β . Indeed , the phenotype of p110βD931A/D931A testes was reminiscent of some aspects of SC-specific AR knockout ( SCARKO ) mice [30] . Compared to p110βD931A/D931A mice , in SCARKO mice the reduction in the diameter of the seminiferous tubules is milder , and sperm cell differentiation is blocked at a later ( round spermatid ) stage ( Fig 3B ) . SCs start to express the AR at P4 [33] , with increased expression at P15 [32] , coinciding with the stage at which the p110βD931A/D931A testis phenotype becomes apparent ( Fig 3F and 3G and S8 Fig ) . We therefore hypothesized that p110β that is expressed in SCs could regulate some aspects of AR activity . Systemic inactivation of p110β in mice did not affect AR mRNA expression in the testes ( Fig 5A , left panel ) . In contrast , the mRNA expression of the SC-specific AR-responsive homeobox-gene Rhox5 was reduced in both p110βD931A/D931A and p110βD931A/WT 12-week-old testes ( Fig 5A , right panel ) . The expression of Rhox5 is critical for the full efficiency of meiosis [29 , 32 , 34] . p110β inactivation also led to reduced expression of other SC-specific AR targets in adult testes , including TJP1 ( Tight Junction Protein 1 ) and claudin11 ( a transmembrane protein important for tight junctions ) , although the reduction in the expression of claudin11 did not reach statistical significance ( Fig 5A , right panel ) . In contrast , systemic p110β inactivation did not affect the expression of Leydig cell-specific AR target genes such as Hsd3b6 [35] ( Fig 3D ) , in line with the lack of obvious defects in this cell population upon p110β inactivation ( Fig 3C and 3D and S8 Fig ) . The AR also has a pivotal role in the regulation of extragonadal reproductive glands , muscle mass , fat deposition and bone or brain function [36 , 37] , none of which were notably affected in p110βD931A/D931A mice ( Fig 3A ) . Taken together , these data indicate that p110β regulates a subset of AR target genes , specifically in SCs . In order to gain further insight into the functional link between p110β and AR , we performed an unbiased global gene expression analysis in WT and p110βD931A/WT testes at P10 . This early time point was selected in order to investigate the events associated with the initiation of the p110β-associated fertility phenotype . The use of p110βD931A/WT testes is also expected to reveal the primary transcriptional targets of AR regulated by this PI3K , as homozygous inactivation of p110β likely results in ‘knock-on’ effects on spermatogenesis regulation . One such effect is the induction of the FSH/LH feedback loop that arises as a consequence of impaired production of spermatozoa . Indeed , the plasma levels of FSH were significantly increased in p110βD931A/D931A but not p110βD931A/WT males ( S7 Fig ) . However , a drawback of using p110βD931A/WT mice is the potentially low magnitude of change in the gene expression as compared to WT mice . For this reason , we considered a 2-fold difference in gene expression significant in this setting . The expression of 42 genes was found to be altered ≥2-fold between WT and p110βD931A/WT testes ( 17 genes downregulated , p-value 0 . 0052–0 . 00013; 25 upregulated , p-value 0 . 015–0 . 00013; S2 Table ) . The functions of these genes span various biological contexts , with genes known to regulate fertility forming the main group ( Fig 5B and S3 Table ) . A comparison of the gene expression profiles of p110βD931A/WT and SCARKO P10 testes [38] ( Fig 5C and S4 Table ) showed that , of the 21 genes significantly modified in SCARKO , 9 also showed an altered expression between WT and p110βD931A/WT testes . p110β activity thus regulates the expression of a fraction of known SC-specific AR-regulated genes , while other genes regulated by p110β appear not to be dependent on Sertoli-cell specific AR activity . This is indicative of the AR in SCs having p110β-independent functions but also of p110β having 1 ) AR-independent functions in SCs and 2 ) SC-independent functions in the testes , such as the regulation of germ cell survival and proliferation . The AR resides in the cytoplasm and upon binding to testosterone translocates to the nucleus where it binds to its DNA-response elements in the promoter or enhancer regions of androgen target genes . To demonstrate that p110β activity has the ability to regulate the genomic functions of AR , we transiently transfected the mouse SC line MSC-1 [39] with SV40 promoter-containing luciferase reporter constructs with hormone-responsive elements , including elements responsive to AR only ( Rhox5 AR elements ( AREs ) and Eppin-AREs ) or to both AR and Glucocorticoid Receptor ( GR ) ( Tat-GRE , a known binding element for both AR and GR [40] ) ( Fig 5D and S9A Fig ) . Stimulation of MSC-1 cells transfected with the Rhox5-ARE reporter with the androgen 5α-dihydrotestosterone ( 5α-DHT ) , which activates the endogenous AR , induced a significant increase in luciferase activity ( S9B Fig ) , with the concomitant transfection of AR strongly enhancing Rhox5-ARE reporter activity ( S9C Fig ) . Importantly , pre-treatment of cells overexpressing AR with the p110β inhibitor TGX-221 decreased 5α-DHT-induced luciferase expression driven by Rhox5-AREs ( Fig 5E ) , Eppin-AREs ( S9D Fig ) and Tat-GREs ( S9E Fig ) . While the observed decrease in AR-dependent transcriptional activation upon p110β inhibition was modest in cell culture , a strong impact on the expression of Rhox5 was seen in vivo , with a 29% , 59% and 81% decrease in gene expression in adult SCβ-DEL , p110βD931A/WT and p110βD931A/D931A males , respectively , compared to WT mice ( Fig 5F and 5A , right panel ) . Taken together , these data show that p110β activity regulates AR transcriptional activity , contributing to the expression of the SC-specific AR target Rhox5 in vivo . The testis phenotype upon global p110β inactivation is stronger than that observed in SCARKO mice ( see above ) . This is possibly due to an additional role that p110β has directly in the spermatogenic germ cell lineage , in addition to its ability to regulate AR signalling in SCs , for example its previously reported involvement in c-kit receptor-positive male germ cells [12] . An important question is also whether other class IA PI3K isoforms than p110β are involved in the regulation of fertility . The male fertility phenotype of the p110βD931A/D931A mice appears to be less pronounced compared to that of c-kit-p85 null mice ( knockin mice in which c-kit can no longer interact with the p85 regulatory subunit of class IA PI3Ks; [19 , 20] ) . In the c-kit-p85 null mice , c-kit expression is drastically reduced in mutant seminiferous tubules already at P8 [19] and the spermatogenic germ cell pool is fully depleted at P21 [20] . In contrast , despite a strong reduction in the mRNA expression of the stem cell marker Trap1a ( Fig 6A ) , the testes of p110βD931A/D931A adult males still showed mRNA expression of the CD9 and c-kit stem cell markers ( Fig 6A ) and protein expression of c-kit ( Fig 6B ) , demonstrating that they contained germ cells . In addition , some c-kit-positive spermatogonial cells , surrounded by c-kit-positive Leydig cells ( respectively indicated by * and # in Fig 6C ) , were present in the seminiferous tubules of p110βD931A/D931A males . These findings suggest that another class IA PI3K isoform than p110β could be involved in spermatogonial signalling , possibly downstream c-kit . We therefore assessed the possible contribution of p110α to male fertility , using mice with a kinase-dead knockin allele of p110α [22 , 23] . Homozygous p110αD933A/D933A mice are embryonic lethal [22] but heterozygous p110αD933A/WT males were found to be subfertile ( Fig 6D ) with a significant decrease in testis size at 35 days after birth and in the adult stage ( Fig 6E ) . An incompletely penetrant ( 2 mice out of 5 ) mixed atrophy of seminiferous tubules of p110αD933A/WT was observed at 5 weeks of age . This atrophy was found to be reversed at 8 weeks of age ( Fig 6F ) . In addition , p110αD933A/WT females , when crossed with WT males , had a 35% reduced average litter frequency , compared to WT mice ( S10 Fig ) . In contrast , analysis of homozygous p110δ kinase-dead testes showed no significant defects in ageing mice ( S11 Fig ) . Given that all class IA PI3K isoforms bind p85 [41] , the association to the c-kit receptor of all p85-bound p110 isoforms is expected to be impaired in c-kit-p85 null mice . We found that in unstimulated 12-week-old testes , only p110α , but not p110β or δ , co-immunoprecipitated with c-kit ( S12 Fig ) , whereas p110α and δ , but not p110β , co-immunoprecipitated with c-kit in spleen ( S12 Fig ) , a tissue that is enriched in leukocytes and in which p110δ is known to transmit c-kit signalling [21 , 41 , 42] . These data suggest that only p110α contributes to c-kit signalling in adult testes , while p110β is not significantly recruited to c-kit receptor at this developmental stage . These data show that both p110α and p110β , but not p110δ , contribute to male and female fertility in mice , with p110α , as a tyrosine kinase-linked class I PI3K , most likely executing this biological function through c-kit . The role of p110α in Sertoli cells is unknown .
Using a mouse model of constitutive inactivation of the ubiquitously expressed p110β isoform of PI3K , we document that only few mice with inactive p110β survive into adulthood , for reasons that are unclear at the moment . Interestingly , the only apparent phenotypes in the p110β kinase-dead mice that are born are subfertility in females and complete infertility in males . The importance of PI3K signalling in fertility was initially uncovered using mice in which the c-kit tyrosine kinase , an essential regulator of fertility in germ cells , was engineered to no longer interact with all class IA PI3Ks [19 , 20] . Our data reveal that both of the ubiquitously expressed class IA isoforms , p110α and p110β , regulate fertility in male germ cells ( Fig 6G ) , with no fertility phenotypes observed upon full inactivation of p110δ , a leukocyte-restricted class I PI3K isoform . A recent study demonstrated that the p110β isoform signals downstream of c-kit [12] , uncovering a potential germ-cell intrinsic function of p110β in mice . However , the male fertility phenotype of p110β kinase-dead mice differs from that of c-kit/PI3K mutant mice , pointing to an additional , germ cell-extrinsic , function of p110β in the regulation of male fertility . Indeed , the testicular phenotype of mice with inactive p110β is reminiscent of that of mice with defective SCs , which are known to control the formation of the lumen of seminiferous tubules , attachment of the germinal cell lineage and efficient sperm formation and maturation [25 , 27 , 32] . Little is known about PI3K function in male germ cell support cells , with some evidence for a role of PI3K signalling in primary culture of SCs [43] . Importantly , SC-specific inactivation of p110β also led to male sterility , highlighting its important role in these support cells . Indeed , a decrease in the mRNA expression of the homeobox gene Rhox5 , critical for the full efficiency of meiosis [29 , 32 , 34] , was observed upon SC-specific genetic deletion of p110β as well as upon global genetic inactivation of p110β , suggesting that the catalytic activity of p110β was also important for SC function . Of note , the progenitor germ cell marker Trap1a was also found to be decreased in both mouse models ( Fig 4E and Fig 6A ) , although the in vivo implication of this is currently unknown . The male fertility phenotype of SC-selective p110β inactivation was less prominent than upon systemic p110β inactivation , in that it did not affect the germ cell composition of the mice , further suggesting potential germ cell-intrinsic roles for p110β , such as in c-kit-positive sperm cells . Of note , we cannot rule out that p110α also plays a role in SCs , as it is expressed in this cell type ( Fig 4A ) . Taken together , our data show that the previously reported male infertility phenotype upon p110β inactivation [12] is not limited to a potential germ cell-intrinsic role of p110β in c-kit signalling , but is also related to an important role for p110β in the SC support cells . The fertility phenotype of mice with inactive p110β strongly resembles that of mice with SC-selective deletion of the AR ( SCARKO mice; [29] ) . We found that p110β activity regulates the expression of SC-specific genes that are essential for the differentiation of germ cell lineage and known to be regulated by AR [40] . Previous work has also implicated p110β as a positive regulator of AR transactivation in prostate cancer cell lines [44] and PI3K/mTOR signalling has been shown to either positively or negatively modulate AR transactivation both in prostate cancer cell lines and genetic mouse models of prostate cancer [45 , 46] . At present , the upstream signals that activate p110β in SCs are unknown . Between P10 and P15 , the later time point being the one at which the p110β-linked phenotype becomes largely apparent in the testes , SCs regulate the induction of germ cell differentiation through the combined action of the AR and FSH , a ligand that signals through the FSH receptor ( FSHR ) , a GPCR only expressed SCs [47 , 48] . FSHR deletion in mice mildly perturbs SC function and the progression of germ cells through spermatogenesis but when combined with AR deletion in SCs severely blocks this process [48] . As p110β mainly signals downstream of GPCRs [9–11 , 49 , 50] , it is conceivable that p110β could mediate some of the action of FSH , and in particular the potential synergistic activity of FSH signalling on AR function . This remains to be investigated further . We find that p110β activity modulates the transcriptional activity of the AR on DNA response elements from Rhox5 or Eppin promoters . However , the exact way in which the lipid kinase activity of p110β signals to the AR is currently unclear . Although p110β is expected to act mainly in the cytosol , recent reports suggest that it could also act inside the nucleus where it has been found to regulate DNA repair and replication [51 , 52] and to directly interact with the AR in ChIP assays [44] . Female mice with full inactivation of p110β had a significant reduction in litter size and frequency . This might be explained by our finding that p110β activity ( either in the maternal environment and/or intrinsically in the developing egg ) contributes to the transition of explanted 2-cell embryos to the morula/blastocyst stage in vitro . These data are in line with previously published evidence , using PDK1 or PTEN inactivation in oocytes , which show that maternal PI3K signalling is crucial for embryonic genome activation and preimplantation embryogenesis in mice [53] . Preimplantation embryos may generate intrinsic signals that promote their survival and development , with paracrine/autocrine factors activating intracellular signalling events needed for early embryonic development [54 , 55] . Class I PI3K activity is known to contribute to the constitutive PtdIns ( 3 , 4 , 5 ) P3 lipid synthesis observed in mouse preimplantation embryos [56] . Moreover , granulosa cells surrounding the oocyte were shown to act via the PI3K/Akt/mTOR pathway to promote the translation of maternal oocyte mRNAs that are critical for preimplantation embryo development [57] . It is possible that p110β signalling downstream of the GPCR agonist LPA [9] , known to be important in preimplantation embryos [58] , contributes to the embryonic lethality upon p110β inactivation . In cancer , p110β is often , but not always , the key PI3K isoform in cells with inactive PTEN [50 , 59–61] . p110β-selective drugs ( such as GSK2636771 [62] , clinicaltrials . gov identifier NCT01458067 ) are currently being tested in cancers with inactive PTEN , including prostate cancer . Our data suggest that such compounds , but also the broader spectrum class I PI3K inhibitors that hit both p110α and p110β , might have side-effects on human fertility . Our data also provide a new lead for the development of male contraceptives . Indeed , in an organismal developmental context , p110β regulates AR targets only in SCs but not in other AR-responsive tissues , including prostate , seminal vesicles and epididymal/retroperitoneal fat , which would ensure minimal off-target effects of a p110β inhibitor . A male contraceptive should be free from side effects with a reversible action on sperm once the "male pill" is no longer taken . Our data suggest that p110β inhibitors could meet these requirements , with no overall phenotypes in p110β-deficient males other than sterility , due to a highly specific blockade of sperm maturation from spermatogonia to the primary spermatocyte stage ( Fig 3B ) , while retaining most of the spermatogonial pool of cells that are at the origin of sperm development . Disorders of male and female fertility are on the increase . Our findings have additional potential clinical implications for unraveling mechanisms of idiopathic male and female infertility . Idiopathic non-obstructive azoo/oligozoospermia is a major health problem , accounting for about 30% of all male infertility cases . It is likely , and widely speculated , that novel mutations in genes regulating spermatogenesis will be discovered as causes of such situations . It is tempting to speculate that Pik3cb could be one of such candidate genes .
Small molecule inhibitors were dissolved in DMSO , with final concentration of DMSO in the assays maximally 0 . 2% . TGX-221 was from Cayman . Some antibodies to class IA PI3Ks ( p110α and p110β ) were generated in-house ( for details , see Ref . [9] ) , p110β antibodies for immunoblotting was from Santa Cruz Biotechnology ( sc-602 ) . Additional antibodies were from Upstate ( p85-pan; 06–195 ) , BD Biosciences ( p110α; 94520–150 ) ; Alexis ( p110γ; clone H1 ) ; Cell Signaling Technology ( pS473-Akt , pT308-Akt , Akt , pS176-IKKα , p-S240/244-S6 or S6 ) ; Santa-Cruz Biotechnology ( pT202-pY204-p44/42 , c-kit ( C19 and M14 ) ) and Sigma ( α-tubulin , β-actin ) . Cell culture reagents were from Invitrogen . Dihydroxytestosterone 5α-DHT was from Sigma . Mice were kept in individually-ventilated cages . All procedures and animal care were conducted under the UK Licence PPL 70/7447 , in accordance with the UK Animals ( Scientific Procedures ) Act 1986 , with local ethics approval at University College London . Embryos or pups from timed pregnant mice were dissected at different time points , and those from E13 . 5 pregnant mice from mixed C57BL/6 x 129S2/Sv or C57BL/6 background were used to prepare MEFs as described [9] . Breeding efficiency was analysed in cages with 1 male and 2 females . Necropsy was performed after perfusion of mice with 4% formalin . Organs were fixed for 24 h in 4% PFA , washed twice in water and stored in 70% ethanol until embedding in paraffin . For analysis of testes , fixation was in Bouin’s solution ( overnight ) rather than PFA . H&E staining was performed on 2 μM sections . The diameter of the seminiferous cords/tubules was measured at 400× magnification using an ocular micrometer calibrated with a stage micrometer ( Hamamatsu ) . Between 100 tubules that were either round or nearly round were chosen randomly and measured for each animal . For IHC , cryosections were stained with antibodies to JAM-A or JAM-C ( kind gift from Sussan Nourshargh , Queen Mary University London ) or anti-goat c-kit ( Santa Cruz; 1/400 ) . Mouse tissue and cultured cells were lysed in 1% w/v Triton X-100 in 50 mM Tris . HCl , pH 7 . 4 , 150 mM NaCl , 1 mM EDTA , supplemented with protease and phosphatase inhibitor cocktails . Protein concentration was quantified by the BCA method for tissue or Bradford assay for cell lysates . IP of c-kit was performed after preclearing lysates with Sepharose protein A/G . Proteins were resolved on 8% SDS/PAGE gels and immunoblotted as described [9] . The murine MSC-1 SC line was transiently transfected with pcDNA3 plasmids with or without the DNA sequence encoding Flag-tagged human AR , together with a Firefly luciferase expression plasmid driven by AR- and/or GR-responsive elements , as shown in S9 Fig . Two controls were applied: 1 ) Firefly luciferase expression was normalised to expression of a co-transfected plasmid in which Renilla luciferase is driven by the SV40 promoter and 2 ) the luciferase values were normalised to values from wells transfected only with the plasmid in which Firefly luciferase is driven by the SV40 promoter , to account for non-specific induction of gene expression . Data are expressed as fold-increase of normalized Firefly/Renilla ratios . All transfections were performed in triplicate in 6-well plates . Indicated are the mean induction factors ± SEM after stimulation with 5–50 nM of 5α-DHT for 24 h . The DNA elements used and their nucleotide sequence were as follows: Rhox5-ARE-1 ( 5'-AGATCTCATTCTGTTCC-3' ) , Eppin-ARE ( 5'-AGAACTTGGTGTTCC-3 ) and TAT-GRE2 ( 5'-TGTACAGGATGTTCT-3' ) and were described in detail in [40 , 63] . Tissue samples of ( P10 , P15 and P35 and week 8 and 12 ) testes were collected and snap-frozen in liquid nitrogen . All samples heterozygous for p110β were from mice on the C57BL/6 background . cDNA was synthesized from DNaseI-treated total RNA ( RNeasy kit , Qiagen , Chatsworth , CA ) using Superscript II RNaseH– reverse transcriptase and random hexamer primers ( Invitrogen ) . Primer pairs spanning an intron were designed by Applied Biosystems or previously published in [29] ( for details see supplemental data ) . For quantification of gene expression , the ABI Prism 7700 sequence detector PCR detection system ( Applied Biosystems ) was used with a two-step RT-quantitative-PCR protocol . Gene expression was corrected for well-to-well loading variation by expressing data as a ratio to 18S rRNA . All samples and standard curves were run in triplicate . Data are analyzed using relative standard curves to allow comparison between all samples . Normalization of data to the total weights of the testes was performed to take into account the differential composition due to differential development of spermatogenesis . For Illumina array , testes from WT and heterozygous mutant P10 pups ( on the C57BL/6 background ) were harvested and snap-frozen . Purified mRNA was subjected to a quality check ( Experion ) and subjected to Illumina array analysis ( Mouse Ref8v2 arrays ) . Five samples from each genotype in duplicate were subjected to the analysis . Quality Control and normalization were performed using BeadStudio ( Illumina ) . Statistical analyses were performed using Bioconductor ( www . bioconductor . org ) packages within the open source R statistical environment ( www . r-project . org ) . After filtering , the Limma package for differential expression analysis was used . Significant changes in gene expression were detected using a False Discovery Rate ( FDR ) < = 0 . 05 . Data are represented as fold modification in log 2 . p110βflox/flox mice ( C57BL/6 background ) were crossed with SC-specific Cre expressing mouse line AMH-Cre ( C57BL/6 background ) [28] . For detection of Cre-mediated excision of exons 21 and 22 of the p110β catalytic domain , mRNA was extracted from the testis and transcribed into cDNA and used as a template for a nested PCR to amplify exon 16–24 of Pik3cb using primers located in exon 16 ( 5’-CACTCCTGCTGTGTCCGTACA-3’ ) and 24 ( 5’-TCAGTGCTTCCTCCTCGCTCT-3’ ) followed by amplification of exons 19–23 using primers located in exon 19 ( 5’-TTGGACCTGCGGATGCTCCCCTAT-3’ ) or exon 23 ( 5’-CGCATCTTCACAGCACTGGCGGA-3’ ) . The generation of a 204 bp ( base pair ) PCR fragment in testis samples from AMH-Cre+p110βflox/flox ( SCβ-DEL ) mice indicated successful splicing of exon 20 onto exon 23 , resulting in the generation of a mRNA encoding an internally truncated p110β protein [9] . 6- to 8-week-old female mice of the indicated genotypes on a C57BL/6 x 129 mixed background were superovulated by intraperitoneal injection of 7 . 5 IU pregnant mare's serum gonadotrophin ( PMSG , Intervet ) followed 48 h later by injection of 5 IU human chorionic gonadotrophin ( hCG , Intervet ) . Female mice were mated with males of the indicated genotype ( mixed background ) at the time of hCG administration , and two-cell embryos were collected from the oviducts 1 . 5 days later ( E1 . 5 ) in HEPES-buffered KSOM ( Specialty Media ) supplemented with amino acids . The numbers of 2-cell embryos recovered from WT and p110βD931A/D931A females were similar , suggesting normal ovulation upon p110β inactivation . Embryos were cultured for 4 days in a 5% CO2 incubator in KSOM supplemented with amino acids . In order to reach high-density culture , embryos were placed into small drops of KSOM under mineral oil , at a density of one embryo per μl ( typically , 15–20 embryos in 15–20 μl drops ) , as previously described [56] . After microscopic scoring of the stage of development , each embryo was digested for 2 h at 55°C in 5 μl of tail digestion buffer ( 100 mM NaCl , 10 mM Tris pH 8 , 25 mM EDTA , 0 . 5% SDS ) with proteinase K and pronase E ( 0 . 4 μg/ml ) ; the reaction was stopped with 45 μl of TE ( Tris . EDTA pH 8 . 0 ) and genotyping PCR performed on 2 μl of the reaction as described above . Embryos with failed genotyping ( 14% of all embryos cultured ) were not taken into account: 6% were blastocysts or morulas and 8% were developmentally arrested embryos . In vitro and in vivo parameters were compared between two groups using the non-parametric Mann–Whitney U-test or unpaired t-test; quantifications and in vitro parameters using Student's t-test . | Class I PI3Ks are important signalling enzymes and drug targets in cancer and inflammation . We report that p110α and p110β , the two ubiquitously expressed class I PI3K isoforms , control fertility , with no evidence for such a role for p110δ , a PI3K highly expressed in leukocytes . Infertility is therefore a possible but reversible side-effect of PI3K-targeted therapies . Using a new mouse model of systemic p110β inactivation , we found that p110β is critical for ensuring the quality of eggs in females and for sperm formation in males . p110β inactivation leads to a specific blockade in sperm development , without affecting the spermatogenic stem cell pool . This , together with the observation that p110β inactivation has no detectable organismal side effects in the adult stage , makes this kinase a potential drug target for a male contraceptive . Besides its previously reported role in the spermatogenic cells themselves , we now report that p110β also regulates the action of androgens , the male sex hormones , specifically in the Sertoli cells that surround the developing sperm , without affecting androgen action in other tissues . In cancer , however , p110β may acquire the capacity to regulate androgen action in tissues other than Sertoli cells , as was previously documented in prostate cancer . | [
"Abstract",
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] | [] | 2015 | Novel Role for p110β PI 3-Kinase in Male Fertility through Regulation of Androgen Receptor Activity in Sertoli Cells |
Tomato ( Solanum lycopersicum ) is one of the highest-value vegetable crops worldwide . Understanding the genetic regulation of primary metabolite levels can inform efforts aimed toward improving the nutrition of commercial tomato cultivars , while maintaining key traits such as yield and stress tolerance . We identified 388 suggestive association loci ( including 126 significant loci ) for 92 metabolic traits including nutrition and flavor-related loci by genome-wide association study from 302 accessions in two different environments . Among them , an ascorbate quantitative trait locus TFA9 ( TOMATO FRUIT ASCORBATEON CHROMOSOME 9 ) co-localized with SlbHLH59 , which promotes high ascorbate accumulation by directly binding to the promoter of structural genes involved in the D-mannose/L-galactose pathway . The causal mutation of TFA9 is an 8-bp InDel , named InDel_8 , located in the promoter region of SlbHLH59 and spanned a 5’UTR Py-rich stretch motif affecting its expression . Phylogenetic analysis revealed that differentially expressed SlbHLH59 alleles were selected during tomato domestication . Our results provide a dramatic illustration of how ascorbate biosynthesis can be regulated and was selected during the domestication of tomato . Furthermore , the findings provide novel genetic insights into natural variation of metabolites in tomato fruit , and will promote efficient utilization of metabolite traits in tomato improvement .
Tomato represents an important source of nutrients and fiber for the human diet and is a model system for studying fruit biology [1] . Cultivated tomatoes carry only a small fraction of the available genetic variation in this crop , since breeders have primarily focused on fruit size and stress resistance [2 , 3] , resulting in decreased flavour quality [4] . To address this issue , breeders must focus on quality as well as high yield [5] . Recent progress has been made in analysing the nutritional and flavour qualities of tomato , which can be assessed by assaying a range of metabolites whose selection can influence organoleptic and nutritional qualities [6 , 7] . The diverse metabolites produced by plants can be divided into primary metabolites and secondary metabolites , in which primary metabolites play a central role in plant growth , cellular replenishment , resource allocation , and differentiation [8] . Primary metabolites include a wide range of intermediate compounds ( such as sugars , organic acids , and amino acids ) involved in glycolysis , the tricarboxylic acid ( TCA ) cycle , and amino acid metabolism [9] . Ascorbic acid ( AsA ) is an organic acid that scavenges reactive oxygen species and dietary AsA can reduce the incidence of important human diseases such as hypertension and diabetes [10] . Four pathways of ascorbate biosynthesis have been established in higher plants [11 , 12] , in which D-Man/L-Gal pathway , starting from glucose , is considered the most important in plants , and genes underlying all biosynthetic steps have been identified [13] . In this pathway , PMM mediates the interconversion between mannose 6-phosphate and mannose 1-phosphate , and is required for the synthesis AsA in both Arabidopsis and N . benthamiana [14] . GMP , a rate-limiting enzyme of the D-Man/L-Gal pathway , catalyzes the conversion of D-mannose-1-P to GDP-D-mannose [15] . An ozone-sensitive Arabidopsis mutant showing significant reduction of AsA has been mapped to the VTC1 locus encoding a GDP-D-mannose pyrophosphorylase [16] . The mRNA levels of GMP are correlated with L-ascorbate levels in several plant species [17 , 18] . An additional important quantitative trait for the tomato processing industry is fruit soluble solids content ( SSC ) , which primarily reflects a combination of fructose , glucose and additional sugars . Altering the content and proportion of sugars and acids is a major breeding strategy for improving the flavour of processing tomato [19] . Several QTLs has been identified in tomato which influence these traits , including Lin5 and SSC11 . 1 [20] . Given their important role , understanding the genetic basis of variations in nutrition and flavor related metabolites among diverse tomato varieties will provide important insight for efforts to facilitate breeding of elite varieties with enhanced nutritional content and improved flavour . Metabolomic quantitative trait locus ( mQTL ) mapping in bi-parental populations is an effective method for exploring the genetic architecture of primary metabolites . Valuable QTLs specific to the parental lines of mapping populations have been detected , including several candidate causal genes [21] , and major genes involved in regulating primary metabolism in tomato [2 , 3] , but our understanding of natural variation in primary metabolites in natural populations and in a given plant species remains limited . Technological developments have extended our ability to understand the genomic diversity in tomato , facilitating the analysis of metabolites and locus–locus interactions in plants [22–24] . Genome-wide association studies ( GWAS ) of metabolic traits enable screening of numerous accessions to explore the genetic basis of metabolic diversity . For example , the high-quality reference genome and rich re-sequencing data have facilitated GWAS investigations of primary metabolic traits in tomato [23 , 25–28] . Using a large population and meticulous genotyping , Tieman et al . ( 2017 ) drew a genetic roadmap related to tomato flavour to facilitate the breeding of high-quality modern commercial varieties [20] . In the present study , we performed GWAS for 92 metabolic traits using 302 diverse tomato accessions that were characterized in two different years and environments and genotyped with 4 , 180 , 023 SNPs . We uncovered a relatively simple genetic architecture for most metabolic traits . A novel transcription factor ( TF ) SlbHLH59 for ascorbate underlying one of these QTLs was functionally and phylogenetically characterized suggesting TFA9 was a domestication target . These findings provide new information on the genetics of fruit quality and provide a foundation for additional discovery of the genetic regulation of metabolic traits .
By combined metabolomic approach ( see Method ) , we detected and quantified 92 metabolic traits in red fruit from the association panel ( 302 inbred lines ) harvested in two environments open field ( E1 ) and greenhouse ( E2 ) ( S1–S3 Tables ) . Most metabolites had coefficients of variation ( CV ) was greater than 40% ( S1A Fig ) . Among the 49 repeatedly detected traits ( 48 primary metabolites including ascorbate , SSC ) , 46 . 9% ( 23 out of 49 ) displayed broad-sense heritability ( H2 ) greater than 0 . 3 , and 22 . 4% had heritability values >0 . 5 ( S1B Fig ) . A wide range of variation was observed for some metabolites in each species and subgroup over the years ( S4 Table ) . Across the groups , several sugars ( sucrose , fructose , and myo-inositol ) and organic acids ( citric acid , malic acid , ascorbic acid and pentanoic acid ) were present at the highest levels in PIM , as were two amino acids ( alanine and L-glutamic acid ) and three flavor-related components ( SSC , total sugar and total acid ) ( S2 Fig ) , suggesting selection during domestication for these metabolites . We performed GWAS using a compressed mixed linear model ( CMLM ) to reveal the metabolic regulatory mechanisms in tomato fruit under different environments ( E1 and E2 ) . Using a Bonferroni correction based on the effective numbers of independent markers [29] , the P-value thresholds were set at 2 . 4 × 10−7 ( suggestive ) and 1 . 2 × 10−8 ( significant ) . The detected 388 lead SNPs ( including 262 suggestive SNPs and 126 significant SNPs ) across the two environments ( S3 Fig and S5 Table ) , including repeated detection of 103 SNPs ( Fig 1 ) . We identified six potential mGWAS hotspots ( density>0 . 015 ) , on chromosomes 1 , 3 , 5 , 9 , and 11 . These loci were often frequently by metabolites that are biochemically related; for instance , a hot spot on chromosome 1 was identified for approximately half ( 46 . 7% or 7/15 ) of the sugars detected in this study ( S5 Table ) . Candidate genes underlying these loci might encode central regulators of these pathways and/or influence rate-limiting reactions . The percentage of phenotypic variation explained by each locus ranged from 2 . 4 to 36 . 1% and from 2 . 1 to 30 . 3% in the two replicates , with mean values of 10 . 2 and 8 . 6% , respectively ( Table 1 ) . To test for possible interactions between high significance QTL loci ( P≤1 . 2 × 10−8 ) , we investigated the pairwise epistatic interactions between the QTLs of each metabolic trait in each environment . We detected 27 significant interactions ( P<0 . 05 ) for 4 metabolites ( 4-hydroxyproline , quininic acid , lactic acid and galactose oxime ) whose levels were controlled by more than one QTL in both environments ( S6 Table ) . The epistatic effect ( i . e . , sum of two-locus interaction effects ) on metabolic variation ranged from 2 . 2 to 51 . 2% , with an average of 8 . 03% , suggesting that epistasis plays an important role for the four metabolites . For lactic acid , the epistatic effect was weaker compared with the main effects of the loci ( i . e . , sum of single-locus effects ) , while for the other three metabolites , the effect was comparable to or greater than the predominant effect ( S4 Fig ) suggesting diverse pathways or interconnected mechanism . The high-density linkage disequilibrium map generated in this study helped us narrow down association signals to regions close to or directly on genes that have been reported previously [3 , 27 , 30–35] . For each significant loci identified in this study , candidate genes within 200kb ( <average LD of tomato ) of the lead SNP are listed in S5 Table , providing a database for investigation of specific metabolites of interest . Taking advantage of the mGWAS results , we searched for candidate genes based on ( i ) gene annotation , ( ii ) prior knowledge , ( iii ) gene expression , and ( iv ) structural variation to identify the most likely causal genes of the identified loci for metabolites measured here . The 37 candidate genes listed in S7 Table , which are located within 32 significant loci for 27 metabolites , are potentially causative for the identified association signals ( S5 Fig ) . Among these 37 candidate genes , four significantly associated loci ( ch02_44249907 , ch05_1882379 , ch06_36888571 , and ch09_3306649 ) on chromosomes 2 , 5 , 6 , and 9 , with P values of 1 . 4 × 10−7 , 2 . 8× 10−8 , 3 . 4 × 10−9 and 3 . 9 × 10−11 were identified by GWAS for SSC . Three of these four loci correspond to HT1 ( Hexose transporter 1 ) , SUT2 ( Sucrose transporter LeSUT2 ) , and Lin5 have been reported previously [3 , 31 , 32] . Ch06_36888571 is a novel locus within LD of Solyc06g054270 , which encodes the sugar transporter STP11 . We further validated the association between its allelic variations and SSC via mQTL mapping ( S6 Fig ) . We generated an experimental F2 population by crossing a tomato accession with low SSC , HG22 ( an elite inbred line in China , relative SSC of 4 . 8% ) with TS-21 , which confers high SSC ( a Solanum pimpinellifolium accession from Peru , relative SSC of 9% ) . By the bulk segregant analysis ( BSA ) , the causal locus of fruit SSC accumulation was mapped to two intervals , 2 . 07 Mb on Chr 2 and 7 . 84 Mb on Chr6 ( from ~38 . 8 to 40 . 8 Mb on Chr2 and 36 . 1 to 43 . 9 Mb on Chr6 , respectively ) , with the peak centered on the mapping interval identified in our GWAS analysis ( S6A and 6H Fig ) . The results of linkage mapping in the F2 population from TS-21×HG22 were consistent with the results of GWAS analysis , further supporting the notion that STP11 on chromosome 6 is a candidate gene driving the observed natural variation in fruit SSC ( S8 Table ) . This demonstrates that our metabolite profiling and GWAS analysis could provide accurate genetic architecture of tomato primary metabolites . We further investigate the putative loci associated with ascorbate concentration in tomato fruit . Three loci ( SL2 . 50ch07_1311726 with P value = 8 . 0 × 10−8 , SL2 . 50ch07_60983724 with P value = 4 . 6 × 10−10 and SL2 . 50ch07_65942036 with P value = 4 . 1 × 10−11 ) on chromosome 7 and one loci ( ch09_64101874 with P value = 3 . 1 × 10−11 ) on chromosome 9 were identified by the GWAS of AsA . Among those loci associated with ascorbate concentration in tomato fruit , one loci was co-localized with a previously reported AsA large-effect QTL on chromosome 9 [36] , we designated this locus as TOMATO FRUIT ASCORBATE ON CHROMOSOME 9 ( TFA9 ) ( Fig 2 ) . The SNP with the highest association to fruit AsA content explained 15 . 9% of the total variance . Two major haplotypes based on the lead SNP ( ch09_64101874 ) of the association signal—High-AsA haplotype ( HAH ) and Low-AsA haplotype ( LAH ) —were associated with high-AsA and low-AsA phenotypes in tomatoes , respectively ( Fig 2C ) . There was a total of 19 genes within 100-kb on either side of ch09_64101874 ( S9 Table ) . But given the estimated LD decay rate of more than 800 kb in BIG group tomato [26] , we carefully analyzed the pairwise LD distance within the 2-Mb interval centered on the lead SNP ( ch09_64101874 ) from the GWAS ( Fig 2D ) . All significant SNPs ( P value ≤ 1 . 2 × 10−8 ) fall into an 830 Kb region for 63 . 95 Mb to 64 . 77 . A haplotype analysis of the region spanning all the significant SNPs on chromosome 9 ( 830 Kb ) identified 145 haploblocks , and many significant SNPs including lead SNP ( ch09_64101874 ) trace back to haploblock 20 ( SL2 . 50ch09_64 , 095 , 308-SL2 . 50ch09_64 , 109 , 883 ) ( Fig 2E ) . The Haploblock 20 ( 14 . 575 kb; Fig 2F ) spans two genes , a bHLH transcription factor ( Solyc09g065820 , 64 , 095 , 481–64 , 100 , 029bp ) and NADH ubiquinone oxidoreductase ( Solyc09g065830 , 64 , 101 , 025–64 , 109 , 232bp ) and contains 47 SNPs , 19 of which show P values were less than 12 . 2× 10−7 ( S10 Table ) , suggesting their potential role in AsA accumulation . To identify the casual gene for AsA content in tomato fruit , we randomly selected 15 PIM accessions of high-AsA and 15 BIG accessions of low-AsA and measured expression of both genes in fruit by quantitative RT-PCR . The expression of Solyc09g065820 showed significantly higher expression in fruit with high-AsA as compared to low-AsA accessions ( Fig 2G and 2H ) . No significant difference was observed in the expression of NADH ubiquinone oxidoreductase ( S7 Fig ) . Basic helix-loop-helix ( bHLH ) proteins are a large superfamily of transcription factors functioning in a wide range of metabolic , physiological , and developmental processes in plants [37 , 38] . Based on these results , Solyc09g065820 gene ( referred to hereafter as SlbHLH59 ) is the likely candidate underlying TFA9 . To investigate functional allelic variation at the SlbHLH59 locus , we analyzed the nucleotide sequence of SlbHLH59 in 369 tomato accessions with diverse AsA content . Sequence analysis suggested that the SlbHLH59 genotype can be classified into four different haplotypes ( Hap 1 , Hap 2 , Hap 3 and Hap 4 ) by a total of 11 polymorphisms ( Fig 3A and S11 Table ) , including one InDel ( InDel_8 , ->TCTCTTTC variant at position -1324 ) and three SNPs ( SNP1 , T>C variant at position -983; SNP2 , A>G variant at position -402; SNP3 , C>T variant at position -399 ) in the promoter region , six intron SNPs ( SNP4 , T>C variant at position 958; SNP5 , A>G variant at position 1419; SNP6 , C>T variant at position 1922; SNP7 , T>A variant at position 2030; SNP8 , T>C variant at position 2718; SNP10 , T>A variant at position 3438 ) , and one nonsynonymous polymorphism in exon 4 ( SNP9 , A>G variant at position 2903 , with amino acid change from I to V ) . Interestingly , Hap 1 ( mainly consists of BIG accessions ) , Hap 2 ( consisting of BIG and CER accessions ) and Hap 3 ( consisting of BIG and CER accessions ) showed lower AsA content than Hap 4 ( mostly PIM accessions ) ( Fig 3B ) , and line with the result mentioned above where PIM tomato accessions showed higher AsA content than CER and BIG tomato accessions ( S2B Fig ) . All accessions in Hap 4 with nine consensus polymorphisms ( InDel_8 , SNP1 , SNP2 and SNP3 in promoter; SNP4 , SNP6 , SNP7 , SNP8 and SNP10 in intron ) exhibited higher AsA content than other haplotypes suggesting variation in AsA content among the SlbHLH59 haplotypes was attributed to polymorphisms in the promoter ( Fig 3A ) . The two SNPs ( SNP1 and SNP2 ) do not change the known cis elements in the promoter of SlbHLH59 but SNP3 resides at a box-1 cis element ( light responsive element ) according to PLACE ( Plant cis-acting regulatory DNA elements ) analysis . Notably , the InDel_8 in the promoter of SlbHLH59 led to the formation of a 5’UTR Py-rich stretch motif ( TTTCTCTCTTTCTC ) associated with elevated expression of downstream genes [39 , 40] , and consistent with increased SlbHLH59 expression in the high-AsA accessions that contain this motif versus the low-AsA accessions without this motif ( Fig 2H ) . Using published data [41] , we observed that SlbHLH59 showed significantly higher expression in Hap 4 accessions than in other haplotypes ( Fig 3C ) . Moreover , we conducted transient assays using site-mutated promoter fragments of SlbHLH59 in Nicotiana benthamiana to test the effects of the four polymorphisms ( three SNPs and InDel _8 ) under the promoter region on SlbHLH59 expression ( Fig 3D ) . Expression was significantly higher in the promoter fragments with the 8bp insertion than not , supporting its role in the expression of SlbHLH59 . In total of 159 tomato bHLH proteins , 68 SlbHLHs ( 43% ) showed I and 60 SlbHLHs ( 38% ) showed V at the SNP9 which is located on the second helix of the conserved bHLH domain , suggesting the conservation of this amino acid residues in tomato ( S8A Fig ) [42] . The non-consensus SNP9 with “A” allele in Hap 1 and “G” allele in Hap 2 , 3 and 4 showed irrelevance with AsA content in tomato ( Fig 3A ) . To further functionally characterize the role of SNP9on SlbHLH59 expression and AsA content , two overexpression ( OE ) constructs containing allele SlbHLH59SNP9A and SlbHLH59SNP9G were separately introduced into TS186 ( a low AsA accession ) . The two SlbHLH59SNP9A transgenic plants , A-OE5 and A-OE9 , with higher SlbHLH59 expression , showed significantly enhanced total and reduced AsA content compared to wild type ( Fig 3E and 3F ) . Similarly , fruits from G-OE4 and G-OE6 , two SlbHLH59SNP9G overexpression transgenic lines exhibited enhanced AsA content than the control , with the comparable AsA levels as A-OE5 and A-OE9 fruits ( Fig 3G and 3H ) . These results indicate that the SNP9A allele and SNP9G allele of SlbHLH59 are both functional , consistent with the results of haplotype analysis indicating SNP9 was non-causal for AsA content ( Fig 3A ) . The differential AsA accumulation within tomato is therefore more likely attributed to nucleotide differences in the promoter region . To further test the function of SlbHLH59 in AsA biosynthesis , an RNA interference ( RNAi ) vector was constructed and transformed into TS-265 ( a high AsA accession ) . Down regulation of SlbHLH59 in TS-265 resulted in significant reduction AsA content ( Fig 3I and 3J ) . All of these results suggested that the InDel_8 in the promoter of SlbHLH59 and the resulting absent/present 5’UTR Py-rich stretch motif , is the major cause underlying the QTL TFA9 on variation in AsA levels and attributable to altered gene expression . Phylogenetic analysis showed that SlbHLH59 belongs to the basic helix-loop-helix ( bHLH ) family transcription factors and showed highest amino acid similarity with UNE12 ( UNFERTILIZED EMBRYO SAC 12 ) which is responsible for the regulation of fertilizationin processes in Arabidopsis [43] ( S8B Fig and S1 File ) . We investigated the spatial and temporal expression patterns of SlbHLH59 in high and low AsA content accessions ( high-AsA accession TS-265 and low-AsA accession TS-186 ) . SlbHLH59 showed high expression in leaves but low in different fruit developmental stages , with the transcript level of SlbHLH59 higher in most tissues of TS-265 versus TS-186 ( Fig 4A ) , supporting a role of SlbHLH59 in positively regulating AsA content in tomato . A previous study revealed that light plays a critical role in regulating AsA metabolism and accumulation [44] . To assess whether SlbHLH59 is involved in light-dependent ascorbate metabolism , we analyzed the expression of SlbHLH59 under successive illumination circulation . Interestingly , we observed light-suppressed SlbHLH59 expression , i . e . it rapidly decreased in the light and increased under dark ( S9 Fig ) . These results explained in part the molecular mechanism of light-dependent accumulation of AsA in tomato . In order to investigate SlbHLH59 cellular localization , we created SlbHLH59SNP9A-YFP and SlbHLH59SNP9G-YFP fusion proteins , which were transiently expressed in Nicotiana benthamiana . Fluorescent signals of YFP overlapped with that of ERF-RFP , a marker for the nucleus , suggesting that both SlbHLH59SNP9A and SlbHLH59SNP9G were located in the cell nucleus ( Fig 4B and 4C ) . When plants were exposed to oxidative stress , AsA acts as an antioxidant protecting cells from oxidative damage by scavenging excessive reactive oxygen species [10] . To evaluate whether overexpressed SlbHLH59 in tomato can increase tolerance to oxidative stress , 1-month-old A-OE lines ( A-OE5 and A-OE9 ) and wild type ( TS-186 ) were subjected to oxidative stress by treatment with 75 μM methyl viologen ( MV ) for 2 days . DAB staining showed that there was no significant difference between TS-186 and A-OE lines under normal conditions ( treatment with ddH2O ) , but more brown spots in the leaves of TS-186 were observed than in the leaves of A-OE after MV treatment ( Fig 4D–4F ) . Additionally , the content of chlorophyll and MDA were not significantly altered in the A-OE lines , but significantly decreased and increased in the wild-type after MV treatment , respectively ( S10 Fig ) . These results demonstrate that SlbHLH59 induces AsA accumulation facilitating increased oxidative stress tolerance . bHLH transcription factors have previously been reported to recognize and bind to the E-box cis-element ( CANNTG ) , thereby affecting the expression of downstream genes [45] . To test whether the expression of structural genes in the AsA biosynthesis pathway were modulated in the SlbHLH59 transgenic plants , we performed qPCR analysis . The expression of SlPMI , SlPMM , SlGMP1 , SlGMP2 , SlGMP3 , SlGMP4 and SlGME1 were higher and lower in the fruits of the SlbHLH59-OE and SlbHLH59-RI lines , respectively , when compared with wild-type fruits , suggesting transcriptional regulation mediated by SlbHLH59 on the AsA biosynthetic genes ( Fig 5A and 5B ) . Using PLACE ( http://www . dna . affrc . go . jp/PLACE/signalscan . html ) and PlantCARE ( http://bioinformatics . psb . ugent . be/webtools/plantcare/html/ ) software , we analyzed the cis-elements in the promoters of SlPMI , SlPMM , SlGMP1 , SlGMP2 , SlGMP3 , SlGMP4 and SlGME1 , and determined that all of the genes were predicted to harbor the E-box cis-elements ( S12 Table ) . We speculated that SlbHLH59 might bind and regulate these genes of the D-Man/L-Gal pathway . To test this hypothesis , we conducted yeast one-hybrid ( Y1H ) analysis to test the binding activity of SlbHLH59 protein to the promoters of SlPMI , SlPMM , SlGMP1 , SlGMP2 , SlGMP3 , SlGMP4 and SlGME1 . Cis-elements from the SlPMM , SlGMP2 and SlGMP3 promoters were bound by SlbHLH59 ( Fig 5C and 5D ) . Also , the physical interactions between SlbHLH59 and the promoter fragments derived from SlPMM , SlGMP2 and SlGMP3 were detected by using the dual luciferase system ( Fig 5E ) . These results indicate that SlbHLH59 can directly bind to the SlPMM , SlGMP2 and SlGMP3 promoter to modify their expression , and thereby positively regulate tomato fruit AsA content . To test how SlbHLH59 affects SlPMM , SlGMP2 and SlGMP3 during the fruit development in tomato , we determined total AsA and the expression of SlPMM , SlGMP2 and SlGMP3 in immature green stage ( IMG ) , green mature stage ( MG ) , breaker stage ( BR ) , yellow ripe stage ( YR ) and red ripe stage ( RR ) fruits . The G-OE4 showed higher total AsA content than TS-186 throughout the whole fruit development stages and the greatest difference was observed at MG stage , consistent with the dynamic change of SlbHLH59 transcript level ( Fig 5F and 5G ) . The expression of SlPMM , SlGMP2 and SlGMP3 were all significantly higher in G-OE4 than in TS-186 except SlPMM and SlGMP2 in IMG fruits , and the greatest difference was observed at MG stage , consistent with the dynamic change of AsA content in TS-186 and SlbHLH59 overexpression line ( Fig 5H–5J ) . This result supports the notion that SlbHLH59 contributes to AsA biosynthesis by directly regulating SlPMM , SlGMP2 and SlGMP3 during fruit development with the highest effect at the MG stage just prior to the onset of ripening . Since InDel_8 represents a functional polymorphism of the TFA9 locus for AsA biosynthesis via SlbHLH59 expression in tomato fruit , we investigated InDel_8 variants in 540 accessions , including 333 BIG , 141 CER , 54 PIM and 12 accessions of wild tomato species ( Fig 6A and S13 Table ) . Only 6 accessions in the BIG group carried insertion_8 , as TS-265 , and all other accessions carried deletion_8 , as TS-186 . Significant differences in both total and reduced AsA were detected between the eight insertion_8 BIG accessions ( BIGinsertion_8 ) and TS-186 ( BIGdeletion_8 ) ( Fig 6B ) . All 12 accessions of wild species carried insertion_8 but the deletion_8 was detected in 9 of 54 tested PIM accessions , suggesting the deletion of InDel_8 occurred in early domestication during the time the PIM group differentiated from wild species . Thirteen of the 23 CERinsertion_8 carrying accessions originated from South America , including those from Ecuador and Peru where wild tomato relatives originated . Thus , it is likely that the deletion_8 initially occurred in South America and subsequently spread into other neighbouring countries . The high fruit AsA-associated insertion_8 was present at a highest frequency of 83 . 3% ( 45/54 ) in wild progenitor variety PIM , but the frequency sharply decreased in CER accessions ( 16 . 3% , 23/141 ) that were domesticated from the wild progenitor variety PIM and BIG landraces ( 1 . 8% , 6/333 ) ( Fig 6A ) . The result revealed that the less fruit AsA content was selected during domestication . To examine the evolutionary history of the TFA9 locus , DNA sequence variation in the genomic region spanning 10 kb upstream fragment and coding region of the SlbHLH59 was investigated . On average , the nucleotide diversity ( π ) in the coding and promoter region of SlbHLH59 was much lower in BIG ( π = 0 . 11×10−3 ) than in CER ( π = 0 . 65×10−3 ) and PIM ( π = 1 . 32×10−3 ) ( Fig 6C ) . The highest ratios of nucleotide diversity in PIM to CER ( πPIM/πCER , 4 . 03 ) and CER to BIG ( πCER/πBIG , 13 . 74 ) occurred at -2 kb to -1 kb region of SlbHLH59 , where the indel_8 was located on ( Fig 6D ) . This result was consistent with previous report that SlbHLH59 was pointed out as a domestication and improvement sweep at the whole genome level [26] . Moreover , Tajima’s D of the region from -2 kb to -1 kb was negative in BIG ( -1 . 53 ) subgroup but positive in the CER ( 0 . 46 ) and PIM ( 2 . 33 ) subgroup , respectively ( S14 Table ) . Taken together , these results suggested that TFM6 region has been subjected to human selection during the domestication of tomato .
Plant breeders have long focused on traits with potential to increase yields while decreasing inputs [33] . More recently , improving nutritional and flavour quality are of economic and social interest to help meet nutritional security needs of an increasing human population [46 , 47] . Despite the success in generating tomato varieties with improved traits [48 , 49] , the efficiency of genetic improvement of this crop has remained relatively limited [8] . Here we implemented GWAS to obtain a detailed understanding of the genetic determinants underlying metabolic variation in tomato with the ultimate goal of enhancing genetic improvement of nutritional and flavour . We measured 92 metabolic traits and identified 388 loci involved in their variation . Among the candidate genes involved in these loci , most were annotated as transporters or regulators as opposed to structural enzymes ( S7 Table ) . Finally , we characterized a candidate gene , SlbHLH59 , involved in the variation of ascorbate . Then , we characterized a major QTL , TFA9 , underlying the variation of ascorbate . Finally , we characterized a candidate gene , SlbHLH59 corresponding to TFA9 , and found it contribute to the variation of ascorbate which improved both fruit nutrition and oxidative stress tolerance in tomato . mGWAS on tomato fruit metabolic traits has been conducted previously [28 , 50] . Sauvage et al . ( 2014 ) preformed mGWAS using 39 primary metabolites and 5 , 995 SNPs among 163 tomato accessions , while 60 primary and secondary metabolites ( including 33 volatiles ) and 10 , 000 SNPs were used in the mGWAS by Bauchet et al . ( 2017 ) . Together these led to identification of 44 and 79 loci significantly associated with 19 and 32 metabolic traits , respectively . We used over 4 million SNPs with 92 measured primary metabolites for genotype-phenotype association , leading to 388 highly suggestive loci for 75 metabolic traits ( Table 1 ) . For example , 17 associated signals of citric acid were identified ( S5 Fig and S5 Table ) , as compared to up to 4 loci previously associated with citric acid [28 , 50] . Four loci significantly associated with SSC including Lin5 were also detected ( S6 Fig ) , while only Lin5 was previously reported [50] . We narrowed mapping resolution to a single gene in some cases . For example , a lead SNP on chr01 ( ch01_79524657 ) located in a glutamine synthetase gene ( Solyc01g080280 ) significantly associated with isoleucine content ( S5 Table ) . Unlike the GWAS results of secondary metabolites [51 , 52] , regulators and transporters rather than structural enzymatic genes involved in organic acid and sugar metabolism were identified ( S7 Table ) . We hypothesized that the transportation and regulation of sugar and organic acids synthesis harbors more exploitable genetic variation than its synthesis in tomato fruits , consistent with previous studies [3 , 27 , 31 , 32] . Two candidate genes , SlbHLH59 as a regulator of ascorbate and SlSTP11 as a transporter of SSC , were functionally identified to support this hypothesis . Previous studies have shown that the accumulation of AsA does not correlate with the expression of genes involved in its biosynthesis [53] . SlbHLH59 , located at the end of chromosome 9 , was associated with fruit ascorbate content consistent with previous reports by linkage mapping [36] . Notably , the regulatory role of bHLH transcription factor , SlbHLH59 , involved in the biosynthesis of AsA was firstly reported in tomato . Given that the expression of SlbHLH59 was associated with ascorbate content in different accessions , we concluded that the InDel_8 in the promoter of SlbHLH59 play an important role in determining natural AsA variation and was selected during domestication ( S11 Fig ) . Taking advantage of this valuable resource for tomato genetic and metabolic variation , we uncovered the genetic basis underlying the variation in primary metabolism among subgroups of our diverse collection . This information could be directly used to help design breeding strategies for the improvement of high-value metabolites . Although a more complex genetic architecture has been revealed for primary metabolism compared with secondary metabolism [21] , the considerable number of metabolites with major loci ( R2>15% ) , suggest that breeding efforts for some metabolites can be simplified by pyramiding favourable alleles of major genes . Moreover , in addition to SlbHLH59 that was verified by transgenic lines , hundreds of additional loci identified in this study remain to be fully explored to help dissect the molecular basis of metabolic variation in tomato . Further evaluation and validation of polymorphisms as was done for SlbHLH59 should help uncover the genetics of natural variation in primary metabolism and expand the crop breeding toolbox for important fruit traits .
A total of 302 tomato accessions , including 171 Solanum lycopersicum ( BIG ) , 104 S . lycopersicumvar . cerasiforme ( CER ) and 27 S . pimpinellifolium ( PIM ) accessions that were selected from the previously described 360 accessionsand used for GWAS in this study . The GWAS was conducted at two sites: E1 ( Spring 2013 , open field at Huazhong Agricultural University , Wuhan , China ) and E2 ( Spring 2016 , greenhouse at the Agri-Academy of Sciences of Wuhan , China ) . A F2 population of 1 , 587 individuals was derived from a cross between TS-21 ( high SSC ) and HG22 ( low SSC ) conducted in an open field at Huazhong Agricultural University in spring 2014 . For GWAS and BSA , at least three fruits from at least three plants per line were harvested at the ripe stage . For fruit development analysis , flowers were tagged at the full-bloom stage to synchronize developmental stages . The fruits were harvested at immature green ( IMG , 21 DAF ) , mature green ( MG , 37 DAF ) , breaker ( BR , 40 DAF ) , yellow ripe ( YR , 42 DAF ) , and red ripe ( RR , 49 DAF ) developmental stages . Three biological replicates of each developmental stage were analyzed . After tissue selection , the outer pericarp was bulked ( five fruits ) and stored at –80°C for metabolic and transcript profiling . The remaining fresh red fruits were used directly to measure SSC , total sugars , total acids , and sugar/acid ratio . Red fruit metabolite profiling of the 302 tomato accessions was performed by GC-MS using the method as described previously [27] . Tomato fruit juice samples were used to measure the SSC , total sugars , total acids , and sugar/acid ratio . SSC was determined within the GWAS population and F2 segregation population using a hand saccharimeter ( B429335 , ATAGO ) . The total sugar and total acid contents were measured using a Brix-Acidity Meter ( PAL-BX|ACID3 , ATAGO ) , followed by calculation of sugar/acid ratios . Total sugars and SSC in the fruit juice were measured by directly dropping the sample onto the meter and recording the value . The fruit juice was then diluted 50-fold with ddH2O water and used to determine total acids . The AsA levels were measured as previously described [44] . The relative content of each metabolite was obtained by comparing the peak area of each metabolite with the peak area of internal standard ( ribitol ) . For each phenotype , normal distribution of the data was tested using a Shapiro-Wilk test . The normality test revealed that 34 of the 92 phenotypes ( 37% ) were not normally distributed and were Box-Cox transformed . The coefficient of variation values was calculated independently for each metabolite ( using the mean of the biological replicates of the untransformed m-trait data ) as follows: σ/μ , where σ and μ are the s . d . and mean of each metabolite in the population , respectively . Broad-sense heritability ( H2 ) was calculated using the following equation by treating accessions as a random effect and the biological replication as a replication effect using one-way ANOVA: H2 = Var ( G ) / ( Var ( G ) + Var ( E ) ) , where Var ( G ) and Var ( E ) are the variance derived from genetic and environmental effects , respectively . Finally , differences in the metabolic traits among the six subgroups ( PIM , SA_CER , NSA_CER , UO_CER , NP_BIG , and P_BIG ) were analysed by ANOVA tested . Significance was declared at P <0 . 05 . The LD heatmap surrounding candidate gene in the GWAS was constructed using Haploview 4 . 2 with default parameters [54] , indicating r2 values between pairs of SNPs multiplied by 100 . To facilitate SNP identification and genotype imputation , two sequencing data sets were used in this study . The first was downloaded from a diverse global collection of 360 tomato accessions ( NCBI Sequence Read Archive [SRA] under accession number SRP045767 ) [26] . Additional sequence data from 398 varieties were generated by Tieman et al . [20] was downloaded from the National Center for Biotechnology Information BioProject site under accession number PRJNA353161 . SOAP2 was used to map all sequencing reads from each accession to the tomato reference genome ( Version SL2 . 50 ) with the following parameters: -m 100 , -x 888 , -s 35 , -l 32 , -v 3 . Mapped reads were filtered to remove PCR duplicates . Both paired-end and single-end mapped reads were used for SNP calling throughout the entire collection of tomato accessions using SOAPsnp with the following parameters: -L 100 -u -F 1 ( 23 ) . After imputation , SNPs with missing rates of less than 20% were selected , resulting in a total of 4 , 180 , 023 SNPs ( MAF > 0 . 05 , the number of varieties with the minor allele ≥ 6 ) for further analysis . Detailed information on called SNPs can be viewed and downloaded from Sol Genomic Network ( https://solgenomics . net/ ) . Association analyses were performed using the compressed MLM [55] with TASSEL 4 . 0 [56] . Suggestive ( 1/n , ≤2 . 4×10−7 ) and significant ( 0 . 05/n , ≤1 . 2×10−8 ) P-value thresholds were defined to control the genome-wide type 1 error rate ( n = total number of markers used ) [51 , 57] . We used Haploview software to perform local LD analysis [54] and calculate LD accordingly with modification [20] . Briefly , the average linkage decay for each 0 . 5 Mb region of the whole genome was evaluated with the following parameters: -maxdistance 2000 -minMAF 0 . 05 -hwcutoff 0 . Pairwise LD between the suggestive/significant SNPs for each metabolic trait was calculated . The physical locations of the SNPs were identified based on tomato genomic sequence version SL2 . 50 ( http://solgenomics . net/ ) . An F2 population of 1 , 587 individuals derived from a cross between TS-21 ( a high-SSC accession ) and HG22 ( a low-SSC accession ) was planted in the Spring of 2014 in an open field at Huazhong Agricultural University , China . For each individual , the average SSC of three representative fruits was recorded , and genomic DNA was isolated from fresh leaves using the CTAB method . For bulked segregant analysis , bulk DNA samples for high- and low-SSC accessions were constructed by mixing equal amounts of DNA from 50 individuals showing extremely high and low SSC , respectively . Subsequently , 34 . 97× genome sequences for TS-21 , 35 . 56× genome sequences for HG22 , and roughly 60× genome sequences for each bulk sample ( high-SSC fruit and low-SSC fruit ) were generated by BIOMARKER Company ( Beijing , China ) . The SLAF ( Specific-Locus Amplified Fragment ) label was located on the reference genome using SOAP , and labels that were sequenced <5× in the parent were filtered out . Short reads were aligned against the reference genome ( release SL2 . 50 ) using the Burrows-Wheeler Aligner ( BWA ) . The ΔSNP index was obtained by subtracting the SNP index of the low-SSC bulk sample from that of the high-SSC bulk sample . The average SNP index for the high-SSC and low-SSC bulk samples was calculated using a 1 , 000-kb sliding window with a step size of 10 kb . The statistical confidence intervals of the ΔSNP index were calculated under the null hypothesis of no QTLs , and 0 . 32 was then set as the threshold . For each metabolic trait in each environment , the pairwise additive-by-additive epistatic interactions were investigated for all identified loci . Epistatic interactions were determined by two-way analysis of variance ( ANOVA ) ( using P< 0 . 05 as a significance threshold ) using all significant loci in pairwise combinations . The proportion of variance explained by epistasis was tested by comparing the residual of the full model containing all single-locus effects and two-locus interaction effects with that of the reduced model containing all single-locus effects but excluding two-locus interaction effects [58] . To detect the variation in SlbHLH59 gene region ( chromosome 9: 64 , 094 , 000–64 , 101 , 000 , release SL2 . 50 ) , DNA sequences of SlbHLH59 in 30 tomato accessions ( 15 high-AsA and 15 low-AsA accessions ) were amplified by PCR using primers listed in ( S15 Table ) . The PCR products were sequenced and compared against the reference genome for polymorphism analysis . In addition , DNA sequences of SlbHLH59 in 367 tomato accessions ( 250 BIG accessions , 94 CER accessions and 23 PIM accessions ) and the data for genotype analysis of InDel_8 in 540 tomato accessions were downloaded from the public database ( National Center for Biotechnology Information BioProject site under the accession PRJNA353161 ) . For overexpression construct , completed SlbHLH59 open reading frame ( ORF ) were amplified from the cDNA of tomato ( TS-186 for SlbHLH59SNP9A and TS-265 for SlbHLH59SNP9G ) , and then incorporated into the pHELLSGATE8 vector using homologous recombination ( ClonExpress II One Step Cloning Kit , Vazyme ) . For RNAi construct , a 200-bp fragment of SlbHLH59 was amplified by SlbHLH59-RI primers ( S15 Table ) , and then cloned into the pHGRV vector using BP Clonase according to the manufacturer’s instructions ( Invitrogen , USA ) . All the recombinant constructs were transformed into Agrobacterium strain C58 by electroporation and subsequently transformed into the tomato genome ( TS-186 for overexpression and Ts-265 for RNAi ) using cotyledon explants as described previously [59] . Transgenic plants were confirmed by PCR using CaMV35S promoter forward primer and SlbHLH59 specific reverse primer ( S15 Table ) . Total RNA was extracted from different accessions and transgenic lines using TRIZOL reagent ( Invitrogen , USA ) . Gene expression was investigated by qRT-PCR . The primer pair sequences ( designed using Primer Premier 3 . 0 [http://frodo . wi . mit . edu/primer3] ) are listed in S15 Table . The cDNA synthesis and qRT-PCR steps were performed as previously described [60] . The Actin gene ( Solyc11g008430 ) was used as an internal standard and qRT-PCR was performed with three repeats per gene ( including ACTIN ) . The coding sequences of SlbHLH59SNP9Aand SlbHLH59SNP9G without the stop codon was amplified from the cDNA of tomato ( TS-186 for SlbHLH59SNP9A and TS-265 for SlbHLH59SNP9G ) by PCR and then cloned into the expression vector p101YFP under the control of the CaMV35S promoter by homologous recombination . CaMV35S: SlbHLH59-YFP vector as well as cell nucleus marker CaMV35S: ERF-YFP was transformed into Agrobacterium tumefaciens strain GV3101 and co-infiltrated into leaves of N . benthamiana with the suspension as previously described [61] . After 48 h incubation at 25 °C , the tobacco leaves were used for YFP and RFP fluorescence signal observation using Leica Confocal software . CaMV35S:YFP acted as positive control . The yeast one-hybrid assay was performed as described in the Matchmaker One-Hybrid Library Construction and Screening Kit ( Clontech ) . Briefly , the full-length of SlbHLH59 ORF sequence ( amplified from TS-186 cDNA ) and promoter sequences of SlPMM , SlPMI , SlGME1 , SlGMP1 , SlGMP2 , SlGMP3 and SlGMP4 ( amplified from TS-186 genomic DNA ) were cloned into the pGADT7 and pAbAi vector ( Clontech ) , respectively . The pAbAi bait vectors were introduced into the GOLD1 yeast and cultured on SD/–Ura . The pGADT7 prey vector was introduced into yeast strains containing pAbAi bait vectors and cultured on SD/–Leu . After 4 d incubation , the positive yeast strains were picked and diluted in double-distilled water to an OD600 of 0 . 1 , and 2 μL of suspension was spotted on SD/–Leu , with or without ABA ( 0–20 ng/mL ) ( Sigma-Aldrich ) , followed by 3 to 7 d incubation at 30°C . The full-length SlbHLH59 ORF was cloned into the effector vector pGreen II 62-SK under the control of CaMV 35S promoter . SlPMM , SlGMP2 and SlGMP3 promoter fragments were PCR amplified using specific primers and cloned into the reporter vector pGreen II 0800-LUC . Individual combinations of effector and reporter vectors were co-transformed into Agrobacterium GV3101 cells alongside the pSoup vector , and the transformed GV3101 cells were used to infiltrate young N . benthamiana leaves , in which transient expression was analyzed following a 2-d incubation . Firefly and Renilla luciferase signals were assayed with the dual luciferase assay reagents ( Promega ) using an Infinite M200 ( Tecan ) . The promoter activity analysis was carried out as described previously [62] . GUS activity and LUC activity were determined by Fluorescence FLx800 microplate fluorescence reader ( BIO-TEK Instruments ) . Ratios of GUS to LUC activities were used to define relative promoter activity . Three biological replicates were performed for each construct . The cis-element analysis was conducted in PLACE ( http://www . dna . affrc . go . jp/PLACE ) . One-month-old seedlings ofSlbHLH59SNP9A overexpression plants as well as TS-186 and TS-265 plants were grown in plastic pots in the greenhouse . For the light response characterization , from 8 am , the plants were exposed to continuous light at 25°C for 12 h followed by 12 h continuous dark under 25°C and recovered by12 h continuous light at 25°C . In a 36-h photoperiod , samples were taken every four hours ( 8:00 , 12:00 , 16:00 and 20:00 under light , 24:00 and 4:00 under light ) to determine the expression of SlbHLH59 in tomato leaves . To evaluate the performance of SlbHLH59SNP9A overexpression plants and wild type ( TS-186 ) plants under oxidative stress , the plants were sprayed with 75 μM MV ( MV dissolved in water with 0 . 1% Tween-20 ) or water with 0 . 1% Tween-20 ( control ) once a day for 2 days . Phenotype was investigated and recorded one week after the end of the treatment . For the 3–3’-diaminobenzidine ( DAB ) staining , the leaves were cleaned and placed in 1 mg/mL DAB , pH 3 . 8 , under light at 25°C for 8 h . The experiment was terminated by immersing the leaves in boiling 96% ethanol for 10 min . After cooling , the leaves were placed in fresh 96% ethanol for 4 h at room temperature and photographed . The deep brown polymerization product was produced via the reaction of DAB with H2O2 . Also , leaves were collected and ground into fine powder in liquid nitrogen after the treatment . To assay chlorophyll levels , 1 ml of 80% ( v/v ) acetone was added to approximately 0 . 1 g of frozen powder in a 2-ml Eppendorf tube under low light intensity by the procedure described by Wellburn [63] . The MDA levels were measured as previously described [64] . For the molecular diversity analysis , the π ratios and Tajima’s D [65] , were used to identify the selective sweeps in SlbHLH59 associated with tomato domestication and improvement events . Briefly , π ( πPIM , πCER and πBIG ) and Tajima’s D ( Tajima’s DPIM , Tajima’s DCER and Tajima’s DBIG ) were calculated using DnaSP5 . 0 version 5 . 0 software [66] , with a sliding window length of 100 bp and step size of 25 bp . | Deciphering the diverse , interconnected plant metabolome can facilitate crop improvement . In this study , the use of a combination of multiple technologies has allowed us to obtain novel functional and genetic insights into our GWAS investigating variation in ascorbate accumulation in tomato . The InDel_8 in the promoter of SlbHLH59 was selected during tomato domestication and determines fruit ascorbate content by directly regulating the expression of structural genes involved in ascorbate biosynthesis in tomato fruit . The genes and polymorphisms responsible for the variations identified in this study lay the foundation for further comparative genomic studies and for improving nutrition quality in tomato and other fruit crops . | [
"Abstract",
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... | 2019 | Genome-wide association analysis identifies a natural variation in basic helix-loop-helix transcription factor regulating ascorbate biosynthesis via D-mannose/L-galactose pathway in tomato |
Fluorescent proteins have been widely used as genetically encodable fusion tags for biological imaging . Recently , a new class of fluorescent proteins was discovered that can be reversibly light-switched between a fluorescent and a non-fluorescent state . Such proteins can not only provide nanoscale resolution in far-field fluorescence optical microscopy much below the diffraction limit , but also hold promise for other nanotechnological applications , such as optical data storage . To systematically exploit the potential of such photoswitchable proteins and to enable rational improvements to their properties requires a detailed understanding of the molecular switching mechanism , which is currently unknown . Here , we have studied the photoswitching mechanism of the reversibly switchable fluoroprotein asFP595 at the atomic level by multiconfigurational ab initio ( CASSCF ) calculations and QM/MM excited state molecular dynamics simulations with explicit surface hopping . Our simulations explain measured quantum yields and excited state lifetimes , and also predict the structures of the hitherto unknown intermediates and of the irreversibly fluorescent state . Further , we find that the proton distribution in the active site of the asFP595 controls the photochemical conversion pathways of the chromophore in the protein matrix . Accordingly , changes in the protonation state of the chromophore and some proximal amino acids lead to different photochemical states , which all turn out to be essential for the photoswitching mechanism . These photochemical states are ( i ) a neutral chromophore , which can trans-cis photoisomerize , ( ii ) an anionic chromophore , which rapidly undergoes radiationless decay after excitation , and ( iii ) a putative fluorescent zwitterionic chromophore . The overall stability of the different protonation states is controlled by the isomeric state of the chromophore . We finally propose that radiation-induced decarboxylation of the glutamic acid Glu215 blocks the proton transfer pathways that enable the deactivation of the zwitterionic chromophore and thus leads to irreversible fluorescence . We have identified the tight coupling of trans-cis isomerization and proton transfers in photoswitchable proteins to be essential for their function and propose a detailed underlying mechanism , which provides a comprehensive picture that explains the available experimental data . The structural similarity between asFP595 and other fluoroproteins of interest for imaging suggests that this coupling is a quite general mechanism for photoswitchable proteins . These insights can guide the rational design and optimization of photoswitchable proteins .
Fluorescent proteins have been widely used as genetically encodable fusion tags to monitor protein localizations and dynamics in live cells [1]–[3] . Recently , a new class of green fluorescent protein ( GFP ) -like proteins has been discovered , which can be reversibly photoswitched between a fluorescent ( on ) and a non-fluorescent ( off ) state [4]–[10] . As the reversible photoswitching of photochromic organic molecules such as fulgides or diarylethenes is usually not accompanied by fluorescence [11] , this switching reversibility is a very remarkable and unique feature that may allow fundamentally new applications . For example , the reversible photoswitching , also known as kindling , may provide nanoscale resolution in far field fluorescence optical microscopy much below the diffraction limit [12]–[15] . Likewise , reversibly switchable fluorescent proteins will enable the repeated tracking of protein location and movement in single cells [16] . Since fluorescence can be sensitively read out from a bulky crystal , the prospect of erasable three-dimensional data storage is equally intriguing [17] . The GFP-like protein asFP595 , isolated from the sea anemone Anemonia sulcata , is a prototype for a reversibly switchable fluorescent protein . The protein can be switched from its non-fluorescent off state to the fluorescent on state by green light of 568 nm wavelength [5] , [6] , [18] , [19] . From this so-called kindled on state , the same green light elicits a red fluorescence emission at 595 nm . Upon kindling , the intensity of the absorption maximum at 568 nm diminishes , and an absorption peak at 445 nm appears . The kindled on state can be promptly switched back to the initial off state by this blue light of 445 nm . Alternatively , the off state is repopulated through thermal relaxation within seconds . In addition , if irradiated with intense green light over a long period of time , asFP595 can also be irreversibly converted into a fluorescent state that cannot be quenched by light any more [5] . The nature of this state is hitherto unknown . The switching cycle of asFP595 is reversible and can be repeated many times without significant photobleaching . These properties render asFP595 a promising fluorescence marker for high-resolution optical far-field microscopy , as recently demonstrated by Hofmann and coworkers [20] . Currently , however , with its low fluorescence quantum yield ( <0 . 1% and 7% before and after activation , respectively [6] , [16] ) and rather slow switching kinetics , the photochromic properties of asFP595 need to be improved . To systematically exploit the potential of such switchable proteins and to enable rational improvements to the properties of asFP595 , a detailed molecular understanding of the photoswitching mechanism is mandatory . The aim of this study is to obtain a detailed mechanistic picture of the photoswitching mechanism of asFP595 at the atomic level , i . e . , to understand the dynamics of both the activation process ( off-to-on switching ) and the de-activation process ( on-to-off switching ) . High-resolution crystal structures of the wild-type ( wt ) asFP595 in its off state [19] , [21] , [22] , of the Ser158Val mutant in its on state [19] , and of the Ala143Ser mutant in its on and off states [19] were recently determined . Similar to GFP , asFP595 adopts a β-barrel fold enclosing the chromophore , a 2-acetyl-5- ( p-hydroxybenzylidene ) imidazolinone ( Figure 1 ) . The chromophore is post-translationally formed in an autocatalytic cyclization-oxidation reaction of the Met63-Tyr64-Gly65 ( MYG ) triad . As compared to the GFP chromophore , the π-system of MYG is elongated by an additional carbonyl group [23] . Reversible photoswitching of asFP595 was possible even within protein crystals , and x-ray analysis showed that the off-on switching of the fluorescence is accompanied by a conformational trans-cis isomerization of the chromophore [19] . In a recent study [24] , we have shown that the isomerization induces changes of the protonation pattern of the chromophore and some of the surrounding amino acids , and that these changes account for the observed shifts in the absorption spectrum upon kindling . Based on the comparison between measured and calculated absorption spectra , the major protonation states in the ground state have been assigned to the zwitterion ( Z ) and the anion ( A ) for the trans conformer , whereas the neutral ( N ) chromophore is dominant for the cis conformation ( Figure 1B ) . Here , we study the photochemical behavior of each of the previously identified protonation states . We have addressed the following questions: How does light absorption induce the isomerization of the chromophore within the protein matrix , and how do the different protonation states affect the internal conversion mechanism ? Which is the fluorescent species , and how can the fluorescence quantum yield be increased ? To address these questions , we have carried out nonadiabatic molecular dynamics ( MD ) simulations using a hybrid quantum-classical QM/MM approach . This approach includes diabatic surface hopping between the excited state and the ground state . The forces acting on the chromophore were calculated using the CASSCF [25] , [26] multi-reference method , which , although not always yielding highly accurate excitation and fluorescence energies , has shown to be a reliable method for mechanistic studies of photochemical reactions involving conical intersections [27] . A number of approaches for modeling nonadiabatic dynamics have been described in the literature , such as Tully's fewest switches surface hopping [28] , and multiple spawning [29] . For recent reviews , see [30] , [31] . In the context of QM/MM simulations , the surface hopping approach to photobiological problems has been pioneered by Warshel and coworkers [32] , [33] . The diabatic surface hopping approach used in this work differs from the other approaches in two main respects . First , in our approach a binary decision ( hop or no hop ) is made at each integration time step of the trajectory , based only on the current wavefunctions of the ground and excited states . Second , hopping is only allowed at the conical intersection ( CI ) seam , where hopping probability approaches unity . This could in principle underestimate the crossing probabilty , because we do not allow for transitions in regions of strong coupling but no real crossing . However , for ultra-fast photochemical reactions in large polyatomic systems , decay predominantly takes place at the CI seam , as also shown by others [31] . Thus , most surface hops are essentially diabatic , justifying our approach . In addition , both energy and momentum are conserved upon a transition , as the trajectory never leaves the diabatic energy surface . The theoretical background and algorithmic implementation of the diabatic surface hopping are detailed in the Supporting Information ( Text S4 ) . Several theoretical studies on the photochemistry of the GFP chromophore have been conducted , applying both static ab initio [34]–[36] and DFT calculations [37] , and dynamics simulations based on a semi-empirical Hamiltonian [38] . In addition , vertical excitation energies of asFP595 model chromophores in the gas phase and in a continuum dielectric were calculated by DFT and ab initio methods [39] , [40] , as well as in a minimal protein environment by means of DFT and CASSCF calculations within a QM/MM approach [41] . By identifying key residues in the cavity of the asFP595 chromophore , our nonadiabatic QM/MM molecular dynamics simulations elucidate how the protein surrounding governs the photoreactivity of this photoswitchable protein . Based on the simulations , we provide a new mechanism that qualitatively explains measured decay times and quantum yields , and that predicts the structures and protonation states of the photochemical intermediates and of the irreversibly fluorescent state . We also suggest excited state proton transfer ( ESPT ) to play an important mechanistic role . However , the detailed study of such ESPT processes is beyond the scope of this paper . Our predictions can be probed by , e . g . , time-resolved Fourier transform infrared ( FTIR ) spectroscopy and x-ray crystallography .
The five excited state simulations that were initiated from the ground state trajectory of the trans neutral chromophore Ntrans are listed in Table 1 . Trans-to-cis photoisomerization of the chromophore was observed in one of these simulations ( run b , Table 1; Video S2 in Supporting Information ) . Figure 2 shows a schematic representation of the S0 ( green ) and S1 ( red ) potential energy surfaces of the neutral chromophore , along with a photoisomerization MD trajectory ( yellow dashed line ) . Two coordinates are shown , the isomerization coordinate and a skeletal deformation coordinate of the chromophore ( see below ) . The dynamics can be separated into three distinct phases: ( i ) evolution on the electronic ground state S0 , ( ii ) excitation and evolution on the excited state S1 , and ( iii ) decay back to S0 at the surface crossing seam followed by subsequent relaxation on the ground state surface . The position of the surface crossing seam controls the passage of the trajectory from S1 to S0 . The seam is accessed from a global twisted minimum on S1 , which is separated by a small S1 barrier from a local planar minimum near the Franck-Condon ( FC ) region . In our simulations , individual excited state ( S1 ) lifetimes between 0 . 224 ps and 0 . 718 ps were observed ( Table 1 ) . A simple exponential fit to the observed lifetimes yielded a decay time of τ = 0 . 34 ps ( σ+ = 0 . 21 ps , σ− = 0 . 13 ps , with σ being the statistical error ) . Given the low number of trajectories , the statistical error of our estimated lifetime may seem unexpectedly low , but results from a rigorous analysis assuming an underlying single exponential decay [42] . Recent femtosecond time-resolved pump/probe experiments by Schüttrigkeit and coworkers have yielded excited state decay time constants of 0 . 32 ps ( 78% ) , 2 . 6 ps ( 19% ) , and 12 . 1 ps ( 3% ) as well as a fluorescence lifetime of 2 . 2 ns for asFP595 [43] . However , although the simulated decay times seem to be in good agreement to the experimental results , we believe that the results should not be directly compared . In previous work [24] , we demonstrated that the Ntrans protonation state is hardly populated in asFP595 and therefore is unlikely to contribute to the observed excited state decay . Instead , the species that is predominantly responsible for the ultra-fast radiationless decay observed in the experiments is the anionic trans chromophore Atrans , as shown in detail below . Figure 3A shows the snapshot from the isomerization trajectory ( run b , Table 1 ) shortly before the surface crossing seam was encountered . After photon absorption ( blue arrow in Figures 2 and 3B ) , the chromophore spontaneously rotated around torsion A ( imidazolinone-twist ) , and the ring-bridging CH group pointed downwards ( away from His197 ) by almost 90° . The time-evolution of the S0 and S1 potential energies and of the two ring-bridging torsion angles during trans-cis photoisomerization are shown in Figures 3B and 3C . After excitation to S1 , the chromophore rapidly relaxed from the FC region into a nearby planar S1 minimum , as is evident from the decreasing S1 energy in panel b ( red curve ) . The system stayed in this planar minimum for about 0 . 2 ps; subsequently the global twisted S1 minimum was reached through rotation around torsion A ( Figures 2 , 3B , and 3C ) . The system then oscillated around this minimum until the conical intersection seam was encountered . After the surface hop to S0 , torsion B ( hydroxyphenyl-twist ) rotated after a short delay of about 0 . 5 ps . Previously , an ideal “hula-twist” isomerization mechanism of the zwitterionic chromophore was proposed based on a force field model [19] . This hula-twist involves a simultaneous rotation around both torsion angles A and B . In the QM/MM simulations of the neutral chromophore presented here , rotation around both torsions was also observed . However , twisting around torsion B was slower than around torsion A ( Figure 3C ) , and the isomerization hence proceeded via the twisted conformer shown in Figure 3A , with perpendicular imidazolinone and hydroxyphenyl moieties . The hula-twist isomerization mechanism of the zwitterion at the QM/MM level will be discussed below . During the initial equilibration of Ntrans , the hydrogen bonding network of the x-ray crystal structures of the anionic and zwitterionic chromophores ( see above ) changed to accommodate the non-native neutral chromophore . First , a stable hydrogen bond formed between the hydroxyphenyl OH group of MYG and Glu145 ( Figure 3A ) . Second , the hydrogen bonds between the imidazolinone nitrogen and Glu215 as well as between His197 and Glu215 ruptured . Interestingly , these two hydrogen bonds were transiently re-established during the end of the second isomerization phase ( blue and orange curves in Figures 3D and 3E , respectively ) in which torsion B followed torsion A . In the twisted intermediate structure , the imidazolinone nitrogen atom was sterically more exposed as compared to the planar conformation , which facilitated the formation of the hydrogen bonds . During an extended 10 ns force field simulation , the His197-Glu215 and MYG-Glu215 hydrogen bonds repeatedly broke and re-formed at a timescale of several hundred picoseconds ( data not shown ) , further underlining the flexibility of these two hydrogen bonds . Figures 3D and 3E furthermore show that during the isomerization , the hydrogen bonding network in the chromophore cavity was stable , as none of the hydrogen bonds that were established at the instant of photoexcitation ruptured . A similar stability was found in all simulations , irrespective of the chromophore conformation or protonation state . Thus , the hydrogen bonding network around the chromophore is flexible enough to allow for photoexcitation and even photoisomerization without being ruptured . For the neutral cis chromophore Ncis , five excited state simulations were initiated from the ground state trajectory ( Table 2 ) . Only two of these trajectories returned to the ground state within 10 ps ( Table 2 , runs a and b ) , which was the maximum affordable trajectory length in terms of computation time . In one of these two simulations , a spontaneous cis-to-trans photoisomerization was observed ( run a; Video S1 in Supporting Information ) . As expected , the isomerization pathway was similar to the reverse trans-to-cis pathway in that the conical intersection seam was accessed via rotation around torsion A , followed by a slightly delayed rotation around torsion B in S0 ( see Figure S4 in Supporting Information ) . However , in contrast to the activation pathway , the ring-bridging CH group rotated upwards ( i . e . , towards His197 ) . Thus , despite the anisotropic protein surrounding , both rotational orientations of the chromophore CH bridge are feasible . In the second simulation , the CI seam was also encountered after rotation around torsion A , but the chromophore returned to the initial cis conformation . For the other three trajectories , the chromophore remained trapped in a planar S1 minimum conformation near the FC region throughout the simulation ( data not shown in Table 2 ) . The starting structures of these trajectories were used for three additional simulations in which the escape from the planar S1 minimum was accelerated by means of conformational flooding [42] , [44] ( Table 2 , runs c–e ) . In these accelerated simulations , the flooding potential successfully induced the escape from the local S1 minimum , and the surface crossing seam was encountered in all cases . Isomerization was observed in two of these simulations . In total , cis-to-trans photoisomerization was seen in three out of five simulations initiated in the Ncis state . Although the number of trajectories is small , our simulations suggest that the probability for cis-to-trans photoisomerization is larger than for the reverse trans-to-cis process discussed before . To further characterize the potential energy surfaces underlying the photochemical conversion processes of the neutral chromophore ( Figure 2 ) , we have optimized three S1 minima and a minimum energy S1/S0 conical intersection ( MECI ) in the gas phase ( see Supporting Information , Figure S1 , Tables S1 and S5 ) . We found a local planar S1 minimum for the trans isomer 76 kJ/mol below the FC region . The structure corresponding to the global S1 minimum is twisted around torsion A by 85° and lies −131 kJ/mol relative to the FC region . The structure of the nearby MECI is twisted around torsion A by 81° . The MECI is energetically lower than the FC region by 62 kJ/mol , and the CI seam is therefore readily accessible . Twisting around torsion B instead of torsion A also leads to a local minimum on S1 , whose energy is 28 kJ/mol below the FC energy . The MD simulations reflect this surface topology . Immediately after excitation , the system relaxed from the FC region to the global S1 minimum by rotation around torsion A ( Figure 3C ) . The system oscillated around this minimum until the conical intersection seam was encountered , with a subsequent surface hop back to S0 . The gradients on S0 and S1 are almost parallel at the CI , which indicates that the CI is sloped . The gradient difference vector and the derivative coupling vector that span the branching space largely correspond to skeletal deformations of the imidazolinone moiety ( see Supporting Information , Figure S1 ) . Thus , as shown in Figure 2 , the rotation coordinate around torsion A is parallel to the seam and does not lift the S1/S0 degeneracy . The seam is accessible anywhere along this torsional rotation coordinate , and therefore such torsional rotation is in principle not essential for the radiationless decay . The extended surface crossing seam parallel to the isomerization coordinate accounts for the low isomerization quantum yield seen in our simulations . In most of our MD simulations , the seam was encountered rather “early” along the torsional rotation coordinate ( Figure 2 ) , and the system thus returned to the ground state before overcoming the S0 barrier maximum . In these cases , relaxation on S0 after the surface hop led back to the starting conformation . To elucidate the influence of the protein environment on the photoisomerization process of the chromophore , we have re-calculated the S1 and S0 energies along two excited state trajectories ( run b , Table 1 and run a , Table 2 ) in the gas phase . In these simulations , the chromophore followed the same trajectory as before , but did not interact with the rest of the system ( protein and solvent surrounding ) . We have not attempted to further characterize the electrostatic influence of the surrounding by , e . g . , pKa calculations . Figure 4A and 4C show the obtained energy traces . In the protein , both S1 and S0 are stabilized with respect to the gas phase . For the trans-to-cis isomerization process , the protein stabilized the energies of the S1 and S0 states on average by −339 kJ/mol and −307 kJ/mol , respectively . For the cis-to-trans process , the average stabilization energies were −173 kJ/mol and −126 kJ/mol , respectively . Thus , the protein ( and solvent ) environment favors S1 over S0 by about 30–50 kJ/mol . Figure 4B and 4D show the energy differences between the protein and the gas phase , ΔE = E ( protein ) −E ( gas phase ) . The S1 stabilization was rather strong at the surface crossing seam ( Figure 4 ) . We found S1 to be stabilized stronger than S0 by 78 kJ/mol and 93 kJ/mol at the conical intersection in both MD simulations . In summary , the protein environment energetically stabilizes S1 more than S0 , thereby enhancing fast radiationless decay . In total , 20 simulations of the anionic chromophore protonation state were carried out , 10 of which were initiated in the trans conformation and the other 10 were initiated in the cis conformation . Ultra-fast radiationless deactivation was observed in all 20 trajectories ( Table S6 in Supporting Information ) . However , trans-cis photoisomerization never occurred . A simple exponential fit to the S1 lifetimes of the trans anion yielded a decay time of τ = 0 . 45 ps ( σ+ = 0 . 19 ps , σ− = 0 . 12 ps ) . Since Atrans is one of the two dominant protonation states in the off state besides Ztrans [24] , we expect Atrans to significantly contribute to the experimentally observed decay . The measured decay time of 0 . 32 ps [43] agrees well with the decay time derived from the simulations . For Acis , an excited state decay time of τ = 1 . 81 ps ( σ+ = 0 . 77 ps , σ− = 0 . 48 ps ) was obtained , which is about four times longer as compared to the decay time of Atrans . Figure 5A shows the conical intersection geometry adopted during a typical trajectory . In contrast to the neutral chromophore , the CI seam was accessed through a phenoxy-twist ( rotation around torsion B , see Figure 5C ) , and the CH bridge remained in the imidazolinone plane . Shortly after excitation , rotation around torsion B drove the system towards the surface crossing seam ( Figure 5B and 5C ) . Back on S0 , the system returned to the initial configuration . The hydrogen bonding network in the chromophore cavity was very similar to the network observed in the x-ray crystal structures and remained stable during the excited state MD simulations . Since rotation around torsion B does not lead to trans-cis isomerization and rotation around torsion A did not occur , the quantum yield for the isomerization of the anion was zero in our simulations . However , due to the limited number of trajectories ( 20 ) , we cannot rule out the trans-cis photoisomerization of the anion . Our results agree with recent MRPT2 computations of Olsen and coworkers on an anionic DsRed-like model chromophore in the gas phase [45] , who have shown that the imidazolinone-twisted S1/S0 CI ( i . e . , twisted around torsion A ) , which leads to cis-trans isomerization , is disfavored by more than 150 kJ/mol as compared to the phenoxy-twisted CI . The latter CI is lower in energy than the planar S1 minimum by 9 . 2 and 48 . 1 kJ/mol at the MRPT2 and CASSCF levels , respectively [45] . In our calculations , this energy difference is about 30 kJ/mol ( see Supporting Information , Table S2 ) , in qualitative agreement with Olsen and coworkers . Due to the slightly different chromophores in DsRed and asFP595 , a quantitative agreement cannot be expected . The deviation from planarity of the trans chromophore observed in the crystal structures was speculated to enhance ultra-fast deactivation [24] , [43] . Indeed , the difference between the S1 lifetimes of the cis and trans conformers seen in our simulations can be attributed to steric constraints imposed by the protein matrix . In the trans conformation the phenoxy-ring deviates from planarity by about 20° , whereas the cis chromophore is essentially planar [19] , [24] . We observed in our simulations that only a slight additional twisting was required for the trans conformer to reach the surface crossing seam . Thus , the pre-twisting of the phenoxy-moiety due to the protein matrix facilitated fast internal conversion of the trans conformer . As shown in Supporting Information ( Figure S2 , Table S2 ) , we have optimized the S1/S0 MECI , a planar and two twisted S1 minima ( imidazolinone-twist and phenoxy-twist ) for an isolated anionic chromophore . The planar minimum lies 31 kJ/mol below the FC point . The structure of the global S1 minimum is twisted around torsion B by 269° and its energy lies −82 kJ/mol relative to the FC energy . The MECI structure is also twisted about torsion angle B by 269° and is energetically lower than the FC point by 61 kJ/mol , thus explaining the ultra-fast decay seen in our MD simulations . Twisting around torsion A leads to a local S1 minimum that is 46 kJ/mol below the FC point . The CI of the anion is sloped , and the gradient difference vector corresponds to a skeletal deformation of the imidazolinone ring , analogous to the neutral chromophore ( see above ) . In contrast to the neutral chromophore , the derivative coupling vector involves rotation around torsion B . However , the amplitude of this vector is small . Thus , the two electronic states may remain close in energy along torsion B , allowing the system to decay at various phenoxy-twist angles . To study the influence of the protein matrix on the deactivation process of the anionic chromophore , we have re-evaluated the S0 and S1 energies along two representative excited state trajectories ( trans and cis ) with all interactions between the QM atoms of the chromophore and the MM surrounding switched off , as was done for the neutral chromophores ( see above ) . As Figure 5D and 5E show , the protein ( and solvent ) environment stabilizes the chromophore with respect to the gas phase . The S0 and S1 states of the trans chromophore are strongly stabilized by −840 kJ/mol and −832 kJ/mol , respectively . The S0 and S1 states of the cis chromophore were stabilized relative to the gas phase by −407 kJ/mol and −433 kJ/mol , respectively , during a representative Acis trajectory ( Figure S5 in Supporting Information ) . Similar to the neutral chromophore , the protein environment favors the trans conformation over cis . Before reaching the CI seam , the S0 and S1 states of the chromophore were stabilized to the same extent . At the CI , however , the protein environment lowered the energy of the S1 state more strongly than the energy of the S0 state by 26 kJ/mol and 20 kJ/mol for Atrans and Acis , respectively . This preferential stabilization of S1 enhanced the ultra-fast radiationless deactivation seen in our MD simulations . Ten simulations were carried out for the zwitterion . Five simulations were started in the Ztrans conformation , and the other five simulations were initiated in the Zcis conformation . No decay back to the ground state was observed within a maximum trajectory length of 10 ps , neither for Ztrans nor for Zcis . The chromophore did not escape from a planar S1 minimum in the vicinity of the FC region in any of the excited state simulations . This suggests that Ztrans and Zcis could be the fluorescent species in asFP595 , although the measured fluorescence lifetime of 2 . 2 ns [43] is still much longer than our maximal trajectory length ( 10 ps ) . For Ztrans and for Zcis , we have carried out three additional simulations each , in which we applied the conformational flooding technique [42] , [44] , [46] to accelerate the escape from the S1 minimum in an unbiased manner ( see Materials and Methods ) . The results of these flooding simulations are shown in the Supporting Information ( Figure S6 , Text S3 ) . The flooding potential induced isomerization of the chromophore , which followed a hula-twist pathway , in agreement with our previous work [19] . From the flooding simulations , we obtained a lower bound for the excited state lifetime of the order of 1 ns . The qualitative agreement with the measured fluorescence decay time of 2 . 2 ns provides further support for the assignment of the zwitterion as the fluorescent species . The results thus obtained for the zwitterionic chromophore suggest that a hula-twist CI may be spontaneously accessed if the trajectories were extended to ( significantly ) longer times , i . e . , nanoseconds . However , at the nanosecond timescale , fluorescence ( and not isomerization ) will be the predominant decay process . Note that in Ref . [19] , the energy barrier for hula-twist isomerization of the zwitterion was underestimated , thus favoring this isomerization over fluorescence . In the next paragraph , we characterize the CI and show that the minimum energy crossing point for the zwitterionic chromophore has a high energy , thus hampering radiationless decay through hula-twist isomerization . To characterize the topology of the S1 and S0 potential energy surfaces and of the conical intersection that occurs between them , multiconfigurational calculations were carried out for the isolated zwitterionic chromophore , as was also done for the other protonation states ( see above ) . We optimized a planar S1 minimum and a hula-twist S1/S0 MECI ( see Supporting Information , Figure S3 , Tables S3 and S4 ) . In contrast to the anion and the neutral chromophore , no twisted S1 minima were found . The gradient difference vector and the derivative coupling vector at the MECI do not involve torsional rotation of either torsion A or torsion B , indicating that the CI seam lies parallel to the isomerization coordinate . The MECI lies 70 kJ/mol above the planar S1 minimum and 23 . 4 kJ/mol above the FC energy . Hence , in contrast to the anion and the neutral chromophore , no low-lying CI is present for the zwitterion , demonstrating that radiationless decay in the gas phase cannot occur in an unactivated manner . For the CI seam to become accessible , a significant stabilization of S1 relative to S0 by the protein environment would be required . However , as shown in Figure S6 in Supporting Information , the protein surrounding does not reduce the S1/S0 energy gap anywhere along the isomerization coordinate . Our results suggest that the zwitterionic chromophore is potentially fluorescent , irrespective of the conformation . However , the x-ray analysis of the emitting species has shown that only the cis chromophore fluoresces , whereas the trans chromophore is dark [19] . A possible explanation for this discrepancy is the presence of an alternative deactivation channel that does not involve isomerization . This deactivation pathway would have to be more easily accessible for Ztrans than for Zcis . Only the latter would therefore be trapped in S1 and fluoresce . The hydrogen bond between the NH group of the imidazolinone ring and Glu215 strongly suggests that the alternative decay involves an excited state proton transfer ( ESPT ) . Such ESPT would quench the fluorescence , because the resulting anion rapidly deactivates , as shown above . However , by including only the chromophore into the QM subsystem , we have excluded the possibility of observing such ESPT in our QM/MM simulations . To identify possible ESPT pathways , we have carried out extended force field MD simulations of both Ztrans and Zcis and analyzed the relevant hydrogen bonds . Figure 6A shows that , during the simulation of Ztrans , two stable hydrogen bonds were formed between the protonated OH group of Glu215 and His197 as well as between the NH proton of MYG and Glu215 . These two hydrogen bonds allow for a proton transfer from Ztrans to the rapidly deactivating Atrans . The OH proton of Glu215 could transfer to the Nδ atom of His197 , with a simultaneous or subsequent transfer of the NH proton of the imidazolinone moiety to Glu215 . In contrast , during the force field simulation of Zcis , the MYG-Glu215 hydrogen bond remained intact , whereas the Glu215-His197 hydrogen bond broke after about 1 ns ( Figure 6B ) . This differential behavior of Ztrans and Zcis was confirmed by two additional independent MD simulations ( data not shown ) . Based on these results , we assume that only the trans zwitterion can be converted to the anion through a short proton wire . Therefore , an ultra-fast deactivation channel is available only for the trans zwitterion , and not for the fluorescent cis zwitterion . From the presence of the hydrogen bonding network in our force field trajectories , we do not obtain insights into the energetics of proton transfer . Studying these transfers along the identified pathways in asFP595 , both in the ground and the excited state , is beyond the scope of the present work . Having established that fluorescence can only originate from the zwitterionic chromophores , the structure of the irreversibly fluorescent state of asFP595 can now be predicted . We expect that intense irradiation over a prolonged period of time leads to a decarboxylation of the Glu215 side chain ( Figure 7 ) . Such process is also known to occur in GFP [47] , [48] and DsRed [49] . A decarboxylated Glu215 can no longer take up the NH proton from the zwitterionic chromophores . The absence of an S1 ESPT deactivation channel leads to fluorescence . The finding that the irreversibly fluorescent state cannot be switched off by light ( see Introduction ) is corroborated by our observation that even in the flooding-induced isomerization trajectories , no radiationless decay back to S0 occurred ( see Figure S6 in Supporting Information ) . Figure 8 summarizes our proposed photoswitching mechanism . The proton distribution at the active site of asFP595 governs the photochemical conversion pathways of the chromophore in the protein matrix . Changes in the protonation state of the chromophore and several proximal amino acids lead to different photochemical states , which are all involved in the photoswitching process . These photochemical states are ( i ) the neutral chromophores Ntrans and Ncis , which can undergo trans-cis photoisomerization , ( ii ) the anionic chromophores Atrans and Acis , which rapidly undergo radiationless decay after excitation , and ( iii ) the potentially fluorescent zwitterions Ztrans and Zcis . The overall stability of the different protonation states is controlled by the isomeric state of the chromophore . To switch from the non-fluorescent off to the fluorescent on state , the chromophore has to isomerize from trans to cis . As shown in Figure 8 , this photoisomerization was only observed for the neutral form of the chromophore ( Ntrans→ Ncis ) . However , the Ntrans state is only marginally populated [24] , thus explaining the low quantum yield for switching asPF595 to the on state . Moreover , green light is used to switch on asFP595 , whereas the absorption maximum of Ntrans is significantly blue-shifted . The use of blue light , however , would lead to an unfavorable Ncis→ Ntrans back-reaction due to the absorption of the blue light by Ncis . The reverse cis-to-trans isomerization , i . e . , on-to-off switching , requires the excitation of the neutral cis chromophore . Since Ncis is the predominant protonation state in the cis conformation , the efficiency for the on-to-off switching is high , as observed experimentally [19] . Fluorescence originates from Zcis , which is also hardly populated , like Ntrans [24] . Taken together , the low populations of the involved states give rise to the low overall fluorescence quantum yield of asFP595 . The insight obtained from our simulations can be exploited for a targeted improvement of asFP595 for applications as a fluorescence marker in optical microscopy . In particular , to improve the signal-to-noise ratio , a higher fluorescence quantum yield is desired . Our results suggest that one way to enhance fluorescence would be to increase the stability of Zcis , e . g . , by introducing additional hydrogen bond donors near the phenoxy-group of the chromophore . Another possibility would be to implement an internal proton relay , similar to that in GFP . In GFP , a hydroxyphenyl-bound serine residue , a water molecule , and a glutamic acid form an internal proton wire that enhances the formation of the fluorescent Acis chromophore from the neutral chromophore via ESPT . GFP has a significantly higher fluorescence quantum yield as compared to asFP595 [50]–[54] . Although the fluorescent species in asFP595 and GFP are different , the similarity between the chromophores suggests that implementing a similar internal proton relay in asFP595 might increase its fluorescence quantum yield . Note , however , that due to the competition between different reaction channels in asFP595 , shifting the relative populations of the protonation states will also affect the photoswitchability . For example , increasing the population of the fluorescent species at the cost of the neutral species will decrease the back-isomerization efficiency . Thus , a compromise has to be found between increasing the fluorescence quantum yield on the one hand while maintaining the photoswitchability of asFP595 on the other hand . Understanding the excited state dynamics of ultra-fast photoactivated processes in biomolecular systems such as the reversible photoswitching of the fluorescent protein asFP595 represents a major challenge , but is essential to unveil the underlying molecular mechanisms . In the present work we have demonstrated that by using an ab initio QM/MM excited state molecular dynamics strategy together with explicit surface hopping , it is not only possible to explain at the atomic level experimentally accessible quantities of asFP595 , such as quantum yields and excited state lifetimes , but also to make predictions that are rigorously testable by experiment , such as the nature of the irreversibly fluorescent state or possible improvement of the fluorescence quantum of this protein . We have revealed that the protonation pattern of the chromophore cavity determines the photochemical behavior of asFP595 , and that photon absorption can lead to trans-cis isomerization of the neutral chromophore . Based on our results , we suggest that a reversibly switchable protein must fulfill three criteria . First , to enable switching , trans-cis photoisomerization is necessary . Second , this photoisomerization has to be coupled to proton transfer events , that is , the preferred protonation state is different for the two conformers . Third , only one of the two isomers fluoresces , while the other can undergo rapid radiationless decay . Interestingly , other fluoroproteins contain a chromophoric moiety similar to asFP595 , like , e . g . , Dronpa [8] , [55] , [56] , DsRed [49] , [57] , Kaede or KiKG [58] , [59] , eqFP611 [60]–[63] , Rtms5 [64] , and HcRed [65] . In these structures cis or trans conformations of the chromophore have been observed . Our simulations on asFP595 suggest that chromophore photoisomerization could also be possible in these fluoroproteins . In particular , the high structural similarity between asFP595 and the reversibly photoswitchable Dronpa protein suggests that the Dronpa chromophore can undergo trans-cis photoisomerization as well . Indeed , the crystal structure of the isomerized state of Dronpa was solved very recently [66] and confirms our prediction . Furthermore , recent experiments on Dronpa [67] , [68] also provide strong support for our proposed protonation/deprotonation mechanism . The similarity between the chromophores in a variety of fluoroproteins suggests that during molecular evolution , the ( p-hydroxybenzylidene ) imidazolinone chromophoric moiety served as a template and that the photochromic properties - and thus the function - was fine-tuned by the protein environment .
To model the dynamics of the photoactivated asFP595 chromophore , we carried out excited state QM/MM [69] molecular dynamics ( MD ) simulations . Energies and forces of the excited ( S1 ) and ground ( S0 ) states were calculated on-the-fly at the CASSCF/3-21G level of theory with a reduced active space of 6 electrons in 6 orbitals . The studied processes start in S1 and end up in S0 . The transitions ( hops ) between the two energy surfaces were modeled by surface selection at the conical intersection ( CI ) seam ( see Supporting Information , Text S4 for details ) . This diabatic hopping procedure is known to potentially underestimate the surface hopping probability [70] , [71] . However , our experience has shown that in large polyatomic systems , low-lying CI hyperlines are easily accessed due to their high dimensionality ( N−2 , where N is the number of internal degrees of freedom ) . We therefore expect that , for the case at hand , diabatic surface hopping is sufficiently accurate . For all MD simulations , Gromacs 3 . 3 [72] with an interface [70] to Gaussian03 [73] was used . Modifications were made to the one-electron integral routines of Gaussian03 to account for the polarization of the CASSCF wavefunctions due to the pointcharges on the MM atoms . The reduced active space used in the MD simulations was validated using geometry optimizations of relevant excited state minima and minimum energy crossing points for the isolated chromophores . The full CASSCF active space for the π-system of the asFP595 chromophore would require 18 π electrons in 16 π orbitals , rendering geometry optimizations prohibitively expensive . To make the respective calculations feasible , we have restricted the number of excitations in the wavefunction by employing the RASSCF method ( see Supporting Information , Text S1 ) [74]–[76] . We have characterized minima and conical intersections at the RASSCF ( 18 , 7+4+5 ) [2 , 2]/6-31G* level of theory . The final 6 electron , 6 orbital active space used in the CASSCF QM/MM MD simulations was selected from the RASSCF calculations such as to enable the simultaneous description of the electronic ground and first excited states . This approach allowed us to compute on-the-fly QM/MM trajectories using a CASSCF/3-21G wavefunction with an accuracy that is comparable to RASSCF/6-31G* at a drastically reduced computational cost . All MD simulations were based on the crystal structure of a mutant of asFP595 [24] . The mutant has similar photochromic properties as the wild-type , but high-resolution crystal structures are available for both the trans and the cis conformations . For both trans and cis isomers , MD simulations were initiated for three different chromophore protonation states; anionic ( A ) , neutral ( N ) , and zwitterionic ( Z ) . The simulations were performed in a rectangular periodic box of about 730 nm3 . Each system contained about 21 , 500 TIP4P water molecules , including 340 crystallographic water molecules . After assigning the protonation pattern of the chromophore pocket , all other polar , aromatic , and aliphatic hydrogen atoms were added to the protein with the HB2MAK [77] routine of WHATIF [78] . To each of the systems , sodium and chloride ions were added at physiological concentration to compensate for the net positive charge of the protein . The actual number of ions varied with the total charge of the protein , which differed for the chosen protonation patterns of the chromophore cavity . The final systems comprised about 90 , 000 atoms . Prior to the MD simulations , the systems were energy minimized ( 1000 steps steepest descent ) . Subsequently , force field based MD simulations were carried out . First , 500 ps MD simulations with harmonic position restraints on all protein heavy atoms ( force constant 1000 kJ mol−1 nm−2 ) were carried out to equilibrate the solvent and the ions . Then , 500 ps free MD simulation were run at 300 K . All simulations were carried out using the OPLS all-atom force field [79] . Parameters for the chromophore were taken from [19] . The simulations were run at constant temperature and pressure by coupling to an external heat bath ( τT = 0 . 1 ps , τp = 1 ps ) [80] . In the force field simulations , LINCS [81] was used to constrain bond lengths , thus allowing a time step of 2 fs . SETTLE [82] was applied to constrain the internal degrees of freedom of the water molecules . A twin-range cut-off was used for the Lennard-Jones interactions . Interactions within 1 . 0 nm were updated at every step , whereas interactions between 1 . 0 nm and 1 . 6 nm were updated every ten steps . Coulomb interactions within 1 . 0 nm were computed at each step as well . Beyond this cut-off , the particle-mesh Ewald ( PME ) method [83] with a grid spacing of 0 . 12 nm was used . The QM subsystem in the excited state QM/MM simulations comprised the chromophore; the rest of the system was described by the OPLS all-atom force field ( Figure 1 ) . The N−Cα bond of Gly65 and the Cα−Cβ bond of Met63 , respectively , were replaced by a constraint , and the QM part was capped with hydrogen link atoms [84] . The forces on the link atoms were distributed over the two heavy atoms at the boundary according to the lever rule . The QM system experienced the Coulomb field of all MM atoms within a sphere of 1 . 6 nm , and Lennard-Jones interactions between QM and MM atoms were included . For the QM/MM simulations , a time step of 1 fs was used , and no constraints were applied in the QM subsystem . Prior to the excited state simulations , the systems were simulated in the ground state , first for 1 ps at the RHF/3-21G//OPLS level of theory and then for an additional 2 . 5 ps at the CASSCF ( 6 , 6 ) /3-21G//OPLS level . From the latter trajectory , frames at equal time intervals ( Δt = 0 . 5 ps ) were used as starting configurations for the excited state MD simulations . To accelerate the escape from the S1 minima , additional excited state conformational flooding [42] , [44] simulations were carried out for the Ncis , Zcis , and Ztrans systems , respectively . A Gaussian-shaped flooding potential Vfl was constructed from a principal component analysis ( PCA ) [85]–[87] of free QM/MM S1 simulations . For all systems , the covariance matrix of the motion of all QM atoms , except the exocyclic carbonyl group of the chromophore and the hydroxyphenyl-OH proton ( for Ncis ) , was computed from two independent 10 ps S1 trajectories . All internal degrees of freedom of the chromophore were affected by the flooding potential to ensure that the escape from the initial minimum was accelerated in an unbiased manner . Unless stated differently , adaptive flooding [42] , [46] with target destabilization free energies of 300 kJ/mol ( Ztrans ) or 100 kJ/mol ( Ncis ) and time constants of τ = 0 . 1 ps was applied . In all flooding simulations , Vfl was switched off as soon as the conical intersection seam was encountered to allow for an unperturbed relaxation on the ground state potential energy surface . In all our simulations of the anionic and zwitterionic chromophores , we considered both the cationic and neutral protonation states of the imidazole side chain of His197 , which lies coplanar to the chromophore ( Figure 1 ) . Recent Poisson-Boltzmann electrostatics calculations have shown that slight structural fluctuations in the local environment change the preferred protonation of the His197 imidazole ring between cationic and neutral and that both protonation states are populated at room temperature [24] . Thus , we ran the same number of trajectories with a cationic and with a neutral His197 . As shown in Supporting Information ( Text S2 ) , the His197 protonation state had no major influence on the decay mechanisms and lifetimes . | Proteins whose fluorescence can be reversibly switched on and off hold great promise for applications in high-resolution optical microscopy and nanotechnology . To systematically exploit the potential of such photoswitchable proteins and to enable rational improvements of their properties requires a detailed understanding of the molecular switching mechanism . Here , we have studied the photoswitching mechanism of the reversibly switchable fluoroprotein asFP595 by atomistic molecular dynamics simulations . Our simulations explain measured quantum yields and excited state lifetimes , and also predict the structures of the hitherto unknown intermediates and of the irreversibly fluorescent state . Further , we find that the proton distribution in the active site of the asFP595 controls the photochemical conversion pathways of the chromophore in the protein matrix . Our results show that a tight coupling between trans-cis isomerization of the chromophore and proton transfer is essential for the function of asFP595 . The structural similarity between asFP595 and other fluoroproteins suggests that this coupling is a quite general mechanism for photoswitchable proteins . These insights can guide the rational design and optimization of photoswitchable proteins . | [
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] | 2008 | Chromophore Protonation State Controls Photoswitching of the Fluoroprotein asFP595 |
Neurons utilize bursts of action potentials as an efficient and reliable way to encode information . It is likely that the intrinsic membrane properties of neurons involved in burst generation may also participate in preserving its temporal features . Here we examined the contribution of the persistent and resurgent components of voltage-gated Na+ currents in modulating the burst discharge in sensory neurons . Using mathematical modeling , theory and dynamic-clamp electrophysiology , we show that , distinct from the persistent Na+ component which is important for membrane resonance and burst generation , the resurgent Na+ can help stabilize burst timing features including the duration and intervals . Moreover , such a physiological role for the resurgent Na+ offered noise tolerance and preserved the regularity of burst patterns . Model analysis further predicted a negative feedback loop between the persistent and resurgent gating variables which mediate such gain in burst stability . These results highlight a novel role for the voltage-gated resurgent Na+ component in moderating the entropy of burst-encoded neural information .
Real-time signal detection in uncertain settings is a fundamental problem for information and communication systems . Our nervous system performs the daunting task of extracting meaningful information from natural environments and guides precise behaviors . Sensory neurons for instance use efficient coding schemes such as bursting that aid in information processing [1] . Mathematical models of bursting have helped explain the basic structure of an underlying dynamical system as one in which a slow process dynamically modulates a faster action potential/spike-generating process , leading to stereotypical alternating phases of spiking and quiescence [2 , 3] . The so-called recovery period of the slow process governs the intervals between bursts which is often susceptible to random perturbations . Uncertainty in spike/burst intervals can alter the timing precision and information in a neural code [4] . Consequently , ionic mechanisms that modulate the recovery of membrane potential during spike/burst intervals , can play a role in maintaining the stability of these timing events and aid neural information processing . Here we examined a candidate mechanism involving neuronal voltage-gated Na+ currents for a role in the stabilization of burst discharge ( durations and intervals ) and noise modulation . Voltage-gated Na+ currents are essential for spike generation in neurons [5] . The molecular and structural diversity of Na+ channels and the resultant functional heterogeneity and complexity , suggest their role beyond mere spike generation [6] . For instance , in addition to the fast/transient Na+ current ( INaT ) mediating action potentials , a subthreshold activated persistent Na+ current ( INaP ) participates in the generation of subthreshold membrane oscillations ( STO ) ( e . g . , see [7] ) . These oscillations can lead to membrane resonance by which a neuron produces the largest response to oscillatory inputs of some preferred frequency [8 , 9] . Neurons utilize this mechanism to amplify weak synaptic inputs at resonant frequencies [10] . The slow inactivation and recovery of INaP further provides for the slow process required for rhythmic burst generation [11–13] and therefore can contribute to efficient information processing in multiple ways . However , during ongoing activity , random membrane fluctuations can alter the precision and order of burst timings which can distort/diminish the information in neural code . Here , we provide evidence that a frequently observed resurgent Na+ current ( INaR ) , often coexistent with INaP might be a mechanism by which neurons stabilize burst discharge while maintaining its order and entropy . The INaR , in neurons and other excitable cells is an unconventional Na+ current which physiologically activates from a brief membrane depolarization followed by repolarization , such as during an action potential [11 , 14–17] . In the well-studied neuronal Nav1 . 6-type Na+ channels , such a macroscopic INaR is biophysically suggested to occur from an open-channel block/unblock mechanism [18 , 19] . Consequently , INaR is known to mediate depolarizing after-potentials and promote high-frequency spike discharge in neurons [14 , 20–24] . Sodium channels containing the Nav1 . 6 subunits carry all three types of sodium currents and are widely distributed in the central and peripheral neurons and participate in burst generation [14 , 25] . Sodium channelopathy involving alteration in INaR and INaP , and its association with irregular firing patterns and ectopic bursting in disease ( e . g . , [26–29] ) , prompted us to investigate distinct roles for these Na+ currents in regulating bursting in sensory neurons . Lack of suitable functional markers and experimental tools to dissociate the molecular mechanism of INaR from INaP , led us to use computational modeling and dynamic-clamp electrophysiology to examine a role for INaR in burst control; however see [19 , 23 , 24] . Although existing Markovian models model a single channel Nav1 . 6 type INa using a kinetic scheme ( e . g . , [30 , 31] ) , they have limited application for studying exclusive roles of INaR and INaP in the control of neural bursting; however see [13 , 23 , 32 , 33] . Here , we developed a novel mathematical model for INaR using the well-known Hodgkin-Huxley ( HH ) formalism which closely mimics the unusual voltage-dependent open-channel unblocking mechanism . We integrated the model INaR into a bursting neuron model with INaP that we previously reported to study their exclusive roles in burst control in the jaw proprioceptive sensory neurons in the brainstem Mesencephalic V ( Mes V ) nucleus [8] . To validate model predictions , we used in vitro dynamic-clamp electrophysiology , and theoretical stability analysis . Using these approaches , we identify a novel role for INaR in stabilizing burst discharge and noise modulation in these sensory neurons ( see Fig 1 for a workflow and approaches used ) .
As noted earlier , the total INa in our model has the novel resurgent component , INaR; the transient and persistent components are similar to our previous report [8] . Fig 3A ( left panel ) illustrates a well-accepted mechanism of Na+ resurgence [30] , wherein a putative blocking particle occludes an open channel following brief depolarization such as during an action potential; subsequently upon repolarization , a voltage dependent unblock results in a resurgent Na+ current . Our INaR formulation recapitulates this unusual behavior of Na+ channels using nonlinear ordinary differential equations for a blocking variable ( br ) and a competing inactivation ( hr ) ( see Methods ) . Different from a traditional activation variable of an ionic current in HH models , we formulated the INaR gating using a term ( 1−br ) to enable an unblocking process ( see INaR equation in Methods and in Fig 3A ) . Here , the model variable br reflects the fraction of channels in the blocked state at any instant , and ‘1’ denotes the maximum proportion of open channels , such that ( 1−br ) represented the fraction of unblocking channels . Such a formulation enabled mimicking the open-channel unblocking process as follows: Normally the br is maintained ‘high’ such that ( 1−br ) term is small and therefore no INaR flows , except when a spike occurs , which causes br to decay in a voltage-dependent manner . This turns on the INaR which peaks as the membrane voltage repolarizes to ~ -40 mV , following which the increasing inactivation variable hr gradually turns off the INaR . The steady-state voltage dependency of unblock , ( 1−br∞ ( V ) ) , and the competing inactivation ( hr∞ ( V ) ) in the model are shown in Fig 3A ( middle panel ) , along with the equation for INaR; the magenta shaded region highlights the voltage-dependency of INaR activation as posited to occur during open-channel unblocking . In Fig 3A ( right panel ) , we show simulated INaR ( in magenta ) , peaking during the recovery phase of spikes ( in black ) . In Fig 3B ( I . ) , we reproduced experimentally observed INa during voltage-clamp recording and highlight the resurgent component in both model ( left ) and experiment ( right ) , ( inset shows experimental protocol; also see legend and Methods ) . A comparative current-voltage relationship for the model and experiments is shown in Fig 3B ( II . ) ; also see S3 Fig for detailed kinetics of model INaR . Taken together , the above tests and comparisons ensured the suitability of our model for further investigation of INaR mediated burst control . Given that INaR is activated during the recovery phase of an action potential , physiologically , any resulting rebound depolarization may control the spike refractory period , and increase spike frequency and burst duration [22 , 37] . We tested this by selectively increasing the maximal resurgent conductance gNaR in our model neuron simulation and validated the predictions using dynamic-clamp experiments as shown in Fig 4A and 4B . In parallel , we also exclusively modified the maximal persistent conductance gNaP using model simulations and verified the effects using dynamic-clamp experiments as shown in Fig 4C and 4D . We only focused on rhythmically bursting Mes V neurons and quantified the burst timing features including the inter-burst intervals ( IBIs ) , burst duration ( BD ) and inter-spike intervals ( ISIs ) as illustrated in the boxed inset in Fig 4 . Figure panels 4e –j show a comparison of the exclusive effects of gNaR versus gNaP on each of these burst features . These experimental manipulations using model currents and quantification of resulting burst features revealed significant differences and some similarity between the action of gNaR and gNaP in burst control . First , increases in gNaR resulted in longer IBIs , which was in contrast with the effects of the persistent Na+ conductance , gNaP , which decreased IBIs ( effect highlighted with red double arrows in Fig 4A–4D and quantified using box plots in Fig 4E and 4H ) . Alternatively , increasing gNaR reduced ISIs , while gNaP had the opposite effect on these events , resulting in an overall increase in ISIs with gNaP increases ( see Fig 4F and 4I ) ; whereas gNaR and gNaP had similar effect in increasing BDs ( see Fig 4G and 4J ) . Box plots in Fig 4E–4J show 1st , 2nd ( median ) and 3rd quartiles; error bars show 1 . 5x deviations from the inter-quartile intervals . For each case ( or cell ) , n values represented events ( ISI , IBI and BD ) and reported here are within-cell effects for different gNaR and gNaP applications during 20 sec step-current stimulation . For the gNaR applications ( or series ) , IBI mean ± std were 210 . 49 ± 69 . 33 ( control , n = 7 ) , 450 . 29 ± 116 . 56 ( 1x , n = 26 ) and 1074 . 06 ± 199 . 17 ( 2x , n = 11 ) , and for gNaP applications , these values were 1448 . 71 ± 450 . 92 ( control , n = 4 ) , 910 . 10 ± 527 . 27 ( 1x , n = 8 ) and 644 . 35 ± 234 . 19 ( 1 . 5x , n = 6 ) . For gNaR series , ISI mean ± std were 20 . 26 ± 1 . 65 ( control , n = 59 ) , 12 . 56 ± 2 . 25 ( 1x , n = 646 ) and 10 . 56 ± 1 . 81 ( 2x , n = 783 ) , and for gNaP series these values were 12 . 76 ± 1 . 13 ( control , n = 159 ) , 12 . 57 ± 1 . 05 ( 1x , n = 611 ) and 13 . 56 ± 1 . 42 ( 1 . 5x , n = 503 ) . For gNaR series , BD mean ± std were 176 . 36 ± 69 . 57 ( control , n = 8 ) , 300 . 56 ± 77 . 90 ( 1x , n = 27 ) and 671 . 16 ± 285 . 96 ( 2x , n = 12 ) , and for gNaP series these values were 346 . 59 ± 88 . 39 ( control , n = 4 ) , 571 . 30 ± 197 . 56 ( 1x , n = 8 ) and 1334 . 20 ± 592 . 59 ( 1 . 5x , n = 7 ) . Treatment effects and group statistics for all the replicates showing the above effects across six Mes V bursting neurons , each from a different animal are summarized in S2 Table . A one-way ANOVA was used to test the treatment effects of gNaR and gNaP applications , and when significant , a post hoc two-sample Student t-test was used for group comparisons between control and 1x , 1x and 2x , and control and 2x for gNaR cases , and between control and 1x , 1x and 1 . 5x , and control and 1 . 5x for gNaP cases . Asterisks above box plots between groups in Fig 4E–4J indicate p<0 . 05 using two-sample Student t-tests for post-hoc comparisons . Furthermore , as shown in Fig 4 , the model simulations predicted consistent effects ( note white circles denote predicted values in panels e—j ) with dynamic-clamp experiments , making the model suitable for further analysis of INaR/INaP mediated mechanism of burst control ( see white circles in panels 4e - j ) . In two additional bursting neurons , we also conducted gNaR and gNaP subtraction experiments which showed consistent reverse effects of additions ( see S4 Fig and legend ) . In the gNaP subtraction experiment , note that a -2x gNaP resulted in the abolition of bursting and subthreshold oscillations as shown in the figure . Such an effect was reproduced in the neuron model by setting gNaP = 0 , as shown in S4C Fig . To test whether INaR and INaP differentially modulate the regularity and precision of spike timing we observed the inter-event intervals ( or IEIs ) during real-time addition of gNaR and gNaP . As shown in Fig 5A and 5B , addition of gNaR improved the regularity of the two types of events: the longer IBIs and the shorter ISIs , whereas , addition of gNaP did not show such an effect . Note that application of real-time gNaR improved both the timing precision and regularity of IBIs ( compare left and right panels of Fig 5A ) , while application of dynamic-clamp gNaP did not seem to affect either of these features ( compare left and right panels of Fig 5B ) . Furthermore , in Fig 5C we highlight that gNaR application also improved spike-to-spike regularity of ISIs within bursts ( shown are two representative bursts for gNaR ( Fig 5C , left panel ) versus gNaP ( Fig 5C , right panel ) application from Fig 5A and 5B ( see figure legend ) . Taken together , these opposite effects of persistent and resurgent Na+ currents arising from a single Na+ channel may act in concert to offer a push-pull modulation of burst timing regulation . The effect of persistent gNaP in reducing IBIs can be explained by its subthreshold activation [11] , wherein increases in gNaP promotes STO and burst initiation which in turn increases burst frequency and reduces IBIs . Additionally , a high gNaP together with its slow inactivation helps maintain depolarization which can increase BDs . During spiking , INaP inactivation slowly accumulates and can contribute to any spike frequency adaptation as a burst terminates . Further increases in gNaP can accentuate such an effect and lead to increases in spike intervals within a burst . In contrast , the rebound depolarization produced due to INaR can decrease ISIs and promote spiking which prolongs the BDs . An effect which is not immediately obvious is an increase in IBI due to increases in gNaR since IBIs are order of magnitude slower events than rise/decay kinetics of INaR . Moreover , this current is not active during IBIs . It is likely that INaR which promotes spike generation produces its effect on lengthening the IBIs by further accumulation of INaP inactivation during spiking and in turn reducing channel availability immediately following a burst . We examined this possibility using model analysis of INaR’s effect on modulating the slow INaP inactivation/recovery variable , hp . The simulated membrane potential ( grey traces ) and the slow INaP inactivation/recovery variable , hp ( overlaid magenta traces ) of the model neuron under three conditions shown in Fig 6A–6C: 1 ) with default values of gNaR and gNaP ( Fig 6A ) , 2 ) an 1 . 5x increase in gNaP ( Fig 6B ) , and , 3 ) a 2x increase in gNaR ( Fig 6C ) . The peak and trough of the slow inactivation/recovery variable , hp correspond to burst onset and offset respectively . Comparing these traces in the three panels , we note that an increase in gNaR effectively lowers the hp value at which burst terminates ( see curvy arrow in Fig 6C and legend ) . This observation was further supported by estimations of theoretical thresholds for burst onset and offset for increasing values of gNaR ( Fig 6D ) and , similar thresholds for increasing values of gNaP are provided for comparison in Fig 6E ( see legend and S1 Text for details ) . Note that changes in gNaR did not alter the burst onset thresholds , consistent with a lack of resurgent current before spike onset ( see brown arrow indicating burst onset threshold in Fig 6D ) . In contrast , increasing gNaR , consistently lowered the threshold values of slow inactivation/recovery for burst offset ( see highlighted dashed box with arrows pointing to the burst offset thresholds decreasing with increasing gNaR values in Fig 6D ) . The net effect is longer recovery time between bursts and therefore prolonged IBIs . Additionally , in Fig 6D , an increase in gNaR extended the range of slow inactivation/recovery for which stable spiking regime exists ( marked by the green circles ) . This gain in stability is indicative of a negative feedback loop in the Na+ current gating variables which model the slow inactivation and the open-channel unblock process . As shown in Fig 6F ( see boxed inset ) , during a burst , presence of a channel unblocking process and the resulting resurgent Na+ can lead to further accumulation of slow inactivation ( a positive effect ) which eventually shuts off the unblocking events with further inactivation ( negative feedback ) , and terminates a burst . The schematic on the left in Fig 6F summarizes a negative feedback loop between the unblocking and slow inactivation processes of Na+ currents which could mediate the stabilization of burst discharge as described above . Next , we examined whether the stability of burst discharge offered by the presence of INaR can also contribute to noise tolerance . During quiescence/recovery periods between bursts , the membrane voltage is vulnerable to perturbations by stochastic influences , which can induce abrupt spikes and therefore disrupt the burst timing precision . We introduced a Gaussian noise to disrupt the rhythmic burst discharge in the model neuron when no gNaR was present as shown in Fig 7A and 7B . Subsequent addition of gNaR restored burst regularity ( Fig 7C ) . Model analyses in the ( hp , V ) plane provides insight into the mechanism of INaR mediated noise tolerance . Briefly , we project a portion of a ( hp , V ) trajectory corresponding to the termination of one burst until the beginning of the next ( see expanded insets in Fig 7A , 7B and 7C ) to the ( hp , V ) diagrams shown in Fig 7D , 7E and 7F respectively ( see S1 Text for details ) . In Fig 7D , beginning at the magenta circle , the ( hp , V ) trajectory ( magenta trace ) moves to the right as hp recovers during an IBI , until a burst onset threshold is crossed; point where the blue circles meet the red and black curves ( see S1 Text for details ) , and eventually bursting begins; see upward arrow marking a jump-up in V at the onset of burst . During a burst , while V jumps up and-down during spikes , hp moves to the left as slow inactivation accumulates during bursting ( left arrow ) . Finally , when hp reduces sufficiently , ( hp , V ) gets closer to the burst offset threshold ( points at which the green and blue circles meet ) , and the burst terminates ( down arrow ) . What is key in this figure is that the IBI is well-defined as the time period in which the ( hp , V ) trajectory moves along the red curve of steady states during the recovery process and moves past the burst onset threshold until a burst begins . However , when stochastic influences are present , the recovery period near-threshold is subject to random perturbations in V and can cause abrupt jump-up/spikes during the recovery period ( see expanded inset in Fig 7B ) . Projecting ( hp , V ) during this period on to Fig 7E , we note that the near-threshold noise amplitudes can occasionally push the ( hp , V ) trajectory ( magenta ) above a green region of attraction and this results in such abrupt spikes . Now , when gNaR is added , the apparent restoration of burst regularity ( see Fig 7C ) can be attributed to an expansion in this green shaded region as shown in Fig 7F ( see arrow pointing to a noise-tolerant region ) . In this situation , near-threshold random perturbations have less of an effect during the recovery process to induce abrupt spikes . This way , the net effect of INaR on slow Na+ inactivation prevents abrupt transitions into spiking regime following burst offset and in turn contributes to burst refractoriness and noise tolerance . We suggest that such a mechanism can make random fluctuations in membrane potential less effective in altering the precision of bursts and therefore aid information processing . The spike/burst intervals , their timing precision and order are important for information coding [38–42] . Given our prediction that INaR can offer noise tolerance and stabilize burst discharge , we examined whether it can reduce uncertainty in spike/burst intervals and restore order in burst discharge . We tested this using model simulations and dynamic-clamp experiments as shown in Fig 8A–8D . In both cases , as shown by spike raster plots in Fig 8E and 8F , we disrupted the inter-event intervals ( IEIs ) by additive White/Wiener noise input while driving rhythmic burst discharge using step depolarization ( also see Methods ) . Subsequent addition of gNaR conductance restored the regularity of rhythmic bursting . We used Shannon’s entropy as a measure of uncertainty in IEIs and show that increases in entropy due to noise addition was reduced to control levels by subsequent increases in gNaR as shown in Fig 8G ( see Methods ) . We also quantified the Coefficient of Variation ( CV ) and noted that adding noise which shortened IBIs , indeed decreased the CV , due to a reduction in the standard deviation ( s . d . ) of the IEI distribution . Subsequent addition of INaR , which significantly lengthened the IBIs , resulted in increases in CV values due to an increase in IEI s . d . Taken together , INaR moderated burst entropy and improved the regularity of spike/burst intervals .
In contrast with INaP , which drives near threshold behavior and burst generation , INaR facilitated slow channel inactivation as bursts terminate ( also note [11] ) . Increased channel inactivation due to INaR in turn prolonged the recovery from inactivation required to initiate subsequent burst of activity . Such an interaction between open-channel unblock process underlying INaR , and , the slow inactivation underlying INaP , offer a closed-loop push-pull modulation of ISIs and IBIs . Specifically , presence of INaR facilitates slow Na+ inactivation as shown by our theoretical analyses of model behavior; such enhanced slow channel inactivation can eventually shut off channel opening and unblocking . This resulted in burst stabilization . Theoretically , this represented an enlarged separatrix ( or boundary ) for transitioning from a sub-threshold non-spiking behavior to spiking behavior ( see enlarged green shaded region in Fig 7F ) , and the neuron becomes refractory to burst generation and hence offers noise tolerance . Is this apparent effect of INaR physiologically plausible ? Biophysical studies indicate that recovery from fast inactivation is facilitated in sodium channels that can pass resurgent current [30]; as shown here , this appears to be true for recovery from slow inactivation as well . Consistently , in the SCN8a knockout Med mouse , which lack the Nav 1 . 6 sodium channel subunit , recordings from mutant cells showed an absence of maintained firing during current injections , limited recovery of sodium channels from inactivation , and failure to accumulate in inactivated states . This is attributed to a significant deficit in INaR [11 , 20 , 37] . Furthermore , maintained or repeated depolarization can allow a fraction of sodium channels in many neurons to enter inactivation states from which recovery is much slower than for normal fast inactivation ( reviewed in [43] ) . Here , our simulations and model analyses predict that the presence , and increase in INaR conductance , provides for a such a physiological mechanism to maintain sustained depolarization and promote fast and slow Na+ inactivation . Neuronal voltage-gated Na+ currents are essential for action potential generation and propagation [5] . However , to enable fight-or-flight responses , an overt spike generation mechanism must be combined with noise modulation to extract behaviorally relevant inputs from an uncertain input space . Here we show that , the voltage-gated Na+ currents can serve an important role in neural signal processing ( see summary in Fig 9 ) . As shown in the figure , a sub-threshold activated persistent Na+ current contributes to membrane resonance , a mechanism of bandpass filtering of preferred input frequencies [9] . We call this type of input gating , which is widely known to be important for brain rhythms [9 , 41] , a tune-in mechanism ( see figure legend ) . In some cases ambient noise or synaptic activity can amplify weak inputs and promote burst generation [44 , 45] . This way , a tune-in mechanism such as the persistent Na+ current can contribute to weak input detection and promote burst coding [46 , 47] . Then again , during rhythmic bursting , presence of resurgent Na+ maintains the order and precision of the timing events of bursts while preventing abrupt transitions into spiking phase due to stochastic influences as shown here . During ongoing sensory processing , such burst timing regulation can provide for noise cancellation or what we call a tune-out mechanism , which can mitigate random irregularities encoded in bursts ( see Fig 9 and legend ) . Whether this leads to improved sensory processing in the presence of natural stimuli and/or sensorimotor integration during normal behaviors needs to be validated . Our biological prediction here that a sensory neuron can utilize these voltage-gated Na+ currents as a tune-in-tune-out mechanism to gate preferred inputs , attenuate random membrane fluctuations and prevent abrupt transitions into spiking activity supports such a putative role .
The conductance-based Mes V neuron model that we used to investigate the physiological role for INaR and INaP components of INa in burst discharge , incorporates a minimal set of ionic conductances essential for producing rhythmic bursting and for maintaining cellular excitability in these neurons [8] . These include: 1 ) a potassium leak current , Ileak , 2 ) sodium current , INa as described above , and , 3 ) a 4-AP sensitive delayed-rectifier type potassium current ( IK ) [8 , 48] . The model equations follow a conductance-based Hodgkin-Huxley formalism [5] and are as follows . V′= ( −INa−IK−Ileak+Iapp ) /C ht′=ht∞ ( V ) −htτt hp′=hp∞ ( V ) −hpτp ( V ) br′=αb ( 1−br ) br∞ ( V ) −kbβbr ( V ) br hr′=αhr ( V ) hr∞ ( V ) −0 . 8βhr ( V ) hr n′=n∞ ( V ) −nτn In what follows , we provide the formulation for each of the ionic currents and describe in detail , the novel INaR model . In vitro action potential clamp studies in normal mouse Mes V neurons , and voltage-clamp studies in Nav1 . 6 subunit SCN8a knockout mice have demonstrated existence of three functional forms of the total sodium current , INa , including the transient ( INaT ) , persistent ( INaP ) and resurgent ( INaR ) components [11 , 14] . Each of these currents is critical for Mes V electrogenesis including burst discharge , however , their exclusive role is yet unclear . Lack of suitable experimental model or manipulation to isolate each of these TTX-sensitive components , led us to pursue an alternative approach involving computational model development of the physiological INa . To further allow model-based experimental manipulation of individual components of the INa , we designed a conductance-based model as follows . Although a single Nav1 . 6 channel can produce all three INa components observed experimentally , we used a set of three HH-type conductances , one for each of the transient , the persistent , and the resurgent components . This allowed us to easily manipulate these components independently to test their specific role in neural burst control . The equation for the total sodium current can be written as: INa=INaT+INaR+INaP where , INaT=gNaT ( mt∞ ( V ) ht ) ( V−ENa ) INaR=gNaR ( ( 1−br ) 3hr5 ) ( V−ENa ) INaP=gNaP ( mp∞ ( V ) hP ) ( V−ENa ) The maximal persistent conductance , gNaP was set 5–10% of the transient , gNaT [49] and the resurgent was set to 15–30% of gNaT , based on the relative percentage of maximum INaR and INaT as revealed by voltage-clamp experiments shown in Fig 3; ENa is the Na+ reversal potential . Based on experimental data , the gating function/variable , mt∞ ( V ) , and ht , for INaT , and , mp∞ ( V ) , and , hP , for INaP are modeled as described in [8] . The rate equations for the inactivation gating variables ht , and , hP , model the fast and slow inactivation of the transient and persistent components respectively . The activation gates are steady-state voltage-dependent functions , consistent with fast voltage-dependent activation of INa . Steady-state voltage-dependent activation and inactivation functions of transient sodium current respectively include: mt∞ ( V ) =11+e ( − ( V+35 ) 4 . 3 ) ;ht∞ ( V ) =11+e ( ( V+55 ) 7 . 1 ) Steady-state activation , inactivation and steady-state voltage-dependent time constant of inactivation for persistent sodium current respectively include: mp∞ ( V ) =11+e ( − ( V+50 ) 6 . 4 ) ;hp∞ ( V ) =11+e ( ( V+52 ) 14 ) ;τp ( V ) =100+100001+e ( ( V+60 ) 10 ) The novel INaR formulation encapsulates the block/unblock mechanism using a block/unblock variable ( br ) , and , a second hypothetical variable for a competing inactivation , which we call , hr . We call this a hybrid model , to highlight the fact that the model implicitly incorporates the history or state-dependent sodium resurgence , following a transient channel opening , and combines this into a traditional Hodgkin-Huxley type conductance-based formulation . In the br′ and hr′ rate equations for br , and , hr , the block/unblock variable , br increases or grows according to the term , αb ( 1−br ) br∞ ( V ) , and decays as per the term , kbβbr ( V ) br , described as follows: αb ( 1−br ) br∞ ( V ) : In this growth term , we incorporate state-dependent increase in br , as follows; we assume that the rate of increase in br is proportional to the probability of channels currently being in the open state , with a rate constant , αb which we call ‘rate of unblocking’; such probability is a function of the membrane voltage given by , br∞ ( V ) , defined as below: br∞ ( V ) =11+e ( ( V+40 ) 12 ) The term ( 1−br∞ ( V ) ) , models the steady-state voltage-dependency guiding the unblocking process . The channels being in open state is represented by the term , ( 1−br ) . Note that if ( 1−br ) is close to 1 , this means that larger proportion of channels are in an open state , and therefore br grows faster , promoting blocking . We modeled br∞ ( V ) as a decreasing sigmoid function , such that , at negative membrane potentials , channels have a high probability to enter future depolarized states and therefore , ( 1−br ) ~0 , in turn , br does not grow fast . kbβbr ( V ) br: In this decay term , we assume that the rate of decay of br , is proportional to the probability of channels being in the blocked state , with a constant of proportionality kb , and , this probability is given by a voltage-dependent function , βbr ( V ) , defined as below: βbr ( V ) =21+e ( − ( V−40 ) 8 ) Note that , βbr ( V ) gives a high probability at depolarized potentials , indicating a blocked state and enables decrease in br in subsequent time steps . Taken together , br , represents a phenomenological implementation of a previously described block/unblock mechanism of a cytoplasmic blocking particle [19] ( see schematic of channel gating in Fig 3A ) . Additionally , a hypothetical competing inactivation variable , hr , sculpts the voltage-dependent rise and decay times and peak amplitude of sodium resurgence at -40 mV following a brief depolarization ( i . e . , transient activation ) , as observed in voltage-clamp experiments ( see Fig 3B ) . The functions , αhr ( V ) , βhr ( V ) and hr∞ ( V ) are defined as voltage-dependent rate equations that guide the voltage-dependent kinetics and activation/inactivation of the INaR component as given below . The steady-state voltage-dependency of the competing inactivation necessary to generate a resurgent Na+ current is defined as follows: hr∞ ( V ) =11+e ( ( V+40 ) 20 ) The voltage-dependent rate functions of such inactivation is defined by two functions as follows: αhr ( V ) =11+e ( − ( V+40 ) 8 ) ;βhr ( V ) =0 . 51+e ( − ( V+40 ) 15 ) The steepness of the voltage-dependent sigmoid functions for activation and inactivation were tuned to obtain the experimentally observed INaR activation ( see Fig 3; also see [11 , 14 , 30] ) . To obtain the kinetics ( rise and decay times ) of INaR comparable to those observed during voltage-clamp experiments ( see S3 Fig ) , the model required three units for the blocking variable ( ( 1−br ) 3 ) and five units for the inactivation variable ( hr5 ) ( see INaR equation ) . Together , the modeled INa reproduced the key contingencies of the Nav1 . 6 sodium currents ( see S2 Fig ) [18 , 30 , 50] . Sensitivity analyses was conducted for the key parameters of INaR gating including αb , and , kb . Note that these two parameters control the rate of blocking . As expected , increasing αb , that controls rate of increase in br , decreased the peak amplitude of INaR , similar to an experimental increase in block efficacy by a β-peptide ( e . g . , [19] ) . On the other hand , kb also moderates br , and increasing kb , enhances br decay rate , that significantly enhanced INaR , and , therefore burst duration ( not shown ) . Large increases in kb significantly enhanced INaR , and indeed transformed bursting to high frequency tonic spiking . However , the effects of INaR on bursting described in the results section were robust for a wide range of values of these parameters ( >100% increase from default values ) , and , for our simulations , the range of values , αb = 0 . 08 to 0 . 1 , kb = 0 . 8 to 1 . 2 , were used to reproduce Mes V neuron discharge properties . To reproduce experimentally observed spike width , we additionally tuned the inactivation time constant , τt = 1 . 5±0 . 5 , for INaT . The 4-AP sensitive delayed-rectifier type potassium current , IK , and the leak current , Ileak were modeled similar to [8] as below; also see [48] . IK=gKn ( V−EK ) Ileak=gleak ( V−Eleak ) where , the steady-state voltage-dependent activation function for the gating variable , n is given as: n∞ ( V ) =11+e ( − ( V−43 ) 3 . 9 ) EK and Eleak are K+ and leak reversal potentials respectively . Model parameter values used are provided in S1 Table . All animal experiments were performed in accordance to the institutional guidelines and regulations using protocols approved by Animal Research Committee at UCLA . Experiments were performed in P8-P14 wild-type mice of either sex . Mice were anesthetized by inhalation of isofluorane and then decapitated . The brainstem was extracted and immersed in ice-cold cutting solution . The brain-cutting solution used during slice preparation was composed of the following ( in mM ) : 194 Sucrose , 30 NaCl , 4 . 5 KCl , 1 . 2 NaH2PO4 , 26 NaHCO3 , 10 glucose , 1 MgCl2 . The extracted brain block was mounted on a vibrating slicer ( DSK Microslicer , Ted Pella ) supported by an agar block . Coronal brainstem sections consisting of rostro-caudal extent of Mes V nucleus , spanning midbrain and pons were obtained for subsequent electrophysiological recording . To obtain direct experimental data to drive INaR model development , we performed voltage-clamp experiments on Mes V neurons and recorded Na+ currents by blocking voltage-gated K+ and Ca2+ currents similar to [11] . The pipette internal solution contained the following composition ( in mM ) : 130 CsF , 9 NaCl , 10 HEPES , 10 EGTA , 1 MgCl2 , 3 K2-ATP , and 1 Na-GTP . The external recording solution contained the following composition ( in mM ) : 131 NaCl , 10 HEPES , 3 KCl , 10 glucose , 2 CaCl2 , 2 MgCl2 , 10 tetraethylammonium ( TEA ) -Cl , 10 CsCl , 1 4-aminopyridine ( 4-AP ) , and 0 . 3 CdCl2 . The voltage-clamp protocol consisted of a holding potential of -90 mV followed by a brief voltage pulse ( 3 ms ) of +30 mV , to remove voltage-dependent block , followed by voltage steps between -70 mV to -10 mV , in steps of 10 mV for ~ 100 ms to activate INaR , and then returned to -90 mV . A 1 μM TTX abolished the Na+ current and the residual leak current was subtracted to isolate evident sodium currents . Recordings with series resistance Rseries>0 . 1Rm were discarded , where Rm is the input resistance of the neuron; we did not apply any series resistance compensation . Dynamic-clamp electrophysiology and in vitro current-clamp recording were used for testing the physiological effects of Na+ currents on burst discharge as well as noise-mediated entropy changes corrected by INaR [51] . We selected neurons responding with a bursting pattern in response to supra-threshold step current injection in the Mes V nucleus in brainstem slice preparation for our study; >50% of neurons showing other patterns ( e . g . , tonic or single spiking cells ) were discarded . Dynamic-clamp was successfully performed in bursting cells ( n = 10 ) . For dynamic-clamp recording , slices were placed in normal ACSF at room temperature ( 22–25°C ) . The ACSF recording solution during patch-clamp recording consisted of the following ( in mM ) : 124 NaCl , 4 . 5 KCl , 1 . 2 NaH2PO4 , 26 NaHCO3 , 10 glucose , 2 CaCl2 , 1 MgCl2 . Cutting and recording solutions were bubbled with carbogen ( 95% O2 , 5% CO2 ) and maintained at pH between 7 . 25–7 . 3 . The pipette internal solution used in current clamp experiments was composed of the following ( in mM ) : 135 K-gluconate , 5 KCl , 0 . 5 CaCl2 , 5 HEPES ( base ) , 5 EGTA , 2 Mg-ATP , and 0 . 3 Na-ATP with a pH between 7 . 28–7 . 3 , and osmolarity between 290 ± 5 mOsm . Patch pipettes ( 3–5 MΩ ) were pulled using a Brown/Flaming P-97 micro pipette puller ( Sutter Instruments ) . Slices were perfused with oxygenated recording solution ( ~2ml/min ) at room temperature while secured in a glass bottom recording chamber mounted on an inverted microscope with differential interface contrast optics ( Zeiss Axiovert 10 ) . Current clamp ( and dynamic-clamp ) data were acquired and analyzed using custom-made software ( G-Patch , Analysis ) with sampling frequency: 10 kHz; cut-off filter frequency: 2 kHz . The Linux-based Real-Time eXperimental Interface ( RTXI v1 . 3 ) was used to implement dynamic-clamp , running on a modified Linux kernel extended with the Real-Time Applications Interface , which allows high-frequency , periodic , real-time calculations [52] . The RTXI computer interfaced with the electrophysiological amplifier ( Axon Instruments Axopatch 200A , in current-clamp mode ) and the data acquisition PC , via a National Instruments PCIe-6251 board . Euler’s method with step size 0 . 05 ms was used for model integration resulting in a computation frequency of 20 kHz . The model INaR current used for dynamic clamping into Mes V neuron in vitro was developed as discussed above . The ionic conductance gNaR was set to suitable values to introduce model INaR current into a Mes V neuron during whole-cell current-clamp recording . For dynamic-clamp experiments involving gNaR mediated noise modulation , two approaches were used to model random noise Inoise , generated in RTXI and injected as pA current: We used stochastic current injection as an external input ( additive noise ) in order to produce irregularities/uncertainties in burst discharge . Our choice of the noise model was to experimentally disrupt spike timing regularity [45] and is not directly based on any known noise characteristics in Mes V neurons . The jaw muscle spindle afferent Mes V neurons are not known to have spontaneous synaptic events and we did not characterize Na+ channel fluctuations in these neurons [53] . Nonetheless , the stochastic noise we used as shown in the representative example in Fig 8 most closely matched a diffusive synaptic noise model with Gaussian distribution [54]; Fig 8C illustrates the temporal features of the noise inputs described above . A Gaussian white noise generated in MATLAB with zero mean and unit standard deviation or a Wiener noise generated in XPPAUT were used to disrupt firing patterns in the model neuron . Model simulation and all the analyses were performed using MATLAB ( Mathworks ) Model code available up on request . Model bifurcation analyses were performed using XPPAUT/AUTO [55] . A variable step Runge-Kutta method ‘ode45’ was used for current-clamp simulations and ‘ode23s’ was used for voltage-clamp simulations . Inter-event intervals ( IEI ) between spikes in dynamic-clamp recordings were detected using Clampfit 9 . 0 software and were classified post hoc as ISIs and IBIs based on a bi-modal distribution of IEIs . Typically , IEI values < 40 ms were considered as ISIs within bursts and IEI values ≥ 40 ms were considered as IBIs . Any occasional isolated spikes were eliminated from analyses for burst duration calculations . To calculate Shannons’ entropy [56] in the inter-event intervals ( IEIs ) , we generated histograms IEI and calculated the probabilities for each bin of the underlying IEI distributions for each 10 sec spike trains . The probability of kth IEI bin from a distribution of n equal size bins was calculated from the bin counts , N ( k ) as: p ( k ) =N ( k ) ∑k=1nN ( k ) The entropy , H was calculated using the following formula: H=−∑k=1npklog2pk where , n is the total number of IEI bins , each with probability , pk . The coefficient of variation ( CV ) in IEIs was calculated as follows: CV=sx¯ where , s is the IEI sample standard deviation , and , x¯ is the sample mean . | The nervous system extracts meaningful information from natural environments to guide precise behaviors . Sensory neurons encode and relay such complex peripheral information as electrical events , known as action potentials or spikes . The timing intervals between the spikes carry stimulus-relevant information . Therefore , disruption of spike timing by random perturbations can compromise the nervous system function . In this study we investigated whether the widely-distributed voltage-gated sodium ( Na+ ) ion channels important for spike generation can also serve as noise modulators in sensory neurons . We developed and utilized mathematical models for the different experimentally inseparable components of a complex Na+ channel current . This enabled phenomenological simplification and examination of the individual roles of Na+ components in spike timing control . We further utilized real-time closed-loop experiments to validate model predictions , and theoretical analysis to explain experimental outcomes . Using such multifaceted approach , we uncovered a novel role for a resurgent Na+ component in enhancing the reliability of spike timing and in noise modulation . Furthermore , our simplified model can be utilized in future computational and experimental studies to better understand the pathological consequences of Na+ channelopathies . | [
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"me... | 2019 | Resurgent Na+ Current Offers Noise Modulation in Bursting Neurons |
We previously reported that autosomal recessive demyelinating Charcot-Marie-Tooth ( CMT ) type 4B1 neuropathy with myelin outfoldings is caused by loss of MTMR2 ( Myotubularin-related 2 ) in humans , and we created a faithful mouse model of the disease . MTMR2 dephosphorylates both PtdIns3P and PtdIns ( 3 , 5 ) P2 , thereby regulating membrane trafficking . However , the function of MTMR2 and the role of the MTMR2 phospholipid phosphatase activity in vivo in the nerve still remain to be assessed . Mutations in FIG4 are associated with CMT4J neuropathy characterized by both axonal and myelin damage in peripheral nerve . Loss of Fig4 function in the plt ( pale tremor ) mouse produces spongiform degeneration of the brain and peripheral neuropathy . Since FIG4 has a role in generation of PtdIns ( 3 , 5 ) P2 and MTMR2 catalyzes its dephosphorylation , these two phosphatases might be expected to have opposite effects in the control of PtdIns ( 3 , 5 ) P2 homeostasis and their mutations might have compensatory effects in vivo . To explore the role of the MTMR2 phospholipid phosphatase activity in vivo , we generated and characterized the Mtmr2/Fig4 double null mutant mice . Here we provide strong evidence that Mtmr2 and Fig4 functionally interact in both Schwann cells and neurons , and we reveal for the first time a role of Mtmr2 in neurons in vivo . Our results also suggest that imbalance of PtdIns ( 3 , 5 ) P2 is at the basis of altered longitudinal myelin growth and of myelin outfolding formation . Reduction of Fig4 by null heterozygosity and downregulation of PIKfyve both rescue Mtmr2-null myelin outfoldings in vivo and in vitro .
Phosphoinositides ( PIs ) constitute potent signaling molecules with a specific and restricted distribution at intracellular membranes that is strictly controlled by the concerted action of kinases and phosphatases [1] , [2] . PIs are key regulators of membrane trafficking as they contribute to assembly of molecular machineries that promote and control membrane dynamics and vesicle movement , tethering and fusion . In the nervous system , both neurons and glia rely on efficient membrane trafficking for many functions , such as axonal transport or myelination . Charcot-Marie-Tooth ( CMT ) neuropathies are very heterogeneous disorders from both the clinical and genetic point of view [3]–[6] . Several CMT genes encode proteins that regulate or are connected with PI metabolism , including FRABIN/FGD4 , FIG4 , DNM2 , RAB7 , SIMPLE , LRSAM1 , SH3TC2 , MTMR2 , and MTMR13 , supporting the idea that regulation of intracellular trafficking is a key process in peripheral nervous system biology [7] ( http://www . molgen . ua . ac . be/CMTMutations/default . cfm ) . We first demonstrated that loss of function mutations in the MTMR2 ( Myotubularin-related 2 ) gene cause autosomal recessive demyelinating Charcot-Marie-Tooth type 4B1 ( CMT4B1 , OMIM #601382 ) neuropathy with myelin outfoldings [8] . MTMR2 is a phospholipid phosphatase that dephosphorylates both PtdIns3P and PtdIns ( 3 , 5 ) P2 phosphoinositides at the D3 position of the inositol ring , thus generating PtdIns5P [9]–[16] . We have generated a Mtmr2-null mouse which models the CMT4B1 neuropathy and we reported that loss of Mtmr2 specifically in Schwann cells is both sufficient and necessary to provoke myelin outfoldings [17] , [18] . We recently identified a potential mechanism using in vivo and in vitro models of CMT4B1 and proposed that Mtmr2 belongs to a molecular machinery that titrates membrane formation during myelination . According to this model , myelin outfoldings arise as a consequence of the loss of negative control on the amount of membrane produced during myelination [19] . Despite these findings , the function of MTMR2 and the role of the MTMR2 phospholipid phosphatase activity in the nerve still remain to be assessed . Loss of the FIG4/SAC3 phospholipid phosphatase in human provokes another form of autosomal recessive demyelinating CMT , the CMT type 4J ( OMIM #611228 ) neuropathy [20] , [21] . FIG4 is a 5-phosphatase involved in the dephosphorylation of PtdIns ( 3 , 5 ) P2 , a predicted substrate of MTMR2 . Loss of Fig4 in the mouse causes the plt ( pale tremor ) phenotype , characterized by extensive neuronal vacuolization and degeneration and by a peripheral neuropathy [20] , . Yeast Fig4 is localized at the vacuolar membrane-the yeast lysosomal compartment- and is required for both the generation and turnover of PtdIns ( 3 , 5 ) P2 [23] , [24] . In addition to the 5-phosphatase activity , yeast Fig4 appears to activate Fab1 , the kinase that produces PtdIns ( 3 , 5 ) P2 from PtdIns3P [23] , [24] . Deletion of yeast Fig4 reduces rather than increases PtdIns ( 3 , 5 ) P2 leading to defects in vacuole homeostasis and function . A significant decrease of PtdIns ( 3 , 5 ) P2 was found also in plt ( Fig4-null ) fibroblasts , suggesting conserved enzymatic and cellular functions of Fig4 from yeast to mouse [20] . Moreover , the most common human mutation of FIG4 acts by reducing its affinity for the PtdIns ( 3 , 5 ) P2 biosynthetic complex [25] . Since FIG4 has a role in generation of PtdIns ( 3 , 5 ) P2 and MTMR2 catalyzes its dephosphorylation , these two phosphatases might have opposite effects in the control of PtdIns ( 3 , 5 ) P2 homeostasis and their mutations might have compensatory effects in vivo . To explore the role of the MTMR2 phospholipid phosphatase activity in vivo , we took advantage of the Fig4 and Mtmr2-null mice and generated and characterized the Mtmr2/Fig4 double null mutant . Here we provide strong evidence that Mtmr2 and Fig4 functionally interact in both Schwann cells and neurons , and reveal for the first time a role of Mtmr2 in neurons in vivo . We also report that the imbalance of PtdIns ( 3 , 5 ) P2 might be at the basis of myelin outfolding in the nerve . Reduction of Fig4 by null heterozygosity and downregulation of PIKfyve both rescue Mtmr2-null myelin outfoldings in vivo and in vitro .
The generation and characterization of Mtmr2-null and Fig4-null ( plt ) mice have been reported [17] , [20] . Mtmr2/Fig4 double null mice and controls were analyzed in the F2 generation . At postnatal day three ( P3 ) Mtmr2−/−Fig4−/− mice had reduced body size and diluted pigmentation of the coat similar to the Mtmr2+/+Fig4−/− mice in the same litter , and as reported for the plt mouse [20] . Tremor and abnormal gait developed in the second week after birth . Mtmr2+/+Fig4−/− mice show juvenile lethality and die around 1 month of age . The viability of Mtmr2−/−Fig4−/− mice was lower than for Mtmr2+/+Fig4−/− littermates . A reduced number of both Mtmr2+/−Fig4−/− and Mtmr2−/−Fig4−/− mice were present at P8 , compared to the expected Mendelian ratio ( Table 1 and Table 2 ) . The longest survival of the double mutant was 20 days . The data indicate that loss of Mtmr2 reduces viability of Mtmr2+/+Fig4−/− . We therefore hypothesized that loss of Mtmr2 might provoke a worsening of the Mtmr2+/+Fig4−/− neurodegeneration . To explore this possibility , we performed semithin section analysis of DRG ganglia , brain and spinal cord from Mtmr2+/+Fig4−/− and Mtmr2−/−Fig4−/− mice . DRG ganglia from both Mtmr2+/+Fig4−/− and Mtmr2−/−Fig4−/− mice at P3 were severely affected , exhibiting neuronal loss and massive vacuolization ( Figure 1A–1C ) . In the cerebellum of both Mtmr2+/+Fig4−/− and Mtmr2−/−Fig4−/− mice at P8 and at P20 we observed a thickening of the molecular layer as compared to wild-type , and cells with cytoplasmic vacuoles were present in the granular layer . At P20 , a consistent loss of Purkinjie and basket cells was observed in both genotypes ( Figures S1 and S2 ) . These cerebellar findings have not been previously reported in the plt mouse . In the cortex and brainstem of Mtmr2−/−Fig4−/− mice at P3 we noted more cells with vacuoles and inclusions than in Mtmr2+/+Fig4−/− mice , which were never been observed in wild-type animals ( Figure 1D–1I ) . In particular , in the brainstem of Mtmr2−/−Fig4−/− mice at P8 the number of neurons carrying pathological abnormalities was significantly increased as compared to Mtmr2+/+Fig4−/− mice ( Figure 1J–1L ) . We also analyzed the spinal cord of Mtmr2+/+Fig4−/− and Mtmr2−/−Fig4−/− mice at P3 and P8 ( Figure 2A–2F ) . Vacuolated cells and cells with inclusions were observed , as previously described for the plt phenotype , which were not present in wild-type spinal cords [20] , [26] . At P8 , we observed a significant decrease in the number of motor neurons and cells in Mtmr2−/−Fig4−/− mice as compared to Mtmr2+/+Fig4−/− mice ( Figure 2H , 2I ) . These findings demonstrate that loss of Mtmr2 exacerbates the Mtmr2+/+Fig4−/− neurodegeneration . This effect could be the consequence of loss of Mtmr2 in neurons or in other cells , such as in astrocytes . For example , in the plt mouse , a block of autophagy in astrocytes has been reported . In plt mice at 1 week of age , the p62 autophagy marker was increased in GFAP-positive astrocytes from brain regions severely affected at later stages , suggesting that autophagy impairment contributes to the pathogenesis [26] . Elevated p62 co-localized with LAMP2-positive late endosomes/lysosomes ( LE/LY ) in astrocytes , showing that the block of autophagy occurred subsequent to the fusion of autophagosomes with LE/LY [26] . To determine whether loss of Mtmr2 in astrocytes might further impair autophagy , we evaluated p62 levels in total brain extracts from Mtmr2+/+Fig4−/− as compared with Mtmr2−/−Fig4−/− mice . Increased p62 , LAMP1 and GFAP expression levels were confirmed in Mtmr2+/+Fig4−/− as compared to wild-type but no differences were detected between Mtmr2+/+Fig4−/− and Mtmr2−/−Fig4−/− double null mice ( Figure 3A ) . This finding indicates that loss of Mtmr2 does not further impair the block in the autophagic process in astrocytes of Fig4-null mice . To further investigate the cell autonomy of the Mtmr2/Fig4 interaction , we established dissociated Schwann cell/DRG neuron co-cultures from Mtmr2+/+Fig4−/− and Mtmr2−/−Fig4−/− mice , in which mutant Schwann cells were replaced with exogenous wild-type rat Schwann cells . Mtmr2−/−Fig4−/− DRG neurons cultured with wild-type Schwann cells were significantly more severely vacuolated ( 57 . 8% ) as compared to Mtmr2+/+Fig4−/− cultures ( 37 . 4% ) ( Figure 3B–3D′ and 3H ) . This finding provides strong evidence that the loss of Mtmr2 in neurons leads to the worsening of the Fig4-null neurodegeneration . Like neurons , mouse primary fibroblasts ( MFs ) from plt mutants display enlargement and vacuolization of the LAMP2-positive LE/LY compartment [20] , [22] . To provide further evidence for functional interaction between MTMR2 and FIG4 , we established MF cultures from Mtmr2+/+Fig4−/− and Mtmr2−/−Fig4−/− mice . By LAMP1 staining and confocal microscopy , we observed that the number of fibroblasts carrying enlarged LE/LY was significantly increased in Mtmr2−/−Fig4−/− double mutants as compared to Mtmr2+/+Fig4−/− ( Figure 4 ) . This finding indicates that Mtmr2 loss exacerbates Fig4-null vacuolar phenotype by further impairment of the endo/lysosomal trafficking pathway . The plt mouse phenotype is characterized by a peripheral neuropathy . Loss of large diameter myelinated axons , hypomyelination ( reduced myelin thickness ) , reduced amplitude of compound motor action potential ( cMAP ) and slowing of the nerve conduction velocity ( NCV ) have been reported in plt mouse nerves at 6 weeks of age [20] , [22] . The extent of the NCV reduction in plt mice and the presence of demyelinating features in CMT4J patient biopsies such as onion bulbs suggested that FIG4 has also a cell autonomous role in Schwann cells [22] . We investigated sciatic nerves from Mtmr2+/+Fig4−/− and Mtmr2−/−Fig4−/− mice . At P3 and P8 , mutant sciatic nerves showed a normal development . In both genotypes at P8 , Schwann cells often contained cytoplasmic inclusions and occasionally contained vacuoles , which were never observed in wild-type nerves . At P20 , the latest time point of survival of Mtmr2/Fig4 double null mice , Mtmr2+/+Fig4−/− sciatic nerves were hypomyelinated ( thin myelin sheath ) with an increased g-ratio ( diameter of the axon divided by the diameter of the fiber ) as compared to wild-type nerves ( Figure 5E ) . At this stage , sciatic nerves from Mtmr2−/−Fig4−/− double null mice were more severely hypomyelinated than Mtmr2+/+Fig4−/− mice with a higher g-ratio , demonstrating that Mtmr2 loss exacerbates the neuropathy of Mtmr2+/+Fig4−/− mice ( Figure 5E ) . The total number of fibers and the axonal diameter distribution at P20 were not significantly altered in mouse nerves of either genotype ( Figure 5F ) . These observations indicate that the hypomyelination is not a developmental defect related to delayed axonal growth . Hypomyelination may result from a defective axonal/Schwann cell interaction due to the severe neuronal degeneration and/or from the loss of FIG4 in Schwann cells . We thus cultured dissociated DRG neurons from Mtmr2−/−Fig4−/− and Mtmr2+/+Fig4−/− mice , seeded with exogenous wild type rat Schwann cells . Following induction of myelination by ascorbic acid treatment , vacuolated DRG neurons from both Mtmr2−/−Fig4−/− and Mtmr2+/+Fig4−/− mouse embryos were able to produce myelinated segments , although significantly fewer than wild-type cultures . Moreover , DRG neurons from Mtmr2−/−Fig4−/− mice cultured with wild-type Schwann cells produced significantly fewer myelinated segments than Mtmr2+/+Fig4−/− neurons seeded with wild-type Schwann cells ( Figure 3E–3G and quantification in panel I ) . This observation suggests that the hypomyelination of Mtmr2+/+Fig4−/− nerves represents at least in part the consequence of impaired Schwann cell-axonal interaction . To further investigate Mtmr2 and Fig4 interaction in the nerve , we evaluated whether loss of Fig4 modifies the myelin outfolding phenotype . Myelin outfoldings in Mtmr2-null mice arise around the third to fourth week after birth , and the number of fibers containing myelin outfoldings and loops progressively increases with age ( up to 6 months or even later ) . Since Mtmr2−/−Fig4−/−double mutants die before 1 month of age , we compared sciatic and peroneal nerves at 6 months of age from Mtmr2−/−Fig4+/+ and Mtmr2−/−Fig4+/− ( Fig4 heterozygous ) mice . Using semithin section analysis , we measured the number of fibers carrying myelin outfoldings in mutant sciatic and peroneal nerves normalized to the total number of fibers . In Mtmr2−/−Fig4+/− nerves myelin outfoldings were significantly reduced as compared to Mtmr2−/−Fig4+/+ mice ( Figure 6A–6D ) . Since loss of Mtmr2 in Schwann cells is both sufficient and necessary to provoke myelin outfoldings , loss of Fig4 in Schwann cells ( rather than axons ) is likely to account for the rescue of the disease phenotype . To further evaluate this finding , we established myelin-forming Schwann cell/DRG neuron co-cultures from Mtmr2−/−Fig4+/+ and Mtmr2−/−Fig4+/− mouse embryos at E13 . 5 ( Figure 7A , 7B ) . By measuring the number of MBP positive fibers carrying myelin outfoldings in the cultures , we confirmed that Mtmr2-null myelin outfoldings were rescued by Fig4 heterozygosity ( Figure 7C ) . Loss of Fig4 in plt fibroblasts leads to a significant decrease in PtdIns ( 3 , 5 ) P2 , whereas Mtmr2 loss should lead to an increase in both PtdIns3P and PtdIns ( 3 , 5 ) P2 in vivo in the nerve [20] . Indeed , by performing a sensitive in vitro mass assay on Mtmr2-null Schwann cell/DRG neuron co-cultures , we found that in null cells PtdIns5P is significantly reduced ( up to 70% ) as expected by the loss of MTMR2 3-phosphatase activity on PtdIns ( 3 , 5 ) P2 ( Figure 8A ) . We hypothesized that the observed rescue by Fig4 heterozygosity might be the consequence of a restored level of PtdIns ( 3 , 5 ) P2 in Schwann cells . Heterozygosity of Fig4 might decrease PIKfyve activity and therefore partially restore PtdIns ( 3 , 5 ) P2 levels in Mtmr2-null cells . To test this hypothesis , we downregulated either the activity or expression of PIKfyve in Mtmr2-null co-cultures to rescue myelin outfoldings . We transduced Mtmr2-null co-cultures with lentiviral vectors ( LV ) carrying PIKfyve shRNA and scored the number of myelinated MBP-positive fibers with myelin outfoldings . Titration of the PIKfyve shRNA LV was previously performed to determine the highest amount of virus which does not significantly affect myelination ( Figure S3A , S3B ) . We found that myelin outfoldings were significantly rescued by downregulating PIKfyve expression ( Figure 7D–7F′″ and 7I ) . We also treated Mtmr2-null cultures with a specific pharmacological inhibitor of PIKfyve , YM201636 [27]–[29] . Titration of the compound was performed to determine the maximum amount of YM201636 that does not inhibit myelination ( Figure S3A , S3B ) . Seventy nM final concentration of YM201636 was freshly added to the culture media every other day together with ascorbic acid to achieve full myelination . A significant reduction of myelin outfoldings was confirmed in Mtmr2-null cultures treated with YM201636 as compared with DMSO alone ( Figure 7G , 7H , 7J ) . The data suggest that reduction of the level of PtdIns ( 3 , 5 ) P2 , either by heterozygosity for Fig4 or by inhibition of PIKfyve , corrects the myelin abnormality of Mtmr2-null cells This result predicts that the level of PtdIns ( 3 , 5 ) P2 may be elevated in Mtmr2-null cells . To correlate MTMR2 and FIG4 functional interaction with changes in PI levels , we measured PtdIns3P and PtdIns ( 3 , 5 ) P2 levels from wild-type; Mtmr2−/−Fig4+/+; Mtmr2+/+Fig4−/−; Mtmr2−/−Fig4−/− , and Mtmr2−/−Fig4+/− fibroblasts by metabolic labeling and HPLC analysis ( Figure 8 ) . PtdIns3P levels were similar in all the genotypes analyzed ( data not shown ) . In mammalian cells , PtdIns3P generation and turnover are controlled by multiple redundant pathways , so that ablation of one particular enzyme such as myotubularins does not necessarily result in an imbalance of PtdIns3P , as already reported [9] , [30] , [31] . On the other hand , we found that loss of Fig4 in Fig4-null fibroblasts results in a significant decrease of PtdIns ( 3 , 5 ) P2 as compared to control cells , thus confirming previous findings [20] ( Figure 8B ) . As also suggested by the in vitro mass assay performed on Mtmr2-null myelin-forming co-cultures ( Figure 8A ) , loss of Mtmr2 in Mtmr2-null fibroblasts leads to a significant increase in PtdIns ( 3 , 5 ) P2 level , consistently with the 3-phosphatase activity of MTMR2 ( Figure 8C ) . Moreover , PtdIns ( 3 , 5 ) P2 was equally reduced in Fig4−/− and in Mtmr2−/−Fig4−/− cells ( Figure 8B ) , possibly because the PtdIns ( 3 , 5 ) P2 substrate is already severely affected by loss of Fig4 , and Mtmr2 acts downstream of Fig4 in the control of this lipid level . To support the hypothesis that myelin outfoldings in Mtmr2−/−Fig4+/− co-cultures were rescued because of restored PtdIns ( 3 , 5 ) P2 levels , we also measured PtdIns ( 3 , 5 ) P2 in Mtmr2−/−Fig4+/+ and Mtmr2−/−Fig4+/− fibroblasts . However , PtdIns ( 3 , 5 ) P2 did not differ in Mtmr2−/−Fig4+/+ and Mtmr2−/−Fig4+/− fibroblasts ( Figure 8C ) . Small changes in PtdIns ( 3 , 5 ) P2 levels due to loss of 50% of phosphatase expression may be below the level of detection of this method . Overall , these findings indicate that Mtmr2 and Fig4 control PtdIns ( 3 , 5 ) P2 with opposite effects . If Fig4 is totally absent and PtdIns ( 3 , 5 ) P2 is low , the absence of Mtmr2 which dephosphorylates PtdIns ( 3 , 5 ) P2 has no influence . On the other hand , when PtdIns ( 3 , 5 ) P2 is high due to loss of Mtmr2 , a partial reduction in PIKfyve activity due to heterozygosity of Fig4 might lead to PtdIns ( 3 , 5 ) P2 rebalance and rescue of myelin outfoldings . Finally , we tested for interaction between phosphatases using a pull-down assay . GST-MTMR2 was not able to pull-down Fig4 from brain or isolated rat Schwann cell lysates , suggesting that the functional interaction between MTMR2 and FIG4 demonstrated here is not mediated by physical interaction between the two proteins ( Figure S3C–S3E ) . The mutant yeast strain fig4Δ displays enlarged vacuoles caused by reduced PtdIns ( 3 , 5 ) P2 , which in yeast controls the homeostasis of the vacuole ( the lysosomal compartment ) . To further test Mtmr2 function , and further test functional interactions between Mtmr2 and Fig4 , we transformed FLAG-MTMR2 in the mutant yeast strain fig4Δ . Overexpression of wild-type MTMR2 in fig4Δ caused a further enlargement of the vacuolar compartment and defects in vacuole fission whereas the catalytically inactive mutant FLAG-MTMR2C417S did not cause these changes ( Figure 9 ) . To determine the substrates and products of mammalian MTMR2 in yeast , we measured phosphorylated phosphoinositide lipid levels from cells expressing FLAG-MTMR2 as compared to the vector alone . To enhance the sensitivity of the assay , we subjected the yeast to hyperosmotic shock . In wild-type yeast , this results in a transient increase in PtdIns ( 3 , 5 ) P2 levels ( green line ) and concomitant decrease in PtdIns3P ( blue line ) ( Figure 10 , 5 min time point ) . If MTMR2 acts on PtdIns ( 3 , 5 ) P2 , then there should be a decrease in PtdIns ( 3 , 5 ) P2 and a corresponding increase in PtdIns5P . Moreover , if MTMR2 acts on PtdIns3P there will be a decrease in that lipid as well . Each of these changes was observed ( Figure 10 , solid lines and Table S1 ) . These findings demonstrate that MTMR2 acts on both PtdIns ( 3 , 5 ) P2 and PtdIns3P in yeast , and strongly suggest that MTMR2 acts on both of these substrates in mammalian cells as well . These observations support the hypothesis that MTMR2 and FIG4 coordinately regulate the PtdIns3P-PtdIns ( 3 , 5 ) P2 pathway in vivo .
The MTMR2 3-phosphatase activity toward PtdIns3P and PtdIns ( 3 , 5 ) P2 has been demonstrated by a number of studies using recombinant MTMR2 in vitro as well as conventional cell lines overexpressing MTMR2 [10]–[16] . Overexpressed MTMR2 has been co-localized with Rab7 in A431 cells at the level of late endosome/lysosomes , where PtdIns ( 3 , 5 ) P2 is generated [16] . Interestingly , another phospholipid phosphatase , FIG4/SAC3 , is involved in both the dephosphorylation and the production of PtdIns ( 3 , 5 ) P2 and is mutated in autosomal recessive demyelinating CMT4J neuropathy [20] . Loss of Fig4 in mouse provokes the plt phenotype characterized by massive neurodegeneration and peripheral neuropathy . In Fig4-null fibroblasts a decrease in PtdIns ( 3 , 5 ) P2 has been demonstrated , suggesting that Fig4 promotes PtdIns ( 3 , 5 ) P2 production by PIKfyve activation or stabilization [20] . Thus , MTMR2 and FIG4 could have opposite effects in the control of PtdIns ( 3 , 5 ) P2 . To explore the biological role of MTMR2 phosphatase activity in the nerve in vivo , we generated a Mtmr2/Fig4 double null mutant . Analysis of these mice provides evidence that Mtmr2 and Fig4 functionally interact in neurons , fibroblasts , and Schwann cells . Loss of Mtmr2 reduces the viability and exacerbates the neurodegeneration of Fig4-null mice . These results also provide the first evidence for a role for MTMR2 in neurons in vivo , consistent with the marked axonal loss in CMT4B1 patients [32] . Although conditional ablation of Mtmr2 in motorneurons in mice did not reveal signs of axonal degeneration or neuronopathy , a cell autonomous role of Mtmr2 in neurons was not excluded since axonopathies are length dependent and not easily reproduced in mice [18] . Interestingly , a role for MTMR2 in neurons in vitro has been recently reported suggesting that Mtmr2 is localized to excitatory synapses of central neurons via direct interaction with the PSD-95 scaffolding protein [33] . Knockdown of Mtmr2 in cultured neurons markedly reduced excitatory synapse density and function and it was proposed that the MTMR2/PSD95 complex contributes to the maintenance of excitatory synapses by inhibiting excessive endosome formation and destructive endosomal traffic to lysosomes . Here , we assessed MTMR2 and FIG4 interaction in yeast and found that overexpression of MTMR2 reduces both PtdIns3P and PtdIns ( 3 , 5 ) P2 leading to an increase in vacuole size in the fig4Δ mutant . These findings support the in vivo role of MTMR2 as a 3-phosphatase that acts on both PtdIns3P and PtdIns ( 3 , 5 ) P2 . Fig4 heterozygosity rescues myelin outfoldings due to Mtmr2 deficiency both in vivo and in vitro , thus providing evidence of the Fig4 and Mtmr2 interaction in Schwann cells as well as neurons . Loss of Mtmr2 specifically in Schwann cells provokes myelin outfoldings . The presence of cytoplasmic inclusions in Schwann cells and the reduced NCV in the Fig4-null mouse , and the typical demyelinating features ( onion bulbs ) of CMT4J patients , all strongly support a Schwann cell autonomous role for Fig4 . But how does loss of Fig4 in Schwann cells rescue Mtmr2-null myelin outfoldings ? We hypothesized that a 50% reduction of Fig4 might be sufficient to rebalance the PtdIns ( 3 , 5 ) P2 elevation in Mtmr2-null cells ( Figure 11 ) , thus reducing myelin outfoldings . MTMR2 loss should lead to an increase of both PtdIns3P and PtdIns ( 3 , 5 ) P2 , whereas FIG4 loss reduces PtdIns ( 3 , 5 ) P2 levels . In agreement with this model , we observed that downregulation of PIKfyve expression or inhibition of its activity in Mtmr2-null co-cultures reduced myelin outfoldings , as also observed with Fig4 heterozygosity ( Figure 11 ) . Our results therefore suggest that imbalance of PtdIns ( 3 , 5 ) P2 is at the basis of altered longitudinal myelin growth and formation of myelin outfoldings . The observed rescue of myelin outfoldings is likely mediated by restored PtdIns ( 3 , 5 ) P2 rather than PtdIns5P . PtdIns5P may be produced via dephosphorylation of PtdIns ( 3 , 5 ) P2 by MTMRs , and can also be generated , at least in vitro , by PIKfyve acting on phosphatidylinositol [34] . Therefore , Fig4 heterozygosity in Mtmr2-null cells would lead to a further reduction in PtdIns5P rather than restoration , as for PtdIns ( 3 , 5 ) P2 . PtdIns ( 3 , 5 ) P2 is thought to be localized to EE and the limiting membranes of LE/LY , although it cannot be excluded that this lipid might also be generated at other membranes . The lack of specific probes to detect PtdIns ( 3 , 5 ) P2 prevents the definition of other membrane localization [35] , [36] . Our studies raise the question of how dys-regulation of PtdIns ( 3 , 5 ) P2 leads to aberrant longitudinal myelin growth . Excessive longitudinal myelin growth and myelin outfoldings might arise as a consequence of reduced endocytosis/recycling and degradation or as a consequence of increased exocytosis . One can speculate that increased PtdIns ( 3 , 5 ) P2 due to loss of MTMR2 might favor exocytosis from the LE/LY compartment during myelin biogenesis . However , this mechanism , which has been recently suggested to occur in oligodendrocytes [37] , accounts for the assembly of myelin components during the active phase of myelination . In myelin outfoldings , myelin thickness is normal , so a more subtle mechanism of regulation would be involved . Increased PtdIns ( 3 , 5 ) P2 might alter autophagy dynamics . However , we did not observe any change on LC3II/I ratio and/or p62 levels in Mtmr2-null nerves or in myelin-forming DRG co-cultures ( unpublished results ) . Alternatively , MTMR2 may favor endocytosis and counteract exocytosis during later stages of myelin biogenesis . The myelin outfoldings may thus arise as a consequence of the loss of negative control on the amount of membrane produced during myelination . Another alternative is that MTMR2 might control endocytosis of specific transmembrane proteins linking Schwann cell plasma membrane to the axonal plasma membrane , which then act as signaling molecules to control longitudinal myelin growth . Note that myelin outfoldings often contain axoplasm and axons branches at paranodal regions thus following myelin membrane outgrowth [17] . Along these lines , enhanced surface localization of putative adhesion molecules due to loss of Mtmr2-mediated endocytosis might result in the loss of control of myelin elongation and thus in myelin outfoldings . Other members of the MTMR family seem to possess similar biological functions . MTMR4 was recently demonstrated to regulate the sorting of endosomal cargos into recycling endosomes [38] . In C . elegans , MTM6 and MTM9 were found to be involved in endocytosis [39] whereas Drosophila Mtm ( homologous to catalytically active MTM1 , MTMR1 , and MTMR2 ) regulates both actin-based plasma membrane morphogenesis and the endosomal influx toward the endo-lysosomal axis [40] . Whether and how MTMR2 might regulate endocytosis in Schwann cells during postnatal development remains to be assessed .
All experiments involving animals were performed in accordance with Italian national regulations and covered by experimental protocols reviewed by local Institutional Animal Care and Use Committees . Mtmr2-null mice were backcrossed for at least 5 generations to strain C57BL/6N . Fig4+/− heterozygous mice were maintained on the recombinant inbred line CB . plt derived predominantly from strains CAST/Ei and C57BL/6J ( 25% ) [25] . Heterozygous Fig4+/− males were crossed with Mtmr2-null females to obtain Mtmr2+/−Fig4+/− double heterozygous mice . Double heterozygotes were crossed to generate Mtmr2−/−Fig4−/− double null mice as well as Mtmr2−/−Fig4+/− mice for analysis . Genotyping was performed as described [17] , [20] . Semithin morphological analysis was performed as described previously [41] . For morphometric analysis in brainstem at P8 , neuronal damage was evaluated in the facial nucleus at the level of the upper medulla oblongata ( Bregma −5 . 88 ) . For each experimental sample , microscopic images ( 130 um×90 um , nine images per slide , three slides for each brain ) were taken with a digital camera and processed by Adobe Photoshop 7 . 0 software . To be counted , a cell ( diameter >20 µm ) had to be located in the facial nucleus and 100–150 cells were scored per section . Cells with abnormal cytoplasm vacuolization were scored as pathological . The average percentage of normal and damaged neurons for each sample was considered for each experimental group to represent the neuronal density . Counts were performed in double blind by 2 investigators on slides with a number-code system , and results were analyzed . The number of motorneurons and of total cells in spinal cord was assessed by performing at least 15 sections for each spinal cord from three animals per genotype as before and by counting the number of cells per area-cell density ( mm2 ) . The proportion of fibers carrying myelin outfoldings in Mtmr2-null nerves as compared to Mtmr2-null mice with Fig4+/− heterozygosity was determined by measuring the number of fibers carrying myelin outfoldings normalized to the total number of axons per section ( the entire nerve section was reconstructed ) . Ultrathin morphological analysis was conducted as reported previously [41] . For morphological analysis , three to five animals were evaluated at each time point in most cases . MFs were established at P3 from tails and legs chopped in pieces and incubated after PBS washing with RPMI medium and 1 mL Collagenase Type II ( Stock = 2000 U/mL in 1×PBS , Worthington , LS004204 ) overnight at 37°C . The next day , cells were plated in RPMI-1640 with 15% FBS/1× L-Glutamine/1× Pen/Strep . Cells were subjected to only two-three passages to allow maximum efficiency of metabolic labelling for PI measurement . Fibroblasts were labeled for 16 h in phosphate free DMEM ( Invitrogen ) containing 200 µCi/ml [32P]orthophosphate ( Perkin Elmer ) . Lipids were extracted , separated on Silica gel G60 plates and analyzed by HPLC as described previously [42] . PtdIns5P was quantified by mass assay as described [43] . Briefly total lipids were extracted from duplicate or triplicate plates of DRG co-cultures from Mtmr2+/− or Mtmr2−/− knock-out mice and separated on Silica gel G60 plate . Monophosphorylated PIs were scraped , eluted from silica and assessed for PtdIns ( 4 , 5 ) P2 formation in vitro using the recombinant specific PIP4KIIalpha and [gamma-32P] ATP . For western blot analysis and immunohistochemistry the following antibodies were used: rat anti-LAMP1 ( Iowa Hybridoma bank ) , Guinea pig anti-P62 ( Progen ) , rabbit anti-GFAP ( Sigma ) , rabbit anti-MAG ( kindly provided by Dr . J . Salzer ) , rat anti-MBP ( kindly provided by Dr . V . Lee ) , mouse anti-MBP ( Covance ) , rabbit anti-NF-L ( Chemicon ) , mouse anti-tubulin ( Sigma ) , and mouse anti-FIG4 ( NeuroMab ) . Myelin-forming Schwann cell/DRG neuron co-cultures were established from E13 . 5 mouse embryos as previously described [19] , [44] . Myelination was induced by treatment for 15 days with ascorbic acid ( final concentration , 50 µg/ml ) ( Sigma-Aldrich ) . Dissociated Schwann cell/DRG neuron co-cultures were established as described but DRGs were first incubated with trypsin ( 0 . 25% ) for 45 min at 37°C . Cells were also mechanically dissociated and then plated at a concentration of one to two DRGs per glass coverslip . Isolated rat Schwann cells were prepared as reported previously and cultured using DMEM with 10% of fetal calf serum , 2 ng/ml recombinant human neuregulin1-b1 ( R&D Systems ) , and 2 mM forskolin ( Calbiochem ) . YM201636 was provided by Symansis . A titration of the compound starting from 800 nM until 30 nM was performed on co-cultures to select the maximum amount of coumpound which did not affect myelination . As already described [27] , [28] , 400 or 800 nM of compound provoked extensive cell vacuolization after overnight incubation . YM201636 was provided to co-cultures at 70 nM every other day together with ascorbic acid for 13 days to achieve full myelination . Schwann cell/DRG neuron co-cultures were fixed for 15 min in 4% paraformaldehyde , permeabilized for 5 min in ice-cold methanol at −20°C , blocked for 20 min with 10% normal goat serum ( Dako ) , 1% bovine serum albumin ( BSA ) ( Sigma-Aldrich ) , and then incubated with primary antibody for 1 h . After extensive washing , the coverslips where incubated with the secondary antibody for 30 min , washed , and mounted . For double immunostaining with anti-NF-L and anti- MBP antibody , the coverslips were blocked with 1% BSA , 10% NGS for 20 min on ice , and primary antibodies were incubated overnight at 4°C . For LAMP1 staining , fibroblasts were permeabilized using 0 . 1% saponin after fixation . For immunolabeling , secondary antibodies included fluorescein-conjugated ( FITC ) and rhodamine ( tetramethylrhodamine isothiocyanate ) ( Jackson ImmunoResearch ) . Coverslips were analyzed using TCS SP5 laser-scanning confocal ( Leica ) or Olympus BX ( Olympus Optical ) fluorescent microscope , and Zeiss Axiovert S100 TV2 with Hamamatsu OrcaII-ER . To quantify the amount of myelination , the number of MBP positive segments in each explant/coverslip was assessed . As myelination is also a function of the amount of neurites/axons and of the Schwann cell number in the culture , the network of NF-L positive filaments and the number of Schwann cells ( DAPI ) were also evaluated in each explant . To quantify MBP-positive fibers displaying myelin outfoldings , at least 200 MBP-positive myelinated fibers per explant/coverslip were evaluated , in at least ten different explants/coverslip . The percentage of MBP-positive fibers showing myelin outfoldings among the total number of MBP-positive fibers was counted . Fibroblasts were stained using LAMP1 antibody and images were acquired using a confocal microscope . Images were then processed using the Image J software and those cells displaying almost all LAMP1 positive endosomes bigger than 1 . 67 µm ( only occasionally observed in wild-type cells ) were considered as carrying enlarged late endosome/lysosomes . Micrographs were acquired using a digital camera ( Leica F300 ) , and figures were prepared using Adobe Photoshop , version 7 . 0 and 8 . 0 ( Adobe Systems ) . Statistical analysis was performed using the Student t test; two tails , unequal variants , and alpha = 0 . 005 were used . Error bars in the graphs represent SEM . To downregulate PIKfyve expression , a shRNA cloned into pLKO . 1 LV ( human U6 promoter ) without a GFP reporter was used ( clone ID TRCN0000150081 ) . Non-concentrated LVs were used for RNA interference . The transfer constructs were transfected into 293FT cells together with packaging plasmids Δ8 . 9 and pCMV-VSGV using Lipofectamine 2000 ( Invitrogen ) . As control , a vector encoding a shRNA to a nonspecific sequence ( luciferase ) was used . Viral supernatants were collected 48 h after transfection , centrifuged at 3000 rpm for 15 min , and frozen at −80°C . To check for PIKfyve depletion , freshly plated rat Schwann cells ( 106 cells per 100-mm plate ) were incubated with the LVs in DMEM , 10% FBS , and 2 mM L-glutamine plus forskolin and rhNRG-1 ( EGF domain , R&D ) . Cells were expanded for an additional week and maintained in MEM , 10% FBS , 2 mM L-glutamine and 2 µM forskolin before use . A western blot using a anti-PIKfyve antibody ( Santa Cruz ) was performed . Using non-concentrated LV , transduction of Schwann cell/DRG neuron co-cultures was performed 4–5 days after dissection by incubating the cells with LVs overnight . Cells were then supplemented with C-media , and myelination was induced after 2 days . Glutathione S-transferase ( GST ) fusion proteins were expressed in Escherichia coli BL21 cells and purified directly from bacterial extract on glutathione-Sepharose 4 Fast Flow beads . Rat isolated Schwann cells and mouse brains were homogenated , and protein lysates were prepared using a binding buffer with 1%NP-40 , 50 mM Tris buffer , pH 7 . 4 , 10% glycerol , 100 mM NaCl , 10 mM NaF , 1 mM Na-vanadate . Equal amounts of protein lysates were incubated for 4 h at 4°C with immobilized GST fusion proteins and GST as control . After three washes with a buffer containing 0 . 5% NP-40 , the pellets were dissolved in SDS sample buffer and analyzed by SDSPAGE and immunoblotting . To show the relative amount of GST fusion proteins used , beads were dissolved in SDS sample buffer and analyzed by SDS-PAGE , and the gel was stained with Coomassie . Yeast cells were labeled with SynaptoRed C2 ( Biotium , Inc . , CA ) . 0 . 1 units of cells ( at 600 nm ) were collected and resuspended in 250 µl fresh media . 6 µl of SynaptoRed C2 ( 10 µg/µl dissolved in dimethyl sulfoxide ) was added to the cells and incubated at 24°C for 1 hour . Cells were then washed 2 times with fresh media and chased for 2 . 5 hours . Fluorescence and differential interference contrast ( DIC ) images were generated using a DeltaVision RT Microscope System ( Applied Precision , WA ) . Images were processed using Softworx and Adobe Photoshop . Measurement of phosphoinositide levels were performed as described previously [45] . Cells were grown in selective media to mid-log phase , harvested , washed , and resuspended in synthetic media lacking inositol . 1–4×106 cells were inoculated into 5 ml of media lacking inositol containing 5 µCi of myo-[2-3H]-inositol . Cells were labeled for 18 h at 24°C , harvested by centrifugation , washed , and resuspended in 100 µl of inositol-free media . For hyperosmotic shock , an equal volume of 1 . 8 M NaCl was added to cells ( for a final concentration of 0 . 9 M NaCl ) and the resulting suspension was incubated at 24°C for the times indicated . 800 µl of ice cold 4 . 5% perchloric acid [46] was added to the cells . Cells were lysed in the presence of 0 . 5-mm zirconia beads ( Biospec , Bartlesville , OK ) on a Beadbeater ( Biospec ) for three cycles of 2 min at room temperature followed by 2 min on ice . Cell extracts were centrifuged at 14 , 000 rpm for 10 min at 4°C . Precipitates were washed with 1 ml of 100 mM EDTA , centrifuged 14 , 000 rpm for 10 min at 4°C , and resuspended in 50 µl of sterile distilled deionized water . Lipids were deacylated by treatment with methylamine [47] . 1 ml methylamine reagent ( 10 . 7% methylamine , 45 . 7% methanol , 11 . 4% n-butanol ) was added to each sample and incubated at 55°C for 1 h . Samples were dried in a SpeedVac and the pellets were resuspended in 300 µl of sterile water , centrifuged at 14 , 000 rpm for 2 min and the supernatants were transferred to new Eppendorf tubes . 300 µl of butanol/ethyl ether/formic acid ethyl ester ( 20∶4∶1 ) was added to each . The samples were vortexed and centrifuged at 14 , 000 rpm for 2 min . The aqueous phase ( bottom layer ) was transferred to new tubes and the extraction was repeated . At the end of the second extraction the aqueous phase was dried in a SpeedVac . Samples were resuspended in 20 µl of sterile water and 15 µl of each was analyzed by HPLC using an anion exchange , PartisphereSAX ( Whatman ) , column . The column was developed with a gradient of 1 M ( NH4 ) 2HPO4 , pH 3 . 8 ( pH adjusted with phosphoric acid ) : 0% for 5 min , 1–2% over 15 min , 2% for 80 min , 2–10% over 20 min , 10% for 65 min , 10–80% over 40 min , 80% for 20 min and finally 80-0%; flow rate , 1 . 0 ml/min [48] . The value of each glycerol-inositol corresponding to PtdIns3P , PtdIns4P , PtdIns5P , PtdIns ( 3 , 5 ) P2 , and PtdIns ( 4 , 5 ) P2 is reported as percent of total phosphoinositol , to normalize number of cells and incorporation of [3H] inositol . | Charcot-Marie-Tooth type 4B1 ( CMT4B1 ) and Charcot-Marie-Tooth type 4J ( CMT4J ) are severe autosomal recessive demyelinating neuropathies with childhood onset . We previously demonstrated that loss of the phospholipid phosphatase MTMR2 causes CMT4B1 with myelin outfoldings in human and mouse and that loss of the phospholipid phosphatase FIG4 causes CMT4J and neurodegeneration in the mouse . MTMR2 has a predicted role in membrane trafficking , which is crucial for myelin membrane biogenesis and homeostasis . However , the biochemical activity of MTMR2 in vivo and the role of MTMR2 in myelination still remain to be assessed . MTMR2 and FIG4 act on the same phospholipid substrate PtdIns ( 3 , 5 ) P2 , but with predicted opposite effects . We generated a double Mtmr2/Fig4-null mouse which showed that Mtmr2 and Fig4 interact in neurons and Schwann cells to control phospholipid metabolism . Moreover , Mtmr2-null myelin outfoldings are rescued by Fig4 heterozygosity , suggesting that imbalance of PtdIns ( 3 , 5 ) P2 is at the basis of the excessive myelin growth and hypermyelination . | [
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] | 2011 | Genetic Interaction between MTMR2 and FIG4 Phospholipid Phosphatases Involved in Charcot-Marie-Tooth Neuropathies |
In some Pacific Island countries , such as Solomon Islands and Fiji , active trachoma is common , but ocular Chlamydia trachomatis ( Ct ) infection and trachomatous trichiasis ( TT ) are rare . On Tarawa , the most populous Kiribati island , both the active trachoma sign “trachomatous inflammation—follicular” ( TF ) and TT are present at prevalences warranting intervention . We sought to estimate prevalences of TF , TT , ocular Ct infection , and anti-Ct antibodies on Kiritimati Island , Kiribati , to assess local relationships between these parameters , and to help determine the need for interventions against trachoma on Kiribati islands other than Tarawa . As part of the Global Trachoma Mapping Project ( GTMP ) , on Kiritimati , we examined 406 children aged 1–9 years for active trachoma . We collected conjunctival swabs ( for droplet digital PCR against Ct plasmid targets ) from 1–9-year-olds with active trachoma , and a systematic selection of 1–9-year-olds without active trachoma . We collected dried blood spots ( for anti-Pgp3 ELISA ) from all 1–9-year-old children . We also examined 416 adults aged ≥15 years for TT . Prevalence of TF and TT was adjusted for age ( TF ) or age and gender ( TT ) in five-year age bands . The age-adjusted prevalence of TF in 1–9-year-olds was 28% ( 95% confidence interval [CI]: 24–35 ) . The age- and gender-adjusted prevalence of TT in those aged ≥15 years was 0 . 2% ( 95% CI: 0 . 1–0 . 3% ) . Twenty-six ( 13 . 5% ) of 193 swabs from children without active trachoma , and 58 ( 49 . 2% ) of 118 swabs from children with active trachoma were positive for Ct DNA . Two hundred and ten ( 53% ) of 397 children had anti-Pgp3 antibodies . Both infection ( p<0 . 0001 ) and seropositivity ( p<0 . 0001 ) were strongly associated with active trachoma . In 1–9-year-olds , the prevalence of anti-Pgp3 antibodies rose steeply with age . Trachoma presents a public health problem on Kiritimati , where the high prevalence of ocular Ct infection and rapid increase in seropositivity with age suggest intense Ct transmission amongst young children . Interventions are required here to prevent future blindness .
Trachoma is the leading infectious cause of blindness and the subject of an international campaign for elimination as a public health problem [1 , 2] . The strategy for elimination uses the acronym “SAFE”: surgery for advanced disease; and antibiotics , facial cleanliness and environmental improvement to clear infection and reduce transmission of Chlamydia trachomatis ( Ct ) , the causative organism . Australia [3] and several Pacific Island nations [4–6] have long been known to be trachoma-endemic . The first systematic investigation of trachoma in the Pacific was conducted in 2007 , when a series of Trachoma Rapid Assessments was undertaken involving examination of 3102 children aged 1–9 years and 903 adults aged ≥40 years in 67 high-risk communities of Kiribati , Nauru , Vanuatu , Solomon Islands and Fiji [7] . In Kiribati , the 2007 Trachoma Rapid Assessments included 15 sites in Betio and Buota on Tarawa Atoll , in the far west of the country . A total of 655 children aged 1–9 years and 160 adults aged ≥40 years were examined; more than one-third of those children had trachomatous inflammation—follicular ( TF ) , and nearly two-thirds of the adults had trachomatous conjunctival scarring ( TS ) [8] . One case of trachomatous trichiasis ( TT ) was identified , and it was reported that 10 TT surgeries had been performed locally ( in a population of about 100 , 000 people ) [9] over a twelve-month period . Trachoma Rapid Assessment data are deliberately biased to increase the likelihood that trachoma will be identified where it is endemic and therefore do not provide the prevalence estimates necessary to facilitate programme planning; these results provided justification for undertaking a formal prevalence survey . In 2012 , therefore , a population-based prevalence survey was undertaken in Kiribati’s capital , South Tarawa , and the adjacent township of Betio ( Fig 1 ) . It estimated the unadjusted TF prevalence in children aged 1–9 years to be 21% and the unadjusted TT prevalence in adults aged ≥15 years to be 1 . 5% [10] . Because , in 2010 , the combined population of South Tarawa and Betio was 49% of the national population [9] , this survey could have been taken as evidence justifying implementation of the A , F and E components of the SAFE strategy [11] throughout Kiribati: even if there was no TF elsewhere in the country , the national TF prevalence would still have been greater than the 10% threshold above which WHO recommends A , F and E for at least three years before a repeat survey [12] . However , Kiribati is made up of 33 atolls and islands dispersed over 3 . 5 million square kilometers of ocean—approximately half the size of Australia—and the expense of rolling out programme-level interventions in a nation of many tiny islands dispersed over such an area would be considerable . In addition , recent evidence suggests that the epidemiology of trachoma in Vanuatu , Fiji and the Solomon Islands is different to that in much of trachoma-endemic Africa , with a relative paucity of TT [13] and low prevalences of ocular Ct infection for the observed prevalences of TF [14–17] . Whether or not TT and ocular Ct infection were present elsewhere in Kiribati was therefore a question of both public health and scientific significance . Situated in the far east of Kiribati , Kiritimati Island of the northern Line Islands ( Fig 1 ) has the greatest land area ( 388 km2 ) of any coral atoll in the world and comprises over 70% of the country’s total land area . It is the second-most highly populated island of Kiribati after Tarawa [9] , with an estimated population in 2010 of 5586 people . We sought to estimate the prevalences of trachoma , ocular Ct infection , and anti-Ct antibodies on Kiritimati Island , Kiribati , in part to assess the relationships between these indices locally , and in part to guide planning processes for a national trachoma elimination programme .
Ethics approval was received from the Research Ethics Committee of the London School of Hygiene & Tropical Medicine ( reference numbers 6319 , 8355 and 10136 ) and the Kiribati Ministry of Health and Medical Services ( 08/11/2015 ) . Verbal consent to involve each village was obtained from its leaders . Written informed consent was obtained from all individuals examined , or a parent or guardian if the participant was aged <15 years . Children aged 1–9 years are the standard indicator group for estimating the prevalence of active trachoma [12] . We wished to have 95% confidence of estimating an expected TF prevalence in 1–9-year-olds of 20% with absolute precision of ±5% . Using the single population proportion for precision formula and correcting for a small , finite population ( because the estimated number of 1–9-year-olds resident on Kiritimati was 1222 ) , and using a design effect of 2 . 65 , our sample size estimate was 426 1–9-year-olds [9 , 18 , 19] . Although we determined the sample size as a number of children , all individuals aged ≥1 year resident in selected clusters and who gave consent to be examined were included in the survey [19] . All four inhabited villages ( the smallest administrative unit ) on Kiritimati Island were visited ( Fig 1 ) , with the number of households to be invited to participate in each village proportional to that village’s relative size: 62 households from London , 106 households from Tabwekea , 42 households from Banana , and 11 households from Poland ( Fig 1 ) . Maps of each village were prepared by hand and compact segment sampling used , with clusters of households selected by random draw . In addition to household visits , data were collected at Presbyterian and Catholic maneabas ( large open community halls where families gather and often stay for months ) within or adjacent to the selected clusters . Graders were trained and certified according to the standardized training protocols of the GTMP [19] . Trachoma grading was undertaken according to the WHO simplified grading scheme [8] , using 2 . 5× binocular magnifying loupes and sunlight illumination . Eyes with trichiasis were considered to have TT if and only if they also had TS . ( The presence or absence of TS was not recorded in the absence of trichiasis . ) Graders cleaned their hands with alcohol-based hand gel after each examination . Participants identified as having active trachoma ( TF and/or trachomatous inflammation—intense [TI] in one or both eyes ) were provided with two tubes of 1% tetracycline eye ointment , and they or their parents or guardian were instructed on how to apply it . Participants with trichiasis were referred for management by a trained ophthalmologist . When a 1–9-year-old child was found to have TF and/or TI , a conjunctival swab was taken by the examiner . In addition , a conjunctival swab was taken from a systematic two-thirds sample of 1–9-year-old children without these signs , with the creation of this sub-sampling approach necessitated by the discovery that a box of sterile swabs had been misplaced in transit to the island , prior to fieldwork—this meant that we would not be able to swab all the children that we intended to examine . Systematic sampling was achieved by selecting the first two of every three children without active trachoma , in order of presentation . Individuals older than 9 years were not swabbed , regardless of whether or not they had active trachoma . In subjects selected for swabbing , a single sterile polyester-coated swab ( Puritan Medical Products , Guilford , USA ) was passed four times across the upper tarsal conjunctiva of the more inflamed eye , rotating the swab 90° after each pass . Using a technique that minimised the likelihood of the retained part of the swab shaft or swab-head touching anything other than the subject’s conjunctiva and the collection tube , swabs were placed in polypropylene tubes and refrigerated at the end of each day; ice was not available locally . Following grading and ( if indicated ) swab collection , examiners collected fingerpick blood onto filter paper ( Cell Labs Pty , Sydney , Australia ) from all 1–9-year-old children assessed . Filter papers were air-dried overnight in an air-conditioned room , then packed in individual resealable plastic bags; up to 100 of those primary plastic bags were enclosed in large secondary resealable plastic bags containing dessicant sachets for refrigeration and transport . Swabs and dried blood spots were shipped at ambient temperature to the London School of Hygiene & Tropical Medicine for droplet digital PCR ( ddPCR ) and anti-Ct antibody testing , respectively . Handling and processing was identical for all samples , being undertaken masked to examination results . Genomic DNA was extracted from swabs using the QIAamp DNA mini kit ( Qiagen , Manchester , UK ) . Proprietary lysis buffer and proteinase K were added directly to the specimen collection tube and incubated for 1 hour at 56°C to lyse the specimen . The DNA was then bound to a silica spin column , washed , and eluted into 100μL Tris-Cl EDTA . One 8μL aliquot was tested for conserved regions of Ct plasmid open reading frame 2 ( diagnostic target ) and human 30kDa ribosomal RNA subunit ( RPP30; endogenous control target ) using an in-house ddPCR , described elsewhere [16 , 20] . An area of each filter paper calibrated to hold 10μL of blood was tested using an ELISA for anti-Pgp3 antibodies [14 , 21] . Serum was eluted from dried blood spots overnight at 4°C into phosphate-buffered saline ( PBS ) supplemented with 0 . 3% volume/volume Tween 20 ( PBST ) and 5% milk powder , for a final serum dilution of 1:50 . Immulon® 2HB plates were coated with GST-tagged Pgp3 overnight at 4°C . The next day , plates were washed and then blocked with PBST for 1h at 4–10°C . After removal of blocking buffer , 50μL of the serum elution was added to each well and plates incubated at room temperature for 2h on an orbital shaker . After removing unbound antibody with PBST , 50μL of rabbit anti-human IgG ( Southern Biotech , Brimingham , USA ) diluted 1:32 , 000 in PBST was added to each well , and plates were incubated for 1h at room temperature on an orbital shaker . After washing with PBST , plates were incubated in the dark at room temperature for 10 minutes with 50μL per well TMB ( KPL , Gaithersburg , USA ) . The reaction was stopped with 50μL 1N H2SO4 and the absorbance read at 450nm on a Spectramax M3 plate reader ( Molecular Devices , Wokingham UK ) . Readings were corrected for background by subtracting the average absorbance of three blank wells containing no serum , using Softmax Pro5 software ( Molecular Devices ) . Serial dilutions of high-titre serum in low-titre serum were run on each plate , and all sample ODs were normalized against that of a middle dilution of the high-titre serum . A finite mixture model [21–23] was used to estimate a negative population in the resulting normalised optical density ( NOD ) values , and specimens were considered anti-Pgp3 positive if their NOD was more than three standard deviations above the mean of the presumed-negative population ( a NOD threshold of 0 . 245 ) . Data entry and real-time server upload was via a bespoke android-based Open Data Kit data capture system , developed as part of the GTMP [19] . Participant data were encrypted and stored in a secure server with only the study investigators having access . TF prevalence data were adjusted for the age of those examined , in five-year age bands . TT prevalence data were adjusted for age and gender of those examined , in five-year age bands . The most recent census data [9] were used as the reference dataset for the purposes of undertaking these adjustments . Results are presented in accordance with STrengthening the Reporting of OBservational studies in Epidemiology ( STROBE ) guidelines ( see S1 Checklist ) [24] .
Fieldwork was conducted in November 2015 . This coincided with the tenth anniversary of the arrival of Christianity to Kiritimati; as a consequence of the associated celebrations , a majority of families had decamped to local maneabas . We examined 406 of the 412 children aged 1–9 years identified as being resident in selected segments; the other six children were absent at the time of the field teams’ visits and could not be located by their parents or guardians . Of those examined , 123 children had TF and 16 had TI; all children with TI also had TF . The age-adjusted prevalence of TF in children was 28% ( 95% CI 24–35 ) . Of 417 adults aged ≥15 years enumerated , 416 were examined , with one person refusing to participate . A total of 8 adults were identified to have TT . The age- and gender-adjusted prevalence of TT in adults was 0 . 2% ( 95% CI 0 . 09–0 . 33% ) . One person had bilateral TT . Swabs were available from 311/406 ( 77% ) children aged 1–9 years: 118 children with TF and/or TI , and 193 systematically selected children without either of these signs . Infection was significantly associated with signs of active trachoma ( Table 1; Pearson’s Χ2 p<0 . 0001 ) . Extrapolating the infection prevalence in swabbed children who had neither TF nor TI ( 13 . 5% ) to all such children ( including the 90 who were not swabbed ) , we expect that 38 of the 283 children who had neither TF nor TI were infected; extrapolating the infection prevalence in swabbed children with TF and/or TI ( 49 . 2% ) to all such children ( including the 5 who were not swabbed ) , we expect that 60 of the children with TF and/or TI were infected . This would give an estimated overall prevalence of infection in examined 1–9-year-olds of 24% . A median of 17 , 330 RPP30 copies were found per swab , equivalent to approximately 8 , 500 host cells per swab . Of PCR-positive swabs ( n = 84 ) , the median Ct load in children with TF and/or TI in either eye was significantly higher than in those with neither TF nor TI ( 86 , 450 vs 6 , 530 plasmid copies/swab , logistic regression p = 0 . 0057; Fig 2 ) . Blood spots were analysed from 397 children aged 1–9 years; 8 of the remaining 9 examined children did not assent to fingerprick blood collection , and 1 child was not bled because his parents reported a bleeding diathesis . Seropositivity was significantly associated with active trachoma ( TF and/or TI in either eye ) ( Χ2 p<0 . 00001; Table 2 ) . Reactivity to Pgp3 was higher in children with active trachoma than in those with neither TF nor TI ( Fig 3 ) , and significantly higher in those with conjunctival Ct infection than in those without ( logistic regression p<0 . 0001 , Fig 4 ) . 90 . 7% of PCR-positive children were seropositive . The age-specific seroprevalence was low in those aged 1 year ( 7% ) , increased rapidly between the ages of 1 and 5 years , and was 76% in those aged 9 years ( Fig 5 ) . This increase in seropositivity with age was highly significant ( logistic regression p = 0 . 0007 ) . There was also a significant increase in NOD with age ( logistic regression p<0 . 0001 ) .
We have estimated the prevalence of trachoma and current and cumulative infection with C . trachomatis on Kiritimati Island . Our GTMP-based approach to sampling via household selection [19] was challenged by the extensive community bonds of the Kiritimati population , which mean that nuclear families tend not to maintain exclusive residence in a single family dwelling . Prolonged spells of communal living in maneabas and individual mobility between households make the question , “Where do you live ? ” difficult for residents to answer . Rather than undertaking a complete census of the island’s population and selecting individuals by simple random sampling , we opted for compact segment sampling , including the geographically adjacent maneaba or maneabas . This was an imperfect solution , in that it will have somewhat biased survey recruitment by including individuals not normally resident within the land area of the selected segment . We did not keep a record of whether individual participants were examined in their home or in a maneaba , entering data on all subjects as though they were residents of the same household . A consequence of this was that we were unable to adjust for clustering in our analyses , which may result in our prevalence estimates being less precise than the confidence intervals that we present here . A further limitation was the lack of access to ice to keep samples cold in the field , where temperatures peaked at >30°C each day . We carried samples in insulated boxes , then refrigerated them at the end of the day’s work and until they were transported to Manila , Philippines , for repackaging and shipment to London . DNA degradation occurs more rapidly at higher temperatures , and it is therefore possible that our estimates of Ct PCR positivity and DNA loads are underestimates , however our own [15] and others’ [25] data suggest this degradation results in ( qualitative ) diagnostic failure only in very low load samples , which are presumably from individuals least likely to pass infection on to contacts [26] . Loss of antibody following storage for several hours at temperatures >30°C is minimal [27] . Finally , although we took great care to avoid carry-over contamination of swabbed material from one subject to the next , we cannot say with certainty that this did not occur . Notwithstanding those limitations , our data suggest that trachoma is a public health problem on Kiritimati Island . The prevalence of TF in 1–9-year-olds observed here ( 28% ) is at a level at which WHO recommends implementation of the A , F and E components of the SAFE strategy for at least three years before re-survey [12] . The Kiritimati TF prevalence that we observed is broadly comparable to that ( 21% ) estimated for South Tarawa and Betio of Kiribati in 2012 . Whether these two survey findings , taken together , should be extrapolated as the basis for a decision to deliver population-based interventions , including mass drug administration of azithromycin [28] , across the country’s 18 other inhabited islands , is a question that will require considerable thought . We submit that such an approach is now justified . The prevalence of TT that we observed in adults—0 . 21% , though two significant digits may be more than is justified given that there were only 8 cases—is just over the TT threshold of 0 . 2% specified by WHO as part of the definition for trachoma’s elimination as a public health problem [29] . Ongoing provision of TT surgical services on Kiritimati is likely to be necessary , but because of the relatively small total population , the number of people needing such services is likely to be small . The finding that there is a burden of TT here is again consistent with the situation in South Tarawa and Betio , but contrasts with that in several other Pacific Island countries in which trachoma mapping has been undertaken to date , where TT is rare to absent in adults despite moderate prevalences of TF in children . We found strong evidence for both current Ct infection ( demonstrated by PCR-positive conjunctival swabs ) and widespread prior exposure to Ct infection ( demonstrated by antibodies to Pgp3 ) in children on Kiritimati . Both the presence and load of infection was closely associated with active trachoma in this population , as is typical of trachoma-endemic environments in sub-Saharan Africa [30] . In contrast , in trachoma-endemic communities of the Solomon Islands and Fiji , the prevalence of ocular Ct infection has recently been shown to be very low; it has been hypothesized that the latter finding may account , in whole or in part , for the scarcity of trichiasis in those environments [13 , 15 , 16] . TI is associated with chronic , repeated conjunctival Ct infection and incident TS [31–33] , the precursor to TT . Of the 406 children aged 1–9 years that we examined , 16 ( 4% ) had TI , compared to 2 ( 0 . 2% ) of 1135 children aged 1–9 years examined in the Solomon Islands population in which the prevalence of Ct was only 1 . 3% [16] . The high prevalence of antibody positivity found in the children bled on Kiritimati indicates a high prevalence of previous exposure to Ct infection , and the steep increase in seroprevalence we observed between those aged 1 and 5 years suggests intense transmission is occurring in that age group [34 , 35] . These observations all point to a significant force of infection present in the Kiritimati population , making it straightforward to understand the occurrence of TT in adults , and underscoring the need for implementation of interventions to reduce the prevalence and transmission of infection here . As elimination activities progress , re-measuring the prevalence of conjunctival Ct infection and prevalence of anti-Pgp3 antibodies in young children will be instructive , with those longitudinal data likely to help us to further understand the potential utility of these tools for monitoring programmes [36] . Our finding that a substantial proportion of 1–9-year-olds with neither TF nor TI were Ct-positive ( 13% , Table 1 ) and anti-Pgp3 antibody-positive ( 41% , Table 2 ) is not new [37–40] , and has been the source of prolonged discussion over how best to evaluate the potential programmatic use of laboratory tests for Ct exposure [41–44] . It is certainly true that the WHO simplified trachoma grading scheme is relatively insensitive , ignoring degrees of conjunctival inflammation less marked than five moderately-sized central follicles or pronounced thickening obscuring at least half of the normal deep tarsal blood vessels [8] . Some children who had neither TF nor TI undoubtedly had lesser degrees of inflammation than this . More sensitive , more detailed trachoma grading systems exist [45] , but the simplified system was specifically developed because those systems were felt to be too complex for use by non-specialist personnel working at community level [8] , and is the current standard against which any proposed new method for evaluating the success of interventions against trachoma should be assessed . The differences observed in the epidemiology of trachoma here with that in neighbouring countries could be due to a number of different factors . Kiribati’s population is predominantly Micronesian , whereas the island nations to the southwest are dominated by peoples of Melanesian descent . The environment is also different: despite the abundant vegetation , fresh water availability is poor throughout Kiribati because infrastructure is inadequate , the evaporation rate is high , droughts are frequent , and the soil is mostly loose and drains quickly [46] . Such conditions are associated with greater risk of trachoma elsewhere: in a national survey of Nigeria , for example , villages with hotter , drier climates have greater risk of TT than cooler , wetter villages [47] . Because the preponderance of people enrolled in this study were examined in maneabas rather than in their own homes , collecting data on household-level water and sanitation risk factors for trachoma was not undertaken [48–50] , but doing so in future data collection exercises here , with appropriate adjustments in the unit of data collection ( to account for communal living ) , would be of interest . Our data indicate a need for interventions against trachoma in this population , and will serve as a baseline for ongoing comparative studies of the epidemiology and pathogenesis of trachoma in the Pacific Islands . | Ocular infection with Chlamydia trachomatis causes trachoma . It is the leading infectious cause of blindness , and the target of an international campaign for elimination as a public health problem . Trachoma is endemic to Tarawa , the most populated island of Kiribati , housing approximately half the national population . However , the country has 20 inhabited islands and there were no previous trachoma prevalence data from Kiribati outside Tarawa . We set out to determine the prevalence of trachoma in the second most populated island , Kiritimati , located over 3000 km from Tarawa . In some other Pacific Island countries , ocular C . trachomatis infection is much less prevalent than the clinical signs that are used to guide interventions; we therefore looked for PCR-based evidence of current infection and antibodies to chlamydial proteins , in addition to recording clinical signs of trachoma . Our results indicate that trachoma and ocular C . trachomatis infection are prevalent on Kiritimati , and suggest that interventions are required here . The combined application of antibody , nucleic acid and clinical tools in an intervention-naïve population provides insight into their inter-relationships and the data are , therefore , of considerable interest to elimination programmes within and beyond the Pacific . | [
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"loca... | 2017 | Prevalence of signs of trachoma, ocular Chlamydia trachomatis infection and antibodies to Pgp3 in residents of Kiritimati Island, Kiribati |
Salmonella enterica infections are a significant global health issue , and development of vaccines against these bacteria requires an improved understanding of how vaccination affects the growth and spread of the bacteria within the host . We have combined in vivo tracking of molecularly tagged bacterial subpopulations with mathematical modelling to gain a novel insight into how different classes of vaccines and branches of the immune response protect against secondary Salmonella enterica infections of the mouse . We have found that a live Salmonella vaccine significantly reduced bacteraemia during a secondary challenge and restrained inter-organ spread of the bacteria in the systemic organs . Further , fitting mechanistic models to the data indicated that live vaccine immunisation enhanced both the bacterial killing in the very early stages of the infection and bacteriostatic control over the first day post-challenge . T-cell immunity induced by this vaccine is not necessary for the enhanced bacteriostasis but is required for subsequent bactericidal clearance of Salmonella in the blood and tissues . Conversely , a non-living vaccine while able to enhance initial blood clearance and killing of virulent secondary challenge bacteria , was unable to alter the subsequent bacterial growth rate in the systemic organs , did not prevent the resurgence of extensive bacteraemia and failed to control the spread of the bacteria in the body .
Salmonella enterica causes systemic diseases ( typhoid and paratyphoid fever ) [1] , food-borne gastroenteritis and non-typhoidal septicaemia ( NTS ) [2]–[4] in humans and in many other animal species world-wide . Current measures to control S . enterica infections are sub-optimal . The emergence of multi-drug resistant strains has reduced the usefulness of many antibiotics [5]–[6] . Prevention of infection of food-production animals by implementation of biosecurity or hygiene measures is expensive and is undermined by increased free-range production . Vaccination remains the most feasible means to counteract S . enterica infections . There is an urgent need for improved vaccines against typhoid fever and there are currently no licensed paratyphoid or NTS vaccines [7] . To attain a high level of protective immunity against systemic infections with virulent strains of Salmonella in susceptible hosts it is necessary to induce both antibody responses and T-helper type 1 ( TH1 ) cell-mediated immunity [8] . This is due to the fact that intracellular control requires TH1 immunity whereas antibodies can only target the bacteria in the extracellular compartment ( reviewed in [9]–[10] ) . New generations of live attenuated vaccines have been constructed in the last two decades and are currently being evaluated in field trials . These vaccines mimic the course of natural infection and are more protective than previous ones , but we do not understand the mechanisms responsible for this [11]–[12] . There is also a recent trend towards the development of non-living vaccines against S . enterica enteric diseases for humans and other animals . Current non-living vaccines are based on inactivated whole cells and surface polysaccharides ( e . g . Vi polysaccharide and Vi conjugate vaccines for humans ) [13]–[14] and subunit protein-based vaccines are being considered . However , non-living S . enterica vaccines vary greatly in their protective ability [15]–[19] . Vaccine design and selection is still largely an empirical process . This is due to our insufficient understanding of how vaccine-induced immune responses impact precisely on the dynamics of a secondary infection in terms of bacterial division , killing , spread and persistence in the tissues . Interactions between infectious agents and their hosts occur in diverse environments and over a range of scales: from initial contact at the single cell level; spread throughout various compartments of the host; and between hosts at a population level . Intervention strategies to control infections can interfere with the host-pathogen relationship at all these levels so understanding the dynamics of infections at all scales is important . Mathematical approaches have been extensively used to model infection dynamics on the population level but until relatively recently within-host dynamics have been measured rather crudely , typically by monitoring total pathogen loads in a host or its organs . These measures cannot disentangle the relative contributions of pathogen replication and death to overall growth . For example an unchanging total pathogen load could be due to both replication and killing occurring in balance , or to a lack of replication and no killing . Attempts have been made to measure bacterial pathogen division rates within hosts and cells by techniques such as using non-replicating elements introduced into the bacteria , or dilution of a fluorescent marker that is not expressed within the cell in vivo [20]–[23] but these are often confounded by uncertainties concerning the dynamics of these elements in complex in vivo milieu and the potential effects on the phenotype of the pathogen being investigated . We have established and used a research approach based on the tracking of bacterial subpopulations at multiple body sites to study infections of mice and to capture the parameters that govern the intra-organ and inter-organ infection processes . The system employs simultaneous infections with individually identifiable wild-type isogenic tagged strains ( WITS ) the relative proportions of which can be determined by quantitative real-time PCR ( qPCR ) [24]–[25] or other molecular techniques . One of the systems to which this technology has been applied is the systemic phase of experimental Salmonella infections in mice , a well-established model for invasive disease [25] . In this system , observation of both total bacterial loads and the changes in WITS population structure enabled inference to be drawn on the rates of bacterial replication , killing and spread between organs . This approach allowed us to capture the key dynamic traits of the primary infection process in unimmunised animals and to explore the impact of innate immunity on the infection . In this previous study , we determined that the infection process is multiphasic , with an initial phase of rapid bacterial replication and killing of part of the inoculum by the innate host response mediated by reactive oxygen species ( ROS ) , followed by a phase of growth and intra-organ spread with the stochastic selection of individual bacterial subpopulations . Later the infection moves to a phase in which bacteraemia and the mixing of different subpopulations of bacteria in the organs occurs [25] . In the present study we refined our framework and used it to understand to what extent and at what stages of the infection different vaccine types and different branches of the vaccine-induced host immune response restrain intracellular division and enhance bacterial killing , whether there are changes in the patterns of local or systemic spread in vaccinated/immune animals , whether immunity acts simultaneously or equally on the global bacterial population , and whether there are intra- or inter-organ differences and heterogeneous traits in the control of individual bacterial subpopulations .
Vaccination with either live or killed Salmonella is known to increase the rate of blood clearance of an intravenous challenge , result in a bias of segregation of bacteria towards the liver rather than the spleen , and to enhance the reduction in bacterial loads in the spleen and liver that occurs within the first hours [26]–[30] . To investigate the contributions made by bacterial killing and growth to these changes in total bacterial numbers we performed two separate experiments vaccinating mice with either live attenuated Salmonella Typhimurium ( STm ) SL3261 ( Live Vaccine group: LV ) or acetone-killed STm SL1344 ( Killed Vaccine group: KV ) . Naive control and vaccinated animals ( n = 9 or 10 , see Table S1 ) were then challenged with ∼300 colony forming units ( CFU ) of STm WITS and net bacterial numbers and WITS proportions were monitored over a period of 72 h post-challenge ( timepoints 30 min , 24 , 48 , 72 h ) . The challenge dose was chosen as the minimum to yield reliable colonisation of both the liver and spleen of vaccinated animals as determined in pilot experiments described in the Methods section . Vaccination with either type of vaccine resulted in accelerated clearance of the WITS challenge inoculum from the blood with no circulating bacteria detected at 30 min post-challenge ( Figure 1 ) . In contrast the majority of naive animals still harboured bacteria in the blood at this time . By 6 hr post-infection ( p . i . ) the majority of vaccinated and non-vaccinated animals had cleared the challenge dose from the blood and at 24 hr no bacteraemia was observed in any of the mice tested . At 24 h post-challenge , animals vaccinated with either preparation had a lower bacterial load in the organs compared to naive controls but the kinetics of this reduction differed somewhat between vaccine types ( Figure 1 ) : at 6 h p . i . bacterial loads in both organs of mice immunised with the LV were lower than in the controls whereas for the KV loads were only significantly lower in the spleen at this timepoint; by 24 h p . i . bacterial loads in both vaccinated groups were lower than in the controls . Subsequently between 24 and 72 h after challenge , mice immunised with the KV and the unimmunised control mice both allowed a rapid increase in bacterial numbers ( ∼10-fold per day ) , with no statistically significant difference between these groups , and a resurgence of bacteraemia from 48 h p . i . In clear contrast , net bacterial growth was much slower in the tissues of mice vaccinated with live bacteria and bacteraemia was observed in only one of nine mice tested and even then at a very low level; Salmonella were absent from the spleens in 4/9 animals by 72 h p . i . Next we analysed the population structure of WITS in the samples to determine when bacterial death and inter-organ spread occurred . The challenge inoculum contained equal numbers of each of 8 WITS . We first considered the number of distinct WITS per mouse that were present in both organs ( liver plus spleen ) , in one organ only , or in neither of the organs ( Figure 2 ) . In the absence of bacterial killing we would expect all individual WITS to be represented in one or both organs . In contrast , if bacterial killing was significant then the number of strains present in only one organ , or absent from both would increase . Based on the disappearance of WITS from either organ , we predicted that bacterial death was higher during the first 24 h in animals vaccinated with either vaccine compared to naive mice . In the naive and KV groups , between 48 and 72 h p . i . , we observed increases in the numbers of individual WITS that were simultaneously present in both spleen and liver ( Figure 2 ) . This was suggestive of bacterial transfer between organs and coincided temporally with detection of bacteraemia in these mice from the 48 hr time-point ( Figure 1 ) . The number of WITS that were present or absent in the organs of mice immunised with the LV remained approximately constant between 24 h and 72 h post-challenge indicating that neither transfer between organs nor substantial bacterial death had occurred in this group of animals . In addition to determining the presence and absence of WITS from the organs , we measured the proportion of each WITS present in the spleen or liver ( Figure 3 ) . For the unvaccinated animals at 30 min each WITS could be found in both the spleen and liver with a proportion of approximately 1/8 , i . e . the population structure was similar to that in the inoculum ( which contained equal numbers of the eight WITS ) . By 6 h p . i . and until the 24 h time-point , the population structures ( i . e . presence , absence and proportions of each WITS ) in each organ of naive mice had diverged ( consistent with stochastic killing of bacteria within the organs ) . By 48 h , coincident with the onset of bacteraemia , the hepatic and splenic WITS population structures became more similar – presumably due to transfer of bacteria between the organs via the blood . Between 48 and 72 h p . i . the liver and spleen populations became nearly homogenous within each individual unvaccinated mouse ( i . e . a given WITS was present in the same proportion in the spleen and liver ) ( Figure 3; Table 1 ) . For animals vaccinated with either LV or KV , there was a bias in segregation of bacteria towards the liver , shown by compression of the points around the vertical in Figure 3 , and enhanced bactericidal activity resulted in a number of WITS being absent from one or both organs at 6 hr post-challenge . From 48 h the WITS population structures in the KV and LV groups diverged: the KV immunised animals showed a similar pattern to the unvaccinated controls with highly correlated WITS organ populations in all animals by 72 h ( Figure 3; Table 1 ) , and with an increase in the number of WITS simultaneously present in both the liver and spleen ( Figure 2 ) ; in contrast , for the LV group there was no increase in the co-occurrence of WITS in both organs ( Figure 2 ) and highly correlated WITS organ populations were only observed in a minority of animals ( Figure 3; Table 1 ) , therefore indicating that in most of these animals significant inter-organ spread had not occurred up to 72 h post-challenge . The bias in bacterial numbers towards the liver that was observed earlier in the infection was still present in the LV group at this late timepoint , presumably as a consequence of the restraint of both bacterial growth and spread in this group resulting in populations similar to the early timepoints . In contrast for the KV group this bias had disappeared , likely a consequence of uncontrolled spread and growth of bacteria in these animals . The enhanced reduction in total bacterial numbers , the fluctuations in the WITS population structure and the disappearance of WITS from the spleen and liver indicated that the overall dynamics of bacterial division and death are different in naive mice and in vaccinated animals in the early stages of the infection . Bacterial dynamics were described using a stochastic model that keeps track of the number of copies of a single WITS in the blood , liver and spleen simultaneously [9] , [25] . The parameters of the model ( inoculum size , rates of bacterial replication , killing and transfer from the blood to the organs ) were all estimated by fitting the model to the data from each experimental group at 0 . 5 , 6 and 24 h post-inoculation , using maximum likelihood . In order to allow for the possibility of early killing or inactivation of bacteria before they start to colonise the organs , we estimated an effective inoculum size consistent with the total number of bacteria 30 min p . i . in each experimental group . By comparing these values with the average inoculum doses actually used we obtained an estimate of the fraction of bacteria eliminated in the very early stage of infection ( i . e . within 30 min of inoculation ) : this fraction was highest ( 44% ) in the LV group ( Table 2 ) . Biologically this could be a consequence of bacterial killing and/or entry into a non-replicative state [21] , [31]–[32] . In our model the remaining bacteria settle into the liver and spleen where they undergo a process that involves replication and killing . Comparing the model estimates for these processes between groups ( Table 2 ) we see that for both vaccine types , the model captures the enhanced blood clearance and increase in the proportion of bacteria going to the liver that was observed in the data . Estimates of the intra-organ replication and killing rates ( Figure 4 ) were very similar in naive mice and in mice immunised with the LV in the first 6 h , whereas higher rates of bacterial replication and killing in the liver were estimated in mice immunized with the KV during this initial period of the secondary infection , resulting in a more rapid net reduction in bacterial numbers . All groups exhibited a large reduction in both the killing and replication rates in the liver after 6 h , with replication rates marginally higher than killing rates . The estimated killing and replication rates were lower in the spleen than in the liver for the first 6 h of the secondary infection , but became similar in both organs between 6 and 24 h . In all cases , the fitted models predicted distributions of bacterial loads and WITS abundancies similar to the data ( see Supporting Information Text S1 ) Thus , based on variations in the population structure of WITS , our model predicts that the reduction in the total numbers of viable bacteria that is seen in the first 24 h in both groups of immunised mice is largely due to enhanced bacterial killing in the first few hours after challenge . More specifically , the models predict that in mice immunised with the LV there was a substantial reduction ( by 44% ) in the number of viable bacteria compared to naive mice . This is predicted to occur before the bacteria can be detected in the spleen and liver . The models predict that once the bacteria are in the spleen and liver of the LV-immunised mice they are subject to enhanced bactericidal activity in the spleen and normal bactericidal activity in the liver as compared to naïve mice . For the KV , in contrast , we did not observe any loss of viable bacteria by 30 min p . i . , there was then more intense bactericidal activity ( combined with faster bacterial replication ) in the liver than any other group . Between 6 and 24 h p . i . bacterial replication and killing rates decreased markedly in animals immunized with either vaccine type showing that the control of bacterial numbers after 6 h proceeds mainly by bacteriostatic mechanisms . Taken together the model estimates , measures of WITS co-occurrence , and correlation between the organs show that immunisation with KV enhances blood clearance , and increases the killing rates in the organs ( up to 24 h ) . Subsequently bacteriostatic effects predominate , but these are not enhanced by the killed vaccine over what is seen in naive animals , as shown by similar increases over time in bacterial numbers in naive and KV mice . In contrast , the immune response induced by the LV rapidly inactivated ( within 30 min ) a large fraction of the challenge dose , enhanced clearance of the bacteria from the blood and their transfer into the organs and subsequently exerted a stronger bacteriostatic effect which restrained bacterial growth more than in naive or KV animals . Animals immunised with a live vaccine can control the growth of a secondary challenge in the spleen and liver . CD4+ and CD8+ T-cells are known to play a key role in immunity conferred by live vaccination [8] , [33] . We therefore wished to determine how and at which time T-cell dependent immune functions impact on the dynamics of a secondary challenge in terms of control of bacterial division , enhancement of death and restraint of spread between systemic sites . LV-immunised mice were treated with either anti-CD4 plus anti-CD8 antibodies , or with control immunoglobulins two days before and after challenge with ∼300 CFU of WITS . Pilot experiments showed that differences in bacterial loads between T-cell positive and T-cell negative animals became significant only by 72 h post-challenge therefore we extended the time-course of the experiments to capture these differences; groups of mice ( n = 5 or 10 , see Table S1 ) were sacrificed at 30 min , 6 , 24 , 48 , 72 , 96 , 120 and 144 h and total bacterial numbers and WITS population structures were assessed in the spleen , liver and blood . Depletion of T-cells had no effect on the bacterial loads of the challenge organisms up to 48 h , but subsequently the groups diverged with the LV immunised T-cell depleted animals having higher loads and from 96 h bacterial numbers in this group were increasing at a rate similar to that seen in naive animal ( Figure 5 ) . Subsequently bacterial loads in T-cell depleted mice were consistently higher than in LV-immunised control animals and a resurgence of bacteraemia was detected in the T-cell depleted mice . Bacterial counts in the control mice immunised with the LV increased more slowly than in LV-immunised T-cell depleted mice . Observation of WITS co-occurrence in the spleens and livers ( Figure 6 ) showed similar overall bactericidal activity ( absence of individual WITS from both organs ) up to 96 h post-challenge in both control and T-cell depleted mice . Fitting the mathematical model to this dataset showed that while estimated killing and replication rates over the first 6 h were somewhat higher than in the previously conducted LV experiment , there was little difference between the T-cell depleted and control ( T-cell positive ) mice ( see Supporting Information Text S1 ) . By 120 h , the number of distinct WITS in the organs decreased in LV immunised control mice , indicating killing of bacteria had started to occur in LV . Conversely there was no evidence of bacterial killing in T-cell depleted animals and the number of WITS simultaneously present in both organs increased markedly from 72 h post-challenge which was also indicative of inter-organ spread . The relative proportions of individual WITS in this group were similar in the spleens and livers of T-cell depleted mice ( Figure 7; Table 3 ) confirming that inter-organ spread was significant in these animals . A resurgence of bacteraemia was observed at later time-points in vaccinated T-cell depleted animals , this could not have been due to a defect in production of antibody following T-cell depletion as there was no difference in circulating Ig levels between T+ and T− animals post-secondary challenge ( Figure S1 ) . Unfortunately it was not possible at this time to quantify these processes using our mathematical model due to the increased complexity of the system at later time points . We also observed highly correlated WITS organ populations in the majority of LV-immunised T-cell positive mice later in the infection , but only from 120 h ( Table 3 ) indicating that in these mice bacteria were able to spread between organs despite the very low-grade bacteraemia . By considering mice individually we see that highly correlated populations were observed in those animals with higher bacterial loads and that only one WITS made up the majority of the population in these mice ( Figure S2 ) . T-cell depleted animals also showed a limited number of WITS predominating at 144 h although more individual WITS were present in each sample presumably as bacterial killing was not significant in these animals . KV-immunised mice showed the same effect by 72 h post-challenge , as did naive mice albeit to a more limited extent . Taken together these data show that neither the LV-induced initial rapid bacterial kill nor the subsequent bacteriostatic mechanisms that predominate early in the infection were T-cell dependent and that T-cell dependent bacterial killing became observable by 120 h p . i .
Here we show that previous immunisation with a live attenuated Salmonella vaccine results in the rapid kill of a great proportion of the challenge inoculum and additionally enhanced bacteriostasis until the robust LV-induced T-cell response controls the infection by bactericidal mechanisms . LV also controls secondary bacteraemia to extremely low levels and delays and reduces inter-organ spread . Immunisation with a whole-cell killed vaccine enhanced blood clearance and only transiently ( within the first 24 h after challenge ) increased the rates of bacterial killing within the organs . Later in the infection , the KV neither controlled the net growth of the virulent challenge in the spleen and liver nor was able to induce clearance of the bacteria from the tissues . The KV did not control the rapid resurgence of bacteraemia and did not control inter-organ spread despite its ability to induce anti-Salmonella antibodies . Mixed infections with individually identifiable wild-type isogenic tagged strains ( WITS ) have recently been used to gather information on the population dynamics of bacteria within hosts [24]–[25] , [34] . Use of multiple isogenic strains facilitates more refined measurement of subpopulations than using a more limited number of strains selectable with different antibiotics [35]–[37] as well as limiting the possibility of phenotypic variation between competing strains with different genotypes . In this study we used WITS to study the in vivo population dynamics of S . Typhimurium during secondary challenge following vaccination . In the early phase of the challenge ( up to 24 h ) we estimated bacterial replication and killing rates by mathematical modelling . For later stages in the infection we could infer the times at which inter-organ spread and bacterial killing became observable by tracking the presence or absence of WITS in the organs and calculating the correlation between the abundances of WITS in the organs . We chose the parenteral route to investigate the secondary challenge dynamics in mice immunised with either a killed vaccine or a live attenuated strain . This route enabled us to study specifically the dynamics that underpin control of the bacteria in the systemic compartment . Systemic control of the infection - in other words the suppression of bacterial growth , restraint of dissemination in the spleen , liver and blood , and clearance of the bacteria from the tissues - is an absolute requirement for both host survival in systemic Salmonella infections and for the successful elimination of the infection . We used secondary challenge inoculum sizes of ∼300 CFU as the lowest dose that consistently prevented rapid clearance in immunised animals . This dose is similar to a recent estimate of a rate of migration of 298 CFU/day of STm from the gastrointestinal tract into the cecal lymph node following oral infection of mice [34] . We used a host-pathogen combination where a virulent strain was injected as the challenge organism into innately susceptible mice . This stringent combination always results in lethal infections in naïve animals . Bacterial numbers initially decline following challenge due to reactive oxygen radical mediated killing which exceeds division , but subsequently killing becomes negligible and intracellular replication results in a constant and exponential increase in bacterial numbers in the spleen and liver until death of the animal [25] . In this stringent model we showed that the LV induces an immune response that controls a secondary infection in four phases: rapid clearance of bacteria from the blood into the organs coupled with an initial very rapid ( within 30 min ) inactivation of a large fraction of the challenge dose; a period ( ∼6 h ) of relatively rapid bacterial replication and killing essentially equivalent to that in naive animals; a T-cell independent bacteriostatic phase that lasts for about three days and restrains bacterial growth more than in naive controls; and a subsequent phase in which T-cell dependent bacterial killing becomes significant . Assessment of net bacterial numbers alone would not have distinguished the time of transition between these latter two phases as total bacterial loads were relatively stable over the time period under investigation . The very rapid initial inactivation of the challenge could be a consequence of bacterial killing , or entry into a non-replicative state [21] , [31]–[32] that cannot be recovered from following direct plating of organ homogenates . Any early bacterial killing is unlikely to be the result of serum bactericidal activity as mouse serum lacks such activity against Salmonella due to a reduced ability to deposit C3 [38]–[39] . We confirmed this experimentally by assessing the survival of S . Typhimurium SL1344 in serum collected from mice immunised with the LV ( Figure S3 ) . Entry into a non-replicative state is also unlikely to account for the large drop in viable bacterial numbers seen as non-replicating salmonellae have not been observed at high levels in the spleen , and resume growth upon culturing [40] . We showed that the whole-cell killed vaccine did not induce the very rapid inactivation of the challenge dose seen in mice immunised with the LV , but did result in a decrease in total bacterial loads as compared to naive controls by 6 h post-challenge . This was primarily due to rapid blood clearance leading to more time being available for bactericidal mechanisms to exert their effect coupled with an increase in killing rate in the liver . While it has been known for many years that living and non-living vaccines can reduce total bacterial loads within the first hours of a re-infection [26]–[30] it is now clear that these reductions proceed with markedly different kinetics . After the first 24 h for animals immunised with the KV there was no additional restraint of bacterial growth , enhancement of killing or control of bacteraemia and the infection proceeded as in naive animals despite the presence of circulating antibodies . We also saw a decrease in the number of WITS absent simultaneously from both livers and spleens at later time-points in KV-immunised animals . This could have been due to re-emergence of bacteria from sites that we did not sample in this study , for example the bone marrow , during the haematogenous spread phase of the infection . This phenomenon was not observed in LV-immunised animals . Vaccination with live attenuated Salmonella is known to induce higher levels of IgG2a ( IgG2c in C57BL/6 mice ) as compared to a killed vaccine [16] , which we also confirmed in our model ( Figure S4 ) . Opsonisation of Salmonella with immune serum increases both the uptake of bacteria by macrophages and bacterial killing via the production of reactive oxygen intermediates ( ROI ) by the phagocyte NADPH oxidase ( phox ) [41] . phox also possesses bacteriostatic functions [42] , which we showed were important in naive animals in vivo after its initial bactericidal effect [25] . Macrophage uptake of IgG-opsonised Salmonella is primarily mediated by FcγRI which binds IgG2c [41] . It is therefore possible that both the LV-induced immediate bacterial killing and extended bacteriostasis are due in part to enhanced production of anti-Salmonella IgG2c . Reactive nitrogen intermediates ( RNI ) , produced by iNOS , are also known to have bacteriostatic activity both in vitro and in vivo [43]–[44] and while opsonisation has not been reported to increase RNI production by macrophages in vitro [41] it is possible that such an effect does occur in vivo . In this study we observed that the bacterial population structures in naive mice , and in KV and LV-immunised mice eventually become homogeneous between the livers and spleens indicating that inter-organ mixing of WITS occurs during the infection process . However , mixing occurs earlier in naive mice , in mice immunised with the KV and in LV-immunised T-cell depleted animals compared to control LV-immunised wild-type mice . This inter-organ mixing coincided with bacteria becoming detectable in the blood suggesting that haematogenous spread is responsible for transfer of bacteria between organs . In the majority of the LV-immunised mice inter-organ mixing was seen in the absence of detectable bacteraemia which leads us to speculate that in these animals very few bacteria are released into the blood at any time due to a more efficient control of bacterial release from the infection foci . In the KV-vaccinated mice , as the secondary infection progressed a small number of individual WITS predominated within the overall population structure . This phenomenon was also observed in naive control mice in this study although to a lesser extent than was previously seen when naive animals were challenged with a lower dose ( ∼90 CFU ) [45] . Given that we do not observe bacterial killing during the net growth phase of WITS in the tissues in these mice ( from 24 h p . i . onwards ) , the increased prevalence of some WITS is likely to be due to a relatively faster expansion of some WITS populations over others within each organ . Salmonella adapt in vivo to enter a state in which net intra-organ growth is faster [10] and individual foci of infection arise from distinct clonal populations [46] . We speculate that the expansion of a limited number of WITS populations is a consequence of a loss of control of the infection at a small number of foci ( each of which contains a single WITS ) resulting in release of bacteria that then establish secondary foci with rapidly growing bacteria [9] , [25] , [47]–[48] . A similar prevalence of a low number of distinct WITS was also seen in the later stages of the secondary infection in mice immunised with LV suggesting that loss of focal control at a limited number of sites also occurs in these animals . However in these mice we observed a T-cell dependent bactericidal activity of the immune system from 120 h onwards and therefore it is possible that bacterial killing is also responsible for the stochastic prevalence of some bacterial subpopulations over others . Overall our study indicates the superiority of a live attenuated vaccine over a whole-cell killed one in controlling the infection process by suppressing bacterial growth , exerting bacterial killing and achieving clearance of the inoculum from the tissues . The latter function is ascribable to the T-cell response induced by the LV since it can be abrogated by depletion of T-cells before challenge . This work indicates that a KV is inadequate to control a secondary infection in a very stringent host pathogen combination . New generations of Salmonella vaccines such as polysaccharide and subunit vaccines for typhoid and NTS are currently being considered and empirically tested . These preparations rely on the induction of antibodies for their protective activity so the optimisation of these vaccines and their delivery would benefit from improvements in their ability to induce T-cell mediated immunity .
S . enterica serovar Typhimurium ( STm ) WITS strains 1 , 2 , 11 , 13 , 17 , 19 , 20 and 21 which have been described previously [25] were made by inserting 40 bp signature tags and a kanamycin resistance cassette between the malXY pseudogenes of STm JH3016 [49] , a gfp+ derivative of wild-type virulent SL1344 . They are phenotypically wild-type for growth in broth and infectivity for mice . The live attenuated STm SL3261 aroA [50] strain was used for all immunizations . Bacteria for infection were grown for 16 h at 37°C in L-broth ( LB ) without aeration and diluted in phosphate-buffered saline ( PBS ) prior to inoculation . Enumeration of bacteria was by plating dilutions on LB agar plates . All experiments involving animals were conducted under project licences approved by the University of Cambridge Animal Welfare and Ethical Review Body , granted by the United Kingdom Home Office ( licence numbers PPL 80/2135 and PPL 80/2572 ) , and performed in observance of licensed procedures under the United Kingdom Animals ( Scientific Procedures ) Act 1986 . Female age-matched C57BL/6 mice were purchase from Harlan Laboratories and used when over 9 weeks of age . Live bacteria for parenteral immunization were prepared from a 16 hr static culture of STm SL3261 , diluted 1/100 in PBS and administered by i . v . injection into the tail vein in 200 µl aliquots ( ∼106 CFU/mouse ) . Actual inoculum dose was determined by plating dilutions . We confirmed that this immunisation regime elicited anti-Salmonella antibodies ( Figure S1 ) and elicited a TH1-type memory response ( Figure S5 ) . Challenge with WITS was performed three months after immunisation with the LV by which time the primary infection was cleared , as determined by sampling livers and spleens of mice in pilot experiments ( n = 2 ) , and demonstrated previously [51] . Acetone killed wild-type bacteria for immunization were prepared as previously described [16] . Briefly , bacteria from a 100 ml culture of STm SL1344 grown for 16 h at 37°C with aeration in tryptic soy broth ( Oxoid ) were harvested by centrifugation at 3220 g , resuspended in 100 ml PBS and viable counts determined by plating . Following three sequential washes in acetone , bacteria were harvested , acetone completely removed by evaporation , cells resuspended in PBS to an equivalent concentration of 5×1010 CFU/ml and stored in aliquots at −20°C or below . Immunization was by administration of two doses of 108 cells given subcutaneously in the back , 3-weeks apart . The subcutaneous route was chosen as in our experience this results in a strong antibody response with the killed vaccine whereas i . v . immunisation does not . Regardless of route the KV is known to be unable to induce protective T-cell immunity against virulent challenge . We confirmed that this immunisation regime elicited anti-Salmonella antibody , although there was a somewhat lower production of IgG as compared to immunisation with the LV , due to a decrease in levels of IgG2c ( Figure S4 ) . Challenge with WITS was performed 6 weeks post-primary immunisation . For T-cell depletion experiments 500 µg each of rat IgG2b anti-mouse CD4 and anti-mouse CD8 ( prepared by Harlan Bioproducts from hybridomas YTS 191 . 1 and YTS 169 . 4 . 2 respectively . Hybridoma lines were a kind gift of Prof . Anne Cooke , University of Cambridge ) in PBS were injected into the tail vein at days −2 and +2 with respect to the challenge [33] , [52]–[53] . Control animals received normal rat globulins ( mpBiomedical ) . In this model the development of immunity post-vaccination proceeds normally as the mice are wild-type; removal of T-cells only occurs around the time of challenge . This regime was confirmed by FACS to deplete the mice of both CD4+ and CD8+ splenocytes ( Figure S6 ) . For infections with WITS , each strain was individually grown statically for 16 h in L-broth and aliquots mixed to obtain a stock with each strain in an equal amount . Bacteria from 1 ml of this stock were harvested by centrifugation for subsequent qPCR . Dilutions of the stock were then made in PBS ( typically 1 in 5×105 ) to achieve the desired final cell density for i . v . injection of 200 µl doses into the tail vein . Actual cell density of the inoculums were determined by plating triplicate 200 µl aliquots , cell density in the individual WITS cultures was also determined . Experimental group sizes are shown in Table S1 . To enable mathematical modelling of subpopulation structures we attempted to determine a single challenge dose that would result both in consistent colonisation of the organs in immunised animals , and a frequency of WITS absence in naive animals that would enable paramaterisation of our previously developed model; in naive animals a dose of ∼90 CFU is appropriate [25] . We conducted pilot experiments where animals immunised with the LV or naive controls were challenged with 90 , 300 , 900 or 9000 CFU total WITS and we determined the WITS population structure in spleens and livers at 6 h post-infection ( Table S2 ) when bacterial counts are at a minimum in naive animals [25] . At the lowest dose there was excessive WITS loss in the spleen of immunised animals while at the other doses there was insufficient loss of WITS from naive animals . We therefore selected a challenge dose of ∼300 CFU which consistently resulted in colonisation of the spleen in immunised animals and modified the mathematical models to account for variations in the relative proportions of WITS between animals , rather than merely for the presence or absence of subpopulations . Blood was obtained from the tail vein in heparin-coated tubes , mice were humanely killed by cervical dislocation , spleens and livers removed and individually homogenised in a Stomacher80 ( Seward ) with 5 ml distilled water . If required , dilutions were made to enable enumeration by pour plating 100 µl aliquots in 6-well plates . Entire blood samples or tissue homogenates in 1 ml aliquots were inoculated onto the surface of 90 mm agar plates . Following overnight incubation at 37°C , colonies were enumerated and total bacteria harvested from the plates by washing with 2 ml PBS . Bacteria were thoroughly mixed by vortexing , harvested by centrifugation and stored at −80°C prior to DNA extraction . DNA was prepared from aliquots of bacterial samples ( typically ∼5×109 CFU ) using a DNeasy Blood and Tissue kit ( QIAGEN ) . DNA concentration was determined using a NanoDrop 1000 spectrophotometer ( Thermo Scientific ) . Approximately 106 total genome copies were analysed for the relative proportions of each WITS by qPCR on a Rotor-Gene Q ( QIAGEN ) . Duplicate reactions were performed for each sample with primer pairs specific for each WITS in separate 20 µl reactions ( primers in Table S3 ) . Each reaction contained 10 µl QuantiTect SYBR Green ( QIAGEN ) , 1 µM each primer , 4 µl sample and DNase-free water to 20 µl . Thermal cycling was 95°C for 15 min; 35 cycles of 94°C for 15 s , 61°C for 30 s , and 72°C for 20 s . The copy number of each WITS genome in the sample was determined by reference to standard curves for each primer pair . Standard curves were generated for each batch of PCR reagents by performing qPCRs in duplicate on 4 separate dilution series of known concentrations of WITS genomic DNA . We used a branching process model that kept track of the distribution of the number of bacteria of a single WITS in three compartments: blood , liver and spleen . The model is outlined here , a full description is in Supporting Information Text S1 . While we had determined the average number of CFU in the inoculum by plating , total bacterial loads in the LV immunized animals at 30 min were markedly lower , presumably due to the rapid activity of bactericidal mechanisms . In order to account for this observation we assumed that at time t = 0 , the bacteria followed a Poisson distribution with unknown mean ν in the blood , whence they migrate into the liver ( at rate cL ) and the spleen ( at rate cS ) . Upon entering the liver , bacteria replicate at rate rL and are killed at rate kL . Likewise , those in the spleen replicate at rate rS and are killed at rate kS . For the sake of parsimony , we assume that these rates remain constant for the first six hours , and we estimate them by fitting the model to the data from first two time points ( 0 . 5 h and 6 h post inoculation ) . We then allow the replication and killing rates to change at t = 6 h and assume they remain constant until t = 24 h; we estimate these new values by fitting the model to the data from the third time point ( 24 h p . i . ) . For each experimental treatment , we thus estimate 11 parameters ( ν , cL , cS , kL1 , kS1 , rL1 , rS1 , kL2 , kS2 , rL2 , rS2 ) from up to 720 data points ( 3 time points ×9 or 10 mice ×3 compartments ×8 strains ) , where subscripts 1 and 2 refer to the two time intervals considered ( 0–6 h and 6–24 h respectively ) We assumed that all strains and all mice were independent and shared identical parameters within each experimental group . Our model did not allow movement of bacteria from the liver or the spleen back into the blood , as data strongly suggested that this does not happen until after 24 h [25] . Inference was done by maximum likelihood; the complete mathematical formulation is presented in Supporting Information Text S1 . Basically , for a given set of model parameters , we computed the joint probability distribution of the number of copies of a single WITS in the blood , liver and spleen during the first 24 h by solving the master equations of the branching process . In order to compute the likelihood of the model , we also needed to determine the probability of observing a set of data ( CFU and proportions of the 8 WITS in the blood , liver and spleen from a given mouse ) given the unobserved numbers of copies of each WITS that were actually present in the blood and organs of that mouse ( which correspond to the variables in our stochastic model ) ; in other words , we had to estimate the noise generated by the experimental procedure . This was achieved by an additional calibration experiment: we plated known numbers of each WITS , harvested the colonies and processed known combinations of the eight WITS by qPCR . We then performed regressions of the observed proportions of the WITS by qPCR against the known numbers of colonies . We compared six models by AIC ( Akaike's Information Criterion ) for the distribution of ω ( the product of the total number of colonies harvested by the proportion of a single WITS reported by qPCR ) given n ( the actual number of colonies of this particular WITS on the plate ) . The most parsimonious was a log-normal distribution where the log-standard-deviation decreases exponentially with n , namely: log ( ω ) ∼N ( log ( n ) , 0 . 267e−0 . 0148n ) . See Supporting Information Text S1 for further detail . All the analyses were performed in R version 3 . 0 [54] , and likelihood estimation was done using the R library Powell [55] . | The bacterium Salmonella enterica causes gastroenteritis and the severe systemic diseases typhoid , paratyphoid fever and non-typhoidal septicaemia ( NTS ) . Treatment of systemic disease with antibiotics is becoming increasingly difficult due to the acquisition of resistance . Licensed vaccines are available for the prevention of typhoid , but not paratyphoid fever or NTS . Vaccines can be either living ( attenuated strains ) or non-living ( e . g . inactivated whole cells or surface polysaccharides ) and these different classes potentially activate different components of the host immune system . Improvements in vaccine design require a better understanding of how different vaccine types differ in their ability to control a subsequent infection . We have improved a previously developed experimental system and mathematical model to investigate how these different vaccine types act . We show that the inactivated vaccine can only control bacterial numbers by a transient increase in bactericidal activity whereas the living vaccine is superior as it can induce an immune response that rapidly kills , then restrains the growth and spread of infecting bacteria . | [
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"v... | 2014 | The Effects of Vaccination and Immunity on Bacterial Infection Dynamics In Vivo |
Arboviruses have overlapping geographical distributions and can cause symptoms that coincide with more common infections . Therefore , arbovirus infections are often neglected by travel diagnostics . Here , we assessed the potential of syndrome-based approaches for diagnosis and surveillance of neglected arboviral diseases in returning travelers . To map the patients high at risk of missed clinical arboviral infections we compared the quantity of all arboviral diagnostic requests by physicians in the Netherlands , from 2009 through 2013 , with a literature-based assessment of the travelers’ likely exposure to an arbovirus . 2153 patients , with travel and clinical history were evaluated . The diagnostic assay for dengue virus ( DENV ) was the most commonly requested ( 86% ) . Of travelers returning from Southeast Asia with symptoms compatible with chikungunya virus ( CHIKV ) , only 55% were tested . For travelers in Europe , arbovirus diagnostics were rarely requested . Over all , diagnostics for most arboviruses were requested only on severe clinical presentation . Travel destination and syndrome were used inconsistently for triage of diagnostics , likely resulting in vast under-diagnosis of arboviral infections of public health significance . This study shows the need for more awareness among physicians and standardization of syndromic diagnostic algorithms .
Globalization has resulted in a steep increase in travel and trade . [1 , 2] In recent decades it has contributed to the spread of diseases that traditionally emerged only regionally but now threaten populations across the globe , stressing the need for global health surveillance . [1 , 2] Among these emerging threats , arboviruses form a unique group , with a large public health impact in endemic countries , a tendency to expand their geographical distribution through trade and travelers , and colonize previously unaffected areas . Due to their vector-borne and often zoonotic nature , they require targeted surveillance and control schemes . This requirement is particularly relevant when evaluating symptoms of illness in travelers . Of all those returning from developing , tropical , or subtropical countries , 8% require medical care on return . [3] For those returning from Africa and Southeast Asia , fever is the most common reason for seeking medical care; for travelers returning from the Caribbean and South America , rash is the most common reason . Around 50% of the cases remain undiagnosed in clinics focused on travel medicine , and this percentage is likely higher in less specialized clinics . [3] The traveler’s personal physician is therefore an important link in ongoing arbovirus surveillance in travelers and the gate-keepers of disease detection . Correct diagnosis of arbovirus infections in travelers is challenging . Arboviruses have overlapping geographical distributions and cause symptoms that coincide with more common infections . [4] If general practitioners consider an arbovirus infection in their differential diagnosis , they commonly test for the best known arboviral disease , Dengue virus ( DENV ) . Laboratory diagnostics for travelers are largely based on serologic testing , since viremia is short-lived and has often already dropped to undetectable levels when severe symptoms appear and diagnostics are performed . [5 , 6] The use of serologic results for arbovirus diagnosis and surveillance requires careful evaluation due to cross-reactivity of antibodies to related viruses . [7] Also , several vaccines , notably for Yellow fever , Tick-borne encephalitis and Japanese encephalitis , can cause false-positive serological tests . [7] For these reasons , arbovirus illness is under-diagnosed , as evidenced by studies of unexplained illness in returned travelers . [8–10] A potential solution would be the development of syndromic arboviral disease detection methods that cover the most common arboviruses and simultaneously provide surveillance information . [11] Here we aimed to assess the potential added value of syndrome-based approaches for diagnosis and surveillance of neglected arboviral diseases in returning Dutch travelers .
For retrospective patient analysis , a database was created by integrating data from the two arbovirus diagnostic reference centers in the Netherlands: Erasmus Medical Centre in Rotterdam and The National Institute for Public Health and the Environment in Bilthoven . Previously , we described trends of DENV diagnostics in the Netherlands from 2000–2010 . [12] The current study included almost all arbovirus diagnostic requests from Dutch physicians from 2009 through 2013 in the Netherlands . In the case of DENV not all data was included because 10% of the DENV diagnostics were performed outside the arbovirus reference centers and were not included in this dataset . For syndromic analysis , only entries were included where travel and clinical history were provided . To define the syndromes , entries in the database were reviewed by a consultant microbiologist , and infectious disease clinicians assigned them to syndrome categories ( Table 1 ) . Due to the laboratory-specific variety in diagnostic methods used , we classified each patient’s test results according to the validated methods and cut-offs for the pertinent laboratory . Results were classified as positive for a disease if the patient had ( 1 ) a positive PCR result with <40 cycles , ( 2 ) an IgM above an individual laboratory-determined cut-off , or ( 3 ) a minimum fourfold increase in IgG titers between two consecutive samples . For DENV patients , ( 4 ) a positive non-structural protein 1 ( NSI ) antigen-capture test was among the criteria . [6] The likelihood of infections by arboviruses other than DENV was based on a previously published article in which we developed syndromic diagnostic algorithms based on data from an exhaustive review of the literature addressing geographic distribution and prevalence of arboviruses by syndrome . [12] Optimal diagnostic algorithms using a combination of clinical syndromes and geographical distribution presented were updated and used as a basis for our current analysis ( Fig 1 ) . In short , criteria used to prioritize arboviruses for the diagnostic algorithm were: a ) circulation in urbanized areas , due to the use of humans as reservoir hosts , or the presence of reservoir hosts colonizing urban areas , b ) known endemic disease , c ) tourist activity in the area , d ) high rate of exposure in resident population , and e ) recorded cases of infections in travelers . [4] These diagnostic algorithms were used in the current article to identify gaps that may occur with a physician-indexed single-virus approach . Travel data for Dutch travelers was based on the year 2011 . They were extracted from a commercial database “ContinuVakantieOnderzoek” ( CVO ) created for trend analysis in the tourism industry . Its data are collected and converted into national numbers every three months by interviewing individuals in about 15 , 000 Dutch households on their travel destinations , activities , lodging , transport , and booking method . [13] Using data from 2011 provided a representative distribution of Dutch travel behavior from 2009–2011 . Only slight country specific fluctuations were reported . [13] The analysis was performed in STATA . [14] Pearson's chi-square test was used to assess for equality of proportions . Multivariable logistic regression models ( Table 2 ) reporting odds ratios were used with a 95% confidence level . [14] Heatmaps were generated using the additional R package “stats”[15] and based on pair-wise correlation between rows and columns . This research was conducted in accordance with the Dutch law on medical research ( WMO ) , article 1 . In compliance with Dutch Law and medical ethical guidelines , no personal identifiers were included and no informed consent was required for use of data in this study .
Over the five year study period 8126 patients were tested for arboviral diseases in the Netherlands . Of the patients , 44% presented to larger hospitals or specialized travel clinics . All other patients were seen at smaller hospitals or local clinics . Molecular tests comprised 1 . 3% of diagnostic tests performed . Larger hospitals and specialized travel/tropical clinics tested on average for 1 . 7 viruses per patient compared to 1 . 2 in smaller hospitals and local clinics . The patient male to female ratio was 1 . 04 . Vaccination history was recorded on the diagnostic request for only 14 patients ( <1% ) . Of all patients , 2153 ( 26% ) had information on travel history and clinical history and were thus included for further syndrome and travel-based analysis . Of these , 23% had provided a second serum sample needed for determination of a potential IgG titer increase . With a median of 7 days , the average number of days elapsed between onset of symptoms and first sampling was 17 . 5 ( 95%CI 14 . 0–20 . 3 ) . This number is based on the 317 patients with clinical and travel history for whom this chronological information was recorded . Elapsed time did not differ between patients seen at specialized hospitals/clinics and those visiting smaller hospitals/clinics . We analyzed the travel data of Dutch travelers in 2011 to determine the range and importance of arbovirus tests needed to cover the differential diagnosis for travelers with illness after return from the various destinations . In 2011 , approximately 84% of Dutch travelers traveling abroad stayed within Europe . Western Asia ( predominantly Turkey ) was the most popular non-European destination , with nearly one million Dutch vacations booked annually ( Fig 2 ) . [13] The most diagnostic requests ( 35% ) by far , however , were for travelers returning from destinations in South and Southeast Asia , while only 3% of all travelers had this region as their destination . The number of diagnostic requests by travel region and the proportion of positive test results ( Fig 2 ) show that DENV testing was by far the most commonly requested ( 86% ) , yielding the highest absolute number of cases ( Fig 2 ) . When comparing the numbers of requests and proportions of positives by region of travel , substantial differences were observed: diagnostic requests for ill travelers returning from sub-Saharan Africa were frequent but not often positive , whereas ill travelers returning from popular arbovirus-endemic regions in Central and Western Asia were rarely tested . A low number of patients who had traveled within Europe were tested . DENV was tested ( N = 41 ) almost as often as tick-borne encephalitis virus ( TBEV ) ( N = 57 ) , for which exposure is far more likely . Of note , two of these European travelers tested DENV-positive . One was a tourist returning from Croatia , who tested DENV-IgM-positive and borderline NS1-positive . The other tourist had taken a five-day trip to Southern France and was DENV IgM- and NS1-positive 10 days after return . However , 14 days previous to onset of symptoms , this traveler had been in Thailand before traveling on to France . Another virus considered endemic to Europe is Sindbis virus ( SINV ) , for which diagnostics are not readily available in the Netherlands . Nor are they available for oropouche virus ( OROV ) , endemic to South America . To assess the potential use of diagnostic requests for syndrome surveillance by region , we analyzed the symptoms recorded for each patient returning from a particular travel destination . Nearly all patients ( 86% ) reported fever , followed by arthralgia/arthritis ( 22% ) and enteric symptoms ( 14% ) . Information divided per travel region showed regional variation in symptoms recorded ( Fig 3 ) . For all regions , fever was the most reported symptom . Proportionally , neurological symptoms were more often reported for travelers returning from a European destination than for travelers from other regions . Arthralgia-arthritis was recorded more frequently for travelers returning from Oceania , with rash being most recorded for Southern Africa compared to other regions . Three heatmaps were created to visualize per continent ( Africa , Asia and the Americas ) the correlation between the physicians’ diagnostic requests and the literature-based syndromic algorithms ( Fig 1 ) . In the heatmaps , diagnostic requests are grouped based on the clinically important arboviral diseases per region within each continent ( Figs 4–6 ) . For most regions , Dutch physicians requested DENV diagnostics for 100% of the travelers who had recorded symptoms corresponding to DENV infection ( fever , rash and joint pain ) . For some regions , a lower percentage of such patients was tested , i . e . Northern Africa ( 67% ) ( Fig 4 ) , Western Asia ( 57% ) ( Fig 5 ) and Central America ( 38% ) ( Fig 6 ) . In all regions , CHIKV testing was less frequently requested than DENV testing , even though the infections overlap in geographical distribution and range of symptoms to a great extent . On average , 45% of patients with febrile symptoms , rash and/or arthralgia after travel to CHIKV-risk areas in Asia were not tested for CHIKV . Patients with symptoms suggesting West Nile Virus ( WNV ) , Japanese encephalitis ( JEV ) , Rift Valley fever virus ( RVFV ) and TBEV were tested infrequently ( 0 to 25% ) and only in association with neurological symptoms . Diagnostics on all other viruses presented in Figs 4–6 were minimally requested . We analyzed the association between symptoms recorded and test outcomes for DENV and CHIKV requests in Dutch travelers ( Table 2 ) . Patients with rash , hemorrhagic symptoms and fever had an increased odds of testing positive for DENV , but respiratory symptoms decreased the odds of being DENV-positive ( OR 0·5 ) . Positive test outcomes for CHIKV were associated with arthralgia combined with rash . Both DENV and CHIKV were positively associated with travel history to Southeast Asia .
A physician’s diagnostic requests for returned travelers can play a key role in infectious disease surveillance . However , while travel destination and syndrome could be used for triage and diagnostics , such use is inconsistent . We found clear evidence of patient groups at risk of under-diagnosis of arboviral disease when evaluated by syndrome and by region . Based on a comparison between all arboviral diagnostic requests by physicians in the Netherlands between 2009 and 2013 with a literature-based assessment of the likely exposure of the patients to an arbovirus , we showed that while dengue virus diagnostics are routinely requested , other relevant arboviruses such as chikungunya virus are neglected , even if travelers present with relevant symptoms and return from countries where the viruses are endemic . We also showed that for travelers to European destinations , arbovirus diagnostics were rarely requested and that for almost all arboviruses and travel destinations , diagnostics were requested only when patients presented with severe symptoms . Whether the low number of requests and overemphasis of physicians on patients presenting with severe symptoms reflects a lack of physician awareness of arboviruses and their risk to travelers , financial restrictions or limited time , it points at possible gaps in preparedness . Our paper shows that in order to limit the amount of missed clinical arboviral infections , and to increase the level of awareness of arboviral infections of public health significance , physicians should rely on diagnostics and surveillance with a syndromic approach and matching laboratory methods . | Physicians attending travelers with particular symptoms often neglect those infections that are transmitted by arthropods like ticks and mosquitoes ( arboviruses ) or don’t test for the appropriate arboviruses . This is because arboviruses cause symptoms that are similar to more common infections and because there is a geographical overlap in the arbovirus infections that people have a large chance of being infected with . We compared the amount of times that Dutch physicians had patients tested for arboviral infections with the likelihood that Dutch travelers would be exposed to particular arboviruses . Whereas research and diagnostics generally focus on only one virus , our study was uniquely comprehensive and systematic in that it analyzed multiple viruses simultaneously on the basis of a unique national database . The research shows that the current viruses travelers are tested for is incomplete and likely many more people carry these kinds of diseases than are diagnosed . As these diseases pose potential public health threats , physicians should be more aware of the diseases that travelers could be infected with , and protocols are needed regarding what infectious diseases physicians should check for when patients present with particular symptoms . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Syndromic Approach to Arboviral Diagnostics for Global Travelers as a Basis for Infectious Disease Surveillance |
Topological , chemical and immunological barriers are thought to limit infection by enteropathogenic bacteria . However , in many cases these barriers and their consequences for the infection process remain incompletely understood . Here , we employed a mouse model for Salmonella colitis and a mixed inoculum approach to identify barriers limiting the gut luminal pathogen population . Mice were infected via the oral route with wild type S . Typhimurium ( S . Tm ) and/or mixtures of phenotypically identical but differentially tagged S . Tm strains ( “WITS” , wild-type isogenic tagged strains ) , which can be individually tracked by quantitative real-time PCR . WITS dilution experiments identified a substantial loss in tag/genetic diversity within the gut luminal S . Tm population by days 2–4 post infection . The diversity-loss was not attributable to overgrowth by S . Tm mutants , but required inflammation , Gr-1+ cells ( mainly neutrophilic granulocytes ) and most likely NADPH-oxidase-mediated defense , but not iNOS . Mathematical modelling indicated that inflammation inflicts a bottleneck transiently restricting the gut luminal S . Tm population to approximately 6000 cells and plating experiments verified a transient , inflammation- and Gr-1+ cell-dependent dip in the gut luminal S . Tm population at day 2 post infection . We conclude that granulocytes , an important clinical hallmark of S . Tm-induced inflammation , impose a drastic bottleneck upon the pathogen population . This extends the current view of inflammation-fuelled gut-luminal Salmonella growth by establishing the host response in the intestinal lumen as a double-edged sword , fostering and diminishing colonization in a dynamic equilibrium . Our work identifies a potent immune defense against gut infection and reveals a potential Achilles' heel of the infection process which might be targeted for therapy .
Acute infections constitute highly complex interactions between pathogens and their hosts . The complexity arises from dynamic changes in pathogen gene expression , pathogen growth , barriers limiting the initial colonization and host defenses which limit further pathogen growth and survival during the course of an infection . Identifying the relevant interactions and how they affect the progression of the disease is of great value for understanding the infection process and may reveal new targets for prevention or therapy . Mixed inoculation provides a powerful approach to decipher pathogen-host interactions [1] . In such experiments , genetic markers carried by some members of the pathogen population are used to follow how the pathogen population disseminates , grows or is killed during the course of an infection [2]–[13] . This can reveal “barriers” , which limit the infection . Barriers can be of varying nature , including chemical barriers ( i . e . antimicrobial peptides , stomach acid ) , physical obstacles ( e . g . the mucus layer separating gut luminal bacteria from the epithelial surface [14] ) or immune responses killing the pathogen . Such barriers can impose “bottlenecks” onto the pathogen population which can be detected as loss of marker diversity . Thus , barriers are important characteristics of an infection process as they indicate how hosts can interfere with pathogen colonization and survival . We employed a mixed inoculum approach to study Salmonella enterica subspecies 1 serovar Typhimurium ( termed S . Tm hereafter ) growth in the inflamed gut using the well-established streptomycin mouse model for Salmonella colitis [15] . In this model , the resident microbiota is transiently suppressed by a single dose of streptomycin [16] . This bypasses the initial phase of the natural infection where the pathogen has to competitively grow in the face of an intact , dense microbiota [17] and allows us to focus on the next stage where the pathogen triggers disease and grows in the inflamed gut [15] , which is still incompletely understood . S . Tm mainly elicits mucosal inflammation via virulence factors encoded in genomic islands , i . e . the SPI-1 and SPI-2 type III secretion systems [18] , [19] . The inflammatory response has been shown to foster efficient colonization of the host's gut lumen by the pathogen , as the milieu in the inflamed gut can help the pathogen to outcompete and/or suppress the resident microbiota [20] . Some of the molecular mechanisms have been identified [15] . This includes the elicitation of antimicrobial peptides which kill some of the microbiota ( but not S . Tm; [21] ) , the limitation of iron and zinc uptake by microbiota species ( but not S . Tm; [22] , [23] ) and the provision of terminal electron acceptors fuelling S . Tm growth by anaerobic respiration [24] , [25] . These findings have established S . Tm as a pathogen subverting gut luminal inflammation to efficiently colonize this niche . However , it was previously unclear whether the inflammatory response also inflicts an additional barrier limiting pathogen colonization of the gut . This would seem reasonable as inflammation is generally mounted to fight infection , i . e . in infected host tissues [26] , [27] . Indeed , some studies have observed transient , 10-fold reductions of gut luminal pathogen loads at day 2 p . i . [20] and Reg3β , an antimicrobial peptide released by the inflamed mucosa , was found to kill fast-growing S . Tm cells [28] . In this study , we have employed a mixed inoculum approach to identify barriers limiting gut luminal colonization by S . Tm . Using S . Tm strains chromosomally tagged with bar coded sequences ( WITS , wild-type isogenic tagged strains; [5] , [6] , [29] ) we identified a pronounced bottleneck in the gut luminal pathogen population and oligo-clonal expansion post crisis . The barrier was attributable to pathogen-induced inflammation , in particular granulocytes , a prominent phagocytic cell type infiltrating the infected mucosa and the gut lumen . Our data extend the current paradigm by establishing that not only the tissue-infiltrating bacteria , but also the gut luminal pathogen population , can be temporarily restricted by the host's inflammatory response .
To identify barriers limiting gut luminal pathogen colonization , we monitored the S . Tm community composition by using seven wild-type isogenic tagged S . Tm strains ( WITS ) , which are phenotypically identical ( S1 Figure ) . Each WITS harbors a 40 nucleotide “bar code” between two pseudogenes which allow quantification of their relative proportion by rtqPCR [5] , [6] , [30] . In case of perturbations , the equal proportions of the WITS-tagged subpopulations will shift towards an uneven population structure in which some WITS are dominant while others disappear ( i . e . loss of diversity; reduced “evenness” ) . Dilution of the tagged strains by untagged S . TmWT probes the degree of diversity loss ( S1C Figure ) . First , we screened for optimal assay conditions by titrating the fraction of tagged WITS strains in the inoculum . A “1∶7” inoculum was prepared by mixing seven different WITS at an equal ratio ( 1∶1∶1∶1∶1∶1∶1; S1 Figure ) . The “1∶70” , 1∶700” and “1∶7000” inocula were generated by diluting the original WITS mixture with an untagged , isogenic wild type strain ( 1∶10 , 1∶100 and 1∶1000 , respectively ) . Streptomycin pretreated C57BL/6 mice were infected with the indicated inocula ( 5×107 cfu total , by gavage; without sodium bicarbonate-mediated neutralization of stomach acid , 2 independent experiments using a total of 5 or 6 mice per group ) and we monitored pathogen loads in the stool by plating fecal pellets ( day 1 p . i . ) , the cecal content , the mesenteric lymph nodes ( mLN ) , the livers and the spleens ( day 4 p . i . ) . All animals featured high pathogen loads in the feces at day 1 and in the cecum lumen at day 4 p . i . ( ≈109 cfu/g; Fig . 1A–D , black bars ) . Furthermore , we observed efficient colonization of the mLN , spleen and liver and all mice displayed profound cecum inflammation by day 4 p . i . ( Fig . 1E ) . This is well in line with previous work [16] , [19] and verified that the tags do not interfere with the infection process . To detect diversity loss and identify optimal assay conditions , we quantified each WITS in the inoculum , the feces ( day 1 p . i . ) and the cecum content ( day 4 p . i . ) . To analyze the WITS-diversity in a quantitative fashion , we have used the “evenness index” , an indicator commonly used to describe differences in the distribution of goods or money in a society ( = Gini-index [31]; see also S1 Figure ) . In our experiments , this index had a value close to 1 in the inoculum ( 0 . 913 , median ) . Slight deviations ( i . e . values from 0 . 9 to 1 ) are most likely attributable to technical errors ( e . g . pipetting errors , PCR bias , cfu determination , DNA recovery , enrichment culture , etc . ) , not loss of genetic diversity in the biological system ( S2 Figure ) . At day 1 p . i . , all WITS were detectable in the feces , no matter which inoculum was applied , and evenness indices ranged from 0 . 914–0 . 950 ( 1∶7 inoculum; median = 0 . 939 ) to 0 . 833–0 . 893 ( 1∶7000 inoculum; median = 0 . 868; Fig . 1A–D ) . This high degree of evenness indicated that the population did not encounter detectable bottlenecks , “selective sweeps” [32] or other effects limiting the genetic diversity during the transit through the gastrointestinal tract or the initial colonization of the host's large intestinal lumen by day 1 p . i . In stark contrast , we detected dramatically reduced evenness indices of cecum luminal S . Tm populations at day 4 p . i . , in particular at the highest WITS-dilutions ( 1∶7000; median = 0 . 033; Table 1 ) . In the 5 mice , 18 of the 35 WITS were lost from the cecum lumen at day 4 , and one animal did not retain any detectable WITS ( mouse #G752; Fig . 1D ) . The identity of the dominant WITS and the WITS lost from the population differed from animal to animal and from experiment to experiment ( Fig . 1A–D , see color code ) . These observations provided additional evidence that all WITS had equivalent fitness in our model and suggested that the diversity is reduced by a stochastic process . Formally , the reduced evenness observed at higher WITS-dilutions could be explained either by bottlenecks in the pathogen population or by selective sweeps ( e . g . up-growth of fitter S . Tm mutants [33] ) . The latter was ruled out in competitive infections with re-isolated clones . These control experiments verified that ( in general ) , the dominant WITS-isolates retained the fitness of the original strain ( S3 Figure ) . Thus , selective sweeps [32] of beneficial mutations cannot explain the loss of WITS-diversity in the experiment shown in Fig . 1 . Therefore , these data provided a first hint that a bottleneck might limit the diversity of the gut luminal pathogen population between days 1–4 post infection . Taken together , our data suggested that a population-bottleneck explains the reduced evenness index at day 4 p . i . Equivalent observations were made in an Nramp-positive mouse line ( 129SvEv; see below ) , indicating that the barrier reducing the evenness may be a general feature , independent of the mouse line used . The gut luminal S . Tm population should encounter this bottleneck after day one and before day 4 p . i . The 1∶7000 inoculum seemed well suited for further analysis of this phenotype . Next , we analyzed the time course of changes in the WITS genetic diversity . We employed a “1∶7000” WITS inoculum , as the data above had indicated that this retains high evenness indices for stool samples by day 1 and should allow sensitive detection of changes during the subsequent days . The 1∶7000 inoculum was prepared as described above and we infected streptomycin pretreated C57BL/6 mice ( 5×107 cfu total , by gavage; 2 independent experiments using a total of 5 mice per group ) . Total pathogen loads and the WITS in the inoculum , the stool , the cecal content , the mLN , the spleen and the liver were quantified by plating and rtqPCR at day 1 , 2 , 3 or 4 post infection ( Materials and Methods ) . In line with earlier results , S . Tm efficiently colonized the feces and the cecum contents within the first day ( 108–109 cfu/g; Fig . 2A , B , black bars ) , elicited pronounced cecum tissue contraction and inflammation ( Fig . 2D , E ) and the pathogen spread to the mesenteric lymph nodes , the spleen and the liver ( S4 Figure , black bars ) . This verified that the infection had proceeded as expected . At day 1 p . i . , the bacterial populations in the feces and in the cecum content featured evenness indices almost as high as the inoculum ( medianfeces = 0 . 801; mediancecum content = 0 . 737; each WITS present; Fig . 2 ABC ) . This was in stark contrast to the S . Tm populations in the mLN , spleens and livers , which harbored no WITS at all ( S4 Figure; evenness index below detection ) . In line with earlier work , this indicated that systemic dissemination is partly restricted by a pronounced barrier and that these organs are populated by local growth of a few “founder” bacteria ( [6] , [29]; not analyzed further in this study ) . In contrast , the gut luminal population retained all WITS and the high evenness indices indicated that S . Tm did not encounter detectable bottlenecks during the transit through the stomach and gastrointestinal tract or the initial colonization of the gut lumen . Based on the 1∶7000 composition of the inoculum , we applied a simple binomial selection model to estimate that the luminal S . Tm population size never drops below 2×104 cfu during this initial day of infection ( Table 2; Materials and Methods ) . Please note that this is an estimate of the lower boundary ( detectable with WITS-dilutions of 1∶7000 and using 10 mice ) and that the actual S . Tm population size might in fact be much larger than this . Reduced evenness indices were observed in the cecum lumen and the feces by day 2 p . i . and evenness further declined until day 4 p . i . ( medianfeces = 0 . 434→0 . 043; mediancecum content = 0 . 222→0 . 044; Fig . 2 A , B ) . By day 4 p . i . , most mice had “lost” one or more WITS from the cecal contents and the feces ( detection limit: 10 cfu/g ) . This indicated that some type of barrier may restrict the gut luminal S . Tm population from day 2 on . A simple estimate of the number of bacteria that can cross this barrier can be done from the data on the presence or absence of WITS at day 4 after inoculation at a dilution of 1∶7000 ( see Materials and Methods ) . In combination with the 1∶7000 dilution experiment shown in Fig . 1D , we had a total of 10 animals infected for 4 days . In these mice , 30 of the 70 WITS were lost from the cecum lumen at day 4 ( Fig . 1D , Fig . 2B ) . Assuming that each WITS succeeds in crossing the barrier according to a binomial process with probability 1/7000 , we estimated a bottleneck size of 5931 bacteria with a 95% confidence interval ranging from 4242 to 8046 bacteria ( Materials and Methods ) . Within each animal , the WITS distribution patterns of the cecal content and the feces were strikingly similar , in particular at days 3 and 4 p . i . ( R2 = 0 . 854 or 0 . 961 , respectively ) . Therefore , these populations are linked and fecal samples can be used to monitor the cecum luminal pathogen population at days 3 and 4 p . i . Conceptually , this suggests that the cecum lumen may produce ( or seed ) the pathogen population shed in the feces . In conclusion , these data established that the gut luminal S . Tm population is restricted by a significant barrier by day 2 p . i . and that this bottleneck can be detected via the reduced evenness index of the WITS-diversity . It is interesting to note that the cecum luminal pathogen density transiently dropped at day 2 p . i . and was as low as 105–106 cfu/g in two of the mice ( Fig . 2B , mouse #R030 and R031 , black bars ) . Thus , a transient reduction of the total gut luminal S . Tm population size ( a “population bottleneck” ) may contribute to the barrier restricting the gut luminal S . Tm population . The nature of this barrier remained to be determined . Next , we analyzed if mucosal inflammation contributes to the gut luminal population bottleneck . This seemed plausible , as inflammatory responses can ( at least in tissues ) eliminate pathogens via an elaborated arsenal of antimicrobial mechanisms [26] . Gut luminal colonization without the elicitation of mucosal inflammation can be achieved by using “avirulent” S . Tm mutants deficient in the SPI-1 and SPI-2 type III secretion systems [18] , [19] . To this end , we constructed “avirulent” WITS by P22 phage transduction of the chromosomal tags into an invGssaV mutant ( WITSSPI-1 & SPI-2; Materials and Methods ) and prepared a “1∶7000” inoculum by mixing with an untagged S . TmSPI-1 & SPI-2 strain as described above . Streptomycin pretreated C57BL/6 mice were infected ( 5×107 cfu total , by gavage ) and we analyzed pathogen loads in the stool , the WITS diversity in stool samples , gut pathology and pathogen loads at systemic sites ( Fig . 3A ) . As expected , the S . Tm mutant yielded only minimal colonization of the spleen and the liver , reduced pathogen loads in the mLN , reduced total gut luminal loads by day 4 p . i . and no signs of overt mucosal inflammation by day 4 p . i . ( pathoscore 2 . 5+/−1 ) . The WITS evenness index in the stool was high at day 1 p . i . and only slightly reduced by day 4 p . i . ( median = 0 . 799→0 . 676; Table 1 ) . Conceivably , the slightly reduced evenness index detected at day 4 p . i . might be attributable ( at least in part ) to a reduced total S . Tm population size , as loads in the stool reached very high levels at day 1 p . i . ( 109–1010 cfu/g ) , but decreased by about 10-fold to 108–109 cfu/g by day 4 p . i . ( Fig . 3A , black bars ) . This is in line with earlier work which established that , in the absence of gut inflammation , the re-growing microbiota can outcompete avirulent S . Tm mutants lacking functional SPI-1 and SPI-2 type III secretion systems [20] . Nevertheless , the WITS-diversity was only slightly reduced by day 4 p . i . , suggesting that the tight bottleneck observed in wild type S . Typhimurium infections is attributable to mucosal inflammation . Next , we tested the individual contributions of SPI-1 and SPI-2 to WITS-diversity loss . For this we used S . Tm mutants lacking either a functional SPI-1 TTSS ( S . TmSPI-1 , ΔinvG; strong defect in eliciting gut inflammation at day 2 p . i . ) or a functional SPI-2 TTSS ( S . TmSPI-2 , ssaV::cat; slight defect in eliciting gut inflammation at day 2 p . i . ) , which do efficiently colonize the gut lumen of streptomycin pretreated mice , but elicit reduced levels of mucosal inflammation on day 2 p . i . [16] , [18] , [19] , the time when the gut luminal bottleneck is observed . The respective WITS were constructed by P22 phage transduction thus yielding WITSSPI-1 ( ΔinvG background; [6] , [34] , [35] ) and WITSSPI-2 ( ssaV::cat background; [6] , [35] Materials and Methods ) and mice were infected with “1∶7000” inocula and analyzed as described for Fig . 1 . As expected , the SPI-1 mutant efficiently colonized the spleen , the liver and the mLN and elicited moderate mucosal inflammation by day 4 p . i . ( Fig . 3B ) . In line with earlier data , the mucosa featured a “patchy” distribution of inflammatory foci typical for the “SPI-2 mediated” disease [19] , [36] which was even at day 4 p . i . significantly less pronounced than the inflammation elicited by wild type S . Tm ( compare Fig . 1E , 2E with Fig . 3B ) . The stool loads remained high ( 109–1010 cfu/g ) throughout the experiment and we observed at most a slight decrease of the evenness index ( median = 0 . 888→0 . 824; Table 1 ) . In line with the key role of SPI-2 in systemic infection [19] , [37] , the SPI-2 mutant did not efficiently colonize the spleen , liver and the mLN , but elicited mucosal inflammation ( Fig . 3C ) . Please note that the “SPI-1 mediated” mucosal inflammation elicited by this strain is much more pronounced than the “SPI-2 mediated” inflammation at day 2 p . i . [19] . In the present experiment , we analyzed the gut inflammation at a much later time point , i . e . day 4 p . i . Here , inflammation was much milder than the pathology elicited by wild type S . Tm ( compare Fig . 1E , 2E with Fig . 3C; [6] , [19] ) . Strikingly , the WITSSPI-2 population featured a moderate drop of the evenness index between days 1 and 4 p . i . ( median = 0 . 758→0 . 358; Table 1 ) . These data are in line with the notion that mucosal inflammation ( i . e . the degree of inflammation at day 2 p . i . ) may drive the WITS-diversity loss and suggest that the grade of inflammation may dictate ( at least in part ) barrier efficiency . To study the effect of mucosal inflammation on strains incapable of eliciting disease one can perform a modified version of our mixed inoculum experiments . The inflammation elicited by wild type S . Tm will affect all bacteria present in the gut lumen ( i . e . wild type and mutant [20] , [38]–[42] ) . Thus , we performed a variant of the assay described above to verify that the bottleneck is indeed affected by the grade of the inflammatory response ( not genetic predisposition of the S . Tm strain ) . In particular , we wanted to assess the evenness index of WITSSPI-2 in the face of full blown mucosal inflammation elicited by wild type S . Tm . This was of particular interest , as WITSSPI-2 can colonize the gut lumen , but fails to efficiently grow in the mucosal lamina propria and at systemic sites [19] . Thus , using WITSSPI-2 would help to exclude possible re-seeding from such sites and therefore allow focusing on the gut luminal pathogen population . To this end , we mixed WITSSPI-2 with an excess of wild type S . Tm [6] , [38] . Streptomycin pretreated C57BL/6 mice were infected for 1 or 4 days with a “1∶7000” inoculum composed of WITSSPI-2 and untagged S . TmWT and we analyzed the infection as in Fig . 1 . Pathogen organ loads ( i . e . WITSSPI-2 plus untagged S . TmWT ) and cecum pathological scoring confirmed an overall “wild type”-like intestinal disease progression during this mixed infection experiment ( Fig . 4A , B , compare to Fig . 2E; black bars ) . Remarkably , in this experiment , the WITSSPI-2 evenness index dropped much further than in infections performed with WITSSPI-2 alone by day 4 p . i . ( median = 0 . 06; Fig . 4A , compare to Fig . 3C ) . In fact , the evenness index at day 4 p . i . was strikingly similar to that of wild type WITS ( median = 0 . 033 and 0 . 044; see Fig . 1D and 2A , B; Table 1 ) . We therefore conclude that the bottleneck is determined by the degree of inflammation , not by the genetic background of the tagged subpopulation . Reduced WITS-evenness can be explained by a drastic reduction in the cecal S . Tm population size and a subsequent ( oligo-clonal ) regrowth of the population . In fact , transient reductions of the cecal and fecal population densities at day 2 p . i . have been observed repeatedly ( though never followed up in detail ) , over the past decade ( [20]; Fig . 2A , B ) . To specifically analyze the population size at this critical time point , we monitored changes in cecum luminal S . Tm population size by sacrificing S . TmWT-infected mice at day 1 and at day 2 p . i . and by determining the respective pathogen loads . In line with earlier data , the mice featured moderate colitis by day 1 and pronounced colitis by day 2 p . i . ( Fig . 5B ) . The pathogen densities in the cecum lumen were high at day 1 p . i . ( ≈109 cfu/g ) , but dropped significantly by day 2 ( median = 3×107 cfu/g , Fig . 5A ) . Strikingly , the gut luminal pathogen density featured pronounced animal-to-animal variation . In some animals , the pathogen density dropped down to 3×105 cfu/g . Without further information , these data cannot tell whether the dip in the gut luminal pathogen density is more pronounced in some animals than in others . Alternatively , equivalent dips may occur in all mice however with slightly different kinetics . Nevertheless , the data suggests that a transient dip in the cecum luminal population size may contribute to the drop in WITS evenness at least in some of the mice . If pathogen-triggered inflammation was responsible for the dip at day 2 p . i . , cecal S . Tm densities should remain higher in cases of reduced mucosal inflammation . To test this hypothesis , we infected mice with S . TmSPI-2 and compared cecal population sizes at day 1 and day 2 p . i . Indeed , under these conditions cecal S . Tm loads remained equally high from day 1 to day 2 ( Fig . 5 ) . Taken together , our observations suggest that S . TmWT triggered inflammation can inflict a transient population bottleneck at day 2 p . i . and that this contributes at least in part to the reduced evenness index , observed in our WITS experiments . Thus far , our data suggested that the grade of the mucosal inflammation is a key determinant of the bottleneck at day 2 p . i . However , the mechanisms explaining this barrier to infection remained to be identified . One hallmark of S . Tm induced inflammation is the tissue infiltration by granulocytes , their transmigration into the gut lumen and the formation of characteristic crypt abscesses [15] . As granulocytes can attack bacteria by bactericidal mechanisms such as phagocytosis , release of antimicrobial substances and formation of neutrophil extracellular traps ( NETs ) [43] , they might contribute to the gut luminal bottleneck . However , to the best of our knowledge , it has not been assessed previously , whether granulocytes can have bactericidal activity upon transmigration into the gut lumen . To test if granulocyte-mediated killing contributes to the gut luminal bottleneck , we employed a cell depletion strategy . Initially , we depleted granulocytes during S . TmWT infection using an α-Gr1 antibody ( 100 µg i . p . , once per day; Materials and Methods; Fig . 6A ) which binds to Ly6G on neutrophilic granulocytes ( PMN ) and to Ly6C , a marker expressed on neutrophils and on dendritic cells , some subpopulations of lymphocytes and some monocytes [44] . For this experiment , we used 129SvEv mice ( Nramp/slc11α1+/+ ) as their monocytes can control S . Tm growth at systemic sites much more efficiently than those from slc11α1−/− C57BL/6 mice [45] , [46] . This allowed infecting Gr1+-depleted mice for 4 days without compromising the viability of the animal ( Fig . 6A ) . First we analyzed the effect of granulocyte depletion on the cecum luminal pathogen density at day 2 p . i . Gr1+-cell-depleted mice , and PBS-treated control animals were pretreated with streptomycin and infected for 1 or 2 days with S . TmWT . The efficiency of granulocyte depletion was verified by flow cytometry ( CD45+ , Ly6G+ , Ly6C+ cells; Fig . 6B ) of murine blood . The detailed effect of Gr-1 neutralization on inflammatory myeloids in the cecal mucosa of 129SvEv mice revealed that the procedure depleted not only granulocytes , but also other phagocyte populations ( S5 Figure , S1 Text ) . Pathogen loads in the mLN , the spleens and the livers did rise in both groups by 10–100 fold between day 1 and day 2 p . i . We detected ( at most ) slightly elevated tissue loads in the Gr1+-depleted mice , while both groups featured equivalent S . TmWT loads in the cecal lumen at day 1 p . i . ( Fig . 6C ) . Strikingly , the cecum luminal pathogen loads remained high at day 2 p . i . in the Gr1+-depleted mice , but plummeted in the PBS controls . This went along with reduced gut luminal granulocyte ( CD18+ ) numbers in the Gr1+-depleted mice ( Fig . 6D , E ) and suggested that granulocytes may indeed affect the bottleneck . A second experiment was performed to assess effects on the WITS evenness index . Gr1+-depleted mice , and PBS-treated control animals were pretreated with streptomycin and infected for 4 days with a “1∶7000” inoculum of S . TmWT WITS as described for Fig . 2 . In line with earlier work , the Gr1+-depleted mice featured increased organ loads in the mLN , the spleens and the livers [47] . This verified that granulocytes indeed significantly limit systemic infection . In contrast , cecum luminal total S . TmWT densities did not differ between both groups ( Fig . 6F , left panel , black bars ) . Strikingly , the WITS evenness index remained much higher in the Gr1+-depleted mice , as compared to the PBS controls ( Fig . 6F , Table 1 ) . In a complementary , yet more specific strategy , we applied a combination of α-Ly6G and α-G-CSF antibodies to selectively reduce granulocytes but spare monocytes from depletion [48] , [49] . Again , the depleted animals featured higher WITS evenness indices than the control group treated with the respective isotype controls of both antibodies ( S6 Figure , S1 Text ) . These data supported our hypothesis that Gr1+ cells ( mainly granulocytes ) form a major bottleneck for the gut luminal S . Tm population by day 2 p . i . However , these results raised the question whether this outcome is a direct effect of the bactericidal activity of granulocytes ( e . g . NO/ROS production ) or an indirect effect ( e . g . reduction of granulocyte-released cytokines or antimicrobial proteins ) . First , we examined WITS diversity loss in Nos2-deficient mice to clarify the requirement of inflammation-mediated NO production in reducing luminal S . Tm . As Nos2 was shown to be dispensable for controlling S . Tm during the initial days of infection [50] , we hypothesized that the Nos2-deficient mice should restrict gut luminal S . Typhimurium loads as efficiently as the wild type animals . In line with this , the WITS evenness indices in Nos2−/− mice and the littermate controls did not differ at day 3 p . i . ( S7A Figure , S2 Table ) suggesting that NO is not the predominant cause for the WITS diversity loss . Next , we tested the contribution of oxidative burst in the reduction of WITS evenness . As NADPH-oxidase is a key antimicrobial factor of granulocytes [43] at systemic sites and in the intestinal tissue [51] and as these cells do transmigrate in large numbers into the lumen of the inflamed gut , we speculated that Cybb−/− mice might be incapable of restricting gut luminal pathogen loads in the inflamed gut . Strikingly , WITS evenness in the NADPH-oxidase deficient ( Cybb−/− ) mice at day 3 p . i . was increased compared to a heterozygous control group ( S8A Figure , S2 Table ) pointing to a possible involvement of direct ROS-mediated killing to the bottleneck effect . However , interpretation of WITS analysis in the Cybb−/− mice is limited by a high S . Tm colonization at systemic sites up to levels where WITS-tagged strains are found systemically ( S8B Figure ) . As possible re-seeding events of these strains into the gut lumen may compromise the unequivocal interpretation of our WITS-diversity analysis , these results should be taken with caution . Taken together , the gut luminal S . Tm population bottleneck is mostly caused by granulocytes , possibly via a direct ROS-mediated killing mechanism . However , our data do not exclude the contribution of additional factors to this bottleneck effect .
To identify barriers limiting S . Tm in the gut lumen , we have performed mixed inoculation experiments . During the first day , the pathogen density rose to ≈109 cfu/g in the large intestinal lumen without encompassing obvious bottlenecks . In the case of wild type S . Tm infection , cecum colonization was accompanied by pronounced mucosal inflammation which arises during the first day ( 8–12 h p . i . ; [15] , [52] ) and lasts for four days ( or longer; [46] , [53] ) . By day 2 p . i . , the inflammatory response dramatically reduces the gut luminal pathogen density . This inflammation-inflicted , granulocyte-dependent bottleneck transiently restricts the gut luminal S . Tm population to about 6000 cfu . By days 3 and 4 , the pathogen density rises again to ≈109 cfu/g of gut content . These data establish that mucosal inflammation can represent a barrier limiting the gut luminal colonization by the pathogen . Our findings significantly extend earlier work on S . Tm growth in the lumen of the inflamed gut . That earlier work had indicated that the pathogen subverts the inflammatory milieu in the gut lumen for its own advantage , i . e . for outcompeting the resident microbiota ( reviewed in [15] , [54] , [55] ) . Granulocytes infiltrating the inflamed mucosa and transmigrating into the gut lumen are thought to contribute in several different ways i . e . , by i ) providing/regenerating terminal electron acceptors ( tetrathionate , nitrate ) used by S . Tm , thereby fuelling anaerobic respiration and fast growth of the pathogen ( not the commensals , which are mainly fermenters [24] , [25] ) ; by ii ) releasing lipocalin-2 , an antimicrobial peptide blocking siderophore-mediated iron acquisition by E . coli . Interestingly , S . Tm is endowed with the iroBCDEN variant of such system which can bypass the lipocalin-2-mediated restriction [23]; by iii ) releasing calprotectin which sequesters Zn2+ , an essential micronutrient , from the gut lumen . This restricts microbiota growth , while S . Tm can bypass this restriction using a high affinity zinc transporter ( znuABC; [22] ) . Furthermore , S . Tm is endowed with enzymes inactivating PMN-derived reactive oxygen and nitrogen compounds [56]–[58] . Overall , these earlier findings indicated that granulocyte activity ( and mucosa inflammation in general ) foster pathogen growth in the intestine and prevent growth of the resident microbiota . However , the quantitative contribution of inflammation-mediated fuelling versus restriction of S . Tm growth in the gut lumen had remained unclear . Our findings demonstrate that the gut luminal S . Tm population is highly vulnerable to the inflammatory response in particular at day 2 p . i . We conclude that the size of the pathogen population in the inflamed gut is controlled by a fine balance between inflammation-fuelled pathogen growth ( and competition against resident microbiota ) and inflammation/granulocyte-inflicted losses to the pathogen population . How many S . Tm cells survive the bottleneck in the gut lumen ? Our best estimate is that 6000+/−2000 bacteria survive this dip at day 2 . This value is obtained from a simple binomial selection model fitted to the data on the detectability ( presence/absence ) of WITS at day 4 for the 1∶7000 dilution . This model assumes that there are no fitness differences between WITS and that the population bottleneck is effective for only a short time . The classical cfu-plating revealed reduced , but highly variable pathogen population sizes at day 2 p . i . ( median = 2×106 cfu/g ) . In a few mice , we detected as few as 3×105 cfu/g of cecum content by day 2 p . i . This corresponds to a total population size of 15000 bacterial cells , considering that the infected cecum ( tissue+lumen ) has a total volume of no more than 200 µl , and that the cecal luminal volume might range at approx . 50 µl at this stage . The high variance of the cfu-data measured at day 2 p . i . and the similarity of the evenness-indices across animals suggest that the pathogen population may be reduced for no more than a few hours before net re-growth occurs . Using 24 h sampling intervals , classical cfu-plating would therefore miss the low in most mice , in particular if the time-course would differ from animal to animal . Therefore , we conclude that the bottleneck allows the survival of just 6000 bacteria . Thus , the mucosal inflammation is a strikingly efficient host defense , reducing the total cecum luminal population by about 105–106 fold ( 108 cfu per cecum at day 1 p . i . to 6000 bacteria by day 2 ) . This is almost as efficient as chemical disinfectants ( defined as reducing bacterial survival by ≥106-fold; [59] ) and far more effective than many other barriers , e . g . the IFNγ or inflammasome mediated restriction of systemic spread ( ≈10-fold restriction by day 2 p . i . ; [60] , [61] ) or the gut luminal growth defect inflicted by adaptive sIgA response directed against the LPS O-sidechain ( ≈10-fold; [53] , [62] ) limiting S . Tm loads at different stages of an infection . In conclusion , the mucosal inflammation severely reduces the gut luminal S . Tm population by day 2 p . i . However , this is not quite sufficient to yield sterilizing protection and to prevent subsequent pathogen-regrowth . In this work , the importance of granulocytes in controlling S . Tm within the gut lumen is re-emphasized and extended . Already in the early studies of streptomycin pretreated mice , neutrophil infiltration was detected in the cecal mucosa by 10 h p . i . [63] . Similarly , granulocytes are prominent in the infected mucosa of cows and man [64] , [65] , they transmigrate into the gut lumen and form the archetypical “crypt abscesses” , i . e . densely packed clusters of granulocytes located within the intestinal crypts [66]–[69] . By extension , these observations suggest that granulocyte-mediated growth and restriction might be of general importance also in other hosts including the human patient . During the first day of infection , granulocytes might also serve as a host cell permissive for intracellular growth ( or at least survival ) of the pathogen . In the streptomycin mouse model , gut luminal neutrophils harbored significant numbers of S . Tm by 20 hours p . i . and most of these bacterial cells were viable , as judged from the expression of iroBCDE- and ttss-2 driven GFP reporters [70] . Other studies have also indicated that S . Tm can survive in murine neutrophils [70]–[72] . The lack of a bottleneck during the first day of infection would be in line with this . Our data suggest that the barrier detected by day 2 p . i . is attributable to some quantitative or qualitative change in the inflammatory response and/or the granulocyte activity , e . g . by modulating NADPH-oxidase dependent defenses . Identifying the underlying mechanism controlling the granulocyte-inflicted bottleneck will be of interest for future work and might identify strategies for bolstering the gut luminal barrier . Still , our data suggest that pronounced granulocyte migration into the gut lumen can have detrimental effects on the S . Tm population , at least by day 2 p . i . Furthermore , our data provide first hints that ROS might play a central role in decimating luminal S . Tm , despite of the low oxygen-availability in the gut lumen and the fact that S . Tm expresses three catalases and three periplasmic Cu , Zn superoxide dismutase enzymes [73] . Thus , the interaction of S . Tm with granulocytes in the gut lumen is clearly important , but more complex than previously anticipated . In summary , investigating mixed inocula allows precious insights into colonization strategies and host defenses limiting infection . The population bottlenecks identified by such approaches may represent attractive points for therapeutic interventions , as pathogen populations are already reduced to a minimum .
The wild type strain SB300 is a clone of S . Tm SL1344 and the SPI-1 and SPI-2 deficient mutant strains ( S . TmWT , S . TmSPI-1 , S . TmSPI-2 ) , as well as their isogenic tagged strains have been described previously [5] , [6] , [34] , [35] , [74] . WITS-tags were introduced into S . TmSPI-1 & SPI-2 ( SL1344 invGssaV; [17] and into S . TmSPI-2 ( SL1344 ssaV::cat; [35] ) by P22 phage transduction and subsequent selection on kanamycin . The presence of the correct WITS-tag was confirmed by PCR using tag-specific primers ( see S1 Table ) . Wild type C57BL/6 mice were bred and kept under specified pathogen free ( SPF ) conditions in individually ventilated cages at the Rodent Center RCHCI ( ETH Zurich ) . They harbor a complex microbiota and are called “conventional” mice . For reliable and efficient infection with S . Tm , streptomycin pretreatment of these mice was performed in order to overcome colonization resistance in all experiments described herein [16] . 129 Sv/Ev mice are Nramp1 ( +/+ ) ( Slc11α1 ( +/+ ) ) mice which can control systemic S . Tm infection with low bacterial loads at systemic sites but develop acute S . Tm colitis upon streptomycin pretreatment [46] . 129 Sv/Ev mice are specified pathogen free animals with a complex microbiota . Cybb−/− ( B6 . 129S-Cybbtm1Din/J; C57BL/6 background ) and Nos2−/− ( B6 . 129P2-Nos2tm1Lau/J; C57BL/6 background ) were bred at the Rodent Center HCI ( ETH Zurich , Switzerland ) . Both mouse lines have been described previously [75] , [76] . Animal infection experiments were performed in 8 to 12 week old mice as described previously [16]: C57BL/6 mice or 129 Sv/Ev mice were pretreated with 20 mg streptomycin 24 hours prior to infection . For infection , bacteria were grown for 12 h in 0 . 3 M NaCl supplemented LB medium containing the appropriate antibiotic ( s ) , diluted 1∶20 and sub-cultured for 4 h in the same medium without supplementation of antibiotics . Tagged and un-tagged strains were mixed as indicated , washed twice with PBS and mice were infected with 5×107 bacteria by gavage . Each animal was kept in a single cage to avoid transmission between mice . Animals were sacrificed at day 4 p . i . by cervical dislocation . Freshly collected fecal pellets or whole cecal content were harvested , homogenized in 500 µl PBS with steel balls in a Tissue Lyser ( Qiagen ) for plating to determine the total population size . 250 µl of homogenized feces or cecal content were used to inoculate an over night culture in LB supplemented with 50 µg/ml kanamycin to select for tagged strains . Bacteria from 1 ml of this over night culture were harvested and frozen for genomic DNA extraction . HE-staining of cryo-embedded tissues and subsequent pathoscoring for granulocyte infiltration was performed as described previously [16] . To test for selection of beneficial mutations which might explain the variation in the subpopulations at day 4 p . i . , we re-isolated dominant WITS-tagged bacteria from cryo-embedded tissues by plating on kanamycin . Untagged strains were isolated in the same way , but were selected for growth on streptomycin and remained kanamycin sensitive . Competitive infection experiments to test for increased fitness of re-isolated strains were performed by infecting streptomycin-pretreated mice with equal amounts of S . TmWT or a WITS-tagged S . TmWT strain and the isolates . By selective plating , population sizes of both were determined and a competitive index was calculated by dividing the ratio of the isolate to the background strains by the ratio of both strains in the inoculum . Genomic DNA from enrichment cultures was isolated with the QIAamp DNA Mini Kit ( Qiagen , Cat . NO . 51306 ) . rtqPCR analysis with FastStart Universal SYBR Green Master ( Roche ) was performed using primers and temperature profiles as described previously [5] . The population size of each tagged strain was calculated by multiplying the number of kanamycin resistant cfu/g recovered S . Tm with the ratio of WITS determined by rtqPCR . The monoclonal anti-Gr1 antibody NIMP-R14 is highly specific for the murine epitopes Ly6G and Ly6C and was shown to selectively deplete mouse neutrophils [77] . For depletion of Gr-1+-cells during S . TmWT infection , we injected 100 µg of anti-Gr1 i . p . on a daily basis , starting 24 h prior to infection . Granulocyte depletion was assessed daily by analysis of peripheral blood ( and lamina propria after sacrificing the mice ) in a flow cytometry assay using the following staining antibodies: anti-CD11b-PECy7 ( clone M1/70 ) , anti-Ly6C-FITC ( clone AL-21 ) , anti-Ly6G-450V ( clone 1A8 ) and anti-CD45 . 2-APC ( clone 104 ) . NIMP-R14 hybridoma was a kind gift of Dr . Nancy Hogg ( Cancer Research UK , London , U . K . ) and antibodies for flow cytometry were purchased from BioLegend . Cecal tissues were recovered , fixed for 5 h in 4% paraformaldehyde/4% sucrose , saturated in PBS/20% sucrose ( overnight , 4°C ) , embedded in OCT medium ( Sakura , Torrance , CA ) , snap-frozen in liquid nitrogen and stored at −80°C . Cryosections ( 20 µm ) were air-dried , rehydrated with PBS and permeabilized with PBS/0 . 5% Triton X-100 . Unspecific binding was blocked with PBS/10% Normal Goat Serum . The tissue sections were immunostained using rat-anti-mouse-CD18 ( clone M18/2 , Biolegend , ( 1∶300 ) ) . Goat-anti-rat-Cy3 ( 112-165-167 , Jackson , 1∶200 ) was used as a secondary antibody . DNA was stained with DAPI ( SIGMA Aldrich ) and F-actin was stained using AlexaFluor647-conjugated phalloidin ( Molecular probes ) . Sections were subsequently mounted with Mowiol ( Calbiochem ) . Imaging was performed using a Zeiss Axiovert 200 m microscope equipped with 10×–100× objectives , a spinning disc confocal laser unit ( Visitron ) and parallel Evolve 512 EMCCD cameras ( Photometrics ) . CD18+ cells were enumerated in 40× field of vision in several non-consecutive sections per mouse in a blind fashion and the average number of CD18+ cells were compared between experimental groups . The exact Mann-Whitney U test was performed using the software Graphpad Prism Version 6 . 0 for Windows ( GraphPad Software , La Jolla California USA , www . graphpad . com ) . P values of less than 0 . 05 ( two-tailed ) were considered as statistically significant . * P<0 . 05 , ** P<0 . 01 , *** P<0 . 001 . Evenness indices were calculated as described [31] . In cases where none of the seven WITS tagged strains were detectable any more , no evenness index was applicable and the corresponding mice were ignored for determination of the median evenness and the comparison between evenness indices in Table 1 . It should be noted that the 1∶7 inoculum also yielded a somewhat reduced evenness by day 4 p . i . ( median = 0 . 528 ) . This cannot be explained by technical error when using our simple population model and may suggest that a second , so far unidentified process may affect the gut luminal S . Tm population . However , this effect was much smaller than the WITS-diversity loss observed with the 1∶7000 inoculum . In the present paper , we have decided to focus on the latter phenomenon . We estimated the bottleneck size using the data on WITS loss after their inoculation at the lowest dilution of 1/7000 . Let w be the number of WITS , which , in our experiments , was always w = 7 . Further , let m be the number of mice in a given treatment group . Then we have a total of mw WITS in that treatment group . Lastly , let x denote the number of WITS that cannot be recovered across all mice . In the control group for example , 30 of the 70 WITS in the 10 mice were undetectable at day 4 , and we thus have w = 7 , m = 10 , x = 30 . The procedure to estimate the bottleneck size is based on the simplest possible model of this process: binomial selection . We assume that during the bottleneck , B bacterial cells are selected . B is the bottleneck size , i . e . the number of bacteria that are able to traverse the bottleneck . The probability of selecting a certain WITS is determined by its dilution , d = 1/7000 . Not selecting this WITS happens with a probability of p0 = ( 1−d ) B . We can construct a log-likelihood for loosing x out of mw WITS by bottlenecking: ℓ = xlnp0+ ( mw−x ) ln ( 1−p0 ) . Maximizing this log-likelihood yields the maximum likelihood estimates for the bottleneck size listed in Table 2 . To calculate confidence intervals for the estimates we used the profile likelihood [78] . If all or no WITS are lost in a treatment group , only upper or lower bounds of the bottleneck size can be calculated , respectively . All animal experiments were reviewed and approved by the Kantonales Veterinäramt , Zürich ( license 223/2010 ) and are subject to the Swiss animal protection law ( TschG ) . | Salmonella Typhimurium can colonize the human intestine and cause severe diarrhea . In recent years , it has become clear that this pathogen profits from inflammatory changes in the intestinal lumen , as the inflamed gut helps Salmonella to out-compete the resident microbiota . Granulocytes transmigrating into the gut lumen were found to “foster” luminal Salmonella growth by providing nutrients ( used by Salmonella , not the microbiota ) and by releasing growth inhibitors affecting the microbiota , but not the pathogen . In this study , we extend this “fostering” concept by showing that gut luminal Salmonella Typhimurium population is itself surprisingly vulnerable to the host's inflammatory response . Indeed , inflammation reduces the size of the gut luminal Salmonella population by as much as 105-fold at day 2 post infection . Thus , triggering of mucosal inflammation is in fact a double-edged sword by providing S . Typhimurium with a relative growth advantage against the microbiota in the gut lumen and by killing 99 . 999% of the gut luminal pathogen population at day 2 . However , the pathogen population can recover and grow up again during the subsequent days . This changes the current view: Inflammation is not simply “beneficial” for the pathogen in the gut lumen . Instead , pathogen growth in the inflamed gut must be considered as an equilibrium between inflammation-inflicted killing and fostering growth of the surviving bacteria . | [
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... | 2014 | Granulocytes Impose a Tight Bottleneck upon the Gut Luminal Pathogen Population during Salmonella Typhimurium Colitis |
Sex-biased demographic events ( “sex-bias” ) involve unequal numbers of females and males . These events are typically inferred from the relative amount of X-chromosomal to autosomal genetic variation and have led to conflicting conclusions about human demographic history . Though population size changes alter the relative amount of X-chromosomal to autosomal genetic diversity even in the absence of sex-bias , this has generally not been accounted for in sex-bias estimators to date . Here , we present a novel method to identify sex-bias from genetic sequence data that models population size changes and estimates the female fraction of the effective population size during each time epoch . Compared to recent sex-bias inference methods , our approach can detect sex-bias that changes on a single population branch without requiring data from an outgroup or knowledge of divergence events . When applied to simulated data , conventional sex-bias estimators are biased by population size changes , especially recent growth or bottlenecks , while our estimator is unbiased . We next apply our method to high-coverage exome data from the 1000 Genomes Project and estimate a male bias in Yorubans ( 47% female ) and Europeans ( 43% ) , possibly due to stronger background selection on the X chromosome than on the autosomes . Finally , we apply our method to the 1000 Genomes Project Phase 3 high-coverage Complete Genomics whole-genome data and estimate a female bias in Yorubans ( 63% female ) , Europeans ( 84% ) , Punjabis ( 82% ) , as well as Peruvians ( 56% ) , and a male bias in the Southern Han Chinese ( 45% ) . Our method additionally identifies a male-biased migration out of Africa based on data from Europeans ( 20% female ) . Our results demonstrate that modeling population size change is necessary to estimate sex-bias parameters accurately . Our approach gives insight into signatures of sex-bias in sexual species , and the demographic models it produces can serve as more accurate null models for tests of selection .
Human population-genetic studies generally assume that the proportions of reproducing females and males are equal . However , human history contains sex-biased demographic events ( “sex-bias” ) which are defined by having unequal female and male effective population sizes , NeF and NeM . Some examples of sex-bias include matrilocality ( the practice of females remaining in their place of birth after marriage ) , and conversely , patrilocality [1 , 2]; patrilineal inheritance in herder groups [3]; polygamy , the practice of a male having multiple female sexual partners , and polyandry , which is the opposite; female- and male-biased migration; and sexual selection . These factors , along with a variance in reproductive success that is greater in males than females [4 , 5] , cause male and female effective sizes to differ [6 , 7] . Initial studies of human sex-bias compared mitochondrial to Y-chromosomal data due to their uniparental inheritance ( maternal and paternal , respectively ) . Recent studies have compared X-chromosomal to autosomal data [8–11] to take advantage of their multiple independent loci [12 , 13] . Most of these studies found evidence for female bias in human populations . Although Labuda et al . initially found evidence for male bias based on recombination rates [14] , their conclusion changed to one of a female bias after an error in their analysis was corrected [15 , 16] . These studies used standard sex-bias estimators of Q , the ratio of X-chromosomal to autosomal effective population sizes . In a neutrally-evolving population of constant size with no migration , Q is 0 . 75 when there is no sex-bias; Q is greater than 0 . 75 when there is a female bias and less than 0 . 75 when there is a male bias . Other recent sex-bias studies analyzed admixture fraction on the X-chromosome and autosomes and found evidence for sex-biased admixture in human populations . Since they have different effective sizes , the X chromosome and autosomes recover genetic diversity following a population size change at different rates , even in the absence of sex-bias [17] . Previous studies considered whether population size change alone could explain the patterns of X-chromosomal and autosomal genetic variation observed in human populations . Though a study of genomic resequencing data estimated a large Q value consistent with a bottleneck more than 100 , 000 years ago followed by recent growth , they rejected this explanation based on simulations [18] . A more recent study , which found that Q increases with distance from genes , studied the impact of human demographic histories on Q^π ( i . e . , Q estimated from π , average pairwise sequence diversity ) and found it was only slightly biased by realistic size changes [19] . The common estimators of sex-bias , Q^π and Q^FST , Q estimated from FST , are sensitive to sex-biases at different time scales in the context of realistic human demographic history [20] . A study assessing a male-biased Out-of-Africa bottleneck found evidence for a more severe bottleneck on the X chromosome than the autosomes in European and East Asians but was not able to estimate the proportion of females during the bottleneck with their FST-based inference method [21 , 22] . Although these studies characterized patterns of relative X-chromosomal to autosomal variation , they did not explicitly model population size change , nor did they provide estimates of the proportion of females during specific epochs . A recent study by Clemente et al . developed a tree-based method , KimTree , to estimate sex-bias parameters for each population branch from multi-population X-chromosomal and autosomal data [23] . Although they found evidence for an overall female bias in human populations and a male bias in Oceanians , their inference could be biased for one of the following reasons , among others: their method assumes a constant population size during epochs , and they did not remove genic regions from their human data , which could be under evolutionary constraints . Furthermore , their method requires data from multiple populations , limiting its applicability . Here , we present a novel method to estimate sex-bias from X-chromosomal and autosomal sequence data . It models demographic history jointly from X-chromosomal and autosomal site frequency spectra and explicitly models complex demographic events , including exponential growth and multiple bottlenecks . Our method estimates the proportion of females overall as well as during each time epoch . In simulations , our method has good power to detect a true sex-bias for a range of demographic histories and performs well when the method of Clemente et al . does not . We apply our method to globally-distributed human populations from the 1000 Genomes Project [24] and compare sex-bias estimates based on exome data to those from whole-genome sequencing data . Our sex-bias estimates , which account for population size changes , give new insight into human demographic history and the male-biased migration out of Africa .
We initially assume a constant per-site mutation rate , μ , shared by the X chromosome and autosomes , and that mutation occurs as a Poisson process [25] . We later account for unequal male and female mutation rates . For a population with Nm males and Nf females where Nm + Nf = N , the total number of individuals , the inbreeding effective sizes of the autosomes and X chromosome can be derived using a coalescent argument [13] . In terms of the proportion of breeding females p = Nf/N , these effective sizes are: NeA ( p ) =4p ( 1−p ) N ( 1 ) NeX ( p ) =9p ( 1−p ) 2 ( 2−p ) N ( 2 ) We drop p from the left-hand side for notational convenience . The autosomal effective size ( NeA ) is maximized at N when p = 0 . 5 and is less than N otherwise; the X-chromosomal effective size ( NeX ) is always less than N ( Fig 1A ) . We define these reductions of effective size due to sex-bias ( i . e . p ≠ 0 . 5 ) as the reduction factors fA ( p ) =NeA/N and fX ( p ) =NeX/N . The ratio NeX/NeA increases with p ( Fig 1B ) and is greater than 1 for very female biased values ( p > 0 . 875 ) , in agreement with classic results ( Fig 1C ) . The unfolded site frequency spectrum ( SFS ) for a sample of n chromosomes is the random vector ( S1 , S2 , … , Sn−1 ) . Under the Poisson random field model , the Si’s are independent Poisson-distributed entries with mean θF ( i ) ( see Eq 2 of [25] for the definition of F ( i ) ) . The probability of observing si sites with i derived and ( n − i ) ancestral mutations under neutrality , given a population-scaled mutation rate of θ = 4Neμ and the demographic model D , is: p ( Si=si|θ , D ) =e−θF ( i ) ( θF ( i ) ) sisi ! ( 3 ) The maximum likelihood estimator ( MLE ) of θ for a sample of n chromosomes with a total of S segregating sites ( i . e . S=∑i=1nSi ) is as follows , where the subscripts A and X denote the autosomes and X chromosome , respectively ( see also Eq 13 of [25] ) : θ^A=SA∑i=1nA−1FA ( i ) ( 4 ) θ^X=SX∑j=1nX−1FX ( j ) ( 5 ) To test for sex-bias in a population of constant size , we develop a likelihood ratio test ( LRT ) . Under the null hypothesis , the parameters θ^A and θ^X are consistent with p = 0 . 5; under the alternate hypothesis , they are not . Let LA and LX be the physical length ( e . g . , in base pairs ) of the sequenced autosomal and X-chromosomal loci . The Poisson density in Eq 3 can be combined with the MLEs in Eqs 4 and 5 to give distributions of the number of autosomal and X-chromosomal segregating sites: SA∼Pois ( θA×LA×∑i=1nA−1FA ( i ) ) ( 6 ) SX∼Pois ( θX×LX×∑j=1nX−1FX ( j ) ) ( 7 ) We combine the definition θ = 4Neμ with Eqs 1 and 6 to get the expectation of SA and with Eqs 2 and 7 to get the expectation of SX: E[SA]=4p ( 1−p ) Nμ×LA×∑i=1nA−1FA ( i ) ( 8 ) E[SX]=9p ( 1−p ) 2 ( 2−p ) Nμ×LX×∑j=1nX−1FX ( j ) ( 9 ) Taking the ratio of SA to SX and solving for p , we obtain our estimator of the effective proportion of females overall , p˜ , in terms of the site frequency spectrum densities FA and FX: p˜=2−9SALX∑j=1nX−1FX ( j ) 8SXLA∑i=1nA−1FA ( i ) ( 10 ) To estimate p˜ for a particular epoch t , we use the effective sizes NetA and NetX for that epoch: pt˜=2−9×4NetAμLA8×4NetXμLX ( 11 ) Using Eqs 6 and 7 , we can write a joint likelihood for the autosomal and X-chromosomal data: L ( θ , p|SA , SX ) =∏i=1nA−1P ( Si , A=si , A|θA ) ∏i=1nX−1P ( Si , X=si , X|θX ) =∏i=1nA−1e−θAFA ( i ) ( θAFA ( i ) ) si , Asi , A ! ∏i=1nX−1e−θXFX ( i ) ( θXFX ( i ) ) si , Xsi , X ! ( 12 ) For a population of constant size , this likelihood reduces to the likelihood in [18] , which we can use to define a likelihood ratio test for sex-bias . Under the null hypothesis of p = 0 . 5 ( i . e . no sex-bias ) , θA = θ and θX = 3/4 × θ based on Eqs 1 and 2 . Substituting these into Eq 12 , we obtain the MLE of θ based on autosomal and X-chromosomal data under the null hypothesis: θ˜0=SA+SXLA×∑i=1nA−1FA ( i ) +3/4×LX×∑j=1nX−1FX ( j ) ( 13 ) Under the alternative hypothesis of sex-bias ( p ≠ 0 . 5 ) , we instead use the reduction factors in θA = fA ( p ) × θ and θX = fX ( p ) × θ and obtain the MLE of θ as: θ˜=SA+SXfA ( p ) ×LA×∑i=1nA−1FA ( i ) +fX ( p ) ×LX×∑j=1nX−1FX ( j ) ( 14 ) We evaluate the log of the likelihood in Eq 12 at θ=θ˜0 to obtain the null log-likelihood , LL0 , and at θ=θ˜ to obtain LL1 , the alternative log-likelihood . The likelihood ratio test statistic , Λ = −2 ( LL0 − LL1 ) , is approximately χ12-distributed . We define a demographic history as a set of population sizes ( Ne1 , Ne2 , …NeT ) which go forward in time ( i . e . , Ne1 is the ancestral population size ) and correspond to a set of T − 1 size changes and T epoch durations . The size changes ν→= ( ν1 , ν2 , … , νT−1 ) , which occur instantaneously or exponentially , are defined as the size at the end of an epoch relative to the ancestral population size . The epoch durations τ→= ( τ1 , τ2 , … , τT ) are in units of genetic time scaled by the ancestral population size . We assume the X chromosome has the same demographic model ( i . e . number and kind of size changes ) as the autosomes . To assess sex-bias in a population , we test nested X-chromosomal models defined in terms of the female fraction of the effective size during an epoch , pt , t = 1…T: Model 0: no sex-bias . p is 0 . 5 for every epoch , so NtX=0 . 75×NtA . Model 1: constant sex-bias . pt is the same for every epoch , so NtX=c×NtA for a constant c . Model T: varying levels of sex-bias . pt can vary among epochs , so NtX=ct×NtA for a constant ct . These models are implemented by constraining the X-chromosomal size change and epoch duration parameters , νX→ and τX→ , by the autosomal parameters νA→ and τA→ , and their likelihoods are used in the likelihood ratio tests for sex-bias ( see S1 Text , “Likelihood ratio tests for sex-bias: general form” ) . In addition to the examples we give for a two-epoch model below ( “Sex-bias tests for a two-epoch model” ) and a three-epoch bottleneck model ( see S1 Text , “Likelihood ratio tests for sex-bias: bottleneck model” ) , sex-bias tests for arbitrarily complex demographic models can be defined . A population at mutation-drift equilibrium changes from its original size of N0 to size N1 ( i . e . the fold-size change ν is N1/N0 ) at time τ ( Fig 2 ) . Though a population expansion is shown in the figure , the same framework is used for a population contraction . There are two free X-chromosomal parameters , νX and τX , so there are three X-chromosomal models , Models 0 , 1 , and 2 , and two likelihood ratio tests . Model 0 has no sex-bias ( p = 0 . 5 ) and the following constraints ensure that the effective size of the X chromosome is 3/4 that of the autosomes: νX = νA , τX = 4/3 × τA , θX = 4/3 × θA . Model 1 has a constant sex-bias ( p is constant ) and these constraints ensure that the X-chromosomal effective sizes are a constant scaling of the autosomal effective sizes: νX = νA , τX = 1/c × τA , and θX = c × θA for some constant c . The final model , Model 2 , corresponds to varying sex-bias ( p varies ) , and its constraints ensure that the size changes happen at the same time as measured in generations: νX = c2/c1 × νA , τX = 1/c2 × τA , θX = c1 × θA . Joint likelihoods for the ith model , i = 0 , 1 , 2 , based on the autosomal log-likelihood llA and X-chromosomal log-likelihood lli , are: LL0 = llA + ll0 , LL1 = llA + ll1 , and LL2 = llA + ll2 . A test for constant sex-bias is based on Λ0 = −2 × ( LL0 − LL1 ) and one for varying sex-bias is based on Λ1 = −2 × ( LL1 − LL2 ) . The best-fitting model has an estimate of the effective proportion of females overall , p˜ , and during each epoch , pt˜ , t = 1…T .
Human sex-bias studies have reached conflicting conclusions due to the type of genomic loci and statistics used [20] . An important confounder is population size change , which can bias sex-bias inferences . To this end , we developed a sex-bias inference method that accounts for demographic history and takes X-chromosomal and autosomal genetic data as input . When applied to coalescent simulations , our method has better power than conventional estimators to estimate an overall sex-bias for arbitrary demographic histories; in addition , our method can detect a changing sex-bias . We also applied our method to human data from the 1000 Genomes Project [24] . There are two main issues with conventional approaches that test for sex-bias with a single summary statistic such as Q . The first issue is that the null expectation of Q is not 0 . 75 for a population which has changed in size , so a test comparing Q to 0 . 75 for a population of non-constant size can be underpowered or have false positives [20] . The second issue is that a single summary statistic cannot localize the source of sex-bias to a particular time epoch . For example , for data simulated with a bottleneck and varying amount of sex-bias ( S1 Table ) , a population with no sex-bias ( p = 0 . 5 ) which underwent a female-biased bottleneck ( p = 0 . 7 ) has a Q of 0 . 731 , which is similar to the Q of 0 . 737 that a population with a strong female bias ( p = 0 . 8 ) and strongly male-biased bottleneck ( p = 0 . 2 ) has . As a result , these scenarios cannot be distinguished by Q alone . Based on simulated data , our test for sex-bias is more powerful than one based on Q and is well-powered for demographic events relevant to human history , such as recent expansions and bottlenecks . Decreasing p from 0 . 5 by some amount , as for a male bias , changes NeX/NeA more than increasing p by the same amount , as for a female bias ( Eqs 1 and 2 ) . Despite this , our test for a changing sex-bias has good power for all values of p on data simulated with a bottleneck . However , a sex-bias estimator that does not account for population size change , such as pπ , is more biased when p is small ( i . e . for a male bias ) . In bottleneck simulations with a strong male bias , pπ is downwardly biased and at times negative . This is because the strong , recent bottleneck combined with the strong male bias reduces X-chromosomal genetic diversity more than autosomal genetic diversity . Bottlenecks with a changing proportion of females are relevant to human history , particularly since some bottlenecks correspond to long-range migrations which are hypothesized to have been sex-biased . A bottleneck alone biases conventional sex-bias estimators [17] . Applied to data simulated with a bottleneck under the null of constant sex-bias ( p1 = p2 = p3 = 0 . 5 ) , a conventional estimator is biased and estimates a persistent male bias ( pπ = 0 . 399 ) , whereas our sex-bias estimator is unbiased ( p1˜=0 . 503 , p2˜=0 . 496 , p3˜=0 . 500 ) . Using our method , we find evidence for a male-biased bottleneck out of Africa and have good power to detect such a sex-bias based on simulated data . To our knowledge , this is the first direct test of this hypothesis based on whole-genome sequence data . A recent method by Clemente et al . , KimTree , estimates sex-bias from multi-population data [23] . Our method compliments KimTree in that both offer insight into sex-bias , and each one has a different focus . Our method operates on data from a single population and explicitly models population size changes , while KimTree requires multi-population data and does not explicitly model population size change . Since KimTree estimates one effective sex ratio ( i . e . , the proportion of females ) per branch , it cannot detect sex-bias that changes on a single branch; our method can , and we have shown in simulations that it has good power to do so . Our method does not require an outgroup or knowledge of divergence events , and so can be applied to datasets where multi-population data is not available , including those from ancient samples . In addition , our method is much faster than KimTree: our method ran on a laptop in a few hours with a single thread , whereas KimTree took several days to run , even when multiple threads were used . Applied to 1000 Genomes Project whole-genome sequence data , our method infers a pervasive female bias in globally-distributed populations . This is consistent with human anthropological literature , which suggests that males have a greater variance in reproductive success than females [5] . In addition , our method identifies a male-biased bottleneck out of Africa based on data from Europeans; the lack of this signal in the other non-African populations may be due to insufficient sample sizes or misspecified demographic models . Finally , our method infers a male bias in the Southern Han Chinese , which is consistent with previous observations . Our results are generally in agreement with those from KimTree , which found either a female bias or no bias in most human populations , and a male bias on the branch ancestral to Europeans and Asians [23] . From filtered , putatively neutral whole-genome sequence data far from genes , our method infers a more extreme female bias than Clemente et al . , possibly because their estimates are downwardly biased by their inclusion of genic regions , their inability to account for sex-bias that changes along a population branch , or their assumption of a constant population size . To assess whether sex-bias estimation from exome data is appropriate , we analyzed synonymous sites as in previous studies [26–28] . We used a range of demographic models and obtained estimates of the proportion of females ranging from negative values to nearly 0 . 5 . For the best-fitting demographic models , p˜ is 0 . 465 for Yorubans and 0 . 435 for Europeans , similar to previously-obtained π-based estimates from non-genic sites closest to genes [19] . Our results also agree with those from another exome study [12] even though it only assessed three values of p , and we assessed the full range of p . Then , sex-bias inference from exomes is most likely confounded by background selection . We make some assumptions in our framework . We use an average mutation rate for the autosomes , μA , and an average rate for the X chromosome , μX . Though the mutation rate varies across the genome , we use a single SFS for each type of locus , autosomal and X-chromosomal , so mutation rate differences are averaged in the scaled mutation rate parameter , θ . This SFS and θ are used together to estimate demographic parameters , as is standard in demographic inference literature . In addition , though we do not require that SNPs be thinned to remove linkage disequilibrium before estimating sex-bias from genomic data , we recommend estimating parameter standard errors with a conventional bootstrap , as commonly done in demographic inference [27] . Our implementation of the sex-bias method we developed uses the program ∂a∂i [27] , and any demographic inference program that calculates likelihoods will work ( e . g . fastsimcoal [32] ) . Our method could be extended to test for sex-biased admixture or to analyze multiple populations simultaneously , which would expand its utility . In addition , although we only consider common variation ( minor allele frequency > 0 . 05 ) from which our method has good power to detect older sex-bias , if high-confidence rare variant calls are available , our method could be used to infer more recent sex-bias . This work underscores the importance of modeling demographic history when estimating sex-bias , and our results give new insight into sex-bias in human populations . Our method can infer sex-bias in any sexual population and provides better null models for selection scans than competing methods , producing a more accurate view of population histories .
To allow for unequal male and female mutation rates in our framework , we assume a constant female per-site mutation rate , μf , and a constant male per-site mutation rate , μm , with ratio given by α . For a given value of μA and α , we obtain the value μX as in [33] and used in [18]: μX=μA×2× ( 2+α ) 3× ( 1+α ) ( 15 ) In humans , α is greater than 1 , which corresponds to a male mutation bias [34] . These values of μA and μX can be substituted into Eqs 11 , 13 and 14 . We developed a novel method to estimate sex-bias from genetic data and that uses custom demographic functions written in the python programming language . Our method first estimates autosomal parameters then optimizes X-chromosomal parameters , some of which are constrained by the autosomal parameter estimates ( see S1 Text , “Likelihood ratio tests for sex-bias: general form” ) . To estimate demographic parameters , we use the program ∂a∂i , which uses a diffusion approximation to the one-locus , two-allele Wright-Fisher process [27] . To estimate parameters from simulated data , we used the “log_fmin” function in ∂a∂i , which uses the Nelder-Mead optimizer . For both simulated and genetic data , if parameter bounds are hit , we re-start the optimizer from a randomly perturbed point . To estimate parameters from the 1000 Genomes Project data , we perform a grid search over parameters , start ∂a∂i’s optimizer from the grid search optimum , and take the best point as the maximum likelihood point . For the complex demographic models used in the 1000 Genomes Project whole-genome data analysis , we fixed the parameter values of an older African growth event and the time of the Out-of-Africa bottleneck [28] and optimized more recent events . For samples of more than 20 individuals , we use a fine grid ( “minGrid” = 150 ) and a smaller ∂a∂i timescale of 10−4 to improve model fitting ( S9 Fig ) . To construct parametric bootstrap confidence intervals , the following procedure is repeated 100 times . A bootstrap sample is simulated with the coalescent simulation program ms [35] using the demographic model , estimated parameters , and linkage structure of the original dataset . We then estimated demographic parameters with ∂a∂i . For each parameter , the 95% confidence interval is estimated as the range of the central 95% of bootstrap samples for that parameter . In the case of p˜ , a bootstrap sample is generated based on autosomal and X-chromosomal data . We first simulated data from independent sites from 1000 unlinked regions that are 5kb in length . To do so , we drew the number of segregating sites for the autosomes and X chromosome as a Poisson random variable with mean parameter given by Eqs 8 and 9 , respectively . We first simulated data under the null hypothesis ( p = 0 . 5 ) and calculated the estimators p˜ and θ˜ with Eqs 6 , 13 and 14 as well as the likelihood ratio test statistic Λ for each simulated set of autosomal and X-chromosomal data . We used the distribution of Λ to obtain the empirical critical value of c* = 3 . 787 . We then simulated data under the alternative hypothesis for p ranging from 0 . 2 to 0 . 8 in steps of 0 . 1 , and calculated power with respect to c* . We next simulated partially linked sites with ms . We simulated 10 , 000 independent samples of a 5KB locus in 10 males and 10 females using a per-site mutation rate of 0 . 001 and a per-site recombination rate of 0 . 001 . Assuming an ancestral population size Ne = 104 , we calculated the population size-scaled mutation rate θ and the population size-scaled recombination rate ρ based on the proportion of females p: θA=fA ( p ) θ=4p ( 1−p ) θθX=fX ( p ) θ=9p ( 1−p ) 2 ( 2−p ) θρA=fA ( p ) ρ=4p ( 1−p ) ρρX=fX ( p ) × ( 2p ) / ( 1+p ) ρ=9p ( 1−p ) 2 ( 2−p ) 2p1+p Autosomal and X-chromosomal data were simulated separately for p ranging from 0 . 2 to 0 . 8 in steps of 0 . 1; commands are in S1 Text , “Simulation Commands: Population of constant size” . We formed datasets of two different sizes , 5kb and 50kb , by combining simulated loci . The values p˜ , θ˜ , Λ , and critical value c* were calculated analogously to those for simulated independent sites . We compare the power of our LRT to a test based on Q . We calculated Q^ as θ^X/θ^A and estimated confidence intervals with a bootstrap . For partially linked sites simulated in ms , Q^ is calculated as π^X/π^A , and confidence intervals are calculate using a bootstrap over independent iterations . We simulated a population of which underwent an instantaneous ten-fold expansion 100 generations ago with ms . We simulated a sample of 40 individuals with mutation rate of 1 . 5 × 10−8 per site . As in the simulations for a population of constant size , the X chromosome per-site mutation rate and recombination rate are functions of p , the proportion of females . For each p ranging from 0 . 2 to 0 . 8 in steps of 0 . 1 , we simulated datasets of 5kb and generated 10 , 000 independent datasets . We made X-chromosomal and autosomal site frequency spectrum and performed likelihood ratio tests ( see Results , “Sex-bias tests for a two-epoch model” ) for each dataset . We simulated a bottleneck with the parameters estimated from European genetic data [26] . The population starts at size 14 , 500 at 5840 generations ago , experiences a bottleneck to 1861 individuals lasting from 2040 to 920 generations ago , then expands to its final size of 100 , 000 . We simulated sex-bias during epochs by setting effective sizes of X chromosomes and autosomes as per Eqs 1 and 2 . The per-site mutation rate is 1 . 5 × 10−8 , the locus length is 100Kb , and 50 females are simulated by sampling 100 X chromosomes and 100 autosomes . We averaged 105 independent ms simulation iterations to construct the autosomal and the X-chromosomal site frequency spectrum . We simulated the same proportion of females before and after the bottleneck . We tested for sex-bias with the likelihood ratio framework for a bottleneck ( see S1 Text , “Likelihood ratio tests for sex-bias: bottleneck model” ) . We simulated data with ms for three populations with a female bias ( p1 = 0 . 8 ) . After population 3 splits off , the population ancestral to population 1 and 2 experiences a male-biased bottleneck ( p2 = 0 . 2 ) on branch 4 , as does population 3 on branch 3 ( S10A Fig ) . We used the same bottleneck parameters ( magnitude and times ) as in “Simulating population bottlenecks” above . We sampled 100 autosomes and X chromosomes from 50 diploid females per population and performed 100 replicate simulations . We estimated the estimated sex ratio ( ESR ) for each branch with KimTree [23] and used the program arguments recommend in the manuscript and program documentation: -npilot 20 -lpilot 500 -burnin 10000 -length 20000 -thin 20 . We applied our method with a bottleneck model to each marginal frequency spectrum of populations 1 , 2 , and 3 . KimTree was run multi-threaded ( 6 threads ) and our method was run with a single thread . Since the male germline per-site mutation rate is higher than the female rate [12] , X-chromosomal and autosomal per-site mutation rates differ . In the 1000 Genomes Project exome analysis , we estimate α = μM/μF via a grid search . In the 1000 Genomes Project whole-genome data analysis , we assume a value of 3 for α ( close to the empirical value of 3 . 6 from [34] ) , which corresponds to an X-chromosomal to autosomal mutation rate ratio of 5/6 ( Eq 15 ) . When estimating α via a grid search , θX is a free parameter in the X-chromosomal optimization and we perform a grid search to obtain the value of θX that results in the best overall likelihood and the optimal value of α for the dataset . When assuming an α value of 3 , it is used to constrain X-chromosomal parameters based on autosomal parameters: we use an autosomal per-site mutation rate of 1 . 2 × 10−8 [29] and divide it by the value of E[NeX/NeA] . Then , the X-chromosomal model is optimized using the ∂a∂i Poisson model where θ is a fixed input parameter . We analyzed males and females from the 1000 Genomes Project exome pilot data ( 2012-03-17 release date ) . We annotated exome variant calls with SNPeff [36] and kept only synonymous variants . We analyzed chromosome X and chromosome 22 , each of which has approximately 3000 segregating sites in the exome targeted sequencing study . We constructed folded site frequency spectra for the European ( CEU ) and Yoruban ( YRI ) population samples . The chromosome 22 SFS has a higher dimension than the chromosome X SFS for both populations because the samples contain males and females . As a result , we projected the chromosome 22 SFS down to the dimension of the chromosome X SFS using the hypergeometric projection [27] for visual comparison and analysis . We downloaded the VCF file from the 1000 Genomes Project FTP site for Complete Genomics SNP calls ( release date 2013-08-08 ) for 159 females from the following five populations: Yorubans ( YRI ) , Punjabis ( PJL ) , Southern Han Chinese ( CHS ) , Peruvians ( PEL ) , and Europeans ( CEU ) . We restricted our analysis to females to control for any differences in assembly and variant calling between males and females . Of the six individuals sequenced based on two cell types ( blood and LCL ) , and we kept calls from one cell type . We used VCFTools [37] version v0 . 1 . 13 to remove multi-allelic SNPs and retain biallelic SNPs with quality VQHIGH . We used to plink [38] to set Complete Genomics half-calls to missing and remove the X chromosome pseudo-autosomal regions . We excluded sites with more than 5% missing genotypes . Sites were filtered as in “Filtering 1000 Genomes Project whole-genome data” below and used to construct autosomal and the X-chromosomal site frequency spectra . The length of each locus is defined as the number of bases where a confident call is made ( reference , variant , etc . ) which was not removed by the filters described earlier . The locus length is used to convert from time in genetic ( i . e . , coalescent ) units to time in generations and to calculate per-base statistics . To adjust the callable length for SNPs removed during filtering , we multiplied the locus length by the ratio of remaining SNPs to original SNPs . For SFS projected down with a hypergeometric projection , the locus length was similarly adjusted by multiplying by the ratio of SNPs in the projected SFS to the number of SNPs in the original SFS . We do not thin SNPs to remove linkage disequilibrium because the expected values of the SFS are the same for independent sites and for partially linked sites , so demographic point estimates are not affected [27] . Confidence intervals were constructed with standard errors estimated from a conventional bootstrap of 1MB blocks across 100 iterations . We used the average per-site mutation rate of 1 . 5 × 10−8 . For analyses described in “1000 Genomes Project whole-genome data” above , we stratified variants by their genetic distance to the closest gene in centimorgans ( cM ) by using closestBed [39] to get the closest gene boundary to each SNP in physical units ( basepairs , bp ) . We then used a linear interpolation on the HapMap sex-averaged recombination map to convert SNP and gene boundary positions to genetic units ( cM ) , and took their difference as the distance of the SNP to the closest gene . We restricted attention to SNPs at least 0 . 2cM from the nearest gene as in [19] because they are expected to be less affected by background selection . We also removed regions which are putatively under selection , prone to sequencing error , or cause differences in local mutation rates which are contained in the following UCSC tracts [30]: phastConsElements46wayPlacental , simpleRepeat , centeromere/telomere , gap , cpgIslandExt , genomicSuperDups , knownGene , selfChain , rnaCluster , intronEst . The python source code for our sex-bias inference method and its documentation are freely available for download at https://github . com/shailamusharoff/sex-bias-inference/ . | Sex-biased demographic events involve unequal numbers of females and males , and is referred to as “sex-bias” . In humans , short-range migrations ( e . g . , due to marriage practices ) are known to be sex-biased , and some long-range migrations , such as the one out of Africa , are hypothesized to be sex-biased . The recent availability of large-scale genomic sequencing data provides a unique opportunity to study sex-bias in human populations . However , existing sex-bias methods do not account for population size changes , like expansions and bottlenecks , or can only estimate a single sex-bias parameter on a population branch , which can lead to incorrect conclusions . We developed a sex-bias method which explicitly models population size changes , and we show that it outperforms competing methods on simulated data . When applied to human genetic data , our method identifies an overall female sex-bias in globally-distributed populations and a male-biased bottleneck in Europeans . Our method can also be used to assess sex-bias in other sexual species . | [
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"bi... | 2019 | The inference of sex-biased human demography from whole-genome data |
FLAGELLIN-SENSING 2 ( FLS2 ) is a leucine-rich repeat/transmembrane domain/protein kinase ( LRR-RLK ) that is the plant receptor for bacterial flagellin or the flagellin-derived flg22 peptide . Previous work has shown that after flg22 binding , FLS2 releases BIK1 kinase and homologs and associates with BAK1 kinase , and that FLS2 kinase activity is critical for FLS2 function . However , the detailed mechanisms for activation of FLS2 signaling remain unclear . The present study initially identified multiple FLS2 in vitro phosphorylation sites and found that Serine-938 is important for FLS2 function in vivo . FLS2-mediated immune responses are abolished in transgenic plants expressing FLS2S938A , while the acidic phosphomimic mutants FLS2S938D and FLS2S938E conferred responses similar to wild-type FLS2 . FLS2-BAK1 association and FLS2-BIK1 disassociation after flg22 exposure still occur with FLS2S938A , demonstrating that flg22-induced BIK1 release and BAK1 binding are not sufficient for FLS2 activity , and that Ser-938 controls other aspects of FLS2 activity . Purified BIK1 still phosphorylated purified FLS2S938A and FLS2S938D mutant kinase domains in vitro . Phosphorylation of BIK1 and homologs after flg22 exposure was disrupted in transgenic Arabidopsis thaliana plants expressing FLS2S938A or FLS2D997A ( a kinase catalytic site mutant ) , but was normally induced in FLS2S938D plants . BIK1 association with FLS2 required a kinase-active FLS2 , but FLS2-BAK1 association did not . Hence FLS2-BIK1 dissociation and FLS2-BAK1 association are not sufficient for FLS2-mediated defense activation , but the proposed FLS2 phosphorylation site Ser-938 and FLS2 kinase activity are needed both for overall defense activation and for appropriate flg22-stimulated phosphorylation of BIK1 and homologs .
Pattern-Recognition Receptors ( PRRs ) can initiate innate immunity by perception of conserved Pathogen- ( or Microbe- ) Associated Molecular Patterns ( PAMPs or MAMPs ) [1] , [2] . This process , which has been termed PAMP-triggered immunity ( PTI ) , serves as an initial defense response against pathogens . Arabidopsis thaliana FLAGELLIN-SENSING 2 ( FLS2 ) [3]–[5] and EF-Tu RECEPTOR ( EFR ) [6] , which detect bacterial flagellin and elongation factor-Tu , respectively , are particularly well-studied examples of plant PRRs along with rice Xa21 , which recognizes a sulfated peptide made by some Xanthomonas pathogens [7] , [8] . FLS2 , EFR , and Xa21 , as well as other plant PRRs and many animal Toll-like receptors ( TLR ) , belong to the non-RD receptor-like kinase ( RLK ) family [9] . Named due to the absence of the otherwise widely conserved arginine ( R ) residue adjacent to the catalytic aspartate ( D ) in the activation loop , non-RD kinases differ from the widespread RD kinases in that at least some non-RD kinases do not autophosphorylate residues located in the activation loop to facilitate phospho-transfer activity [10] , [11] . Hence the phosphorylation of these PRR non-RD kinases is of particular interest for further study . Prior to ligand exposure , FLS2 is present in FLS2-FLS2 and FLS2-BIK1 complexes that are detectable by co-immunoprecipitation [12]–[14] . FLS2 also associates with close homologs of BIK1 such as PBS1 , PBL1 and PBL2 , all of which are predicted cytoplasmic protein kinases [13] . BIK1 is phosphorylated and released from FLS2 complexes after cellular exposure to flg22 ( a synthetic 22-amino acid peptide based on the recognized epitope of flagellin ) [13] , [14] . Upon perception of flg22 , FLS2 very rapidly forms a heteromer with the LRR-RLK BAK1 or its homolog BKK1 [5] , [15] , [16] . Moreover , the cellular level of FLS2 was recently shown to be post-translationally regulated by ubiquitination [17] . The fact that FLS2 is targeted for degradation by the two U-box E3 ubiquitin ligases PUB12 and PUB13 after they are phosphorylated by BAK1 suggests that BAK1 regulates the degradation of FLS2 [17] . During PTI , early responses include production of reactive oxygen species ( ROS ) , MPK phosphorylation , and ion channel activation [18]–[21] . Longer-term PTI responses include callose deposition , ethylene production , seedling growth inhibition , and elevated expression of downstream genes associated with antimicrobial defenses [18] . The kinase-associated protein phosphatase ( KAPP ) was also shown to impact FLS2 signaling , and to associate with FLS2 in yeast two-hybrid experiments [22] . Despite the above advances , many aspects of FLS2 activation , including the site and functional significance of specific phosphorylation events involving FLS2 , remain unclear . Protein phosphorylation , a reversible switching between phosphorylated and unphosphorylated forms , is a common mechanism by which cell signaling pathways are regulated [23] . Autophosphorylation of protein kinases plays an important role in signal transduction pathways for a wide range of plant processes including hormone responses , development , and responses to biotic and abiotic stresses [24]–[29] . Although they resemble serine/threonine protein kinases , BRI1 and BAK1 were recently found to have both serine/threonine kinase and tyrosine kinase activities , which are critical for brassinosteroid signal transduction [28] , [30]–[32] . Among plant PRR kinases , rice Xa21 is known to autophosphorylate at Thr-705 , in the juxtamembrane domain [33] . But for other PRRs in plants , very little information about autophosphorylation has been reported . Although FLS2 has a possible phosphorylation site at Thr-867 [34] , no direct evidence has shown that Thr-867 is an autophosphorylation site . Early phosphoproteomic screens with Arabidopsis did not reveal FLS2 phosphopeptides [35] , [36] . A recent high-throughput screen for the autophosphorylation activity of 759 annotated protein kinases from Arabidopsis did not detect autophosphorylation activity for FLS2 ( albeit after in vitro translation of a full-length cDNA , and using an anti-phospho-Ser/Thr antibody that may be prone to context-specific limitations in recognition of phosphorylated sites ) [37] . These and other studies have , however , indicated that modern wheat germ cell-free translation systems have minimal endogenous phosphorylation activity [37] . In the present study , mass spectrometry ( MS ) was used to detect apparent autophosphorylation sites of FLS2 kinase purified from a cell-free translation system . Among these sites , we show that Ser-938 is particularly important . FLS2-mediated immune responses including seedling growth inhibition , ROS burst , callose deposition , and MPK phosphorylation are shut down in Arabidopsis fls2− mutants expressing FLS2S938A protein , as is the phosphorylation of FLS2 , while plants expressing the phosphomimic proteins FLS2S938D/E exhibit defense responses similar to those of wild-type plants . The data suggest that phosphorylation of Ser-938 is critical for FLS2-mediated immune responses . flg22-stimulated FLS2-BIK1 dissociation and FLS2-BAK1 association still occur with FLS2S938A protein , but phosphorylation of BIK1 and homologs is absent , suggesting revisions to mechanistic models of FLS2 function .
To characterize the phosphorylation activity of FLS2 , the inferred intracellular portion of FLS2 ( amino acids 840-end , predominantly a protein kinase domain ) was expressed and purified from a wheat germ cell-free expression system and then subjected to MS analysis . A majority of the protein had a mass 80 Da greater than the predicated molecular weight , while a smaller fraction had a mass 159 Da greater than the predicated molecular weight , equivalent to the mass increases expected for the addition of either one or two phosphate groups , respectively ( Figure 1A ) . Although phosphorylation from the expression system is not ruled out , this result is consistent with autophosphorylation activity of the in vitro-synthesized FLS2 kinase . When the purified kinase domain of FLS2 was treated with antarctic phosphatase and analyzed by MS , the abundance of the putative phosphorylated FLS2 peptides was decreased ( Figure 1B ) , a result that is further consistent with the presence of reversible autophosphorylation sites on FLS2 . To identify the detected phosphorylation sites , the in vitro-expressed intracellular domain of FLS2 was digested with trypsin and subjected to further mass spectrometry . As shown in Figure 1C and Figure S1 , peptide fragments containing phosphorylated Serine 909 ( Ser-909 ) , Ser-938 and Ser-1084 were detected with high confidence , suggesting these three serine amino acids as likely in vitro autophosphorylation sites and thus as candidate in vivo autophosphorylation sites . To allow in vivo studies of protein function the above three serines were then converted by site-directed mutagenesis to phosphorylation-blocking alanine ( Ala or A ) , a substitution that typically does not disrupt overall protein structure [38] , or to the carboxylates aspartate ( Asp or D ) or glutamate Glu or E ) , whose covalently attached negative charge has been shown in numerous cases to functionally mimic phosphorylated Ser or Thr [39] . These variants of FLS2 were constructed with a C-terminal 3× HA tag , placed downstream of a native FLS2 promoter , and then transformed into the Arabidopsis fls2 null mutant line Col-0 fls2-101 . Wild-type FLS2-HA in a similar configuration has previously been shown to function normally [12] , [40] . To detect the response to flg22 , leaf samples from four to six independent T1 transgenic plants were used for ROS detection after flg22 treatment . Figure 1D and Figure S3E show that for Ser-938 , the phosphomimic FLS2S938D and FLS2S938E transgenic plants produced an ROS burst comparable to FLS2WT transgenic Arabidopsis after flg22 treatment , while FLS2S938A failed to mediate an ROS response to flg22 . This suggests that Ser-938 is a functionally significant site on FLS2 . Immunoblot analysis showed that all three mutated Ser-938 variants , including the non-functional FLS2S938A , were present in transgenic Arabidopsis ( Figure S2A ) . For wild-type and all three FLS2 variants , further experiments showed presence of the Endoglycosidase H ( Endo H ) -insensitive form that arises after appropriate processing through the ER and Golgi ( Figure S2B; Endo H can cleave mannose rich-oligosaccharides from immature proteins in endoplasmic reticulum ( ER ) but not the oligosaccharides from mature proteins that have been processed in the Golgi [41] ) . Plants expressing FLS2S909A exhibited a reduced ROS burst , approximately 1/3 the magnitude of the burst mediated by FLS2WT ( Figure 1E and Figure S3E ) . However , plants expressing FLS2S909D and FLS2S909E produced little or no ROS after exposure to flg22 ( Figure 1E and Figure S3E ) . These data suggest that integrity of Ser-909 can contribute to FLS2 function , but that Ser-909 phosphorylation apparently does not positively regulate FLS2 activity . For the site Ser-1084 , transgenic Arabidopsis plants expressing FLS2S1084A , FLS2S1084D and FLS2S1084E showed only a minor reduction of ROS production relative to plants expressing a FLS2WT transgene ( Figure 1F and Figure S3E ) . This suggests that Ser-1084 is relatively unimportant for the tested function of FLS2 . For all of the Ser-909 , Ser-938 and Ser-1084 mutant plant lines , ROS production was also monitored in the absence of flg22 treatment and only baseline ROS production similar to that of FLS2WT or fls2− plants was detected ( Figures S3A–C ) . A previous study had suggested that Thr-867 in the juxtamembrane domain is a possible autophosphorylation site , because the mutant form FLS2T867V could bind flg22 but lost responsiveness to flg22 treatment [34] . To further examine Thr-867 , we constructed FLS2T867V , FLS2T867D and FLS2WT alleles under control of the CaMV 35S promoter and transformed them into the Arabidopsis fls2-101 line . Expression of the mutated proteins was confirmed by immunoblot analysis , and responsiveness to flg22 was tested using the seedling growth inhibition assay ( Figure S4 ) . Plants expressing FLS2T867V or the phosphorylation-mimic FLS2T867D lacked responsiveness to flg22 , suggesting as for Ser-909 that Thr-867 is important for FLS2 function , but that phosphorylation of Thr-867 is not likely to be a requirement for FLS2 activity . The location of Ser-938 and other residues on a folded FLS2 intracellular domain was predicted by homology modeling ( Figure S5 ) . The SwissModel and 3D-Jury servers produced multiple high-scoring models of the FLS2 protein kinase domain ( ∼288 contiguous residues out of the 344 total predicted intracellular residues of FLS2 ) . As is often the case with high-scoring homology models , the small regions of uncertain modeling within the ∼288 amino acid FLS2 kinase models were primarily at surface-exposed loop regions that join two well-anchored helices or sheets . These surface-exposed loop regions are predicted to be relatively flexible in the protein kinases with solved crystal structures . Ser-938 is predicted to reside in kinase subdomain IV , which is near the apex of one such loop ( Figure S5 ) . Ser-909 is located in subdomain II , while Ser-1084 is located in subdomain IX ( Figure S5 ) . The ∼40 residue “juxtamembrane” region of the intracellular portion of FLS2 was not sufficiently similar to available protein structures to allow reliable homology modeling . Unlike the sites that are conserved across many or all non-RD kinases ( such as kinase catalytic site residues ) , the functionally important Ser-938 residue and nearby residues ( FLS2 residues 933–942 ) are not well conserved among other non-RD kinases in the LRR-RLK subfamily XII [42] ( Figure S6 ) . The conclusion that the FLS2 Ser-938 site is not a universal component of non-RD kinases was further confirmed by functionally testing serine residues in the analogous region of EFR . EFR sites coding for Ser-777 , Ser-778 , and Ser-781 were converted to alanine ( A ) and aspartate ( D ) by site-directed mutagenesis and transformed into Col-0 efr− mutant plants under control of the EFR native promoter . Multiple independent T1 transgenic plants were tested for EFR function using the seedling growth inhibition assay . As shown in Figure S7 , these phosphorylation-blocked ( EFRS777A , EFRS778A , and EFRS781A ) and phosphomimic ( EFRS777D , EFRS778D , and EFRS781D ) variants still conferred responsiveness to elf18 , suggesting that unlike FLS2 Ser-938 , these EFR serine residues are not essential for EFR function . In preliminary in vivo phosphorylation assays carried out using protoplasts , phosphorylated FLS2S938D was observed ( Figure S8 ) , suggesting that Ser-938 is not the only phosphorylation site on FLS2 . To further study the role of the FLS2 phosphorylation site Ser-938 , transgenic Arabidopsis expressing FLS2S938A/D/E were tested for characteristic FLS2-dependent plant responses after elicitation with flg22 . As shown in Figure 2A–D and Figure S3D , FLS2WT , FLS2S938D , and FLS2S938E transgenic plants accumulated callose after flg22 treatment , but for FLS2S938A transgenic plants , callose presence did not exceed that of untreated controls . MPK3 and MPK6 became phosphorylated after flg22 exposure of plants expressing FLS2WT , FLS2S938D , and FLS2S938E , but MPK3/6 phosphorylation was absent in FLS2S938A transgenic Arabidopsis ( Figure 2E ) . A similar loss of flg22-responsiveness was observed in seedling growth inhibition assays ( Figure 2F ) . Lastly , we inoculated different transgenic plants with Pseudomonas syringae pv . tomato strain DC3000 and found ( Figure 2G ) that FLS2S938A transgenic Arabidopsis was more susceptible than Col-0 , while FLS2S938D/E plants were similar to Col-0 plants , demonstrating that availability of FLS2 Ser-938 for phosphorylation or for phosphomimic status is critical in mediating the overall response to P . syringae pathogens . With the knowledge that Ser-938 is a functionally important site of FLS2 , we turned to investigate mechanisms that may regulate signal transduction downstream of flagellin or flg22 perception . First , we tested whether the interactions of FLS2 with BIK1 and BAK1 are affected by FLS2 Ser-938 . BIK1-cMyc and BAK1-cMyc fusion proteins were transiently expressed together with FLS2WT , FLS2S938A or FLS2S938D in protoplasts made from Arabidopsis fls2-101 plants , and then coimmunoprecipitation of these FLS2 proteins was monitored after immunoprecipitation using anti-cMyc antibodies . The interactions between FLS2 and BIK1 or BAK1 were not altered when Ser-938 of FLS2 was mutated . As shown in Figure 3A and 3B , the positive control interactions of FLS2WT with BIK1 and BAK1 , before and after exposure to flg22 , respectively , were as expected from the published literature [5] , [13] , [14] , [43] . BIK1 and BAK1 interaction patterns very similar to those of FLS2WT were also observed with mutated FLS2S938A and FLS2S938D . These data indicate not only that Ser-938 is critical for FLS2 activities other than reversible FLS2-BIK1 and FLS2-BAK1 interactions , but also that flg22-elicited FLS2-BIK1 dissociation and FLS2-BAK1 association are not sufficient for FLS2 activity . Wild-type FLS2 is present in FLS2-FLS2 associations both before and after exposure to flg22 [12] . FLS2S938A , FLS2S938D , and FLS2D997A also readily formed FLS2-FLS2 associations ( Figure 3C and 3E; experiments done in an fls2-101 genetic background ) . As with FLS2-BIK1 dissociation and FLS2-BAK1 association , no alteration in FLS2-FLS2 associations was detected when Ser-938 was mutated into a non-phosphorylatable alanine . Kinase-dead versions of FLS2 , BIK1 and BAK1 were used to further dissect flg22-dependent protein association/dissociation events . In fls2-101 cells that carry a wild-type ( non-epitope tagged ) BAK1 and BIK1 but lack any FLS2 not supplied transgenically , kinase-dead FLS2D997A still associated with kinase-dead BAK1D416A after cells were exposed to flg22 ( Figure 3D ) . Kinase-dead BIK1D202A still associates with wild-type FLS2 and dissociates after cells are exposed to flg22 ( Figure 3D ) . This confirms recent findings [14] , [44] . The novel results focus on FLS2 interaction with BIK1 when FLS2 lacks kinase activity . We found that FLS2 kinase activity is required for normal levels of association between FLS2 and kinase-dead BIK1D202A prior to flg22 exposure ( Figure 3D ) . BIK1 and some of its homologs were recently shown to contribute to PTI , and to interact with FLS2 when flg22 is absent , and to be released when flg22 is present [13] , [14] . The interaction between BIK1 and FLS2 is independent of the protein kinase activity of BIK1 [14] . To test whether the phosphorylation of FLS2 by BIK1 is dependent on FLS2 phosphorylation activity , we carried out in vitro inter-protein phosphorylation assays with BIK1 and different variants of FLS2 . We used FLS2D997A as a kinase-dead version of FLS2 that fails to carry out defense signaling [12] , [44]; homology models indicate that Asp-997 of FLS2 is a conserved catalytic site residue that is essential in the vast majority of eukaryotic protein kinases [44] , [45] . Interestingly , BIK1 could phosphorylate all the variants of FLS2 ( Figure 4 ) , but the phosphorylation of FLS2S938A and FLS2D997A proteins was reduced while that of FLS2S938D was strong . An analogous experiment with no GST tag on FLS2 is shown in Figure S9 , and gave similar results . These experiments show that in vitro , presence of a phosphomimic or phosphorylatable residue at FLS2 Ser-938 or kinase activity of FLS2 are not essential for phosphorylation of FLS2 by BIK1 , but they enhance FLS2 phosphorylation by BIK1 . The second and third lanes of Figure 4 , and the analogous lanes without and with FLS2WT in Figure S9 , also show that any phosphorylation of BIK1 by FLS2 was not detectable over the autophosphorylation activity of BIK1 in these experiments . Figure 4 also shows an FLS2 autophosphorylation assay ( no BIK1 present ) , conducted within the same Figure 4 FLS2+BIK1 experiment ( performed on the same day and exposed similarly to the left blot of Figure 4 ) . This latter result further documents the very low autophosphorylation activity of FLS2 relative to the phosphorylation activity of BIK1 in these types of experiments ( [14] , [44] , [46]; see also Figure S8 ) . Upon flg22 treatment , BIK1 is phosphorylated and released from FLS2 complexes [13] , [14] . This phosphorylation of BIK1 is FLS2-dependent [13] , [14] , and we showed ( above ) that the phosphorylation of FLS2 by BIK1 is enhanced by , but does not require , Ser-938 and the kinase activity of FLS2 . However , it remained to be shown if the FLS2 Ser-938 site or the kinase activity of FLS2 impacts the phosphorylation status of BIK1 or its partially similar homologs . To address this question , BIK1 and the genes for the BIK1 homologs PBS1 , PBL1 , and PBL2 were amplified from Arabidopsis , cloned downstream of the CaMV 35S promoter to encode C-terminal cMyc fusion proteins , and then transiently expressed in Arabidopsis protoplasts made from different transgenic plants . As previously demonstrated for these proteins [13] , [14] , separation by SDS-PAGE was used to detect altered phosphorylation states as an upward shift in protein mobility . Figure 5 shows that the flg22-elicited phosphorylation of BIK1 , PBS1 , PBL1 , and PBL2 was blocked in FLS2S938A and FLS2D997A transgenic Arabidopsis . In the presence of FLS2WT , the flg22-elicited phosphorylation levels of PBS1 and PBL2 were lower than those of BIK1 and PBL1 , making inferences about PBS1 and PBL2 less certain . However , flg22-elicited phosphorylation of BIK1 and PBL1 , and probably also PBS1 and PBL2 , was dependent not only on the protein kinase activity of FLS2 , but also on the availability of the proposed phosphorylation site Ser-938 or a Ser-938 phosphomimic of FLS2 .
The present study sought to identify FLS2 phosphorylation sites and their roles in FLS2-mediated defense signaling . The project was initiated when purified FLS2 obtained from cell-free ( in vitro ) translation was suggested to have autophosphorylating protein kinase activity . MS analysis identified Ser-909 , Ser-938 and Ser-1084 as in vitro autophosphorylation sites or sites phosphorylatable by the wheat germ in vitro translation extract , in either case making them candidate in vivo autophosphorylation sites . Published phosphoproteomic screens of plant extracts have not detected phosphorylation of FLS2 [35]–[37] , and our efforts to use MS to detect phosphorylation of FLS2 immunoprecipitated from plant extracts also were not successful ( not shown ) . However , the finding that S938A severely disrupts FLS2-mediated defense activation in vivo without disrupting other FLS2-mediated processes , while FLS2S938D and FLS2S938E retain defense-activating function , strongly implicated Ser-938 as a probable phosphorylation site of substantial functional importance . The small ‘non-RD kinase’ sub-group of protein kinases was identified based on their lack of the highly conserved Arg-Asp ( R-D ) residue pair in kinase subdomain VIb [9] . Interestingly , almost all plant and animal immune system PRRs characterized to date are non-RD kinases , suggesting that use of this clade in immune responses became established prior to the divergence of plants and metazoans [9] . Most RD kinases need to autophosphorylate residues located in this activation loop immediately adjacent to the enzyme active site in order to facilitate the phosphotransfer activity of the kinase [47] . For example , Arabidopsis RD kinases BRI1 and BAK1 contain demonstrated autophosphorylation sites in the activation loop ( Thr-1049 and either Ser-1044 or Thr-1045 of BRI1 [30]; Thr-446 , Thr-449 , Thr-450 , and Thr-455 of BAK1 [29] , [32] , [48] ) . Among predicted Arabidopsis LRR-RLKs , 99% of the RD kinases have a Ser or Thr at the position aligned with BRI1 Thr-1049 , compared with 30% for the non-RD kinases [30] . Apparently , this activation loop autophosphorylation site is crucial to the function of RD kinases while it is dispensable for many non-RD kinases , raising questions about the location of important phosphorylation sites on non-RD kinases . The functionally important candidate phosphorylation sites of two non-RD kinases , Thr-705 of Xa21 [33] and Ser-938 of FLS2 ( this study ) , are not located in the kinase activation loop . Some potential FLS2 autophosphorylation or phosphorylation sites received lower priority in the present study , but merit brief discussion . Arabidopsis FLS2 Thr-867 was previously reported as a potential autophosphorylation site [34] , and its location is equivalent to the rice Xa21 autophosphorylation site at Thr-705 [33] . Our data show that fls2-101 Arabidopsis plants transgenically expressing a phosphomimic FLS2T867D protein or the previously studied non-phosphorylatable FLS2T867V both lose flg22-responsiveness . We also did not detect in vitro autophosphorylation of Thr-867 in mass spectrometry experiments . There are several possible explanations for these results , including: ( i ) Thr-867 is not a phosphorylation site , but mutations to Asp or Val cause function-blocking disruptions to FLS2 structure; ( ii ) Thr-867 is not an autophosphorylation site but it is a phosphorylation site , and both phosphorylated and non-phosphorylated status are necessary at some juncture for FLS2 signaling; ( iii ) Thr-867 is a phosphorylation site but only in vivo , and for this site the T867D mutation does not correctly mimic phosphorylation , or ( iv ) Thr-867 is a phosphorylation site but only in vivo , and both phosphorylated and non-phosphorylated status are necessary at some juncture for FLS2 signaling . Similar logic may apply to Ser-909 and Ser-1084 , the other candidate autophosphorylation sites identified and tested in this study . Integrity of Ser-909 is needed for full FLS2 signaling , but the loss of function of S909D and S909E phosphomimic proteins suggests that phosphorylation of this residue is not needed to activate FLS2 function . Neither phosphorylation nor lack of phosphomimic status at Ser-1084 is required for substantial signaling by FLS2 . It is well established in mammalian systems that autophosphorylation of protein kinase-containing receptors plays a critical role in regulating signal transduction to downstream components after perception of ligand [49] , [50] . After discovering that FLS2 Ser-938 is an important candidate phosphorylation site , a number of additional insights into FLS2 function were obtained . If Ser-938 of FLS2 is non-phosphorylatable ( FLS2S938A ) , flg22 induction of downstream responses including seedling growth inhibition , ROS burst , callose deposition , MPK phosphorylation and restriction of P . syringae growth were all blocked . This was not true of FLS2S938D or FLS2S938E . Detectable FLS2 autophosphorylation activity and phosphorylation of BIK1 , PBL1 , PBS1 and PBL2 were also lost with FLS2S938A . However , the ligand-dependent release of BIK1 from FLS2 and the ligand-dependent association of BAK1 with FLS2 were not detectably altered by mutation of Ser-938 . As noted in Results , the above yields two important conclusions: ( i ) Ser-938 controls essential elements of FLS2 activity other than FLS2-BIK1 and FLS2-BAK1 dissociation/association , and ( ii ) flg22-elicited FLS2-BIK1 dissociation and FLS2-BAK1 association are not sufficient for FLS2 activity . In vitro , a quantitative contribution of Ser-938 phosphorylation to BIK1 phosphorylation of FLS2 was suggested . However , some phosphorylation of FLS2 by BIK1 still occurred in the absence of FLS2 kinase activity ( Figure 3 , see FLS2D997A ) , and could occur at sites other than Ser-938 ( see FLS2S938D and FLS2S938A ) , and did not strictly require that Ser-938 be phosphorylatable or in the phosphomic state ( see FLS2S938A ) . In future studies , it will be of interest not only to directly confirm phosphorylation of Ser-938 , but also to learn about the in vivo stoichiometry , timing and target sites of FLS2 phosphorylation by BIK1 . Ser-938 may be a target for autophosphorylation or trans-phosphorylation of FLS2 after flg22 exposure . This too constitutes an interesting topic for future study . However , the findings that S938D or S938E do not constitutively activate FLS2 signaling in the absence of flg22 ligand suggest that phosphorylation of Ser-938 is not sufficient on its own to activate FLS2 signaling . The tested FLS2 defense signaling functions that were lost due to the FLS2S938A mutation were all retained by FLS2S938D or FLS2S938E phosphomimic proteins . This included flg22-induced phosphorylation of BIK1 , PBL1 , PBS1 and PBL2 ( Figure 5 ) , and the weak in vitro FLS2 autophosphorylation activity ( Figure 2A ) . Although flg22-elicited in vivo phosphorylation of BIK1 is also lost after direct ablation of FLS2 protein kinase activity via the D997A catalytic site mutation ( this study ) , and in plants entirely lacking FLS2 [13] , [14] , and interactions of FLS2 and kinase-dead BIK1 occurred with wild-type FLS2 but were reduced with a kinase-dead FLS2 ( this study ) , neither we nor others have directly demonstrated phosphorylation of BIK1 by FLS2 . Such a demonstration is impeded by inherent biological challenges ( high in vitro kinase activity of BIK1 relative to FLS2 , including probable BIK1 autophosphorylation activity; presence in vivo of functionally redundant PBS1 , PBL1 , PBL2 in experiments that might use kinase-dead BIK1 ) . After flg22 stimulation and the postulated FLS2 Ser-938 phosphorylation , BIK1 may be phosphorylated by FLS2 , by BIK1 ( or homologs ) , and/or by BAK1 . However , the present study did demonstrate that in vivo flg22-dependent phosphorylation of BIK1 and PBL1 ( and probably PBS1 and PBL2 ) requires not only the cellular presence of FLS2 but also a kinase-active FLS2 , and an FLS2 with a phosphorylatable or phosphomimic Ser-938 . The above set of findings suggests a chain of events in which FLS2 kinase activity is required for flg22-elicited autophosphorylation or trans-phosphorylation at the proposed Ser-938 phosphorylation site of FLS2 , which is a requirement for flg22-induced phosphorylation of BIK1 and its homologs , which is required [13] , [14] for FLS2-mediated responses to bacterial flagellin . Although autophosphorylation of FLS2 at Ser-938 by definition would require FLS2 protein kinase activity , that FLS2 protein kinase activity subsequently may be redirected toward other proteins ( such as BIK1 ) after flg22-induced phosphorylation of Ser-938 . Alternatively , the change in FLS2 conformation caused by phosphorylation of FLS2 Ser-938 may be sufficient to stimulate BIK1 , BAK1 and/or other proteins to activate downstream defense responses , with no further FLS2 kinase activity required . Ser-938 of FLS2 was not required for release of BIK1 , nor was that release sufficient for FLS2-mediated signaling , but it remains to be determined if flagellin/flg22-stimulated release of BIK1 and homologs from FLS2 is required for effective FLS2-mediated signaling . A BIK1-K105E ATP-binding site mutant blocks BIK1 dissociation from FLS2 and exerts a dominant negative impact on PTI [14] , but also disrupts BIK1 kinase activity , so although a necessity for release seems likely , it remains unclear if release of BIK1 or homologs from FLS2 is necessary for PTI signaling . The phosphorylation of BIK1 is induced not only by MAMPs but also by ethylene [13] , [14] , [51] , suggesting that BIK1 plays multiple roles in regulating PTI , ethylene signaling and plant growth [51] , [52] . It is interesting that key phosphorylation site mutants of BIK1 and BAK1 can still complement the plant growth defect phenotypes associated with many other types of bik1 or bak1 mutations , yet these phosphorylation sites are required for flg22-dependent signaling [14] , [32] , [53] . This may suggest one of the ways that these proteins achieve specificity as they participate in diverse plant signaling pathways [14] . The present findings also suggest refinements to spatial/temporal models for FLS2 signaling ( e . g . , [5] , [12]–[14] , [43] ) . Autophosphorylation in most cases is not intra-protein self-phosphorylation , but rather , cross-phosphorylation of one protein by a second copy of that protein [54] . Prior to ligand exposure , FLS2 can be found both in FLS2-FLS2 associations and in FLS2-BIK1 ( or BIK1 homolog ) associations [12]–[14] . If , after flg22 exposure , the protein kinase catalytic site of an FLS2 protein is to be occupied first by the Ser-938 target on a separate FLS2 protein , and then by a BIK1 target , FLS2-FLS2 and FLS2-BIK1 associations would likely be mutually exclusive and involve a swap of interacting partners . For example , FLS2-FLS2 complexes cross-phosphorylate at Ser-938 and dissociate , FLS2-BIK1 associate , quickly followed by BIK1 phosphorylation if FLS2 is phosphorylated at Ser-938 , and then BIK1 is released from FLS2 because FLS2 had interacted with flg22 or flagellin . This scheme is somewhat inconsistent with co-IP experimental results , but is consistent with the concepts that equilibrium states generally are not static states ( i . e . , many of these interactions may have short half-lives within the cell ) , and is also consistent with the concept that protein-protein interactions that are not sufficiently long-lived to persist throughout co-immunoprecipitation procedures still may be sufficient to foster meaningful signal transduction . In a separate FLS2/BIK1 model , BIK1-FLS2-FLS2-BIK1 tetramers would be the predominant form prior to ligand exposure . This is more consistent with the available data . In either model , BAK1 apparently participates in the shifts in FLS2/BIK1 interaction , because BAK1 is required for flg22-dependent BIK1 phosphorylation and release from FLS2 [13] , and flg22-induced BIK1 phosphorylation requires the FLS2-BAK1 complex [14] . A phosphorylatable FLS2 Ser-938 is not required for flg22-elicited FLS2-BAK1 association , and a kinase-active FLS2 also is not required for flg22-elicited FLS2-BAK1 association , but these FLS2 elements may be required to activate BAK1 for phosphorylation of BIK1 and homologs . A less likely third model is that the pool of FLS2 that associates with BAK1 is not FLS2 that was bound to BIK1 immediately prior to FLS2-BAK1 association . The FLS2 that interacts with BAK1 may come , for example , from free FLS2 or from FLS2 that resided in FLS2-FLS2 associations that lack BIK1 . This model is consistent with previous work suggesting that FLS2-BIK1 dissociation does not need to occur in order for flg22-stimulated FLS2-BAK1 association to occur , but is less consistent with the required role of BAK1 in flg22-dependent BIK1 phosphorylation and release from FLS2 [13] , [14] . In yet another model , phosphorylation of Ser-938 might be hypothesized to cause FLS2/BIK1 dimers to be released from FLS2/BIK1-FLS2/BIK1 tetramer complexes , and promote formation of transient FLS2/BIK1/BAK1 complexes that stimulate BIK1 phosphorylation . However , because the detectable levels of FLS2-FLS2 stay constant or increase slightly after flg22 exposure [12] , it seems more likely that flg22 does not drive FLS2 dissociation from FLS2 . The data are more consistent with a flg22-elicited Ser-938 phosphorylation event altering the FLS2-FLS2 and/or FLS2-BIK1 configuration to activate phosphorylation of BIK1 , without dissociation of FLS2 from FLS2 . If larger multi-protein complexes stay temporarily associated after flg22 exposure but prior to BIK1 release ( for example , pre-existing FLS2/BIK1-FLS2/BIK1 tetramers remain intact and associate with BAK1 upon flg22 exposure ) , they would need to exhibit substantial flexibility if one kinase catalytic site is to phosphorylate more than one substrate in that complex . Such flexibility has been proposed for human growth hormone receptors , albeit with only partial supporting data [55] . Alternatively , it is conceptually possible that in a receptor complex carrying proteins in a relatively fixed position , one FLS2 could be oriented to phosphorylate a second FLS2 whose catalytic site is oriented not toward the first FLS2 but instead toward BIK1 . However , this type of asymmetric arrangement is not typical of well-described receptor complexes that carry two copies of the same protein [56]–[58] . If the proteins in the receptor complex do stay temporarily bound to each other in relatively fixed positions , it is more likely that they phosphorylate only one partner , supporting a model in which flg22-stimulated FLS2-FLS2 autophosphorylation at Ser-938 does not cause FLS2 to then rotate and phosphorylate BAK1 or BIK1 , but instead stimulates BIK1-BIK1 autophosphorylation and/or BAK1 phosphorylation of BIK1 . Spatial models for FLS2 function must also accommodate the fact that the above inter-protein events are physically launched by extracellular interaction of the FLS2 LRR with flagellin or flg22 . This FLS2-ligand binding event drives BIK1 release and BAK1 association , and can do so independent of FLS2 Ser-938 and plant defense activation . With each infusion of new experimental data , refined experimental questions arise . In the case of transmembrane LRR-RLK receptors , the demand is increasingly present for methods that can track the activity of single protein molecules over time , or that supply spatially defined protein complex data .
The standard Center for Eukaryotic Structural Genomics ( CESG ) platform for cloning [59] , protein expression [60] , purification [61] , and bioinformatics management [62] , was utilized to produce FLS2 . Briefly , cDNA was cloned into a pEU-His-Flexi vector . Cell-free expression was conducted on two 4-ml scale reactions using WEPRO2240H wheat-germ extract ( CellFree Sciences , Yokohama , Japan ) , unlabeled 20 amino acids , and a Protemist100 protein synthesizer ( CellFree Sciences ) . His-tagged protein was purified by nickel affinity chromatography . The N-terminal His-tag was cleaved with tobacco etch virus protease; and tag-free protein was isolated by subtractive nickel affinity chromatography . Size-exclusion chromatography provided additional purification and permitted exchange of FLS2 into the final buffer ( 10 mM Bis-Tris , 100 mM sodium chloride , 5 mM dithiothreitol ( DTT ) , 0 . 02% ( w/v ) sodium azide , pH 7 . 0 ) . FLS2 was concentrated to a volume of 150 µl . “In Liquid” digestion and mass spectrometric analysis was done at the Mass Spectrometry Facility ( Biotechnology Center , University of Wisconsin-Madison ) . In short , 50 µg of purified protein was concentrated via acetone precipitation then re-solubilized under denaturing conditions for tryptic digestion with 10 µl of 8 M urea/100 mM NH4HCO3 , 2 . 5 µl of 25 mM DTT , 2 . 5 µl ACN , 32 µl 25 mM NH4HCO3 and 3 µl trypsin solution ( 100 ng/µl Trypsin Gold from PROMEGA Corp . in 25 mM NH4HCO3 ) . Digestion was for 2 hours at 42°C followed by the addition of 3 µl more of trypsin solution ( final enzyme: substrate 1∶80 ) and o/n incubation at 32°C . The reaction was terminated by acidification with 2 . 5% TFA ( Trifluoroacetic Acid ) to 0 . 3% final . 4 µl was loaded on the instrument . Peptides were analyzed by nanoLC-MS/MS using the Agilent 1100 nanoflow system ( Agilent Technologies , Palo Alto , CA ) connected to a hybrid linear ion trap-orbitrap mass spectrometer ( LTQ-Orbitrap XL , Thermo Fisher Scientific , San Jose , CA ) equipped with a nanoelectrospray ion source . Capillary HPLC was performed using an in-house fabricated column with integrated electrospray emitter using 360 µm×75 µm fused silica tubing packed with Jupiter 4 µm C12 particles ( Phenomenex Inc . , Torrance , CA ) . Sample loading and desalting were achieved using a trapping column inline with the autosampler ( Zorbax 300SB-C18 , 5 µM , 5×0 . 3 mm , Agilent Technologies ) . HPLC solvents were as follows: for isocratic loading , 1% ( v/v ) ACN , 0 . 1% Formic acid; for gradient elution , Buffer A ( 0 . 1% formic acid in water ) and Buffer B ( 95% ( v/v ) acetonitrile , 0 . 1% formic acid in water ) . Gradient elution was performed at 200 nL/min and increasing %B from A of 1 to 40% in 135 minutes , 40 to 60% in 20 minutes , and 60 to 100% in 5 minutes . The LTQ-Orbitrap was set to acquire MS/MS spectra in data-dependent mode with MS survey scans from m/z 300 to 2000 collected in centroid mode at a resolving power of 100 , 000 . MS/MS spectra were collected on the 5 most-abundant signals in each survey scan . Dynamic exclusion of 40 seconds intervals with 1 . 05 m/z above and 0 . 55 m/z below previously selected precursors was employed . Singly-charged ions and ions for which the charge state could not be assigned were rejected for MS/MS . Raw MS/MS data was compared to a user-defined amino acid sequence database ( 108 , 325 protein entries ) using an in-house Mascot search engine ( Matrix Science , London , UK ) with methionine oxidation , asparagine and glutamine deamidation , and serine , tyrosine and threonine phosphorylation as variable modifications . Peptide mass tolerance was set at 20 ppm and fragment mass at 0 . 6 Da . All oligonucleotide primers used for PCR are listed in Table S1 . A genomic DNA fragment carrying 1 . 4 kb directly upstream of the FLS2 translational start site , and the FLS2 coding region up to the stop codon was previously amplified by PCR with Pfu Ultra II polymerase and cloned into the pCR8 vector ( Invitrogen ) , resulting in pCR8-Prom-gFLS2 [12] . For site-directed mutagenesis , two complementary primers containing the desired mutation were synthesized , and used to amplify the double-strands plasmid pCR8-Prom-FLS2 by Pfu Ultra II polymerase . After digestion with Dpn I , PCR products were transformed into E . Coli DH5α . The resultant plasmids were verified by sequencing . The wild type and point-mutated versions of FLS2 under its native promoter were recombined into pGWB13 binary vector [63] for further use . Genomic DNA sequences for BAK1 , BIK1 , PBS1 , PBL1 , and PBL2 were amplified by PCR with Pfu Ultra II polymerase and inserted into pCR8 vector . The DNA fragment flanking 35S promoter and NOS terminator was amplified from pGWB17 and ligated into pre-linearized plasmid pUC19 by Sma I , resulting pUC-GW17 [12] . The genomic DNAs for BAK1 , BIK1 , PBS1 , PBL1 , and PBL2 were recombined into pUC-GW17 and verified by sequencing . All the binary vectors were electroporated into Agrobacterium tumefaciens GV3101 ( pMP90 ) and transformed by the floral dip method [64] into the fls2 T-DNA insertion mutant Col-0 fls2-101 [65] . Positive transgenic plants were selected on 0 . 5× MS plate with 25 mg/L kanamycin and 25 mg/L hygromycin after seed surface sterilization by the vapor-phase method [64] . Transgenic seeds ( T1 ) were screened on 0 . 5× MS agar plates containing 25 mg/L kanamycin and 25 mg/L hygromycin in a growth chamber for 7 days . Positive transgenic seedlings were transferred into 24-well plates containing liquid 0 . 5× MS with or without 30 nM flg22 for 10 days of additional growth . Seedling growth inhibition was determined by dividing the weight of flg22-treated seedlings by the mean weight of untreated seedlings from the same experiment . For MPK phosphorylation , T2 transgenic seedlings from 0 . 5× MS liquid were treated with 2 µM flg22 for 15 minutes , and crude protein was extracted with 2× SDS buffer and separated on 12% SDS-PAGE gel . Phosphorylated MPK3 and MPK6 were detected by anti-P44/P42 antibody ( Cell Signaling Technology , Beverly , MA ) . Callose deposition analysis was performed as described by Adams-Phillips , et al . [66] . In brief , 7-day-old seedlings were treated with 1 µM flg22 in 24-well plates for 24 hours . After fixing overnight in FAA ( 50% ethanol , 5% glacial acetic acid , 10% formaldehyde solution ( initial concentration 37% ) ) , seedlings were stained with aniline blue and callose was visualized using ultraviolet epifluorescence microscopy . ROS burst experiments performed as described by Sun et al . [12] . In brief , leaf discs were taken from 6-week-old T1 transgenic Arabidopsis and floated on 50 µl 1% DMSO solution in a 96-well plate overnight . 50 µl of aqueous solution carrying 0 . 5 µl 2 mg/ml luminol in DMSO , 0 . 5 µl 2 mg/ml horseradish peroxidase ( Sigma ) and 2 µM flg22 was added just before measurement by plate reader ( Centro Xs3 LB 960 , Berthold Technology ) . Six-week-old Arabidopsis seedlings were inoculated with P . syringae pv . tomato strain DC3000 at OD600 = 0 . 0001 in 10 mM MgCl2 solution . After 3 days , leaf discs were taken from four inoculated rosette leaves and ground in 10 mM MgCl2 . The samples were then diluted serially , plated on NYGA plates with 25 mM rifampicin , and colony counts were recorded two days after incubation at 28°C . FLS2 intracellular domain ( encoding amino acids 840–1172 ) and full length BIK1 were amplified by PCR and inserted into pGEX 4T-2 at BamHI and HindIII sites . The resultant plasmids were transformed into BL21 for expression of recombinant protein . BL21 strains harboring plasmid were induced at OD600 0 . 6 by adding 0 . 2 mM isopropyl β-D-thiogalactopyranoside ( IPTG ) at 12°C for 24 hours . The cells were collected at 4°C and resuspended in 10 ml 1× PBS ( Sigma ) supplemented with 1 mM EDTA , 1 mM phenylmethylsulfonyl fluoride ( PMSF ) , 0 . 1% Triton X-100 , 1 mg/ml lysozyme and put on ice for 30 minutes with slow shaking . After lysis using a French pressure cell ( AMINCO , Urbana , IL ) , the lysate was centrifuged at 14 , 000 rpm for 30 minutes at 4°C . The resultant supernatant was applied to a column containing glutathione-agarose beads for protein purification . The column was washed with at least 20 bed volumes of 1× PBS . For thrombin digestion to separate FLS2 or BIK1 protein from GST tag , the beads were added to 2 µg/ml thrombin and incubated at 4°C overnight . GST-fusion proteins were eluted with reduced glutathione . GST-free proteins were eluted with 1×PBS . Both GST-fusion proteins and proteins without GST were dialyzed with exchange buffer ( 50 mM Tris ( PH 7 . 6 ) , 50 mM KCl , 2 mM DTT , and 10% glycerol ) . About 1 µg of purified FLS2 and/or BIK1 intracellular domain as described above were used for in vitro phosphorylation assays . Typically , assays were performed in buffer containing 50 mM Tris ( PH 7 . 6 ) , 50 mM KCl , 2 mM DTT , 5 mM MnCl2 , 5 mM MgCl2 , 10 µM ATP and 10 µCi γ-32P-ATP at 30°C for 30 minutes . The reaction was stopped by adding EDTA to 1 mM final concentration . Samples were separated on 10% SDS-PAGE gel , and stained with Coomassie blue prior to autoradiography using storage phosphor screens and a Storm 840 phosphorimager ( Amersham Biosciences ) . Arabidopsis mesophyll protoplasts were isolated from 6-week-old plants according to the method described by Yoo et al [67] . For coimmunoprecipitation assays , 1 ml of protoplasts ( ∼106 cells ) were transfected with 100 µg plasmid [67] , and the transfected protoplasts were incubated at room temperature for about 14 hours . After centrifuge at 500 g for 3 minutes , the protoplasts were frozen in liquid nitrogen and stored in −80°C for use in coimmunoprecipitation assays . Protoplasts with expression of different proteins as described were lysed with 0 . 6 ml protein extraction buffer ( 50 mM Tris ( PH 7 . 6 ) , 150 mM NaCl , 0 . 5% Triton X-100 , and 1× Cocktail protease inhibitor ( Sigma ) ) for 30 minutes on ice . After centrifugation at 14 , 000 rpm for 15 minutes at 4°C , anti-cMyc was added to the supernatant and incubated at 4°C for 3 hours or overnight , and then 20 µl pre-equilibrated protein A agarose was added for 2 hours with end-to-end shaking at 4°C . Beads were collected by centrifugation at 500 g at 4°C and washed with 1 ml wash buffer ( 50 mM Tris ( PH 7 . 6 ) , 150 mM NaCl ) at least 4 times . After washing , beads were eluted with 50 µl 1× SDS loading buffer by heating at 100°C for 10 min . Eluted proteins were separated on 10% SDS-PAGE gels and transferred to PVDF membrane by electroblotting . Immunoblot detection using the designated horseradish peroxidase-conjugated antibodies was performed using SuperSignal West Pico chemiluminescent substrate ( Thermo Scientific , Rockford , IL ) . Protoplasts from fls2-101 plant transfected with plasmids of FLS2-cMyc , FLS2S938A-cMyc , and FLS2S938D-cMyc were incubated in W5 solution [67] with 40 µCi/ml inorganic 32P-phosphate for 10 hours , and the subjected to immunoprecipitation as above using anti-cMyc . After washing 6 times with 1 ml protein extraction buffer , samples were separated on 10% SDS-PAGE gels and detected by autoradiography using a storage phosphor screen and Storm 840 phosphorimager ( Amersham Biosciences ) . | As a Pattern-Recognition Receptor ( PRR ) that detects conserved microbial molecules , FLS2 mediates the plant innate immunity responses triggered by bacterial flagellin or the flagellin epitope flg22 . Even though several protein kinases including BAK1 , BKK1 , BIK1 are known to play early roles in FLS2-mediated signaling , the specific FLS2 phosphorylation and kinase activities during receptor activation after ligand binding remained unclear . We used in vitro and in vivo methods to identify Ser-938 as a potential phosphorylation site of FLS2 . Immune responses are blocked when Ser-938 is mutated to alanine but remain normal when mutated to the phosphorylation-mimic residues aspartate or glutamate , suggesting that phosphorylation of Ser-938 is critical for defense activation after flagellin is detected . Phosphorylation of BIK1 and BIK1-homologous proteins is dependent on FLS2 Ser-938 , but the widely noted release of BIK1 from FLS2 and binding of BAK1 to FLS2 after flg22 exposure proceed even when Ser-938 is mutated and defense signaling is blocked , suggesting that phosphorylation of Ser-938 and BIK1 phosphorylation prior to release are key steps controlling defense activation after the FLS2 receptor initially detects flagellin . | [
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] | 2013 | Mutations in FLS2 Ser-938 Dissect Signaling Activation in FLS2-Mediated Arabidopsis Immunity |
Genomic mapping of DNA replication origins ( ORIs ) in mammals provides a powerful means for understanding the regulatory complexity of our genome . Here we combine a genome-wide approach to identify preferential sites of DNA replication initiation at 0 . 4% of the mouse genome with detailed molecular analysis at distinct classes of ORIs according to their location relative to the genes . Our study reveals that 85% of the replication initiation sites in mouse embryonic stem ( ES ) cells are associated with transcriptional units . Nearly half of the identified ORIs map at promoter regions and , interestingly , ORI density strongly correlates with promoter density , reflecting the coordinated organisation of replication and transcription in the mouse genome . Detailed analysis of ORI activity showed that CpG island promoter-ORIs are the most efficient ORIs in ES cells and both ORI specification and firing efficiency are maintained across cell types . Remarkably , the distribution of replication initiation sites at promoter-ORIs exactly parallels that of transcription start sites ( TSS ) , suggesting a co-evolution of the regulatory regions driving replication and transcription . Moreover , we found that promoter-ORIs are significantly enriched in CAGE tags derived from early embryos relative to all promoters . This association implies that transcription initiation early in development sets the probability of ORI activation , unveiling a new hallmark in ORI efficiency regulation in mammalian cells .
DNA replication initiation is thought to be the most highly regulated process in genome duplication as cells must ensure that replication origins ( ORIs ) fire precisely once before cell division . A large number of studies during the last twenty years have provided a good understanding of the molecular mechanisms that regulate the initiation of DNA synthesis to occur at specific chromosomal sites and during a specific window in the cell cycle to avoid undesired re- or under-replication of any part of the eukaryotic genome [1]–[3] . Less understood is how ORI specification is achieved , particularly in metazoa where ORIs are not defined by DNA sequence and the origin recognition complex ( ORC ) does not show sequence specificity in vitro [4] , [5] . However , metazoan ORIs are strongly linked to other genomic functions , most notably with transcription . Transcription itself can modulate ORI activity [6]–[8] , transcription factors can interact with ORC [9]–[12] and the binding of transcription factors to a plasmid can localise replication initiation to that specific site [13] . In addition , recent high-throughput studies in various experimental systems have confirmed the long observed link between early replication timing and active transcription [14]–[18] . Despite these findings , the steps in the initiation process that are influenced by transcription are poorly understood . It is possible that changes in transcriptional status could modulate the initial selection of potential ORIs either during the G1 phase of the cell cycle ( pre-RC formation ) or during the activation of pre-RC in S-phase . Identification and characterisation of metazoan ORIs has been hindered by the complexity of these genomes and the lack of robust assays to comprehensively monitor DNA replication initiation . A recent genome-wide ORI mapping in HeLa cells over the regions covered by the ENCODE project has revealed that most initiation sites overlap with transcriptional regulatory elements , although there is not a direct link with gene regulation [19] . To further investigate the nature of the relationship between active transcription and ORI specification we have carried out an unbiased study of ORI location and efficiency in undifferentiated mouse embryonic stem ( ES ) cells . The chromatin environment of ES cells appears to be extremely permissive for gene transcription [20] . This status is maintained by hyperdynamic chromatin [21] , bivalent chromatin marks [22] and Polycomb group proteins that suppress transcription at specific sites [23] , [24] , making the ES cell genome an excellent scenario to address the role of transcription in ORI selection and regulation . Here , we performed a high-resolution mapping of ORIs along 10 . 1 Mb of the mouse genome ( ∼0 . 4% ) encompassing a range of genomic features characteristic of gene-rich and gene-poor regions . Replication initiation sites were identified by hybridisation of short nascent strands on tiled genomic arrays and using a stringent algorithm that takes into account the size distribution of replication intermediates relative to the initiation point . In agreement with results from human cells , we found that in mouse ES cells most of the ORIs associate with annotated transcriptional units and nearly half of them locate at promoter regions . Moreover , we found that CpG island promoter-ORIs are the most efficient ORIs in the mouse genome and that ORI specification and firing efficiency is generally maintained across cell types . The organisation of replication initiation sites at promoter-ORIs mirrors the distribution of transcription start sites ( TSS ) suggesting a co-evolution of the regulatory regions of replication and transcription in the genome . Interestingly , promoter-ORIs are significantly enriched in CAGE tags derived from early embryos relative to all promoters . Our findings suggest that transcription initiation early in development sets the probability of ORI firing .
In asynchronously growing undifferentiated mouse ES cells a large proportion of the population is in the S-phase of the cell cycle . This specific property allowed us to obtain a large enough yield in purified replication intermediates to directly hybridise genomic arrays without previous amplification ( see Materials and Methods ) . Two biological replicates of λ-exonuclease treated short nascent strands ( 300–800 nt in length ) were co-hybridised with genomic DNA from the same cells to tiled genomic arrays covering 10 . 1 Mb of the mouse genome ( Agilent Technologies ) . Arrays were analysed by a modification of ACME ( Algorithm for Capturing Microarray Enrichment ) [25] . ACME identifies signals in tiled array data using a sliding window centered in each probe and returning a p-value that assesses the enrichment by comparing observed and expected number of probes above a user-specified threshold ( see Materials and Methods for further details ) . Preparations of short nascent strands purified from asynchronously growing cells are preferentially enriched in regions close to ORIs and less enriched in their immediately adjacent sequences , showing a pine-tree distribution peaking at the ORI that allows their fine mapping by quantitative real-time PCR methods ( Q-PCR ) [26]–[28] . Based on this property of the nascent DNA hybridised on the arrays we filtered the results from ACME to reliably identify replication initiation sites . Windows from ACME's analysis with a p-value<0 . 005 were further required to have a minimum of two probes per window , an average log2 ratio within the window larger than the 75th percentile of the data , and the defining probe of a window above the threshold and with a p-value<0 . 005 ( see Materials and Methods ) . Replicate experiments showed a high degree of correlation and were averaged ( R2 values of 0 . 954 ) . Applying this stringent algorithm we identified 97 ORIs that mostly map associated to annotated transcriptional units ( 85% ) and , specifically , at promoter regions ( 44% , from which 88% correspond to CpG island-promoters ) ( Table S1 and Figure 1A , left column ) . Replication initiation at CpG islands in mammalian cells is well documented [29] , [30] and our method identifies the ORIs associated with the CpG islands of the Hprt1 and Mecp2 genes precisely at the previously described sites , validating the quality of our ORI maps ( ORIs 45236 and 67276 , Table S2 ) [31] , [32] . Our criterion detects ORI activity at 32% of all known promoters covered by the array ( 50% of the annotated CpG islands and 8% of the annotated non-CpG island promoters , Table S1 ) . This result highlights at genomic scale the link between the regions that trigger replication and transcription initiation that has been previously suggested in studies at specific loci [26] , [27] , [29] , [33]–[35] . Our results increase by more than one order of magnitude the number of characterised ORIs in the mouse genome . In addition , the small length of the nascent strands hybridised on the arrays and the window size chosen for the analysis allowed us to accurately define replication initiation sites within an 800 bp region ( Table S2 and Figures 2–4 ) . The identified ORIs were distributed at an average interorigin distance of 103 kb , however , half of them map within 60 kb distance suggesting a degree of ORI clustering ( Figure 1B ) . To test whether this distribution was related to gene organisation , we analysed separately gene rich regions ( 3 . 2 Mb on chromosome 3 and 4 Mb at region 2 of chromosome X ) and gene-poor regions ( 2 . 9 Mb at region 1 of chromosome X ) ( Table S1 ) . At both gene-rich regions , ORI localisation and interorigin distances were comparable and ORI density positively correlated with promoter density ( Figure 1C , 1D and 1E , upper two graphs ) . By contrast , at the gene-poor region 1 of chromosome X we found no ORI clustering and no correlation between promoter density and ORI density , although the percentage of ORIs associated with promoters was similar at the three regions analysed ( Figure 1C , 1D , and 1E , lower graphs ) . These differences in ORI density were due to non promoter-ORIs being very sparsely distributed along gene-desert regions and suggest a coordinated organisation of replication and transcription in the mouse genome , in line with conclusions reached by genome-wide studies in other systems [16] , [19] . To validate our algorithm for ORI identification we selected 18 positive and 3 negative regions and analysed their abundance in independent preparations of purified 300–800 nt nascent strands by Q-PCR . Since Q-PCR defines ORIs as regions preferentially amplified in relation to their flanking sequences , we interrogated each region with 4 to 6 primer pairs spanning 2 kb across the probes defining the ORI and normalised the values to the flanking pair detecting the lowest amount of nascent strands in each case . The regions studied were representative of the observed ORI location relative to the genes . The average log2 ratios of the array duplicates for each region are shown in the top panels of the figure below the corresponding genomic maps ( Figure 2 ) . Seven of these mapped at CpG island promoters ( including the ORI previously identified at the Mecp2 CpG island , ORI 67276 , Figure 2A ) [32] , six mapped at or immediately adjacent to exons , one mapped at the 3′UTR of two genes with convergent transcription ( Figure 2B ) , and four mapped to intergenic regions ( Figure 2C ) . In all 18 regions , the significant probes identified on the arrays coincided with the point of higher enrichment in nascent strands relative to its immediate flanking sequences by Q-PCR . Detected enrichments ranged between 16 to 40 times at CpG island-ORIs ( Figure 2A ) and between 4 and 10 times at non promoter-ORIs ( Figure 2B and 2C ) . An interesting exception was ORI 108639 ( Figure 2B , third panel ) that maps at the last exon of the Zfp697 gene and was 18 times enriched in nascent strands relative to its local flank . This region harbours a C+G composition and CpG density that qualifies it as a 3′ CpG island , suggesting that this ORI could map at an unannotated promoter ( see below ) . In contrast , the regions that were scored as negative by our algorithm showed no enrichment in nascent strands by Q-PCR ( Figure 2D ) , indicating that even low efficiency replication initiation sites detected in the arrays were indeed ORIs . It is worth noting that the peaks defined by the nascent strand profiles were in all cases within 800 bp width , coinciding with the upper size of the replication intermediates hybridised in the arrays and constituting the highest resolution genomic mapping of mammalian ORIs to date . Q-PCR results suggested that CpG island promoter-ORIs were generally more efficient than non promoter-ORIs . As the arrays were hybridised with non-amplified short nascent strands , the output log2 ratios should give semi-quantitative information about ORI efficiency . Consistently , the hybridisation signals obtained at the CpG island-ORI class ( mean values of 3 . 899 ) were significantly higher ( p = 0 . 00005 , Welch Two Sample T-test ) than those at the non promoter-ORI class ( mean values of 3 . 008 ) . To be able to compare ORI efficiencies directly , we performed Q-PCR on three consecutive sucrose gradient fractions containing nascent strands of 100–600 , 300–800 and 600–2000 nt in length , respectively , and normalised the abundance relative to that obtained at the negative regions in each gradient fraction ( Figures 3 and 4 ) . We found that the maximum enrichment in replication intermediates detected by Q-PCR coincided with the highest point of the log2 ratio profile in all gradient fractions ( Figures 3A and 4A ) , confirming that positive regions identified in the arrays were genuine ORIs . In addition , ORI enrichment relative to the non-ORI regions increased with the size of the nascent strands analysed ( fold enrichment of the ORI peak relative to the negative regions are indicated below each histogram ) , further supporting that DNA synthesis elongates from these regions to replicate the genome ( Figures 3B and 4B ) . Remarkably , nascent strand enrichments detected at CpG island-ORIs ( Figure 3B ) were one order of magnitude higher than those detected at non promoter-ORIs across all gradient fractions ( Figure 4B ) , implying that CpG island-ORIs are the subset of ORIs that are preferentially activated in the analysed cell population . Preparations of 100–600 nt nascent strands likely contained Okazaki fragments that co-purified with these small replication intermediates . Given that asynchronously growing ES cells were used , Okazaki fragments were expected to derive from the entire genome and to diminish the overall level of enrichment without a bias for any particular loci . Increasing the background signal , however , could critically affect the detection of weak ORIs , as seen at most non promoter-ORIs in the 100–600 nt nascent strand fraction ( Figure 4B ) . We reasoned that only the ORIs that are active in the majority of cells of the analysed population would be enriched enough in small size nascent strand preparations to give a significant signal on the arrays and could , therefore , identify a collection of the most efficient ORIs . To test this possibility , we hybridised two more arrays with preparations of shorter nascent strands ( 100–600 nt in length ) derived from the same cells . When considering the data from the four arrays altogether , the number of identified ORIs dropped from 97 to 38 and , interestingly , their genomic distribution changed dramatically ( Figure 1A , right column and Table S2 ) . In this case , more than 97% of the identified ORIs mapped at transcriptional units and 78% at promoter regions ( of those , 96% correspond to CpG island promoters ) , indicating that CpG island promoter-ORIs are the subset of ORIs that fire with higher efficiency in mouse ES cells . A similar conclusion can be reached when examining the distribution of the 97 identified ORIs in gene poor versus gene rich regions ( Table S1 ) . While the proportion of mapped ORIs associated with CpG islands was similar in both cases ( 38% vs 44 and 36% ) , the proportion of annotated CpG island-promoters showing ORI activity at gene poor versus gene rich regions was 75% vs 54 and 45% , respectively ( Figure 1C and Table S1 ) . It should be noted that our experimental approach for ORI identification is not suited to detect ORIs dispersed across large regions , such as the ORI downstream of the DHFR gene in hamster CHO cells [36] . We could not , therefore , address either the abundance of broad initiation regions in the genome nor their firing efficiency . To check whether this difference in ORI usage was conserved in other cell types we studied ORI firing efficiency by Q-PCR at 9 CpG island-ORIs and 10 non promoter-ORIs in preparations of 300–800 nt long nascent strands derived from mouse embryonic fibroblasts ( MEFs ) and NIH/3T3 transformed fibroblasts . We first analysed if DNA replication initiated at these sites in differentiated cell types by scanning a 2 kb region surrounding the ORI in experiments analogous to those shown in Figures 3 and 4 ( Figure S1 ) . Overall enrichments in replication intermediates detected at ORI regions relative to the negative zones were smaller in MEFs and 3T3 fibroblasts compared to ES cells , likely reflecting the differences in cell division rates between the three cell types . Despite this , the analysed regions showed peaks of enrichment relative to flanking sequences and to the non-ORI regions at the same positions observed in ES cells , suggesting that ORI specification is maintained at these sites across the three cell types . Although relative activity varies between ORIs in the three cell lines , two clearly distinct groups of ORIs can be distinguished on the basis of their efficiency ( Figure 5 ) . Strong ORIs correspond to CpG island ORIs ( black histograms ) , while weak ORIs correspond to non promoter-ORIs ( grey histograms ) . Interestingly , the replication initiation activity of the CpG island associated to the last exon of the Zfp697 gene ( ORI 108639 ) was indistinguishable from CpG island promoter-ORIs in the three cell types studied , suggesting that CpG regions might contain a hallmark for efficient replication initiation regardless of their position relative to the gene . An exception to this general tendency is ORI 67276 ( MeCp2 CpG island ) , the weakest CpG island-ORI even in ES cells . This is possibly due to the fact that the primer pairs detecting the highest enrichment in nascent strands were not adjacent to the TSS underestimating the real activity of this ORI ( Figures 3 and 6A , and see below ) . Altogether , these results indicate that ORI prominence is generally retained from pluripotent cells to differentiated cells and cell lines . It is important to note that the higher efficiency in ORI activity found at CpG islands is not due to the overreplication occurring at promoter-ORIs that we recently reported [33] . Overreplicated intermediates are typically 100–200 bp long and their detection strictly relies on the use of cloned DNA to normalise primer pair efficiency . In this work we consistently normalised the data with genomic DNA that suppresses all possible contribution of overreplicated short fragments ( either for array hybridisations or for Q-PCR measurements , see Materials and Methods ) . In addition , ORI firing efficiency at CpG islands was found to be consistently higher than at non-promoter ORIs along nascent strand preparations of increasing sizes , where the contribution of short overreplicated fragments is negligible ( Figure 3B ) . Closer examination of the log2 ratio profiles across several CpG island regions similar to those shown in Figure 3 indicated that maximum enrichments in short replication intermediates were usually detected around the major transcription initiation site annotated at each promoter ( mouse NCBI database build 36 . 1 ) . We investigated this correlation in more detail taking advantage of the accuracy of our ORI mapping and the recently available high coverage annotation of transcription start sites ( TSS ) derived from 145 mouse libraries by extensive CAGE and PET analysis ( http://gerg01 . gsc . riken . jp/cage/mm5 ) [37] . Figure 6 shows several examples of CpG island promoter-ORIs with unique or multiple TSS displaying the number and position of annotated tags defining each TSS alongside our array results . The replication initiation points defined by the log2 ratios exactly parallel the transcription initiation sites defined by tag sequencing at less than 30 bp resolution in most cases ( Figure 6A ) . This correlation was more striking in the case of CpG island-promoters with alternative transcription initiation sites or bidirectional activity where , when array probe distribution allowed it , distinct replication initiation points located immediately adjacent to the mapped tags at those regions could be clearly distinguished ( Figure 6B ) . Based on these observations , we asked whether two independent clusters of TSS for the same gene , but not located within the same CpG island region , were also associated with replication initiation sites . We analysed the Flna gene , which is transcribed from two alternative promoters , one located in a CpG island and another one 3 . 4 kb upstream that is not CpG island-associated . Our algorithm identified two separated peaks of nascent strands enrichment pointing exactly to the two tag clusters from where the transcription of the gene initiates ( Figure 6C , left graph ) . Then we asked the reciprocal question , can alternative or novel TSS be identified from the nascent strand profiles ? To test this possibility we analysed 12 . 6 kb surrounding the CpG island associated with the Tbx15 gene , where three distinct ORIs were identified in our arrays ( Figure 6C , right graph ) . As anticipated , the peak located at the CpG island pointed to the mapped tags for that gene . The peak located 4 kb upstream marked exactly the predicted 5′ position of the transcript of a model gene , Hmm112720 , and the peak located 3 . 5 kb downstream of the Tbx15 ATG initiation codon was orphan in terms of transcription initiation activity . To check whether this ORI was associated with an uncharacterised TSS we analysed the chemical modifications of the histone components of the resident nucleosomes by chromatin immunoprecipitation ( ChIP ) and its capacity to drive transcription in plasmid reporter assays ( Figure 7A and 7B , respectively ) . We found that the nucleosomes at this ORI were enriched in histone H3 lysine 4 trimethyl ( H3K4me3 ) and histone H3 lysine 9 and 14 acetyl ( H3K9 , 14ac ) marks , characteristic of transcription initiation ( ORI 109331 , Figure 7A ) [38] . The enrichment in both histone modifications detected at ORI 109331 was significantly lower compared to that detected at CpG island promoter-ORIs , consistent with H3K4me3 levels being positively correlated to gene expression rates and therefore indicating that this TSS might correspond to a low transcribed RNA [39] . In agreement with this , a 944 bp fragment containing ORI 109331 displayed promoter activity in reporter assays in the antisense orientation , although less efficiently than canonical CpG island promoters ( Notch2 and Aprt promoters , Figure 7B ) . Our results help to explain previous observations of ORI clustering in CG-rich regions [19] , [40] , that most likely correspond to discrete replication initiation sites associated to distinct TSS that are activated in different cells of the analysed population . The above results demonstrate a strong correlation between the initiation of replication and transcription at CpG island promoters and at clustered ORIs at promoter-rich regions . To address how general this association was and to test whether non-promoter ORIs might be good predictors of novel TSS regardless of their location in the genome , we extended the analysis of the histone signatures by ChIP to another 10 randomly selected non-promoter ORI regions . Figure 7A shows the proportion of H3K4me3 and H3K9 , 14ac modifications relative to total H3 detected at the studied regions in comparison with that detected at Mecp2 and Zad20d1 CpG island-ORIs and their flanks ( ORIs 67276 and 105455 ) . Three out of the ten regions tested ( ORIs 106169 , 106334 and 108639 ) were enriched in both histone modifications relative to background and the negative controls , indicating that these ORIs could be linked to transcription initiation [39] , [41] . Consistently , ORIs 106169 and 108639 also displayed promoter activity in reporter assays , as well as another two ORIs that were not enriched in H3K4me3 or H3K9 , 14ac histone marks ( ORIs 65321 and 65456 , Figure 7B ) . As 44% of the ORIs identified in this study were located at well characterised promoters , altogether these data suggest that a minimum estimate of 60% of the ORIs in the genome of mouse ES cells are associated to TSS . The data presented in Figure 6 shows that ORI architecture at CpG island regions is reminiscent of that of promoters , where discrete initiation sites can be distinguished , each of them mapping immediately adjacent to annotated TSS . This similar organisation of the regions driving replication and transcription initiation , together with the finding that CpG islands are the most efficient ORIs in the genome , suggests that both processes might benefit from each other . This hypothesis makes several testable predictions that we evaluate in turn . First , highly efficient ORIs would be expected to be preferentially associated to promoters driving ubiquitous expression . We considered the number of tags mapped at the TSS of each promoter-ORI as an indicator of relative promoter usage across several tissues and cell types [37] and analysed them relative to ORI occurrence . As expected , 78% of the ORIs locate at TSS where more than 75 clustered tags have been identified , representing the promoters of the most widely expressed genes at the studied regions ( Figure 7C , grey histograms ) . Second , many promoters in the genome should display replication initiation activity . To test this possibility we reanalysed our array data using a less stringent algorithm ( see Materials and Methods ) . The strict algorithm detected ORI activity at 32% of annotated promoters ( Table S1 ) ; applying the less stringent criterion we now detected ORI activity at 60% of the known promoters ( 83% of annotated CpG islands and 33% of the annotated non-CpG island promoters ) ( Table S1 ) . Although in this case the association is slightly less prominent , ORIs also occur with higher frequency at the promoters with a higher number of mapped tags ( Figure 7C , white histograms ) . Finally , if the spatial coincidence between replication and transcription initiation sites has a functional significance we would predict that promoter-ORIs would be transcriptionaly active in early development . To test this hypothesis we again surveyed the mouse CAGE database and analysed the tags derived from embryonic or germ line libraries mapped at each promoter-ORI relative to all known promoters [37] . CpG island associated genes , including those of tissue specific expression , are transcribed in the germ line [42]–[45] , and we consistently found tags derived from early embryos and testis libraries at 90% of the CpG islands present in the array . When we performed similar analysis for CpG island-ORIs , the proportion increased to 100 and 95% ( Figure 7B , blue histograms , first and second algorithm , respectively ) . More strikingly , we found CAGE tags derived from early embryos at 60 and 67% of non-CpG island promoter-ORIs , which shows statistical evidence of enrichment as the observed frequency of expression of this type of promoters at early developmental stages is only 38% ( Figure 7D , red histograms , first and second algorithm , respectively ) . Interestingly , the ORI 108639 , located at a 3′ CpG island spanning the last exon of the gene Zfp697 that is highly active in all cell types analysed ( Figure 5 ) , has also associated tags derived from early embryos . These results strongly indicate that the most efficient ORIs in the genome are those associated with sites of embryonic transcription initiation .
By combining a genome-wide approach to identify preferential sites of DNA replication initiation with in depth analysis at distinct classes of ORIs according to their genomic location , we were able to conclude that ORI firing efficiency is strongly associated to transcription initiation activity . The short size of nascent strands hybridised in the arrays and the stringent algorithm chosen to analyse the datasets allowed us to draw a highly accurate map of 97 new ORIs along 10 . 1 Mb of the mouse ES genome . A systematic analysis of the location of the identified ORIs revealed a strong correlation with annotated transcriptional units and specifically with the annotated 5′ ends of genes ( Figure 1 ) . This genomic distribution of ORIs is similar to that reported in a recent analogous study in HeLa cells [19] , a remarkable fact given the diverse genetic and epigenetic status of both cell types and the differences in the technical approaches used to prepare the nascent strands for array hybridisations . Therefore , these two high-resolution ORI maps likely represent a comprehensive picture of the coordinated organisation of replication and transcription in the mammalian genome . However , a significant fraction of the ORIs identified in both experimental systems are not associated with known promoters nor carry the histone modifications indicative of transcriptional activity , implying that ORI specification can be achieved by several mechanisms . Detailed measurements of nascent strand abundance at both classes of ORIs in preparations of replication intermediates of increasing sizes ( Figures 3 and 4 ) , and array hybridisation with very short nascent strands that mainly represent highly active ORIs ( Figure 1A and Table S2 ) , indicated that the most efficient replication initiation sites are those associated with CpG island promoters . Interestingly , this hierarchy of ORI usage not only occurs in mouse ES cells but is also maintained in differentiated cells and cell lines ( Figure 5 ) , suggesting that firing efficiency is linked to transcription initiation activity . Recently it has been reported that more than half of all mouse and human genes are associated with TSS driving divergent transcription over short distances , proposed to help maintain promoter regions in a state poised for subsequent regulation [46] , [47] . We did not find any preferential representation of this class of promoters at the promoter-ORI class , suggesting that neither ORI specification nor activity are linked to this type of transcriptional regulation . Our results support the Jesuit model of ORI initiation proposed in the late 90's by Melvin DePamphilis ( “many are called , but few are chosen” ) [48] , [49] . According to this model , the metazoan genome contains multiple potential sites of replication initiation whose activity is modulated during the G1 phase of each cell cycle by a combination of parameters such as nuclear organisation , chromatin structure , gene transcription or DNA sequence . This study identifies transcription initiation early in development as a strong determinant of ORI efficiency in mammalian cells . Transcription start sites of active genes are usually nucleosome-free indicating a more open chromatin conformation [38] , [39] and presumably the parasitism of ORIs at TSS would increase the chances of firing through the facilitation of the assembly of the replication complexes to these sites . Indeed , a recent report showed that ORC binding to the Epstein-Barr virus origin of plasmid replication is stabilised by RNA [50] , opening the possibility that nascent RNA molecules could contribute to ORC recruitment in mammalian cells . Interestingly , we found that ORI and promoter organisation are virtually identical ( Figure 6 ) , likely reflecting that the initiation of replication and transcription are influenced by the same chromatin constraints . Moreover , we were able to show that the probability of ORI activation is set by transcription initiation early in development: we found that promoter-ORIs are significantly enriched in CAGE tags derived from early embryos relative to the rest of promoters ( Figure 7D ) [37] . Our results point to a scenario where active promoters in germ cells and early embryonic cells will recruit pre-RCs and acquire the capability to drive replication . It is possible that the initiation of both replication and transcription at these promoter-ORIs will contribute to the configuration of a competent chromatin conformation that is a prerequisite for efficient replication initiation . This epigenetic state would then be transmitted and maintained in somatic cells . The above scenario can accommodate several observations made in various developmental systems . For example , in somatic cells , silent CpG islands on the inactive X chromosome function as ORIs as efficiently as their counterparts on the active X [32] . On the other hand , upon activation at specific developmental stages new ORIs are switched on while others are maintained [51] , [52] . In addition , our work could provide experimental evidence in support of a hypothesis for the origin of CpG islands [53] . These authors proposed that CpG islands have acquired their distinct properties of C+G composition , CpG density and lack of DNA methylation due to their dual role as promoters and ORIs early in development . Since the number of CpG island associated genes is significantly smaller in mouse than in humans [54]–[56] , presumably due to the different rates of CpG loss occurring during mammalian evolution [57] , [58] , we hypothesise that promoter-ORIs showing early embryonic expression that are not linked to CpG islands in the mouse genome would be CpG island associated in the human genome . To test this possibility we thoroughly searched for the presence of CpG islands at the human orthologous regions of the mouse promoter-ORIs identified in our work ( human NCBI database build 36 . 3 ) . We found that 50% of the non-CpG island associated promoter-ORIs expressed early in mouse development indeed harbour a CpG island in the human genome . However , the observed frequency of this association when considering all other promoters is only 10% ( p-value = 0 . 007 ) , making it tempting to speculate that the co-evolution of the regulatory regions driving replication and transcription initiation could have contributed to the shape of the mammalian genome .
The mouse embryonic stem cell line PGK12 . 1 was grown as described [59] . Mouse embryonic fibroblasts ( MEFs ) were derived from 12 . 5 dpc CD1 embryos and grown in F12 Nutrient Mixture ( Ham ) medium supplemented with 10% FCS , 1×105 U/ml penicillin , 100 mg/ml streptomycin , 2 mM L-glutamine , 1× non-essential amino acids , and 50 µM β-mercaptoethanol ( Invitrogen ) . NIH/3T3 cells were cultivated as recommended in the ATCC . Genomic DNA isolation and nascent strands fractionation was performed as described [33] . Sucrose gradient fractions containing replication intermediates ranging between 100–600 nt , 300–800 nt and 600–2000 nt in size were subjected to digestion by λ-exonuclease , which degrades contaminating random sheared DNA leaving untouched DNA replication intermediates that are protected by a 5′ RNA-primer , as described [28] . ORI enrichment in 300–800 nt nascent strand preparations was routinely monitored by Q-PCR for Mecp2 CpG island-ORI region [32] and a flanking region located 1 kb downstream as control . Primer sequences are provided in Table S3 . Only preparations showing a minimum of 5-times enrichment were used in array hybridisation experiments or Q-PCR validations . Three to four µg of λ-exonuclease treated nascent strands purified from 5×109 mouse embryonic stem cells were co-hybridised with the same amount of total genomic DNA for each array replicate . Sample labelling , hybridisation and data extraction were performed according to standard procedures from Agilent Technologies ( 2005 ) . Agilent 22K feature arrays were designed to cover two 4 Mb regions on the X chromosome ( 45 . 5–49 . 5 Mb and 65–69 Mb ) and a 4 Mb region on chromosome 3 ( 95 . 5–99 . 5 Mb ) of non-repetitive DNA sequences , with an average coverage of one 60-mer probe each 250 bp ( Oxford Gene Technology ) . Probe design was based on Ensembl mouse build 35 . Raw datasets from each experiment were loess normalised to remove signal intensity-dependent bias using GeneSpringX software ( Agilent ) . Normalised data were analysed with the ACME algorithm [25] , that uses the following approach to examine enrichment . First , using a user-specified threshold ( 0 . 95 in our case ) probes are divided into positive probes ( those with a log2 ratio larger than the specified quantile ) and negative ones . ACME then uses a sliding window of fixed size ( 800 bp in our case ) centered on each probe . Within each window , a chi-square test is used to examine enrichment by comparing the observed number of positive probes with the expected number . The p-value can be used as a rough guide to determine regions of interest ( in our case , we used as cut-point a p-value<0 . 005 ) . The original results from ACME are referred to as “Algorithm 2” in Figure 6 , and “the less stringent algorithm” in the text . We further filtered the regions identified with ACME as follows: ( i ) regions were required to contain at least two probes , ( ii ) the average log2 ratio within a window had to be larger than the 75th percentile of the data and ( iii ) the defining probe of a window had to be above the threshold and have a p-value<0 . 005 . These additional conditions were used to minimise false positives by excluding single-probe windows , requiring all of the probes within a region to show at least some evidence of enrichment , and preventing a window from being labelled as interesting simply because of it being next to a highly enriched window . The final list of significant probes defining each 800 bp window is shown in Table S2 . These filtered results are what we refer to as “Algorithm 1” in Figure 6 , and “the more stringent algorithm” in the text . ACME analyses were carried out using R [60] and the BioConductor package ACME [25] . All data have been deposited in the Gene Expression Omnibus ( GEO ) , http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? accGSE15082 . Replication intermediates abundance relative to annotated genomic features was first analysed by visual inspection using the GEB browser ( http://web . bioinformatics . ic . ac . uk/geb ) and manually validated in the mouse NCBI database build 36 . 1 . Annotations for genes and transcripts were obtained from RefSeq , Ensembl and UniGene databases . CpG islands were identified using the strict algorithm displayed in the NCBI database: minimum length of 500 bp , minimum C+G content of 50% and minimum observed CpG/expected CpG of 0 . 6 [61] . Quantitative real-time PCR was performed with an ABI Prism 7000 Detection System ( Applied Biosystems ) , using SYBR Premix Ex Taq ( Takara Bio Inc . ) and following manufacturer's instructions . Four serial 10-fold dilutions of sonicated genomic DNA were amplified using the same reaction mixture as the samples to construct the standard curves . Primer sequences are indicated in Table S3 . All real-time PCR reactions were performed in duplicate and in at least two independent preparations of nascent strands or immunoprecipitated material . Quantitative analyses were carried out using the ABI Prism 7000 SDS Software ( version 1 . 2 . 3 ) . PGK12 . 1 cells cross-linking and chromatin immunoprecipitations were performed as described [62] , with the following modifications . Cells were harvested in Lysis Buffer I ( 5 mM PIPES pH 8 . 0 , 85 mM KCl , 0 . 5% NP-40 , and protease inhibitors ) . Nuclei were pelleted by centrifugation , resuspended in Lysis Buffer II ( 50 mM Tris pH 8 . 0 , 1% SDS , and 10 mM EDTA pH 8 . 0 , and protease inhibitors ) and disrupted by sonication using Bioruptor ( Diagenode ) , yielding genomic DNA fragments with a size distribution of 100–800 bp . For each ChIP 25 µg of chromatin were immunoprecipitated with the following polyclonal antibodies: H3 acetyl K9 , 14 ( 5 µg , Upstate ) , H3 tri methyl K4 ( 2 µg , Abcam ) , or H3 ( 2 µg , Abcam ) . Immune complexes were recovered by the addition of 20 µL of blocked protein A/G Plus beads ( Santa Cruz ) and washed and eluted as described [62] . PCR-amplified DNA fragments were cloned in both orientations upstream of the luciferase gene in the pGL3 basic vector ( Promega ) . Constructs were cotransfected with a Renilla Luciferase Control Reporter Vector ( pRL-SV40 , Promega ) using Lipofectamine 2000 ( Invitrogen ) and following manufacturer's instructions . Firefly and Renilla luciferase signals were quantified 30 h post-transfection using the Dual-Luciferase Reporter Assay System ( Promega ) . Reporter expression was normalised with the Renilla luciferase signal and averaged across two independent transfections carried out in duplicate . Primer sequences used to amplify the fragments for cloning and insert sizes are provided in Table S3 . | The duplication of the genetic information of a cell starts from specific sites on the chromosomes called DNA replication origins . Their number varies from a few hundred in yeast cells to several thousands in human cells , distributed along the genome at comparable distances in both systems . An important question in the field is to understand how origins of replication are specified and regulated in the mammalian genome , as neither their location nor their activity can be directly inferred from the DNA sequence . Previous studies at individual origins and , more recently , at large scale across 1% of the human genome , have revealed that most origins overlap with transcriptional regulatory elements , and specifically with gene promoters . To gain insight into the nature of the relationship between active transcription and origin specification we have combined a genomic mapping of origins at 0 . 4% of the mouse genome with detailed studies of activation efficiency . The data identify two types of origins with distinct regulatory properties: highly efficient origins map at CpG island-promoters and low efficient origins locate elsewhere in association with transcriptional units . We also find a remarkable parallel organisation of the replication initiation sites and transcription start sites at efficient promoter-origins that suggests a prominent role of transcription initiation in setting the efficiency of replication origin activation . | [
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"gen... | 2009 | Transcription Initiation Activity Sets Replication Origin Efficiency in Mammalian Cells |
Methane produced by methanogenic archaea in ruminants contributes significantly to anthropogenic greenhouse gas emissions . The host genetic link controlling microbial methane production is unknown and appropriate genetic selection strategies are not developed . We used sire progeny group differences to estimate the host genetic influence on rumen microbial methane production in a factorial experiment consisting of crossbred breed types and diets . Rumen metagenomic profiling was undertaken to investigate links between microbial genes and methane emissions or feed conversion efficiency . Sire progeny groups differed significantly in their methane emissions measured in respiration chambers . Ranking of the sire progeny groups based on methane emissions or relative archaeal abundance was consistent overall and within diet , suggesting that archaeal abundance in ruminal digesta is under host genetic control and can be used to genetically select animals without measuring methane directly . In the metagenomic analysis of rumen contents , we identified 3970 microbial genes of which 20 and 49 genes were significantly associated with methane emissions and feed conversion efficiency respectively . These explained 81% and 86% of the respective variation and were clustered in distinct functional gene networks . Methanogenesis genes ( e . g . mcrA and fmdB ) were associated with methane emissions , whilst host-microbiome cross talk genes ( e . g . TSTA3 and FucI ) were associated with feed conversion efficiency . These results strengthen the idea that the host animal controls its own microbiota to a significant extent and open up the implementation of effective breeding strategies using rumen microbial gene abundance as a predictor for difficult-to-measure traits on a large number of hosts . Generally , the results provide a proof of principle to use the relative abundance of microbial genes in the gastrointestinal tract of different species to predict their influence on traits e . g . human metabolism , health and behaviour , as well as to understand the genetic link between host and microbiome .
By 2050 , the human population will grow to over 9 billion people , and in the same time frame , global meat consumption is projected to increase by 73% [1] . However , intensive food production puts a strain on the environment , and there is a need to produce more food ethically and in a way that does not harm the environment . Methane is a greenhouse gas with a global warming potential 28-times that of carbon dioxide [2] and ruminants are the major source of methane emissions from anthropogenic activities . Finding means to mitigate methane emissions is an intractable problem , despite large international research efforts . A fundamental problem is that the ruminal microbiota is able to adapt rapidly to intervention methods that have been tried so far—such as different dietary formulations , chemical and biological feed additives , chemo-genomics and anti-methanogen vaccines [3] . In this study we show that genetic selection of low methane emitting animals is a viable option . The gut microbial ecosystem is particularly important in ruminants due to its ability to convert indigestible fibrous plant material into absorbable nutrients . From the environmental and energetic efficiency point of view , there is a disadvantage in that the anaerobic microbial fermentation process can result in excess hydrogen that is used by methanogenic archaea to produce methane and then eructed into the atmosphere . The loss of feed gross energy as methane has been estimated at 2 to 12% [4] . In order to address food security as well as economic and environmental impacts of food production , sustainable intensification has been suggested [5] with genetic improvement of feed conversion efficiency of highest importance in farm animals . Therefore , the overall aim of our work was to improve the efficiency of the rumen microbial community in converting feed into nutrients with minimal production of methane . The host animal provides the environment for the microbial ecosystem in the rumen and may therefore have an impact on its composition and efficiency . Studies in rodents and humans suggest that there is a host genetic influence on the microbiome [6–9] . In addition , research in bovine and ovine indicates that there is a host genetic influence on methane emissions and feed conversion efficiency without considering and evaluating the impact of the microbiome [10–13] . Our previous study found a phenotypic correlation between the composition of the rumen community and methane emissions [14] . However , direct evidence for a genetic control of the microbiota by the host in ruminants is rather weak . Therefore , the main aim of this study was to investigate whether there is a genetic influence of the host on the ruminal microbial community which affects methane production . If the genetics of the host animal has a significant role in determining key activities of the microbiota , then breeding would be a cost-effective tool to reduce methane emissions and improve feed conversion efficiency , provided that an accurate selection criterion is available . Therefore , this study also aimed to find the best selection criterion for mitigation or improvement of these traits . Metagenomics allows the identification of the composition of the whole microbial community , as well as the abundance of their genes . It could be used to develop new selection criteria for difficult-to-measure traits or to understand the link between host genetics , the microbiome and its activity . Our study design allowed us to provide an insight into the genetic influence of the host animal on methane production by archaea , the impact of diets on methane emissions and their interactions with the host genetics . We found novel selection criteria related to microbial characteristics of each host which can be used to select for low methane emitting animals . Specifically , the relative abundance of microbial genes , identified in a metagenomic analysis , was highly informative for predicting methane emissions , but also for other traits such as feed conversion efficiency , and is recommended for exploitation in genetic selection of hosts or to understand the additive genetic link between host genetics and microbiome . Host selection based on a functional microbiome microarray containing microbial genes associated with methane emissions , feed conversion efficiency , health and other traits will provide a novel and cost-effective selection opportunity without measuring these difficult and costly to record traits and has the potential to enable large scale breeding for these performances . This study was carried out using cattle but the identified best microbial criterion ( microbial composition , genes and pathways ) to achieve insight into the host-microbiome interactions should be transferable to other traits and species .
The basis for an efficient selection program to mitigate methane emissions due change in microbial community depends on the genetic variation of these characteristics among animals . There were large phenotypic ranges in methane emissions between the extreme low and high emitting animals within breed type and diet groups ( Fig 1 , S1 Table and S1 Dataset ) . The differences in daily methane emissions between the extremes within crossbred breed type were similar between diets suggesting that the diet effect represents only a scaling effect . If at least part of the variation is influenced by the host animal , then selection for mitigation of methane emissions is expected to be efficient . Even larger variation was obtained for relative archaeal abundance measured as archaea:bacteria ratios , within breed type and diet , as shown by coefficients of variation in the range of 35% to 50% and 39% to 65% for forage and concentrate-based diets respectively . A first indication for a host genetic influence on methane emissions and on the composition of the microbial community can be derived from breed type differences . The least squares means ( LSM ) for daily methane emissions were significantly different at 184 g/d and 164 g/d for the Aberdeen Angus ( AA ) and Limousin ( LIM ) breed types respectively , but not significantly different for methane emissions per kg dry matter intake ( DMI ) ( Table 1 ) . These results indicate that the significant difference between breed types in daily methane emissions were due to higher feed intake of AA ( 11 . 3 ± 0 . 36 kg and 10 . 2 ± 0 . 36 kg DMI for the concentrate and forage based diet , respectively ) compared to LIM ( 9 . 8 ± 0 . 37 kg and 8 . 8 ± 0 . 36 kg , for the same diets , respectively ) . Animals offered the forage based diet had significantly higher methane emissions than those offered the concentrate based diet . This difference is due to higher propionate production from fermentation of starch in concentrate diets , which leads to less hydrogen being available for methanogenesis [15–17] . Estimated LSM for archaea:bacteria ratios taken from live or slaughtered animals were significantly different for diet effects , but not for breed type effects . In the interpretation of the breed type results , it has to be considered that this effect represents only an expected 2/3 of the additive genetic contribution of the sire breed and that non-additive genetic effects can also have an impact . Consistent with diet effects on methane emissions , low archaea:bacteria ratios were obtained for animals offered the concentrate- in comparison to forage-based diet . The differences in archaea:bacteria ratios between diets were 3 . 7 for both rumen contents samples taken from live and slaughtered animals , suggesting that these measurements can be used interchangeably . Sire progeny groups differences were used to identify the host genetic influence on methane emissions . Estimates of LSM for daily methane emissions among sire progeny groups showed significant differences ranging from 136 to 205 g/d ( Fig 2 ) . In contrast to the breed type effects , there were also significant differences between LSM for sire progeny group effects on methane emissions relative to the amount of feed consumed ( Fig 2 ) . Slightly different rankings of sires based on methane emissions per day in comparison to those based on per kg DMI are likely due to differences in feed intake among sire progeny groups . In some cases , the differences in methane emissions between sire progeny groups were even larger than the differences between the diets , indicating a substantial genetic influence of the host animal . The differences in LSM for methane emissions among sire progeny groups , as well as the similar ranking ( r = 0 . 6 ) when methane emissions are expressed per day or per DMI , indicate that there is a direct genetic influence of the host on the rumen microbial methane production independent of the amount of feed consumed . Population genetic studies using beef cattle [10] , dairy cattle [11] and sheep [12 , 13] lend supporting evidence for a genetic influence of the host on methane production . The advantage of the present study is that methane was measured using the considered “gold standard” measurement technique of respiration chambers and that the genetic and diet effects , as well as their interaction were estimated in a powerful experimental design under standardised conditions . There were no significant interactions between breed type ( or sire ) and diet effects in the present study . The absence of interactions indicates that the genetic ranking of sires would not change according to diet . This observation is of substantial importance for implementation of this approach within genetic improvement programmes , and should be confirmed in further independent studies . These results provide fundamental insight into the regulation of methane emissions , indicating that there is an additive genetic influence of the host on methane production by the archaea and that the genetic influence of the host on methane emissions does not change with the diet . The scaling effect of the diet on methane production could be adjusted for in genetic models . In contrast , if interactions between host genetics and diet are present , it would be necessary to use more complex selection strategies . The host additive genetic influence on the microbiome was estimated based on differences in the archaea:bacteria ratio in rumen contents among sire progeny groups . Our earlier studies showed that the archaea:bacteria ratio in rumen contents from live animals can be used to predict methane emissions with a reasonable phenotypic correlation of 0 . 49 [14] . Other methods have also be investigated to predict methane emissions of animals e . g . the use of laser methane detector in sheep or beef cattle [18] and milk mid-infrared spectra in dairy cows , however , further discussion are beyond the scope of this study [19] . The ratio of archaea:bacteria in the rumen contents sample from each animal was more informative than the absolute amount of those microbes , most likely because the ratio is e . g . independent of dilution effects and differences in PCR amplification of 16S rRNA genes between samples . Comparison of sire progeny group estimates for the archaea:bacteria ratio ( taken from live animals shortly after they left the respiration chambers ) with those of methane emissions measured as g/day and kg/DMI showed similar ranking with correlations of r = 0 . 8 and 0 . 65 , respectively . In addition , similar rank correlations ( r = 0 . 72 and 0 . 67 , respectively ) with methane emissions were found for the archaea:bacteria ratios based on rumen contents samples taken in the abattoir even after a time lag between leaving the respiration chambers and slaughter of up to 15 days ( Fig 2 ) . Most of the deviations in ranks were due to two small progeny groups associated with the highest standard error . Therefore , the general consistency in ranking of sire progeny groups based on microbial and methane emission levels provides evidence that there is an additive genetic influence of the host on the rumen microbial community and their metabolic activity to produce methane . Thus , the archaea:bacteria ratio could be used as selection criteria for reduction of methane emissions . In particular , the rumen samples taken in the abattoir could be used to test a large cohort of sire progeny groups to accurately estimate their breeding values for methane emissions . Using the same experimental data , in a previous study we reported similar correlations between methane emissions per kg DMI and archaea:bacteria ratio in rumen contents samples taken in the abattoir as those taken from live animals [14] , which opens up many opportunities to collect rumen microbial information as a basis for mitigating methane emissions and other traits such as feed conversion efficiency . To investigate the use of microbial gene abundance as an alternative selection criterion to mitigate methane emissions , the extreme animals for methane emissions within breed type and diet were selected and their rumen microbial genes determined using a metagenomic analysis . A previous study showed that the metagenomic data are highly informative , e . g . that higher abundance of the Proteobacteria Succinovibrionaceae was significantly associated with low emitting animals [20] . The high methane emission group had 88% higher emissions than the low group ( S1 Fig ) . In the metagenomic study , 3970 KEGG genes ( S2 Dataset ) were identified in rumen contents samples taken in the abattoir , of which 1570 genes were used based on the relative abundance of more than 0 . 001% and the predictability within the univariate GLM analysis . The relative abundance of microbial genes is expected to be more informative than their absolute abundance because it is e . g . independent from dilution effects and the difference in amplifications of the genes between samples . Based on the relative abundance of these 1570 KEGG genes , we carried out a network analysis and found distinct functional clusters of gene networks ( Fig 3A and S2 Table ) . In particular , cluster 4 and 6 formed a distinct group compared to all other clusters . Interestingly , these two clusters contained most genes known to be associated with methane metabolism ( e . g . KEGG database pathway information ) . In contrast , in some other clusters , microbial genes directly or indirectly related to methane production occurred only sporadically e . g . cluster 13 ( phosphoenolpyruvate carboxylase ) , cluster 21 ( acetate kinase ) and cluster 26 ( tetrahydromethanopterin S-methyltransferase subunit B ) . These clusters comprise only on a small number of genes and are well dispersed and distinct from cluster 4 and 6 . To identify the importance of the different microbial genes to predict methane emissions we performed a partial least squares analysis , firstly on all microbial genes in cluster 4 and 6 and thereafter only on those genes directly stated in the literature or in the KEGG database to be involved in the methane metabolism pathway [20–24] . For further discussion of the microbial genes within metabolic pathways of methane metabolism see our previous study [20] . Using this information , the relative abundances of 20 microbial genes explained ( including the diet effect ) 81 . 7% of the variation in methane emissions ( Fig 4 and S3 Table ) . The identified microbial genes are only in cluster 4 and interact closely with each other ( Fig 3B ) . Excluding the diet effect from the model reduced the explained variation in methane emissions only slightly to 77 . 1% . However , inclusion of the diet effects in the prediction equation is recommended because then the influence of the microbial enzyme genes on methane emissions is estimated whilst taking diet effects into account . Based on a regression analysis of methane emission on the relative abundance of different microbial genes within diet we will later show that the slope of the regression lines are similar for the different diets and only shifted to a different level depending on the diet , as we would expect for a fixed effect . In general , the analysis suggests that methane emissions for the large cohort of animals necessary to obtain accurate genetic breeding values could be predicted accurately from the relative abundance of these 20 KEGG genes . All analysed genomes of methanogenic archaea carry the methyl-coenzyme M reductase alpha subunit ( mcrA ) gene , which catalyses the last step in the methanogenesis [25 , 26] . Comparing the mcrA gene abundance between the low and high emission groups ( 170% increase ) resulted in a highly significant difference ( S2 Fig ) . An association between mcrA gene abundance and methane emissions has been reported in dairy cattle [27] and sheep ( at the transcriptomic level ) [28] , whilst this gene is recommended for monitoring the process performance of anaerobic digesters [29] . Another identified archaeal gene was formylmethanofuran dehydrogenase subunit B ( fmdB ) , which is also involved directly in methanogenesis and catalyses the reversible reduction of CO2 and methanofuran via N-carboxymethanofuran ( carbamate ) to N-formylmethanofuran , the first and second steps in the methanogenesis from CO2 [30 , 31] . For this gene , the high methane emissions group showed 173% greater relative abundance than the low group ( S3 Fig ) . Within each of these genes , similar slopes of the regression lines for the diets provided were obtained which indicates that there were no interactions between microbial gene abundances and diet effects ( S4 and S5 Figs ) . This is consistent with the absence of interactions between sire and diet effects described earlier . There was only a constant effect relating to the different diets , which can be considered as a fixed effect in the genetic evaluation model . The study of [28] found a significant association between methane emissions and KEGG genes using data from metatranscriptomic , but not metagenomic , sequencing . In contrast , we obtained significant associations in data from metagenomic sequencing . This may be partly due to the higher statistical power of the present experiment , with a difference between selected high and low methane emitter groups of 11 . 8 g / kg DMI compared to 4 . 4 g / kg DMI in the study of [28] . The relative KEGG gene abundances need to be determined only once in a metagenomic study and can then be further used to investigate their relationship with other potential traits . In this study we also analysed association with feed conversion ratio and found that 49 genes were important to predict this trait and explained 88 . 3% of the variation ( including breed type and diet effects ) and 85 . 5% ( excluding these effects ) as illustrated in Fig 5 and summarised in S4 Table . Most of those genes were in clusters 2 and 5 , indicating a close network of the genes associated with feed conversion efficiency ( S3 Fig and S2 Table ) . However , these clusters were much more disperse and closer connected to other clusters than the clusters associated with methane . The reason is most likely that the animals were selected on the basis of extreme methane emissions , which provided more power to distinguish microbial gene networks associated with methane emissions than those related to feed conversion efficiency . The microbial genes associated with feed conversion efficiency encoded enzymes involved in host-microbe interactions ( e . g . GDP-L-fucose synthetase ( TSTA3 ) , L-fucose isomerase ( FucI ) ) , the synthesis of amino acids and vitamins ( e . g . anthranilate phosphoribosyltransferase , uroporphyrinogen III methyltransferase ) , degradation of amino acids and proteins ( e . g . aminopeptidase ) , enzymes associated with genetic information processing ( e . g . aspartyl-tRNA synthetase ) and membrane processes ( cobalt/nickel transport system permease protein ) . None of the 49 genes associated with feed conversion ratio were associated with methane emissions . Of particular interest is the abundance of TSTA3 and FucI , which may reveal the importance of host-microbe cross talk in ruminants . These two genes related to feed conversion efficiency are involved in fucose metabolism . Fucose is a component of innate immunity glycoproteins ( mucins ) produced by the intestinal mucosa [32] and in saliva to help maintain the integrity of the mucosal barrier . ‘Fucose sensing’ has been identified as an important cross-talk between the intestinal microbiome and host tissues in studies with mice [33] and rabbits [34] . The degradation of mucins often requires enzymes from a range of bacteria , but some Bacteroides and Ruminococcus spp . are able to degrade mucins completely [35] . In particular , a cluster of bacterial genes involved in fucose uptake ( FucP: L-fucose permease ) and fucose utilisation ( FucI: L-fucose isomerase; FucA: L-fucose aldolase; and FucK: L-fucose kinase ) are controlled by a transcriptional repressor gene ( FucR: L-fucose operon activator ) . FucR also controls bacterial signal production affecting host production of fucosylated glycans ( i . e . mucins ) . This provides a mechanism to match bacterial demands for fucose with supply [35] and so affect the development of the microbiome [34] . Ross et al . [36] used the vector of counts of sequenced reads aligned to each contig in a database to create the metagenomic relationship matrix . We used an alternative approach of aligning the reads to identify the microbial genes first and then using the relative abundance of those genes to predict their influence on the trait of interest . The approach used in this study may have the advantage that the abundance of the microbial genes is highly related to the activity of the microbial ecosystem in the rumen . The mechanisms behind genetic influences of the host on the microbial community composition are expected to be based on many different biological factors . The pH of ruminal digesta is known to have a substantial effect on the microbial community structure and diversity in the rumen ecosystem . Saliva contains bicarbonate and tends to maintain rumen pH between 6 and 7 [37] . Adult cattle produce a substantial amount of saliva with an average of 150 L/day [38] , though with substantial variation that is most likely influenced by host genetics as well as diet [39] . Furthermore , differences in bicarbonate secretion , short chain fatty acid absorption and passage rate of protons out of the rumen , all affect ruminal pH [37] and may be partly genetically determined . Variation in the physical structure and size of the rumen , as well as the intensity of contractions and rate of passage of digesta are all expected to have an influence on the rumen microbial community . Lower methane emissions were found in sheep with small rumens , most likely as a result of reduced digesta retention time [40] . Digesta retention time in ruminants has been shown to be heritable [41] . A recent review illustrates the highly complex interactions between microbiome and host [42] , with the concept of microbiome-gut-brain axis interactions emerging . For example , the host’s central nervous system affects the gut microbiome through satiation signaling peptides affecting nutrient availability , hormones such as cortisol released by the hypothalamus-pituitary-adrenals axis during stress regulating gut contraction and integrity , and the immune system can be activated to alter gut flora—which links to ‘fucose sensing’ as discussed earlier based on our results . We showed that there is an additive genetic effect of the host animal on the amount of methane produced by cattle via effects on the rumen microbial community . As a consequence , the characteristics of the rumen microbial community of each host ( e . g . archaea:bacteria ratio ) can be used as selection criteria to mitigate methane emissions . Even better prediction of methane emissions were obtained by using the relative abundance of microbial genes of each host . The relative abundance of rumen microbial genes can be related to any other trait associated with rumen function . In the present study , we demonstrated this for feed conversion efficiency , but there may also be associations between the relative abundance of microbial genes and animal health , meat quality , animal behaviour , milk composition , fatty acid composition , etc . Deep metagenomic sequencing remains relatively costly; therefore a functional metagenomic microarray to cost-effectively determine the relative abundance of rumen microbial genes would be a useful development . This would provide the opportunity to develop new selection strategies for these difficult to measure traits ( e . g . methane emissions , feed conversion efficiency , animal health , and animal behaviour ) —similar to the adoption of genome-wide selection in dairy breeding [43] . Using a reference population in which the traits of interest are measured , the prediction equations could be developed based on the relative abundance of rumen microbial genes and then used to predict e . g . methane emissions of other animals based only on the microbial composition in rumen contents samples without measuring the traits directly . Alternatively , the relative abundance of rumen microbial genes could be used directly for selection using e . g . relative weights equivalent to their effects on methane emissions . In addition , the recommended approach enables us to understand the biological significance of the specific genes in order to add further confidence for the application of the prediction equations . We may go further and hypothesize that selection of the host genetics based on the microbial gene abundances may be more efficient for improvement of feed conversion efficiency than using measured feed intake per unit of weight gained because the true conversion of feed may be more strongly related to the rumen microbial metabolism than to the measured feed conversion ratio , which are influenced by other factors ( e . g . errors in measurements of feed intake and weight gain ) . A further attraction of this approach is that the relative abundance of microbial genes can be based on rumen samples taken from either live or slaughtered animals so that efficient selection strategies can be developed using potential breeding animals as well as their slaughtered relatives . A potential adverse consequence of a successful selection of animals for reduced methane may be the accumulation of the substrate gas , H2 , which is a product of fermentation by acetate and butyrate producing microorganisms , and that this accumulation would suppress fermentation rates in the rumen [44] . This result was founded mainly upon pure-culture studies in which H2 accumulation by a single H2-producing bacterial species resulted in thermodynamic inhibition of fermentation and growth [44–46] . Co-culture with a methanogen relieved this inhibition . As the main cellulolytic species are H2 producers , it was feared that preventing methane emissions would lead to H2 accumulation which would in turn slow fibre breakdown . The effects of H2 concentration are in fact much more complex [16] . Studies in gnotobiotic lambs lacking methanogens [47] and inhibiting methane emissions in goats and cattle using experimental halogenated compounds [48] suggested that growth was normal and other effects such as on feed intake were minor . The overall outcome of inhibiting methanogenesis seems to be fairly neutral , neither beneficial nor detrimental [49–51] although further research is necessary to clarify this issue . More generally , the results may open up opportunities to use the relative abundance of microbial genes in the gastrointestinal tract of different species to predict their influence on traits e . g . health and behaviour . There is substantial evidence in humans that individuals harbour different microbial communities in their gut , with implications for host health in areas as diverse as obesity , cognitive function and allergy [52–54] . Experiments in rodents indicate a host-driven regulation of the gut microbiota that is genetically encoded [6–8] . Research in humans also indicates a host genetic influence on the gut microbiota using the abundance of microbial taxa , in particular the family of Christensenellaceae , which formed a co-occurrence network with other bacteria and with methanogenic archaea and impacts metabolism [9] . Here , we show that the use of the abundance of the microbial genes is much more closely associated with metabolism than the abundance of the microbial community ( for which the archaea:bacteria ratio was the best predictor [14] ) and therefore a much better criterion to predict the host genetic influence on those traits . In addition , specific microbial genes , their networks and pathways can be used to better understand the association between host genetics and microbial activity related to the trait of interest . This could provide opportunities for personalized medicine considering the genetic link between host and microbiome and its activity , e . g . for treatment of inflammatory bowel disease in humans , which showed strong host-microbe interactions [55] .
This study was conducted at the Beef and Sheep Research Centre of Scotland’s Rural College ( 6 miles south of Edinburgh , UK ) . The experiment was approved by the Animal Experiment Committee of SRUC and was conducted in accordance with the requirements of the UK Animals ( Scientific Procedures ) Act 1986 . The data were obtained from a 2 × 2 factorial design experiment of breed types and diets using 72 steers from a two-breed rotational cross between AA and LIM . Equal numbers of experimental animals were sired by purebred AA and LIM . Depending on the purebred sire used , the expected additive genetic contributions were 2/3 and 1/3 from each of the two breeds . Progeny groups were from 5 AA and 4 LIM sires . The average number ( range ) of progenies per sire were 7 ( 2 to 12 ) and 9 ( 6 to 14 ) for AA and LIM , respectively . The animals were offered two complete diets ad libitum consisting ( g/kg DM ) of either 480 forage to 520 concentrate or 75 forage to 925 concentrate; these are subsequently described as forage and concentrate diets , respectively . The detailed diet composition has been published by [56] . The growing-finishing beef cattle were bred , raised and performance tested at the Beef and Sheep Research Centre of SRUC . Before artificial insemination ( AI ) , the dams of the experimental animals were housed outdoors at grass . All dams were synchronised for AI using Progesterone ( Eazi-Breed CIDR cattle insert , Zoetis UK Ltd . , UK ) , Estrumate , PMSG and Prostaglandin , ( Intervet UK Ltd . , UK ) . AI took place from June through to August 2009 . All cows were transferred indoors at the beginning of November 2009 where they remained in group-pens until calving which took place from March through to May 2010 . At late spring time , cows with the experimental calves at foot were transferred outdoors and kept on grass until mid-November . Cows and calves were transferred to indoor group-pens and after few days calves were weaned . Weaned calves were transferred to group-pens at the facility until the experiment commenced . All dams were routinely vaccinated for leptospirosis ( Leptavoid H , Intervet UK Ltd . , UK ) , bovine viral diarrhoea ( BVD ) ( Bovilis , Intervet UK Ltd . , UK ) and rotavirus ( Rotavec Corona , Intervet UK Ltd . , UK ) and treated for nematodes , lice and mites ( Dectomax , Zoetis UK Ltd . , UK ) . All calves were routinely vaccinated for infectious bovine rhinotracheitis , BVD , Bovine Parainfluenza 3 virus and Bovine Respiratory Syncytial Virus ( Rispoval 4 , Zoetis UK Ltd , UK ) and treated for nematodes , lice and mites ( Dectomax , Zoetis UK Ltd . , UK ) . Due to EU legislation the application of hormones enhancing growth is prohibited and antibiotics and drugs were only administered in exceptional cases and those animals were excluded from the trial . All mothers with calves were offered the same diet each day . No twin calves were used in the trial so that each experimental animal had its own specific maternal effect influencing the microbiota . Prior to the start of the trial , animals were adapted to the experimental diets over a 5 week period . During this period , the animals acclimatised to the group-housed environment and were trained to use the electronic feeders ( HOKO , Insentec , Marknesse , The Netherlands ) . During the performance test period of 56 days , the animals were group-housed in two pens of 36 each , balanced for breed type . Within each pen , half of the animals had access to one of the two diets , again balanced for breed type . In addition , treatments were balanced for age at start of test and body weight . All animals were bedded on wood fines to ensure that there was no consumption of bedding . Using electronic feeders , daily feed intake was recorded and daily dry matter intake ( DMI , kg/day ) calculated using analysed dry matter content of duplicated samples of each diet component taken twice weekly . Body weight of each animal was measured weekly and average daily gain ( ADG ) was obtained by fitting a linear regression of body weight on test date . Feed conversion ratio ( FCR ) was calculated as average DMI per day divided by ADG . One week before entering the respiration chambers , the animals were housed individually in training pens , identical in size and shape to the pens inside the chambers , to allow them to adapt to being housed individually . Methane emissions were individually measured for 48h within 6 respiration chambers . The animals were allocated to the respiration chambers in a randomised block design with 3 replicates . Data from 4 animals could not be considered due to health issues and an air leak in one of the respiration chamber . The method of measurement in the respiration chambers is described in detail by [56] . Rumen samples were obtained from the animals when they were alive ( n = 50 ) and after slaughter ( n = 68 ) . Rumen samples were taken from live animals within 2 hours of leaving the respiration chambers . Approximately 50 mL rumen contents were taken by inserting a stomach tube ( 16 × 2700 mm Equivet Stomach Tube , JørgenKruuse A/S , Langeskov , Denmark ) nasally and aspirating manually . Between 3 to 17 days after leaving the respiration chamber the animals were slaughtered in a commercial abattoir where two rumen fluid samples ( approximately 50 mL ) were taken immediately after the rumen was opened to be drained . The slaughter process results in well mixed samples of rumen contents . DNA was extracted from the rumen samples and subjected to qPCR for the 16S rRNA genes as described in [14] to determine the abundance of archaea and bacteria and their ratio . Eight extreme animals ( 4 high and 4 low ) for methane emissions , balanced for breed type and diet , were used in a metagenomic study , in which deep sequencing was applied . Illumina TruSeq libraries were prepared from genomic DNA and sequenced on an Illumina HiSeq 2500 instrument by Edinburgh Genomics . Paired-end reads ( 2 × 100 bp ) were generated , resulting in between 8 . 6 and 14 . 5 GB per sample ( between 43 . 4 and 72 . 7 million paired reads ) . The genomic reads were aligned to the KEGG genes database . Parameters were adjusted such that all hits were reported that were equal in quality to the best hit for each genomic read . The read and best hits have to be more than 90% identical and have to be belonging to a single KEGG orthologue group to be kept in the data . If the best hits are spread over more than one KEGG orthologue group , the read were disregarded . Read counts for KEGG orthologues were summed and normalised to the total number of hits . For the analysis of methane emissions and qPCR determined microbial KINGDOM ( Archaea and Bacteria ) , least squares means ( LSM ) were estimated using a general linear model analysis ( GLM , Version 9 . 1 for Windows , SAS Institute Inc . , Cary , NC , USA ) , including the effects of breed type ( or sire within breed type ) , diet , respiration chamber and randomised block . Using a sire model with each progeny originating from a different mother , maternal effects are expected to be included in the residual effects and therefore did not bias the estimated LSM of sires . In a network analysis using BioLayout Express3D [57] we identified the distinct functional clusters of microbial genes . These networks consist of nodes representing microbial genes and the connecting edges determining the functional linkages between these genes . Preliminary GLM analysis was carried out to estimate the influence of the KEGG genes on methane emissions and feed conversion efficiency by fitting the significant effects ( diet for methane emissions , diet and breed type for feed conversion efficiency ) as well as the relative abundance of one KEGG gene each time . The residuals of each model were normal distributed . We used partial least squares analysis ( PLS , Version 9 . 1 for Windows , SAS Institute Inc . , Cary , NC , USA ) to identify the most important of genes association with methane and feed conversion efficiency . The PLS analysis accounts for multiple testing and the correlation between microbial genes . In addition to microbial genes , the model included the diet effect ( for methane emissions ) and additionally the breed type effect ( for feed conversion ratio ) . The model selection were based on the variable importance for projection ( VIP ) criterion [58] , whereby microbial genes with a VIP < 0 . 8 contribute little to the prediction . In the PLS analysis to predict methane emissions , microbial genes of the two gene network clusters ( 4 and 6 ) which included most of the genes associated with methane metabolism were used , whereas for feed conversion ratio we used all KEGG genes that had a P-value <0 . 1 in the GLM analysis . Different strategies for the two analysed traits were applied because the animals were selected based on maximum differences in magnitude of methane emissions , so that the network analysis showed most discrimination for methane emissions . | Methane is a highly potent greenhouse gas and ruminants are the major source of methane emissions from anthropogenic activities . Here we show in an experiment with cattle that genetic selection of low-emitting animals is a viable option based on a newly developed selection criterion . The experimental data provided a comprehensive insight into the host additive genetic influence on the microbiome , the impact of nutrition on genetics and the microbiome , and the effect of metagenomic microbial genes on the analysed traits . We developed a selection criterion to change those traits by evaluation of hosts based on the relative abundance of microbial genes . This criterion is shown to be highly informative and it is therefore suggested to be used in studies analysing different traits and species . This study provides a proof of principle that there is an additive genetic influence of the host on its microbiome and that selection for the desired host can be based on the abundance of a suite of genes in the ruminal metagenome associated with the trait . The use of this criterion will allow genetic analysis on a large number of hosts , previously a significant barrier to determination of host genetic effects on such traits . | [
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"met... | 2016 | Bovine Host Genetic Variation Influences Rumen Microbial Methane Production with Best Selection Criterion for Low Methane Emitting and Efficiently Feed Converting Hosts Based on Metagenomic Gene Abundance |
Bacillus anthracis , the etiological agent of anthrax , is a spore-forming Gram-positive bacterium . Infection with this pathogen results in multisystem dysfunction and death . The pathogenicity of B . anthracis is due to the production of virulence factors , including edema toxin ( ET ) . Recently , we established the protective role of type-IIA secreted phospholipase A2 ( sPLA2-IIA ) against B . anthracis . A component of innate immunity produced by alveolar macrophages ( AMs ) , sPLA2-IIA is found in human and animal bronchoalveolar lavages at sufficient levels to kill B . anthracis . However , pulmonary anthrax is almost always fatal , suggesting the potential impairment of sPLA2-IIA synthesis and/or action by B . anthracis factors . We investigated the effect of purified ET and ET-deficient B . anthracis strains on sPLA2-IIA expression in primary guinea pig AMs . We report that ET inhibits sPLA2-IIA expression in AMs at the transcriptional level via a cAMP/protein kinase A–dependent process . Moreover , we show that live B . anthracis strains expressing functional ET inhibit sPLA2-IIA expression , whereas ET-deficient strains induced this expression . This stimulatory effect , mediated partly by the cell wall peptidoglycan , can be counterbalanced by ET . We conclude that B . anthracis down-regulates sPLA2-IIA expression in AMs through a process involving ET . Our study , therefore , describes a new molecular mechanism implemented by B . anthracis to escape innate host defense . These pioneering data will provide new molecular targets for future intervention against this deathly pathogen .
Bacillus anthracis , the etiological agent of anthrax , is a spore-forming Gram-positive bacterium [1] . Even though anthrax is primarily a disease of herbivores , all mammals are susceptible to B . anthracis infection . Human infection can occur via cutaneous , gastrointestinal , or respiratory routes , either accidentally or intentionally as a potential consequence of a bioweapon or a terrorism threat . Whatever the infection route used by this bacterium , spores are taken up by macrophages and/or dendritic cells , and subsequently migrate and germinate in the draining lymph nodes [2 , 3] . The infection then spreads through the whole organism , leading to respiratory failure and multiple hemorrhagic lesions . Despite appropriate therapy , all these forms of infection may progress to fatal systemic anthrax , which is characterized by shock-like symptoms , sepsis , and respiratory failure [4] . Pulmonary infection by B . anthracis has been shown to be the most life-threatening form of the disease , causing a near 100% mortality . Innate immune response is the first line of host defense against invading pathogens . Type-IIA secreted phospholipase A2 ( sPLA2-IIA ) [5 , 6] is one of the major components involved in innate host defense against bacteria [7 , 8] . This enzyme belongs to a family of enzymes catalyzing the hydrolysis of phospholipids at the sn-2 position , leading to the generation of lysophospholipids and free fatty acids [5 , 6] . sPLA2-IIA is produced by several cell types , including guinea pig alveolar macrophages ( AMs ) [9] , which play a central role in innate immunity and are the first line of defense against inhaled pathogens . These cells are the major pulmonary source of sPLA2-IIA in experimental models of acute lung injury [9 , 10] . Besides its ability to hydrolyze pulmonary surfactant phospholipids [11] and release arachidonic acid [12] , sPLA2-IIA exhibits potent bactericidal activity , especially against Gram-positive bacteria [13–15] . The bactericidal activity is exhibited through a process involving rapid hydrolysis of bacterial membrane phospholipids [16 , 17] . This activity is the most significant biological property of sPLA2-IIA , being observed at much lower concentrations of this enzyme than for other properties . sPLA2-IIA is constitutively present in guinea pig airways at concentrations [11] above those required for killing B . anthracis [17] . We have also shown that isolated guinea pig AMs constitutively release enough sPLA2-IIA to kill B . anthracis [17] . sPLA2-IIA is highly bactericidal for B . anthracis , either in vitro or in vivo [17 , 18] . This anthracidal effect occurs both against germinated spores and bacilli . A sPLA2-IIA–dependent anthracidal activity was found in human bronchoalveolar lavage fluids ( BALF ) of patients with acute respiratory distress syndrome [17] . In a more recent study , we showed that transgenic mice expressing sPLA2-IIA are resistant to experimental infection with B . anthracis , in contrast to sPLA2-IIA−/− mice [18] . Interestingly , treating sPLA2-IIA−/− mice with recombinant sPLA2-IIA protected them against mortality caused by B . anthracis infection . The exact mechanisms by which B . anthracis induces anthrax are not fully understood; however , it is clearly established that this bacterium spreads rapidly in the host at the early stages of infection without a detectable immune response [2 , 19 , 20] . This allows bacteria to replicate to very high numbers in the blood , ultimately leading to death of the host [4] . This is due to the ability of B . anthracis to subvert the host immune response [19 , 20] by the action of B . anthracis toxins . Indeed , B . anthracis secretes a binary A-B toxin composed of a single B transporter called protective antigen ( PA ) and two alternative A components , lethal factor ( LF ) or edema factor ( EF ) [1 , 19] . LF and EF act in pairs , with PA leading to lethal toxin ( LT = PA + LF ) and edema toxin ( ET = PA + EF ) , respectively . PA serves as a transporter delivering LF and EF inside host cell cytosol where they act on specific molecular targets . EF is a calmodulin-activated adenyl-cyclase ( AC ) leading to a sustained increase in cAMP levels [21] . However , little is known about the genes targeted by ET and their implication in the pathogenesis of anthrax . Here , we report that ET inhibits sPLA2-IIA expression in AMs and demonstrate that this inhibition occurs through the sequential accumulation of cAMP and stimulation of protein kinase A ( PKA ) activity . In the absence of ET , B . anthracis was able to induce sPLA2-IIA expression via a process at least partly involving the cell wall component , peptidoglycan ( PG ) . ET also stopped this induction . This study is important because AMs are a major source of sPLA2-IIA , a critical component in host defense against B . anthracis . Inhibition of sPLA2-IIA expression in AMs by ET may represent an effective strategy for subverting pulmonary host immune response by B . anthracis .
AMs were preincubated with ET ( PA + EF ) 1 h before adding lipopolysaccharide ( LPS ) to analyze the effect of ET on sPLA2-IIA expression . ET stopped both basal and LPS-induced sPLA2-IIA secretion in a concentration-dependent manner ( Figure 1A ) . No effect was observed when EF or PA was added separately to AMs ( unpublished data ) . Inhibition of sPLA2-IIA secretion by ET was also observed when AMs were stimulated by tumor necrosis factor-α ( TNFα ) instead of LPS ( Figure 1B ) . We showed that LPS induced a marked increase in sPLA2-IIA mRNA levels , and that the increase was subsequently abolished by the addition of ET ( Figure 1C ) . We next investigated the effect of ET on two other inflammatory mediators produced by AMs , interleukin 8 ( IL-8 ) and prostaglandin E2 ( PGE2 ) . ET failed to interfere with LPS-induced IL-8 ( Figure 1D ) and PGE2 ( Figure 1E ) secretion . We also examined the effect of ET on nuclear factor κ B ( NF-κB ) translocation . ET had no effect on LPS-induced NF-κB translocation ( Figure 1F ) , as assessed by electrophoretic mobility shift assay ( EMSA ) . These results together indicated that ET inhibits sPLA2-IIA expression in AMs through a different signaling pathway from those inducing IL-8 and PGE2 secretion or NF-κB translocation . Because ET exhibits a calmodulin-dependant AC , we examined its effect on intracellular cAMP levels in our cell system . A 30-min incubation of AMs with ET led to an increase in cAMP levels , whereas LPS had no effect ( Figure 2A ) . The induction of cAMP accumulation by ET was transient; cAMP levels returned to near basal levels after AMs were incubated with ET for 24 h ( Figure 2A , insert ) . In agreement , a cAMP-elevating agent , forskolin , significantly inhibited LPS-induced sPLA2-IIA secretion ( Figure 2B ) . AC inhibitors ( adefovir and ddA ) reversed ET inhibition of LPS-induced sPLA2-IIA secretion ( Figure 2C ) . cAMP is known to activate protein kinases , such as PKA; thus , this kinase may be involved in the inhibition of sPLA2-IIA expression by ET . ET induced a marked and transient activation of PKA in AMs ( Figure 2D ) . Indeed , this activation was observed 2 h after adding ET , and was undetectable 20 h later ( unpublished data ) . To mimic the ET-induced PKA activation , we examined the effect of 6-Bnz-AMP , a specific agonist for PKA . 6-Bnz-AMP inhibited both basal and LPS-induced sPLA2-IIA expression ( Figure 2E ) . By contrast , O-Me-cAMP , a specific agonist for the exchange protein directly activated by cAMP ( Epac ) [22] , had no effect on sPLA2-IIA expression . Taken together , our results suggested that ET inhibits LPS-induced sPLA2-IIA expression in AMs via a cAMP/PKA-dependent process . Because PKA is known to phosphorylate cAMP-responsive element binding protein ( CREB ) , we examined whether this transcription factor mediates the inhibition of sPLA2-IIA expression by ET . ET induced a time-dependent CREB phosphorylation ( Figure 3A ) , but had no effect on the total level of CREB ( Figure 3B ) in AMs , as assessed by western blot analysis . We also investigated the effects of ET on CREB activation using Chinese hamster ovary ( CHO ) cells transfected with a CREB ( [CRE]4-Luc ) reporter plasmid construct . ET significantly increased the CREB luciferase activity ( Figure 3C ) . However , LPS had no effect on this activity and failed to interfere with ET-induced CREB activation . This activation was prevented by cotransfecting cells with a dominant-negative CREB construct , pGR-CREBM1 , as opposed to pGR ( Figure 3C ) . Transfection of CHO cells with a sPLA2-IIA promoter luciferase construct demonstrated that ET inhibits LPS-induced sPLA2-IIA gene transcription activity ( Figure 3D ) . Similar results were observed when LPS was replaced by IL-1β as the inducer of sPLA2-IIA expression ( unpublished data ) . However , cotransfection of a dominant-negative CREB construct failed to reverse the inhibition of sPLA2-IIA gene transcription activity ( Figure 3D ) , indicating that CREB does not mediate ET inhibition of sPLA2-IIA expression . Using a more pathophysiological approach , we examined whether infecting AMs with B . anthracis bacilli modulates sPLA2-IIA expression and whether ET participates in this modulation . AMs were incubated in an antibiotic-free culture medium for 3 h with either RP10 or RPLC2 bacilli; RP10 produces functional and RPLC2 produces inactive ET . After removing bacilli not having undergone phagocytosis , AMs were stimulated overnight with LPS in culture medium supplemented with antibiotics . The RP10 strain inhibited LPS-stimulated sPLA2-IIA secretion , whereas the RPLC2 strain had no effect ( Figure 4A ) . Inhibition by the RP10 strain occurred in a multiplicity of infection ( MOI ) -dependent manner and was selective for sPLA2-IIA . Indeed , this strain failed to inhibit LPS-induced PGE2 ( Figure 4B ) and IL-8 ( Figure 4C ) production . These findings demonstrated that in LPS-stimulated AMs , B . anthracis strains producing functionally active ET down-regulated sPLA2-IIA expression . We next examined the effect of B . anthracis on sPLA2-IIA expression in unstimulated AMs . RPLC2 bacilli induced sPLA2-IIA expression ( Figure 4D ) , and PGE2 ( Figure 4E ) and IL-8 ( Figure 4F ) secretion . The RP10 bacilli strain induced PGE2 and IL-8 secretion , but had no effect on sPLA2-IIA expression ( Figure 4D–4F ) . Interestingly , RP10 and RPLC2 spores induced sPLA2-IIA expression , even after 3 h of infection ( Figure 4G ) . These findings indicate that in the sporular state , RP10 and RPCL2 strains induce sPLA2-IIA expression . However , in the bacilli state , the RPCL2 strain ( devoid of ET ) induced sPLA2-IIA expression , whereas the RP10 strain ( producing ET ) exerted an inhibitory effect . AMs were incubated with cytochalasin D ( Cyto D ) before adding the RP10 strain , to examine the impact of B . anthracis phagocytosis on sPLA2-IIA expression . Cyto D reduced the inhibitory effect of bacilli on sPLA2-IIA expression , but failed to interfere with the stimulatory effect of spores ( Figure 4H and 4I ) . This suggests that both extracellular and intracellular bacilli are involved in inhibiting sPLA2-IIA expression , whereas extracellular spores seem to play a more important role in inducing this enzyme . As RPLC2 strain induces sPLA2-IIA expression in AMs , we searched for which B . anthracis component was involved in this induction . PG purified from B . anthracis stimulated sPLA2-IIA expression ( Figure 5A ) . PG , as well as LPS , induced NF-κB translocation , as assessed by EMSA ( Figure 5B ) . PG-induced sPLA2-IIA expression was abolished if AMs were pretreated with the NF-κB inhibitor CAPE ( Figure 5C ) . Interestingly , pretreating AMs with ET stopped sPLA2-IIA expression induced by B . anthracis PG ( Figure 5D ) . PG-induced sPLA2-IIA expression was also inhibited by the cAMP-elevating agent , forskolin , and the PKA agonist , 6-Bnz-cAMP ( Figure 5E ) .
In this study , we investigated the effect of B . anthracis , the causative agent of anthrax [1–4] , on the expression of sPLA2-IIA , an important component of host defense against invading bacteria . This enzyme is bactericidal in vitro or in vivo , and is especially active against Gram-positive bacteria , including B . anthracis [7 , 8 , 17 , 18] . sPLA2-IIA is produced by AMs and found in human and animal BALF at sufficient levels to kill B . anthracis [17]; these findings are consistent with the enzyme having a role in host defense against pulmonary anthrax . However , despite the ability of lungs to produce sPLA2-IIA , the pulmonary form of anthrax has been shown to be fatal , causing almost 100% mortality [1–4] . This led us to postulate that B . anthracis may inhibit sPLA2-IIA synthesis by AMs , subvert host pulmonary defense , and allow this pathogen to spread extensively in the host . We show here that ET inhibits sPLA2-IIA secretion by AMs , interfering with its expression at the transcriptional level . Inhibiting sPLA2-IIA secretion may decrease the capacity of AMs to kill B . anthracis bacilli and germinated spores . Indeed , AM activity against B . anthracis has been shown to be at least partly associated with sPLA2-IIA , as it was reduced by an sPLA2-IIA inhibitor [17] . This inhibition was observed whatever the stimuli used ( LPS , TNFα , IL1β , or PG ) . We analyzed the signaling pathways by which ET down-regulates sPLA2-IIA expression; our analysis suggested that this inhibition occurs via a process involving cAMP accumulation . Our studies showed that this accumulation was transient , reaching near basal values within 24 h . This contrasts with previous studies reporting that cAMP accumulation was elevated for 48 h or more after ET incubation with NIH/3T3 fibroblasts and RAW 267 macrophages [23] . Thus , it is likely that the duration and amplitude of cAMP accumulation induced by ET may vary with the cell type considered . Because cAMP activates several kinases , we examined whether PKA and Epac , two cAMP-dependent kinases , were involved in this process . PKA but not Epac , appeared to mediate ET-induced inhibition of sPLA2-IIA expression . Our results also suggested that elevating intracellular cAMP concentrations ( either by ET or 6-Bnz-cAMP ) interfered with basal and LPS-induced sPLA2-IIA expression by different mechanisms . The inhibition of induced expression appeared to occur through a process that interferes , at least partly , with the sPLA2-IIA promoter , whereas the inhibition of basal expression appeared to be independent of the sPLA2-IIA transcription . PKA phosphorylates proteins , such as CREB , that are involved in regulating gene expression in mammalian cells [24] . This factor can modulate , either positively or negatively , gene expression in several cell-activation processes [24 , 25] . Although ET induces CREB activation , this transcription factor does not mediate the inhibition of sPLA2-IIA expression by ET . However , it is likely that CREB activation by ET could modulate the expression of other genes involved in host defense , which remain to be identified . We next investigated whether ET inhibits sPLA2-IIA expression by interfering with the activation of NF-κB , known to be critical in inducing sPLA2-IIA expression [26] . ET had no effect on stimulated NF-κB translocation in AMs . Also , ET had no effect on the secretion of IL-8 , whose expression is controlled by NF-κB . However , we cannot exclude that ET may interfere with stimulating cofactors involved in NF-κB coactivation at the sPLA2-IIA promoter level . Studies in progress in our laboratory showed that trichostatin A , an inhibitor of histone deacetylase ( HDAC ) activity [27] , significantly decreased sPLA2-IIA expression in LPS-stimulated AMs . Because HDAC activity is altered by a PKA-dependent phosphorylation [28] , it is likely that HDAC may play a role in the inhibition of sPLA2-IIA expression by ET . Further studies are required to verify this hypothesis . In a more physiological approach , we investigated whether ET modulates sPLA2-IIA expression during infection of AMs with live B . anthracis . This bacterium inhibits LPS-induced sPLA2-IIA expression via ET . Indeed , RPLC2 , the bacterial mutant with inactive ET , had no effect on this induction , whereas the RP10 strain expressing functional ET abolished LPS-induced sPLA2-IIA expression . Incubating RPLC2 bacilli , which produce inactive ET , with unstimulated AMs induced sPLA2-IIA expression . This suggested the existence of bacterial component ( s ) that are able to induce sPLA2-IIA synthesis , and that their actions are masked by the ET inhibitory effect produced by RP10 bacilli . Our findings showed that the cell wall PG purified from B . anthracis induces sPLA2-IIA expression via a process involving NF-κB activation . It is still not clear whether PG-induced sPLA2-IIA expression occurs via an activation of TLR2 or Nod , two PG recognition proteins [29] . A recent study has reported that Nod may be involved in cell activation by B . anthracis spores [30] . We cannot exclude , however , that other bacterial components present in the cell wall or released by B . anthracis may also be involved in inducing sPLA2-IIA expression . Interestingly , ET suppressed PG-induced sPLA2-IIA expression , confirming the relevance of our studies , and showing that ET also suppresses the sPLA2-IIA expression induced by B . anthracis itself . Therefore , during host infection , B . anthracis may modulate sPLA2-IIA expression , either positively or negatively , depending on the status of ET synthesis in the bacterium ( Figure 6 ) . Mammalian pulmonary infection with B . anthracis is initiated by the inhalation of spores , the cell walls of which contain PG . Infecting spores therefore induce sPLA2-IIA expression in the earlier stages of infection . This is consistent with previous studies , which have reported that B . anthracis spores stimulate cytokine production in various cells [31–33] . The susceptibility of inhaled spores to the bactericidal activity of sPLA2-IIA present in airways is dependent on their germination velocity; this is because sPLA2-IIA only kills germinated spores and bacilli [17] . Previous in vivo studies [3] have shown that germination occurred rapidly upon entry into the lung ( 35–60 min ) , and that the spores were mostly found inside the AM . This was followed by a rapid onset ( <3 h ) of expression of genes encoding virulence factors , such as LF , PA , and EF [34] . Elimination of inhaled B . anthracis by the host would thus depend on the balance between sPLA2-IIA levels in the airways and bacterial load . If the balance favors sPLA2-IIA , germinated spores and bacilli would be killed quickly . Our previous studies have shown that the constitutive ( basal ) levels of sPLA2-IIA present in guinea pig airways [11] are greater than those required for killing B . anthracis [17] , and that these levels were greater in inflamed lungs [11] . sPLA2-IIA was also found in BALF of patients with lung inflammatory diseases ( ARDS ) at sufficient levels to exert this anthracidal effect . However , it is still unknown whether BALF of healthy subjects contains functionally significant amounts of sPLA2-IIA , and whether this enzyme would be available soon after the invasion of inhaled spores . It is also possible that some germinated bacteria may escape killing by sPLA2-IIA . Therefore , bacilli derived from geminated spores rapidly produce ET , which may in turn inhibit sPLA2-IIA expression and shift the balance in favor of bacteria . In vivo studies , in which guinea pigs are infected with inhaled B . anthracis spores , are required to investigate this possibility . Our previous studies clearly established a role for sPLA2-IIA in host defense against B . anthracis; however , it is still unknown whether this enzyme participates in killing germinated spores previously ingested by AMs . sPLA2-IIA is involved in the killing of Staphylococcus aureus ingested by neutrophils [35] . Indeed , added sPLA2-IIA binds to S . aureus before its internalization by neutrophils and participates in the cytotoxicity of these cells towards ingested bacteria . Therefore , we suggest that sPLA2-IIA is involved in killing B . anthracis ingested by AMs . In conclusion , we report here that B . anthracis represses the expression of sPLA2-IIA , a major component in innate host response with anthracidal properties , in AMs . This inhibition occurs through a process involving ET-mediated cAMP accumulation and PKA activation , and represents a novel mechanism for evading the innate immune response of the host . Other bacteria ( for example Bordetella pertussis or Yersinia pestis ) [19] are known to produce toxins with AC activity; thus , we can speculate that the inhibition of sPLA2-IIA expression in AMs may be a more general process occurring during bacterial infection . Therefore , using pharmacological approaches to inhibit ACs of invading bacteria may represent a therapeutic strategy for treating not only pulmonary anthrax , but also other bacterial pulmonary infections .
Male Hartley guinea pigs were purchased from Charles River Laboratories . RPMI 1640 cell culture medium was purchased from Invitrogen , and fetal calf serum ( FCS ) from Hyclone . Caffeic acid phenethyl ester ( CAPE ) , and cytochalasin D ( Cyto D ) were purchased from Biomol . LPS from Pseudomonas aeruginosa and 2′ , 5′-dideoxyadenosine 3′-triphosphate ( ddA ) were purchased from Sigma Aldrich . N6-Benzoyladenosine-3′ , 5′-cyclic monophosphate ( 6-Bnz-cAMP ) and 8- ( p-Chlorophenylthio ) -2′-O-methyl-adenosine-3′ , 5′-cyclic monophosphate ( O-Me-cAMP ) were purchased from Biolog . CREB and phospho-CREB antibodies were obtained from Cell Signaling Technology . EF , PA , and PG from B . anthracis were produced and purified as described previously [36] . BIS-POM-PMEA ( adefovir ) was provided by Dr . W . J . Tang ( University of Chicago , Chicago , Illinois ) . The following isogenic B . anthracis strains were studied: ( 1 ) the single mutant RP10 Δlef producing only PA-EF and ( 2 ) the double-mutant RPLC2 on lef and cya genes producing PA-LF and PA-EF , respectively , without enzymatic functions [37] . Guinea pig bronchoalveolar lavages ( BAL ) were performed with PBS , and AMs were isolated , as previously described [9] . AMs were then adjusted at 2 . 106 cells/ml in RPMI 1640 with 3% FCS and 1% of antibiotic , and were pretreated with ET ( PA + EF ) , 6-Bnz-cAMP , O-Me-cAMP , or TSA 1 h before incubation with LPS , PG , or TNFα . In certain experiments , AMs were pretreated 5 h with adefovir before incubation with ET . In other experiments , ET was preincubated with ddA for 1 h before being added to AMs . These reagents were used at the concentrations indicated in the figures . Subsequent analyzes were performed as detailed below . Cells were infected with B . anthracis bacilli or spores for 3 h at various MOI values . Cells were then washed twice and incubated overnight in RPMI 1640 supplemented with 3% FCS and 2 . 5 μg/ml gentamicin in the presence or absence of LPS . In certain experiments , AMs were pretreated with Cyto D for 30 min before adding bacteria . At the end of the incubation , media were harvested and centrifuged . The resulting supernatants were collected and stored at −20 °C for subsequent analyzes . Cells were grown on a cell culture plate and total RNA was extracted using an RNeasy kit ( Qiagen ) . DNase treatment was performed using 2 μg of extracted RNA , 1 μl of DNase I ( Amersham Biosciences ) , and 0 . 5 μl of RNasin ( Promega ) in a total volume of 20 μl in the manufacturer's buffer . cDNA were obtained by incubating RNA with 1 mM dNTP ( Eurobio ) , 1 . 5 μl of hexamers as primers , 20 units of RNasin ( Promega ) , and 300 units of Moloney murine leukemia virus reverse transcriptase RNase H minus ( Promega ) in a total volume of 50 μl of the manufacturer's buffer; the incubation was for 1 h at 42 °C and was followed by a 10-min incubation at 70 °C . PCR was performed using specific primers ( Proligo ) for guinea pig sPLA2-IIA ( sense , 5′-ACA AGT TAT GGC GCC TAT GG-3′; antisense , 5′-GCC CAG TGT AGC TGT GAA GC-3′ ) . As an internal control , we used primers for the detection of guinea pig β-actin ( sense , 5′-AAA CTG GAA CGG TGA AGG TG-3′; antisense , 5′-TCA AGT TGG GGG ACA AAA AG-3′ ) . Amplifications were performed in a Peltier thermal cycler ( MJ Research ) using Q-BioTaq polymerase ( Qbiogene ) . For the detection of sPLA2-IIA , PCR thermo-cycling included 30 cycles of denaturation at 95 °C for 45 s and annealing at 60 °C for 45 s . Nuclear proteins were extracted from 2 . 106 AMs , as previously described [38] . The NF-κB double-stranded oligonucleotides ( Santa Cruz Biotechnology ) corresponded to an NF-κB binding site consensus sequence of 5′-AGT TGA GGG GAC TTTT CCC AGG C-3′ . The overhanging ends were γ-32P–labeled with T4 polynucleotide kinase ( Biolabs ) . Protein concentrations were determined using a Nanodrop spectrophotometer ( Nyxor Biotech ) . Binding reactions were performed in a total volume of 20 μl for 20 min at room temperature , by adding 5 μg of nuclear extract , 10 μl of 2× binding buffer ( 40 mM HEPES [pH 7] , 140 mM KCl , 4 mM DTT , 0 . 02% Nonidet P-40 , 8% Ficoll , 200 μg/ml BSA , 1 μg of poly ( dI:dC ) ) , and 1 μl of labeled probe . The reaction mixtures were separated on a 5% polyacrylamide gel in 0 . 5% Tris/borate/EDTA buffer at 150 V for 2 h . Gels were dried and exposed for 2 to 12 h . We have previously shown , using supershift analysis , that antibodies directed against NF-κB's p50 and p65 subunits displaced the NF-κB band in LPS-stimulated AMs; this confirmed that the observed complexes belong to the NF-κB family [39] . Proteins from AMs were extracted in lysis buffer ( 10 mM Tris-HCl , 10 mM NaCl , 3 mM EDTA , 100 μM leupeptin , 100 mM aprotinin , 1 mM soybean trypsin inhibitor , 5 mM NEM , 1 mM PMSF , 5 mM benzamidine , and 1% Triton W-100 [pH 7 . 4] ) and were run on a gel under reducing conditions . Semidry transferred proteins were applied to polyvinylidene difluoride membranes . Nonspecific binding sites were blocked overnight with 5% BSA in 20 mM Tris-HCl ( pH 7 . 6 ) , 140 mM NaCl , and 0 . 1% Tween 20 . Blots were probed for 1 h with rabbit polyclonal anti-human phospho-CREB ( ser 133 ) or CREB antibodies ( 1/2 , 000 dilution ) . These antibodies also recognize activating transcription factor-1 ( ATF-1 ) , which belongs to the CREB family . After washing , the immunoreactive bands were visualized using a peroxidase-conjugated goat anti-rabbit immunoglobulin G ( IgG ) ( 1/10 , 000 dilution ) antibody and an ECL Plus Western Blotting Detecting System ( Amersham Biosciences ) . Quantifications were carried out using the Image J software and were expressed as arbitrary units . cAMP concentrations were measured in disrupted cells using a specific enzyme immunoassay kit purchased from Cayman Chemical Co; the concentrations were measured after incubating AMs with LPS and/or ET for 30 min or 24 h . Protein concentrations were measured in cell lysates using a kit from Pearce , and then the concentrations of cAMP were expressed in picomoles per milligram of protein . IL-8 and PGE2 concentrations were measured in culture medium after 24 h incubation of AMs with LPS and/or ET; these concentrations were measured using a specific PGE2 enzyme immunoassay ( Cayman Chemical Co ) and human IL-8 Kit DuoSet ELISA ( R&D Systems ) , which cross-reacts with guinea pig IL-8 [40] . AMs were incubated with LPS and/or ET for 2 h . The PKA activity was then measured in disrupted cells using a specific enzyme immunoassay kit purchased from Promega . sPLA2-IIA activity was measured in culture medium using [3H]-oleic acid-labeled membranes of Escherichia coli , following a modification [41] of the method by Franson et al . [42] . Mutated constructs [−488; +46]-sPLA2-Luc were prepared , as described previously [43] . CHO cells were seeded on dishes cultured in HAM F12 , supplemented with 10% ( v/v ) FCS ( Gibco BRL ) , 4 mM glutamine , 100 U/ml penicillin , and 100 mg/ml streptomycin . CHO cells were seeded in 24-well plates at a concentration of 2 . 104 cells per plate at 70% confluence , 24 h before transfection . Transfections with mutated constructs [−488; +46]-sPLA2-Luc , CREB , and DN CREB were performed using 0 . 75 ml of LIPOFECTAMINE Plus ( Invitrogen ) , 0 . 4 mg of reporter DNA , as indicated in Figure 4 , and 0 . 1 mg of pCMV-β-galactosidase per well . The cells were incubated with HAM-F12 medium 3 h after adding the DNA , and incubation was continued for 24 h . CHO cells were incubated with PA ( 1 μg/ml ) and EF ( 500 ng/ml ) for 1 h . LPS ( 1 μg/ml ) was then added , and incubation was continued for an additional 24 h . Luciferase activity was measured using a luciferase reporter assay kit , with signal detection for 12 s by a luminometer ( Berthold ) , and was normalized by dividing the relative light units by β-galactosidase activity [43] . The degree of induction was calculated relative to the control . Cell viability was checked by the trypan blue dye exclusion test . Cell lysis was controlled by measuring the release of lactate dehydrogenase ( LDH ) activity using a commercial kit from Boehringer . No cell mortality was observed in all the experiments presented in this study . Data are expressed as the mean ± standard error of the mean ( S . E . M . ) of at least three separate experiments , and statistical analyzes were performed using the unpaired Student t-test . | All mammals are susceptible to infection by Bacillus anthracis , the etiological agent of anthrax . Infection can occur either accidentally or as a potential consequence of a terrorism threat . Pulmonary infection is the most life-threatening form of the disease , causing a near 100% mortality . Despite appropriate therapy , all forms of infection may progress to fatal systemic anthrax , characterized by sepsis and respiratory failure . Thus , it is important to understand the mechanisms of host defense against B . anthracis . We have previously shown that alveolar macrophages produce an enzyme involved in innate defense that can kill B . anthracis: the enzyme is known as secreted phospholipase A2-IIA ( sPLA2-IIA ) . The alveolar macrophage is one of the first cell types to come in contact with B . anthracis . In this study , we show that live B . anthracis spores stimulate the synthesis of sPLA2-IIA , this stimulation being counterbalanced by the inhibitory effect of the edema toxin produced by germinated spores and bacilli . Our study suggests that inhibition of sPLA2-IIA synthesis by edema toxin is a mechanism by which B . anthracis can escape innate host defense . These pioneering data provide new molecular targets for future intervention against this deadly pathogen . | [
"Abstract",
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] | [
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] | 2007 | Edema Toxin Impairs Anthracidal Phospholipase A2 Expression by Alveolar Macrophages |
The intestinal ecosystem is formed by a complex , yet highly characteristic microbial community . The parameters defining whether this community permits invasion of a new bacterial species are unclear . In particular , inhibition of enteropathogen infection by the gut microbiota ( = colonization resistance ) is poorly understood . To analyze the mechanisms of microbiota-mediated protection from Salmonella enterica induced enterocolitis , we used a mouse infection model and large scale high-throughput pyrosequencing . In contrast to conventional mice ( CON ) , mice with a gut microbiota of low complexity ( LCM ) were highly susceptible to S . enterica induced colonization and enterocolitis . Colonization resistance was partially restored in LCM-animals by co-housing with conventional mice for 21 days ( LCMcon21 ) . 16S rRNA sequence analysis comparing LCM , LCMcon21 and CON gut microbiota revealed that gut microbiota complexity increased upon conventionalization and correlated with increased resistance to S . enterica infection . Comparative microbiota analysis of mice with varying degrees of colonization resistance allowed us to identify intestinal ecosystem characteristics associated with susceptibility to S . enterica infection . Moreover , this system enabled us to gain further insights into the general principles of gut ecosystem invasion by non-pathogenic , commensal bacteria . Mice harboring high commensal E . coli densities were more susceptible to S . enterica induced gut inflammation . Similarly , mice with high titers of Lactobacilli were more efficiently colonized by a commensal Lactobacillus reuteri RR strain after oral inoculation . Upon examination of 16S rRNA sequence data from 9 CON mice we found that closely related phylotypes generally display significantly correlated abundances ( co-occurrence ) , more so than distantly related phylotypes . Thus , in essence , the presence of closely related species can increase the chance of invasion of newly incoming species into the gut ecosystem . We provide evidence that this principle might be of general validity for invasion of bacteria in preformed gut ecosystems . This might be of relevance for human enteropathogen infections as well as therapeutic use of probiotic commensal bacteria .
The mammalian intestine hosts a microbial community of astonishing density and complexity . This intricate association presumably required significant coevolution of the host and its microbiota . Apparently , this coevolution has been guided by positive selection for factors that result in a state of both mutual tolerance and benefit . Microbial colonization of the intestine takes place right after birth and complexity steadily increases henceforward . The temporal and spatial assembly of the gut microbiota is apparently not guided by specific rules but eventually , after weaning , a stable microbial ecosystem is formed [1] . The adult human intestine hosts 1013 to 1014 bacteria belonging to at least 500 different species or strains [2] . Up to 9 different bacterial phyla are usually found; however , the Firmicutes and Bacteroidetes account for over 90% of all bacteria [3] . Despite its striking conservation on a higher phylogenetic level , the abundance of bacteria on species or strain level varies extensively between non-related individuals . Nevertheless , a core gut microbiome ( = sum of microbial genes ) that is shared among different individuals ensures conservation of metabolic functions provided by the microbiota [4] . It is assumed that the microbial ecosystem , once it is formed , efficiently prevents invasion by foreign species . This has been extensively studied in the case of enteric pathogens and is known as ‘colonization resistance’ ( CR ) [5] . The gut microbiota protects its host against infection by life-threatening pathogens such as Vibrio cholerae , pathogenic Escherichia coli strains , Shigella spp . , Clostridium difficile and Salmonella spp . [6] , [7] . To date , the molecular bases of CR as well as the key bacterial species involved remain poorly defined . It is clear that if the gut microbiota is absent or disturbed ( i . e . germfree status , antibiotic treatment , gut inflammation ) the infection risk increases drastically [8] , [9] , [10] , [11] , [12] . CR might not only exclude pathogenic bacteria but also acts against harmless or even beneficial bacteria , such as probiotics . For example , the efficiency of probiotic therapy can differ greatly among individuals [13] , [14] , [15] , [16] . To increase effectiveness of probiotic therapy , research aims at improving the half-life of probiotic strains in the gut [17] . In this study we set out to identify characteristics of the bacterial gut microbiota that are linked to infectivity of the human pathogen Salmonella enterica . Conventional mice ( CON ) harbouring a complex gut microbiota are highly resistant to oral Salmonella enterica infection and concomitant induction of gut inflammation [18] . We tested colonization resistance of mice harbouring different types of gut microbiota . On a quantitative level , we found that mice having a higher gut microbiota complexity exhibited increased protection against Salmonella-induced gut inflammation . In addition we found that the invasion-success of novel species into an established gut ecosystem ( i . e . Salmonella enterica , Lactobacillus reuteri RR ) may be predetermined by the abundance of species that are closely related to the invader .
We generated LCM mice by colonizing germfree mice with the Altered Schaedler flora ( ASF ) according to the protocol published on the Taconic webpage . Mice were inoculated at eight weeks of age by intra-gastric and intra-rectal administration of 107–108 c . f . u . of ASF bacteria on consecutive days ( www . taconic . com/library ) . LCM mice ( C57Bl/6 background ) were maintained under barrier conditions in individually ventilated cages with autoclaved chow and autoclaved , acidified water . No mice with complex gut microbiota were housed in the same room to prevent contamination with natural gut bacteria . CON C57Bl/6 mice were obtained from Janvier ( France ) , Charles River Laboratories ( Sulzfeld , Germany ) , from the Rodent Center HCI ( RCHCI Zürich ) and the Biologisches Zentrallabor ( BZL; Univeristy Hospital Zurich ) . CON transgene negative B6 . 129P-CX3CR1tm1Litt/J mice ( CX3CR1 ) [19] and CON Ly5 . 1 ( B6 . SJL-Ptprca Pepcb ) were bred at the RCHCI Zürich and CON heterozygous MyD88+/− mice ( C57BL/6 background ) [20] at RCC Füllinsdorf , respectively . All mice were bred and kept specified pathogen free in individually ventilated cages . This restricts microbial transfers between mice housed in the same room and animal facility . LCM mice , CON mice or streptomycin-pretreated CON mice ( 20 mg/animal 24h prior to Salmonella infection ) were infected by gavage with 5×107 CFU S . Typhimurium SL1344 wildtype or avirulent ( sseD::aphT [21] ) strains or S . Enteritidis 125109 ( streptomycin-resistant variant M1525 [22] ) . Live bacterial loads in mesenteric lymph nodes ( MLN ) , spleen and cecal content were determined by plating on MacConkey-agar ( Oxoid ) with respective antibiotics [21] . Lactobacillus reuteri RR ( 8*106 cfu i . g . ) was administered by gavage and cultured anaerobically on MRS media ( Biolife; 100 µg/ml rifampicin ) . To enoumerate bacteria , cecal content was stained with Sytox-green and bacteria were counted in a Neubauer-chamber . Bacterial density is given as Sytox-green positive bacteria per gram cecal content . All animal experiments were approved ( license 201/2004 and 201/2007 Kantonales Veterinäramt Zürich ) and performed as legally required . The streptomycin-resistant wild type strain S . Typhimurium ( SL1344 wildtype [23] ) , the isogenic mutant S . Typhimurium avir ( ΔinvG sseD::aphT; kanR [24] ) and wild type S . Enteritidis ( M1525 [22] ) were grown in LB 0 . 3 M NaCl as described [24] . L . reuteri RR [12] was isolated from our mouse colony selected on MRS media ( 100 µg/ml rifampicin ) ( Biolife ) and grown anaerobically . HE-stained cecum cryosections were scored as described , evaluating submucosal edema , PMN infiltration , goblet cells and epithelial damage yielding a total severity score of 0-13 points [21] . 0–3 = no to minimal signs of inflammation which are not sign of a disease; this is frequently found in the cecum of conventional mice . 4–8 = moderate inflammation; 9–13 = profound inflammation . Statistical analysis of Salmonella colonization titers was performed using the exact Mann-Whitney U Test ( SPSS Version 14 . 0 ) . P-values less than 0 . 05 ( 2-tailed ) were considered statistically significant . Pearson- and Spearman correlation coefficients for bacterial colonization levels were calculated using Graphpad Prism ( Version 5 . 01 ) . Other statistical analyses ( Pearson correlation , Kolmogorov-Smirnov test ) were performed using the statistical language and environment R ( http://www . r-project . org/ ) . To systematically detect differentially abundant OTUs in all mice and for different clustering distances , we used the R software Metastats [25] . Total DNA was extracted from cecal contents using a QIAmp DNA stool mini kit ( Qiagen ) . Bacterial lysis was enhanced using 0 . 1 mm glass beads in buffer ASF and a Tissuelyzer device ( 5 minutes , 30 Hz; Qiagen ) . V5-V6 regions of bacterial 16S rRNA were amplified using primers B-V5 ( 5′ GCCTTGCCAGCCCGCTCAG ATT AGA TAC CCY GGT AGT CC 3′ ) and A-V6-TAGC ( 5′ GCCTCCCTCGCGCCATCAG [TAGC] ACGAGCTGACGACARCCATG 3′ ) . The brackets contain one of the 20 different 4-mer tag identifiers [TAGC , TCGA , TCGC , TAGA , TGCA , ATCG , AGCT , AGCG , ATCT , ACGT , GATC , GCTA , GCTC , GATA , GTCA , CAGT , CTGA , CAGA , CTGT , CGTA;] . Cycling condition were as follows: 95°C , 10 min; 22 cycles of ( 94°C , 30 s; 57°C , 30 s; 72°C , 30 s ) ; 72°C , 8 min; 4°C , ∞; Reaction conditions ( 50 µl ) were as follows: 50 ng template DNA; 50 mM KCl , 10 mM Tris-HCl pH 8 . 3 , 1 , 5 mM Mg2+ , 0 , 2 mM dNTPs; 40 pmol of each primer , 5U of Taq DNA polymerase ( Mastertaq; Eppendorf ) . PCR products of different reactions were pooled , ethanol-precipitated and fragments of ∼300 bp were purified by gel electrophoresis , excised and recovered using a gel-extraction kit ( Machery-Nagel ) . Amplicon sequencing of the PCR products was performed using a 454 FLX instrument ( 70×70 Picotitre plate ) according to the protocol recommended by the supplier ( www . 454 . com ) . PCR to detect ASF bacteria in the feces was done as described in [26] . Candidate E . coli strains yielding large , red colonies on MacConkey agar were typed using Enterotubes ( BD Biosciences ) . Additionally , in some cases 16S rRNA gene sequencing was performed . The amplification was performed with extracted DNA using ”broad-range” bacterial primers fD1 and rP1 [27] . Reaction conditions were as follows: Deoxyribonucleoside triphosphates ( 0 . 25 mM ) , primers ( 1 pmol/µl each ) , 5UTaq-DNA polymerase ( Mastertaq; Eppendorf ) , 50 ng of template DNA . The following cycling parameters were used: 5 min of initial denaturation at 94°C followed by 35 cycles of denaturation ( 1 min at 94°C ) , annealing ( 1 min at 43°C ) , and elongation ( 2 min at 72°C ) , with a final extension at 72°C for 7 min . Amplified PCR products were purified by gel electrophoresis and sequenced using rP1 as sequencing primer . Sequences were assigned to the RDP taxonomy using the RDP classifier ( http://rdp . cme . msu . edu/; [28] ) . Fecal samples were re-suspended in PBS and plated in appropriate dilutions on MRS agar ( DE MAN , ROGOSA und SHARPE; Biolife ) that supports growth of Lactobacillus spp . as well as Leuconostoc spp . and Pediococcus spp . Plates were incubated for 24 h in an atmosphere of 7% H2 , 10% CO2 and 83% N2 at 37°C in anaerobic jars . The amplicon library was sequenced according to the 454 Amplicon Sequencing protocols provided by the manufacturer ( Roche 454 ) at the McMaster University Hamilton ( Canada ) . The sequence determination was made using GS Run Processor in Roche 454 Genome Sequencer FLX Software Package 2 . 0 . 00 . 22 . Performance of the sequencing run was gauged using known pieces of DNA introduced in the sequencing run as DNAControl Beads . On average , 94% of reads from DNA Control Beads matched the corresponding known sequences with at least 98% accuracy over the first 200 bases , which was above the typical threshold ( 80% matches of 98% accuracy over 200 bases ) . To estimate the reliability of sample separation using our primer-tagging approach , we assessed the number of reads observed to have an illegitimate 4-mer tag ( i . e . , different from our set of 20 tags ) . The sequencing plate ( including other non-analyzed samples ) produced a total of 264 , 503 reads from which 1 , 339 contained a wrong tag ( 0 . 506% ) . Given that 256 distinct 4-mer tags are possible and that we used only 20 of these , the majority of sequencing errors in this region are detectable . Correcting for the small fraction of undetectable errors ( 20/256 ) and division by four yields a sequencing error rate of 0 . 137% per single nucleotide - at the position of the tag in the primer ( this includes errors during primer synthesis as well as sequencing ) . Because most errors are actually visible as errors , the rate of unintentional ‘miscall’ of the sample is 0 . 043% . We applied quality control of 454 reads in order to avoid artificial inflation of ecosystem diversity estimates [29] . Reads containing one of the exact 4 nt tag sequences were filtered with respect to their length ( 200 nt ≤ length ≤300 nt ) . Quality filtering was then applied to include only sequences containing the consensus sequence ( ‘ACGAGCTGACGACA[AG]CCATG’ ) of the V6 reverse primer and displaying at maximum one ambiguous nt ‘N’ . The latter criterion has been reported as a good indicator of sequence quality for a single read [30] . We identified 5 , 268 reads shorter than 200 nt , 228 reads longer than 300 nt and 2 , 169 reads containing more than one ‘N’ . After filtering , 190 , 728 reads remained ( initial total of 197 , 949 reads containing the exact primer sequence and tag ) and were processed as described below . OTUs were defined using the complete filtered dataset , with the exception of exactly identical reads , which were made non-redundant to reduce computational complexity . Before OTU generation , we added reference sequences for subsequent taxonomic classification of OTUs; for this , we used a reference database of selected 16S rRNA gene sequences downloaded from the Greengenes database ( http://greengenes . lbl . gov/Download/Sequence_Data/Greengenes_format/greengenes16SrRNAgenes . txt . gz , release 01-28-2009 [31] ) . In Greengenes , all entries are pre-annotated using several independent taxonomy inferences including the RDP taxonomy . Our reference database was built using full-length non-chimeric sequences with a minimum length of 1100 nt ( in order to fully cover the V6 region of all entries ) . No archaeal sequences were included . The alignment of non-redundant reads from all mice with the reference database was performed using the secondary-structure aware Infernal aligner ( http://infernal . janelia . org/ , release 1 . 0 , [32] ) and based on the 16S rRNA bacterial covariance model of the RDP database ( http://rdp . cme . msu . edu/; [28] ) . Before defining OTUs , we first removed reference sequences for which the alignment was not successful ( Infernal bitscore <0 ) . The alignment was then processed to include an equivalent amount of information from every read . To do so , we identified the consensus reverse primer sequence of the V6 region within the aligned sequence of Escherichia coli K12 , as a reference . The full alignment was then trimmed from the start position ( defined by the E . coli V6 reverse primer ) and ended after 200 nt's . This also insured the limitation of the effect of pyrosequencing errors by trimming the 3′ end of each read , a region which is more sequencing-error prone ( the trimmed and aligned reads length ranged from 192 to 241 nt ) [29] . Using this alignment , OTUs were built by hierarchical cluster analysis at various distances ( 0 . 01 , 0 . 03 , 0 . 05 , 0 . 10 , 0 . 15 and 0 . 2 ) using the ‘complete linkage clustering’ tool of the RDP pyrosequencing pipeline ( http://pyro . cme . msu . edu/ [28] ) . As a first step , taxonomy was predicted for all reads using the stand-alone version of the RDP classifier ( http://sourceforge . net/projects/rdp-classifier , revision 2 . 0 , [33] ) . Taxon predictions were considered reliable if supported by a minimum bootstrap value of 80% . In order to predict taxonomy for each OTU , we either used any reference sequences present within a cluster , or the taxonomy of the reads present in the cluster , as predicted by the RDP classifier . To increase the resolution of the prediction , we privileged any reference sequences over the reads . For each OTU , taxonomy was inferred by a simple majority vote: if more than half of the reference sequences ( or reads ) present within a cluster agreed on a taxon , the OTU was annotated according to this taxon . In case of conflicts , we assigned a consensus taxon to a higher phylogenetic level for which the majority vote condition was respected . OTU distribution between the different experimental groups and predicted taxonomies were visualized as heatmaps generated by custom Python scripting and the statistical software package R ( www . r-project . org ) . Deep pyrosequencing on the 454 platform has revealed extensive microbial diversity that was previously undetected with culture-dependent methods [34] . Nevertheless , the details of protocols to generate this type of data should always be carefully considered; various types of bias can be introduced at different steps . Here , sequencing was performed on pools of PCR products , thus limitations and biases of this technique have to be taken into account to interpret the results . The abundances of amplicons may not accurately reflect the relative abundances of the template DNA because of differential primer binding- and elongation-efficiencies . Moreover , during amplification , chimeric sequences can be generated . On such short sequences , recombination points ( recombination can occur from an incompletely extended primer or by template-switching [35] ) are extremely difficult to detect . Recently , a new tool to filter noise and remove chimera in 454 pyrosequencing data has been published [36] . In this study , the authors suggest that because of sequencing errors , diversity estimates may be at least an order of magnitude too high . To our best knowledge , at the time of analysis , there were no available tools to detect chimera within libraries of short 454 reads . Therefore , to detect chimera we decided to compare taxonomies assigned to N-terminal and C-terminal read fragments . A read was regarded as ‘non-chimeric’ if the best hits ( BLASTn ) for both of its fragments had a minimum identity of 95% and a minimum bit-score of 150 . These cutoffs were selected heuristically in order to insure a reasonable alignment length and a relatively high identity to the matching reference sequence . A given read was deemed chimeric when the taxonomies of the best hits of each half were clearly not congruent ( i . e . , differing at the phylum level ) . Our simple chimeric reads detection method resulted in a higher rate of detected chimera compared to the method of Quince et al . , 2009 ( ∼7% compare to ∼3% in their example ) adapted from the Mallard algorithm [37] , suggesting that our approach is probably stringent enough at detecting chimera [36] . In order to test the general hypothesis that closely related bacteria are present at similar levels in CON mice , we systematically compared the relative abundance between all OTUs detected in 9 distinct CON mice . Here , a detected OTU was defined as present in at least 6 mice ( 2/3 ) . For each possible pair of OTUs , we computed the Pearson correlation coefficient of their relative abundance ( number of reads normalized by the total number of reads in a given sample ) in each CON mouse . To compare these results to the distance between 2 OTUs we computed identities between all considered OTUs using their representative sequences in the complete alignment ( all reads and all reference sequences ) . An OTU's representative sequence is defined as the sequence that has the minimum sum of the square of the distances to all other sequences within that cluster . For statistics inference , we semi-randomized our results by shuffling non-null abundances between all detected OTUs . For both distributions we plotted running medians ( y-axis ) with a window size of 500 data points ( the window size was decreased towards the beginning and end of the distributions ) . The Kolmogorov-Smirnov test ( one- and two-tailed ) was used to compare both distributions ( actual data and random data ) with respect to the deviation of the running median from the random expectation . The test was computed on x-axis bins ( 0 . 1 ) in order to better interpret the results of the analysis . The data processing and plotting were performed using Python scripting .
Germfree mice and CON mice orally treated with a single dose of antibiotic ( i . e . aminoglycosides , β-lactams , vancomycin ) are highly susceptible to enteric S . Typhimurium colonization and develop acute inflammation of the lower intestine ( cecum , colon ) upon oral infection [11] , [18] , [38] . Here , we tested susceptibility of gnotobiotic mice , associated with a standardized low complex type of gut microbiota ( termed LCM ) , to oral S . Typhimurium infection . In contrast to CON mice ( ∼500 different bacterial strains in the gut ) , the gut microbiota of LCM mice includes a mixture of only 8 bacterial strains , the Altered Schaedler Flora ( ASF ) , which are typically found in the gut of rodents [39] . In order to test whether LCM mice were susceptible to oral S . Typhimurium infection , we infected unmanipulated LCM mice ( n = 5 ) by orally gavaging them with S . Typhimurium wild type ( 5×107 cfu ) . As control , we infected age-matched groups of CON mice ( n = 5 ) harboring a normal fully differentiated gut microbiota and CON mice pretreated with streptomycin 24 h prior to infection ( smCON ) . All mice were sacrificed at 3 days p . i . S . Typhimurium titers at in the mLN and spleen were highest in smCON mice , while no difference was observed comparing CON and LCM groups ( Fig . 1A , B ) . In keeping with previous work , the cecum of untreated CON mice was poorly colonized by S . Typhimurium ( below 105 cfu/g ) while smCON mice displayed high S . Typhimurium levels in their gut ( >108 cfu/gram; p<0 . 05; Fig . 1C ) . Interestingly , LCM mice also displayed high pathogen titers in the cecum . Owing to this high-level colonization , wild type S . Typhimurium triggered a fulminant inflammatory response in the cecum and colon of both smCON and LCM mice , while no pathological changes could be observed in the CON mice not pretreated with antibiotics ( Fig . 1D , E; Fig . S1 ) . This demonstrates that , in contrast to normal complex type of gut microbiota , colonization of mice with a LCM gut microbiota does not confer CR against S . Typhimurium . To verify that mucosal inflammation induced by S . Typhimurium in infected LCM mice is induced by Salmonella-specific virulence factors , we infected LCM mice with an avirulent mutant lacking a functional TTSS-1 and 2 ( S . Typhimuriumavir; 5×107 cfu ) . Despite colonizing the gut to high titers , S . Typhimuriumavir did not cause observable signs of intestinal pathology in LCM mice , demonstrating that gut inflammation in LCM mice was triggered by the same pathogenetic mechanisms as shown for smCON mice ( Fig . S2 ) . LCM mice , with a low complexity gut microbiota are susceptible to oral S . Typhimurium infection and develop severe acute colitis comparable to germfree or antibiotic-treated mice . Of note , microbiota in the cecum of LCM mice had a similar density as in CON mice ( Fig . S3 ) . These findings suggested that their gut microbiota lacks key bacterial species responsible for mediating CR . We reasoned that these protective bacteria would be transferable by co-housing LCM together with CON mice in the same cage . To test this hypothesis , we re-associated 2 groups of LCM mice ( n = 2 , 4 ) with one CON donor mouse each for 21 days . As controls , we used groups of non re-associated LCM and CON mice . We infected all animals with S . Typhimurium wild type ( 5×107 cfu by oral gavage ) to measure the degree of CR . Compared to unmanipulated LCM , all re-associated LCM mice had significantly lower S . Typhimurium loads in their feces at 1 day p . i . ( Fig . 2A ) . 4 out of 6 animals were completely protected from Salmonella-colitis and did not show any signs of cecal pathology ( Fig . 2E , F ) while 2 out of 6 animals developed signs of inflammation ( pathoscore 6 and 7 ) at day 3 p . i . , which correlated with higher S . Typhimurium loads in the cecum of these mice ( Fig . 2B ) . Systemic S . Typhimurium colonization appeared also slightly reduced in re-associated LCM mice ( Fig . 2C , D ) . This revealed that CR is transferable and suggested that discrete bacterial species transferred during the 3 week re-association contributed to colonization resistance and protection from colitis . This offered the opportunity to correlate the changes in microbiota composition in the LCM mice with acquisition of colonization resistance . Protection from Salmonella diarrhea is conferred by bacteria entering the gut microbiota of LCM mice . To identify bacteria transferred during re-association , we analyzed gut microbiota composition by high-throughput sequencing of bacterial 16S rRNA genes . We analyzed the fecal microbiota because this non-invasive sampling method allows monitoring the microbiota of a given animal at various time points ( i . e . before/after re-association or Salmonella infection ) . In contrast to other studies [2] , [4] , [34] , we decided to sequence the 16S rRNA hypervariable regions V5 and V6 ( length in E . coli: ∼280 bp ) . Several studies have shown that sequencing of different hypervariable regions or full-length 16S rRNA genes yields to comparable results [30] . Thus we reasoned that V5V6 sequencing would not lead to a major bias in microbiota composition and at the same time would allow us to fully use current pyrosequencing capacity ( the average output length of the 454FLX instrument is 250 bp ) . After read-quality filtering , we obtained 190 , 728 reads with a length between 200–300 bps in total . Among those , 50 , 860 were non-redundant . The frequency of chimera , using a simple identification approach was 6 . 9% of the total reads ( 13 , 206 ) and 14 . 7% of non-redundant reads ( 7 , 499 ) ( Fig . S4 ) . This chimera-frequency is relatively high considering that we probably detected only a fraction of chimeric reads using our method ( materials and methods ) . Sequence reads were aligned with all quality-filtered sequences of our reference database generated from the Greengenes database [31] and operational taxonomic units ( OTUs ) were defined by hierarchical clustering at various distances , from 0 . 01 to 0 . 2 . Taxonomy assignment was inferred using annotation from the reference sequences , if possible , or by predictions generated by the RDP classifier from the RDP database [28] . Comparing the average number of OTUs at various distances , clearly the CON donor mice display the highest level of complexity ( Tables S1 , S2 and S3 ) . We found an average of 767±233 OTUs at a Clustering Distance ( CD ) of 0 . 03 and 499±139 OTUs at a CD of 0 . 05 ( before chimera removal: 971±290 OTUs at a distance of 0 . 03 and 662±186 OTUs at a CD of 0 . 05 ) . Complexity of the LCM gut microbiota was , as expected , relatively low . By strain-specific PCR [26] , we only detected 4 members of the ASF ( ASF361 , ASF457 , ASF500 and ASF519; Fig . S5 ) . However , 29±10 OTUs at a 0 . 03 CD , and 17±5 OTUs at a 0 . 05 CD were detected ( before chimera removal: 38±10 OTUs at a CD of 0 . 03 and 23±5 OTUs at a CD of 0 . 05 ) . This was expected considering the way the LCM mice were generated . LCM status was created by inoculating germfree mice with bacteria of the ASF . Afterwards , LCM mice were kept in individually ventilated cages ( IVCs ) . During this phase , a limited number of additional species might have been acquired . This might explain why our sequence analysis detected more than 8 different phylotypes in unmanipulated LCM mice . Alternatively , the relatively high number of phylotypes could be explained by PCR artifacts or most likely by the intrinsic error rate of pyrosequencing that can lead to a severe over-estimation of microbial diversity using the 16S rRNA marker gene [29] . In LCMCON21 mice , we observed a significant increase in gut microbiota complexity compared to LCM mice . At a 0 . 03 CD , 295±34 OTUs and at a CD of 0 . 05 , 188±23 OTUs were detected ( before chimera removal: 409±60 OTUs at a CD of 0 . 03 and 279±45 OTUs at a CD of 0 . 05 ) . However , complexity in LCMCON21 mice remains significantly lower than that in CON mice . We assessed the richness ( actual diversity ) of our samples by calculating the Shannon index ( H ) and species evenness ( E ) as well as the Chao1 diversity estimate ( Tables S1 , S2 and S3 ) . These calculations revealed that the community was clearly under-sampled; for small CDs ( 0 . 01 to 0 . 05 ) , the Chao1 estimator was , for each mouse , higher than the total number of OTUs . Although under-sampling is limiting our view on the true microbial diversity , it is legitimate to use diversity measures for relative comparisons among samples . Within this context , it is interesting to ask whether , after re-association , LCM mice display similar or different species evenness E compared to the CON mice . Here , species evenness can be regarded as the equilibrium between community members; the less variation is observed between species , the higher is the E value ( in other words , evenness is greatest when species are equally abundant ) . The E-value is defined as the ratio of the theoretically maximal Shannon-index ( if all observed phylotypes were equally abundant ) divided by the actual Shannon-index . For a 0 . 05 CD , CON mice displayed an average E-value of 0 . 76 compared to an average of 0 . 70 for the LCMCON21 mice ( compared to E = 0 . 15 for LCM mice ) . Thus , there is no major difference between CON mice and re-associated LCM mice with respect to evenness . Hence the 21 days of co-housing were sufficient in order to adopt a relatively complex and ‘in equilibrium’ microbial gut community . To compare species richness between the 3 different groups , rarefaction curves were created for different CDs ( Fig . 3; Fig . S6 ) . For a CD of 0 . 01 , slopes for CON and re-associated CON mice are rather steep , revealing again a considerable under-sampling in our experiment . However , slopes for 0 . 05 ( for re-associated LCM ) and 0 . 1 CD ( for CON ) seem to reach saturation , suggesting that for this level of analysis , the sampling was sufficiently complete . Therefore we decided to perform OTU analyses using a CD higher or equal to 0 . 05 . This CD is in accordance with a recent report advising a stringent quality-based filtering of 16S- 454 reads and the use of a clustering threshold no greater than 97% [29] . Given the clear under-sampling and the sequencing strategy applied here , a species-level analysis is not conclusive and we decided to focus our further analysis at a higher taxonomic level ( from the Family up to the Phylum ) . We next analyzed qualitative changes in microbiota composition during re-association . In particular , we focused at identifying which OTUs were transferred from the CON donor mice to the LCM recipients within 21 days . Those bacteria may contribute to protection against S . Typhimurium colonization . In order to predict taxonomy for each OTU , we used either the reference sequence taxonomy information present within an OTU-cluster , if any , or the reads taxonomy predicted by the RDP classifier . To test if the taxonomy assignment via reference sequences provided a more resolved taxonomy , we compared taxonomy resolution obtained via reference sequences and via RDP-classifier annotated reads for OTUs which contained both reads and reference sequences ( Fig . S7 ) . For different CD and different taxon levels , the reference taxonomy always provided better taxonomic resolution from the phylum level ( taxon_1 ) down to the genus level ( taxon_5 ) . Euclidean distances between relative abundance profiles were computed for each mouse and every time-point sampled . Hierarchical clustering ( average method ) of all mice for taxon_2 ( class ) taxon_3 ( order ) and taxon_4 ( family ) were visualized on distinct heatmaps ( Fig . 4; Fig . S8A , B ) . All CON mice ( day 0 and day 21 ) clustered together as well as all the LCM mice before re-association . Additionally , we included two unmanipulated CON mice ( donor 9855 and 9856 ) that were only sampled at one time-point to provide more samples of independent CON mice from the same mouse colony ( n = 4 in total ) . All samples of LCM mice from day 0 ( before re-association ) were highly similar and clustered together . The highest identity ( determined by BLAST , all against all ) between the V5V6 regions of the 8 different ASF members is of 93% ( data not shown ) ; therefore it is theoretically possible , for a small clustering distance , to detect each ASF species by our sequencing and taxonomy inference approach . Seven OTUs were systematically detected in the LCM mice , all assigned to the Firmicutes and Bacteroidetes phyla . Thus , we assume that the most abundant species in the feces of LCM-mice are ASF500 ( Firmicutes; Clostridia; Clostridiales; Lachnospiraceae; unclassified_Lachnospiraceae ) and ASF519 ( Bacteroidetes; Bacteroidetes; Bacteroidales; Porphyromonadaceae; Parabacteroides; ) . Abundance of ASF strains in different mice can be influenced by various factors [26] , [40] . Hence , the sampling depth could explain the non-detection of the other ASF members , which were most probably less abundant . The qualitative microbiota analysis revealed that within the 21 days of re-association , bacteria from all detected phyla in the CON donor mice were transferred ( Fig . 4; Fig . S8A , B ) . However , the gut microbiota of LCMCON21 was significantly less complex than that of CON mice , suggesting that the microbiota might also differ on a qualitative basis . This might be causally linked to the increased susceptibility to Salmonella infection . Thus , we compared the microbiota of CON and LCMCON21 with respect to lack or enrichment of specific clusters of bacteria ( i . e . on order or family level ) . We analyzed which OTUs were significantly over- or underrepresented comparing LCMCON21 and CON mice . Interestingly , among others , OTUs assigned to the family of the Enterobacteriaceae were enriched in LCMCON21 mice , as compared to CON mice ( Fig . 4; Dataset S1 ) . Since Salmonella Typhimurium is also a member of the Enterobacteriaceae , the enrichment of such close relatives in LCMCON21 mice might be an indicator of favorable growth conditions for this type of bacteria . This finding prompted us to investigate , whether there is a positive correlation between the abundance of Enterobacteriaceae ( i . e . E . coli ) and the susceptibility to Salmonella infection . We have previously observed that C57Bl/6 mice obtained from different sources ( commercial breeders , other laboratories ) exhibit differential degrees of CR against Salmonella . To analyze whether CR is linked to different E . coli titres , we defined fecal E . coli levels of mice from five different breedings ( C57Bl6 background from our animal facility and others ) before infecting them with S . Enteritidis wild type by oral gavage ( 5×107 cfu; no antibiotic-treatment ) . We used S . Enteritidis because pilot experiments in our laboratory had shown that this serovar generally leads to a higher disease incidence ( colitis at day 4 after oral infection ) in non-antibiotic-treated mice , than S . Typhimurium . E . coli is readily differentiated from other Enterobacteriaceae by colony color and morphology on MacConkey agar ( see Materials and Methods for typing details ) . One day after infection , we determined fecal S . Enteritidis titers by plating . The mice were sacrificed at day 4 postinfection and we analyzed S . Enteritidis titers at systemic sites , in the intestine as well as cecal pathology ( Fig . 5A; Fig . S9 ) . Indeed , we observed a positive linear correlation between fecal E . coli levels before infection , S . Enteritidis colonization efficiency ( r2 = 0 . 434: Spearman p = 0 . 0015 ) . If S . Enteritidis titres were above 1 . 5×105 cfu/g feces at day 1 p . i . , mice developed colitis at day 4 p . i . This suggests that E . coli titres may predict whether mice are susceptible to Salmonella induced gut inflammation . We observed that higher E . coli levels positively correlate with increased Salmonella infectivity . This might be due to the close relatedness of these two species as they might have similar environmental requirements . Thus , we hypothesized that the same principle might apply for other intestinal bacteria . We tested this hypothesis using Lactobacillus reuteri RR , a rifampicin-resistant isolate from our mouse colony that can be specifically detected by culture [12] . We determined whether higher titres of intestinal Lactobacilli would correlate with increased gut colonization by Lactobacillus reuteri RR upon oral gavage . Lactobacilli are Gram-positive , of low G+C content , non-spore-forming , aerotolerant anaerobes and can be differentiated on selective media ( i . e . MRS-agar ) . We determined fecal levels of Lactobacilli of mice from different sources and subsequently infected them with Lactobacillus reuteri RR ( 107 cfu by oral gavage ) . 1 and 5 days post infection we determined Lactobacillus reuteri RR titres in the feces . Indeed , we found significantly enhanced colonization of Lactobacillus reuteri RR in mice with higher titres of Lactobacilli ( Fig . 5B ) . This suggests that , like in the case of E . coli and Salmonella , higher levels of Lactobacilli correlate with increased colonization efficiency by a commensal Lactobacillus strain . In order to investigate whether our observations with Enterobacteriaceae and Lactobacillaceae correspond to a more universal phenomenon that applies to closely related bacterial groups in general , we performed a systematic abundance correlation analysis between OTUs detected in 9 distinct CON mice ( Fig . 6; Fig . S10 ) . We limited our analysis to OTUs detected in at least 6 mice in order to lower the under-sampling bias in our 454 sequence data . Upon examination of OTUs defined at various CDs , we found that closely related phylotypes ( i . e . 0<reads divergence<0 . 2 ) generally display significantly correlated abundances ( co-occurrence ) , more so than distantly related phylotypes . In summary , our results indicate that the invasion-success of novel species into a complex gut microbiota might be predetermined by the presence of closely related species or by factors that also influence the abundance of closely related species in this ecosystem .
It has been known for a long time , that the normal gut microbiota plays a key role in protection from infection with pathogenic bacteria . Germfree mice lack CR and thus are highly susceptible to infections with various pathogens [41] . They regain CR upon conventionalization with a normal microbiota [42] . This process has been studied extensively in the 1970s and 1980s; these earlier studies mainly addressed the question , which parts of the complex gut microbiota play a role in ‘conventionalization’ and inhibition of pathogen growth [43] , [44] , [45] . Due to technical limitations at that time , the studies were confined to the analysis of cultivated bacteria . In contrast to this earlier work , we use LCM mice that are colonized with a stable , low-complexity gut microbiota being composed of typical gut bacteria i . e . Bacteroides spp . , Clostridium spp . , Mucispirillum spp . and Lactobacilli ( ASF361 , ASF457 , ASF500 and ASF519 ) as starting point for the re-association studies . We show that LCM mice , despite being colonized with a numerically dense gut microbiota , are still susceptible to Salmonella gut infection and colitis . This system represents major advantages over the use of germfree mice . First of all , the maintenance of LCM mice is by far less extensive than that of germfree mice . We have maintained a colony of LCM mice for 24 months in IVC cages without significantly altering complexity of their gut microbiota . Therefore , LCM mice harbor ‘typical’ gut bacteria which , to some extent , protects against contamination with environmental bacteria , meaning that their gut ecosystem is somewhat normalized . Secondly , compared to germfree mice , the gut mucosal immune system and innate defense is partially normalized . Consequently , Salmonella-induced intestinal pathology is milder in LCM that in germfree mice ( this work and [11] ) . However , the LCM gut microbiota apparently lacks certain parts of the conventional gut microbiota important for protection against infection with enteropathogens ( i . e . Salmonella , E . coli ) . Thus , LCM mice represent a very useful system to screen for protective bacteria and characterize the mode of protection . What mechanisms underlie protection against enteropathogens by the gut microbiota ? Host factors induced by bacterial colonization could be one mediator of CR . The gut microbiota instructs and shapes the mucosal innate and adaptive immunity and keeps the host in a defense-competent state [46] , [47] , [48] . Alternatively the bacteria forming the gut ecosystem directly suppress pathogen growth . This could be mediated by blocking of pathogen receptor sites or the production of antibacterial substances and metabolic by-products like short-chain fatty acids ( SCFA ) [49] , [50] , [51] , [52] , [53] . Moreover , conventionalization also involves drastic changes in intestinal physiology , such as decrease of relative cecal size , free nutrient depletion , oxygen limitation and lowering of the redox potential [54] , [55] , [56] . The intestinal microbiota consists to the greatest part of obligate anaerobic and extremely oxygen-sensitive bacteria [57] and oxygen tension in the gut decreases gradually from stomach to rectum while bacterial density increases [58] . These conditions keep colonization levels of facultative aerobic bacteria , which comprise most enteropathogens , relatively low [57] , [59] . To date , the key bacteria inducing CR have not been unambiguously identified . Rolf Freter and coworkers aimed at identifying single strains that accomplish conventionalization of germfree mice . He demonstrated that a collection of 95 anaerobic intestinal isolates or even a combination of Clostridia and Lactobacillus spp . isolates is sufficient to restore CR [43] , [45] . To our knowledge , these ‘CR-mediators’ were never further described or characterized in detail . Since this would be a critical step towards understanding the molecular basis of CR , isolation and characterization of ‘CR-mediators’ will be subject of future analyses . To this end , LCM mice will be a useful tool . We analyzed microbiota changes during conventionalization of LCM mice using deep sequencing of 16S rRNA genes . This extends earlier studies that have focused only on culturable bacteria , to non-culturable strains . It is assumed that LCM mice pick up fecal bacteria from the CON donor mouse by coprophagy . The efficiency of microbiota transfer by coprophagy may be questionable , since a great part of conventional gut microbiota is extremely oxygen sensitive . Still , we found that representatives of all five major eubacterial phyla typically present in the mammalian gut were transferred to LCM mice within 21 days . Although microbiota complexity drastically increased within 21 days of conventionalization , it was still significantly lower than in CON mice . Interestingly , in the same way as microbiota complexity , CR of LCMCON21 mice against S . Typhimurium was at a somewhat intermediate level . Still , LCMCON21 mice were , at least partially , protected from Salmonella-induced gut inflammation . Overall complexity of LCMCON21 mice was not restored to the levels of CON mice , suggesting that conventionalization takes longer than 21 days to reach a high-density equilibrium state . For example , relatively few members of the Firmicutes and a high number of Verrucomicrobia were detected . Conventionalization is proposed to be a process of ecologic succession whereby the relative composition of the microbiota constantly changes , a sequence that mirrors microbiota-colonization after birth [60] . Alternatively , as the Firmicutes branch comprises most of the extremely oxygen-sensitive species , it is conceivable that they might be transferred less efficiently , or , only after oxygen tension in the gut is low enough to allow growth . It would be very interesting to analyze microbiota composition in fecal samples of LCM mice at different time points during conventionalization and also extend the analysis to longer time points beyond 21 days . Since LCMCON21 mice have partially gained CR during the 21 days re-association period , we aimed at identifying certain protective bacterial species , which are absent in LCM mice . Although microbiota complexity in LCMCON21 mice was too high for unequivocal species identification , comparative microbiota analysis detected an enrichment of Enterobacteriaceae in LCMCON21 mice . We concluded that this group of bacteria does not mediate CR ( ‘CR-mediator’ ) but may rather indicate the level of CR ( ‘CR-indicator’ ) . Upon screening a variety of conventional mice from different sources , we observed that higher E . coli titers positively correlated with Salmonella infectivity . Thus , E . coli can be regarded as ‘CR-indicators’ for Salmonella infection . Higher concentrations or diversity of Enterobactericeae can be indicative for alleviated CR [61] , [62] . This may explain why infection with ‘CR-indicator’ E . coli strains has been previously used as a method to judge the intensity of CR [63] . E . coli and Salmonella spp . are very close phylogenetic relatives . Strikingly , E . coli levels also correlated with susceptibility to Salmonella-induced colitis . When E . coli and concomitant S . Typhimurium levels were above 106 cfu/g at day 1 post pathogen infection , mice reliably developed gut inflammation . Interestingly , we found a similar correlation between the level of intrinsic Lactobacilli and the colonization levels of orally inoculated Lactobacillus reuteriRR strain . Therefore , we speculated that the finding could be a general principle that applies to closely related bacterial groups in the intestinal ecosystem . How can closely related species actually coexist in the same ecosystem ? In theory , closely related species could occupy the same niche in the intestine although they had similar nutrient requirements or share the same adhesion receptors . However , in praxis , species A will perform slightly better than species B , which would lead to out-competition and elimination of B . Alternatively , species B could switch to the use of a different available nutrient source ( or receptor ) and coexist with species A in the same ecosystem . This principle has been demonstrated in case of E . coli . Different commensal E . coli strains can coexist in the intestine by using different nutrients [64] . But how is colonization level of a certain species A connected to the colonization efficiency of its close relative B ? This might be explained by the fact that the same global selective pressure acts on both species . This global pressure could be the presence of a third species C that inhibits both A and B ( i . e . by inhibitor production ) . Alternatively , A and B might have the same requirements of oxygen or the same sensitivity to antimicrobial peptides that only allows the bacteria to grow at a certain , defined density . This correlation would only be maintained , if none of the two strains produced a direct inhibitor against the other species ( i . e . colicin , nisin , metabolites ) . Taken together , this principle suggested for Enterobacteriaceae and Lactobacillaceae might also apply for other bacterial groups , sharing common growth requirements . Our data suggest , that subtle fluctuations in intestinal ecosystem composition between individuals might partly explain their differential susceptibility to gut infections or probiotic therapy . This knowledge could be exploited for screens of the human population to identify certain risk-or susceptibility groups . This would then enable the correlation of these data to other parameters ( lifestyle , age , gender , nutrition ) . The existence of a highly dynamic niche for growth of Enterobacteriaceae , varying between different individuals , might reflect the differential susceptibility to gut infections within the human population . Some patients might have suffered from insults that induce a transient ‘out of equilibrium’ state of the microbiota that renders it less protective . Such conditions could be nutrient deficiencies , stress , illness or a history of antibiotic treatment . Screening of people at risk ( elderly , immune-suppressed ) might thus help in early disease prevention and potentially enable more targeted use of antibiotics . | The commensal microbiota , populating the intestinal tract to high levels , is fundamental to human health . It exerts beneficial effects on the immune system and contributes to protection against gastrointestinal infections ( = colonization resistance ) by largely unknown mechanisms . Here , we reveal characteristics of the commensal microbiota indicative for a high or low degree of colonization resistance . Using a mouse model for Salmonella enterica induced gut inflammation and microbiota analysis by 454 amplicon sequencing , we show that mice having different types of microbiota exhibit differential susceptibility to pathogen infection . In addition , our data lead to the description of a new concept in gut ecosystem biology: the intrusion-success of an extrinsic bacterial species into an established gut ecosystem is related to the abundance of closely related bacteria , already present in this gut ecosystem . We show that this principle applies not only to enteropathogen infection but also to inoculation with beneficial gut bacteria . Humans can display largely different degrees of susceptibility to enteric infections . Similarly , the effectiveness of probiotic therapy varies greatly from person to person . Our data might explain these differences and could be used for increasing the efficacy of probiotic therapy and for identifying patients at risk of developing enteric infections . | [
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"diseases/gastrointestinal"... | 2010 | Like Will to Like: Abundances of Closely Related Species Can Predict Susceptibility to Intestinal Colonization by Pathogenic and Commensal Bacteria |
During development , neural competence is conferred and maintained by integrating spatial and temporal regulations . The Drosophila sensory bristles that detect mechanical and chemical stimulations are arranged in stereotypical positions . The anterior wing margin ( AWM ) is arrayed with neuron-innervated sensory bristles , while posterior wing margin ( PWM ) bristles are non-innervated . We found that the COP9 signalosome ( CSN ) suppresses the neural competence of non-innervated bristles at the PWM . In CSN mutants , PWM bristles are transformed into neuron-innervated , which is attributed to sustained expression of the neural-determining factor Senseless ( Sens ) . The CSN suppresses Sens through repression of the ecdysone signaling target gene broad ( br ) that encodes the BR-Z1 transcription factor to activate sens expression . Strikingly , CSN suppression of BR-Z1 is initiated at the prepupa-to-pupa transition , leading to Sens downregulation , and termination of the neural competence of PWM bristles . The role of ecdysone signaling to repress br after the prepupa-to-pupa transition is distinct from its conventional role in activation , and requires CSN deneddylating activity and multiple cullins , the major substrates of deneddylation . Several CSN subunits physically associate with ecdysone receptors to represses br at the transcriptional level . We propose a model in which nuclear hormone receptors cooperate with the deneddylation machinery to temporally shutdown downstream target gene expression , conferring a spatial restriction on neural competence at the PWM .
Neural specification generates diverse neural cells that are located at exact positions necessary for specialized functions . It is well established that positional cues , such as extrinsic signals and intrinsic tissue-specific transcriptional factors , play key roles in neural specification and neuronal patterning . In Drosophila , the sensory bristles occupy stereotypical positions on the head , notum , abdomen , legs and anterior wing margins ( AWMs ) . Patterning of sensory bristles is primarily defined by the spatially restricted expressions of bHLH proneural proteins Achaete ( Ac ) and Scute ( Sc ) . High-level Ac and Sc expressions in the presumptive sensory organ precursors ( SOPs ) activate genetic programs for further specification and differentiation , leading to the generation of sensory bristles comprising neuron , sheath , hair , and socket cells [1] , [2] . A key target of the proneural proteins , the Zinc finger protein Senseless ( Sens ) , is turned on in smaller subsets of proneural cells , and the expression levels are further elevated in SOPs and SOP lineage cells [3] . Sens is required for the specifications of SOPs and SOP daughter cells pIIa [3] , [4] . Misexpression of Sens in epithelial cells is sufficient to induce sensory organ formation in a process bypassing the requirement of proneural proteins , indicating that Sens plays a key role in sensory organ formation [4] . In addition to spatially restricted expression of transcription factors , specification of SOPs also depends on steroid hormonal ecdysone ( 20-hydroxyecdysone , 20E ) signaling . For example , ecdysone signaling is required for the specification of chemosensory bristles at the AWM at late third instar larval stages [5] . Pulses of ecdysones set temporal boundaries in developmental processes like inducing molting and metamorphosis [6]–[10] . Ecdysone signaling is mediated through the heterodimeric complex between the ecdysone receptor EcR and the RXR ortholog Ultraspiracle ( USP ) that binds to the ecdysone response element ( EcRE ) [11] , [12] . Ligand binding transforms the EcR/USP complex from a repressed to active state , leading to transcriptional activation of a hierarchy of target genes . Among them , broad ( br ) is the key early gene that mediates ecdysone signaling in the induction of metamorphosis . The br gene encodes four isoforms BR-Z1 to BR-Z4 through alternative splicing [13] , [14] . In br mutants , responses of a large set of early and late genes to ecdysone signaling are abrogated [15] , [16] , and mutants die as wandering larvae unable to initiate puparium formation [17] , [18] . Dysregulation of br also disrupts sensory neuron differentiation and suppresses adult abdominal cuticle formation [5] , [19] . Therefore , tight regulation of br expression is crucial for development of various insect tissues . The COP9 signalosome ( CSN ) is a highly conserved protein complex initially identified in Arabidopsis for suppression of photomorphogenesis [20]–[22] . Subsequent identification and characterization in mammalian cells , insects and yeast reveal that the CSN complex participates in diverse cellular and developmental processes ( for reviews , see references [23] and [24] ) . The major CSN function is to deconjugate the ubiquitin-like peptide Nedd8 from cullins , the scaffolding proteins in cullin-RING ubiquitin ligases ( CRLs ) [25] . CSN-mediated deneddylation of cullins inactivates ubiquitin ligase activity and protects CRLs from turnover . Thus , cycling between neddylation and deneddylation maintains the physiological CRL activity [26] , [27] . Additional CSN-associated activities such as deubiquitination and phosphorylation are linked to the maintenance of target protein stability [28]–[30] . Studies in Drosophila have revealed several CSN functions in development and adult physiology . Transcriptome analysis has shown a role for CSN4 and CSN5 , two subunits of the CSN complex , in transcriptional repression of developmentally regulated genes [31] . CSN4 mutants show defect in larval molting while CSN5 mutations induce melanotic formation at larvae stages [32] . The CSN complex regulates protein degradation of CycE and Timeless during oogenesis and circadian rhythm , respectively [33] , [34] . By regulating Cul1 and Cul3 , the CSN complex has dual functions in dendrite morphogenesis [35] , [36] . Interestingly , the CSN activity confers specifically the intermediate response in graded Hedgehog signaling [37] . In an RNAi-based screening to identify genes involved in Notch signaling , CSN subunits were identified to be essential in binary cell fate determination in sensory organ development [38] . The bristles along the anterior and posterior wing margin succumb to different fates; AWM bristles are innervated by neurons beneath the cuticle while posterior wing margin ( PWM ) bristles are non-innervated . Both types of bristles require transcription factors Sens and the bHLH protein Daughterless for precursor cell selection during third instar larval and prepupal stages [4] . However , it is still unclear how the neural competence of PWM bristles is suppressed to prevent formation of neurons and associated neural cells . We have identified a role of the CSN complex in inhibition of the neural competence of non-innervated bristles through Sens downregulation at the onset of pupal development . Activation of ecdysone signaling at the prepupa-to-pupa transition is required for pupal BR-Z1 suppression , which leads to Sens suppression . The CSN complex and ecdysone receptors act in the same genetic pathway and form protein complexes to block BR-Z1 expression in pupal wings . Thus , our results demonstrated that the CSN complex acts as a nexus to convert temporal hormone stimulation to spatial restriction of neural competence .
Innervated sensory bristles , including stout and slender mechanosensory and spaced chemosensory bristles , are located along the AWM of wild-type wings . These sensory bristles grow dome-shaped sockets at the base ( Figure 1A ) , and are innervated by neurons [39] . Non-innervated bristles along the PWM , however , display only thin hairs without sockets ( Figure 1B ) . In CSN4null and CSN5null mutant clones , PWM bristles adopted morphological characteristics of innervated sensory bristles . Mutant bristles had thicker hairs surrounded by sockets ( arrowheads in Figure 1C , 1D ) . Some bristles had double hairs emerging from one large socket ( arrows ) , reminiscent of the double hair/double socket phenotype caused by fate transformation of SOP lineage cells [40] , [41] . Similar morphological changes of PWM bristles were also observed in wings expressing interference RNA ( RNAi ) by en-GAL4 to knockdown CSN1b , CSN2 , CSN3 , CSN6 and CSN7 in the posterior compartment ( Figure 1F–H , and Figure S1A , S1B ) , suggesting that these CSN subunits function together as a holoenzyme to suppress formation of innervated bristles at the PWM . The altered developmental process of PWM bristles in CSN mutants was examined for the expression of nuclear protein Hindsight ( Hnt ) /Pebbled in bristle lineage cells [42] . In wild-type wing discs at 20–24 hours ( h ) after puparium formation ( APF ) , clusters of AWM innervated bristle lineage cells had larger nuclei and expressed high levels of Hnt , while PWM non-innervated bristle cells , with smaller nuclei , expressed low levels of Hnt ( Figure S1C–C″ ) . In CSN4null and CSN5null PWM clones , cells with larger nuclei and high-level Hnt were observed ( Figure 1I , 1I′ and Figure S1D , S1D′ ) . Accumulations of Hnt in PWM cells were also detected in CSN1b , CSN2 and CSN7 knockdowns by en-GAL4 ( Figure 1M–P ) . While non-innervated PWM bristles contain no neurons or sheath cells [4] , [39] , these two types of cells were detected in PWM clones for CSN4null , as shown by Prospero ( Pros ) expression in pIIb precursors and sheath cells [43] and Elav expression in neurons [44] ( Figure 1J–K ) . These ectopic neurons extended axons as revealed by immunostaining for the microtubule-associated protein Futsch ( Figure 1L , 1L′ ) . Taken together , these morphological and molecular analyses indicate that the CSN inhibits the neural potential of PWM bristles . Overexpression of Sens in epithelial cells can induce innervated bristles [3] . As shown , overexpression of Sens by C96-GAL4 along the wing margin induced bristles with sockets around the PWM ( Figure 2A , arrowheads ) . Concomitantly , high levels of Hnt expressions were also detected ( Figure 2B ) , confirming that ectopic Sens expression induces innervated bristles at the PWM . With the induction of innervated sensory bristles by high levels of Sens , we then examined whether Sens was upregulated in CSN mutants . In CSN4null and CSN5null PWM cells , the Sens levels were elevated ( Figure 2C–D′ ) . The elevation of Sens expression was also detected in CSN6 , CSN2 and CSN3 knockdown cells , suggesting that CSN functions as a complex to suppress Sens expression ( Figure 2E , 2F and Figure S2A , S2B ) . Next we addressed whether Sens is required for innervated bristle formation in CSN mutant cells at the PWM . Double knockdown of CSN1b and CSN7 in bristle lineage cells by neur-GAL4 resulted in socket formation in PWM bristles ( Figure 2G ) , ectopic Sens expression ( Figure S2C ) , and consistently , higher levels of Hnt in PWM cells with large nuclei ( Figure 2I ) . When sens was knocked down simultaneously ( Figure S2D ) , those features of innervated bristles in CSN mutants were suppressed . Socket morphology was no longer visible ( Figure 2H ) , and most PWM cells had smaller nuclei and expressed lower levels of Hnt ( Figure 2J ) . Thus , Sens induction in CSN mutant cells is required for the formation of innervated bristles at the PWM . The proneural proteins Ac and Sc are specifically expressed in proneural clusters and precursors of AWM chemosensory bristles [45] . Formation of innervated sensory bristles in CSN mutant cells could be the consequence of ectopic proneural protein induction . However , Ac expression was not induced in CSN4null and CSN5null PWM cells . ( Figure 2K , 2K′ and Figure S2E–F′ ) . Also , removal of ac and sc in CSN4null clones still accumulated Sens in PWM cells ( Figure 2L , 2L′ ) . Thus , proneural proteins Ac and Sc are not required for Sens upregulation in CSN mutant cells at the PWM . The Zinc-finger transcriptional factor BR-Z1 induces Sens expression in response to ecdysone signaling in chemosensory precursors [5] . Thus , we tested if the CSN also downregulates BR-Z1 , leading to the suppression of Sens in PWM cells . As expected , the BR-Z1 levels were strongly elevated in CSN4null and CSN5null wing-disc cells 20–24 h APF ( Figure 3A–B′ ) . Elevated BR-Z1 levels were also detected in CSN2-knockdown cells ( Figure S3A ) . Distinct from Sens regulation , CSN suppression of BR-Z1 was ubiquitous in all wing-disc cells , not restricted to the wing margin . Furthermore , forced expression of BR-Z1 in bristle lineage cells by neur-GAL4 elevated Sens and Hnt expression and induced Elav-positive neurons at the PWM ( Figure 3C–E ) . In adult wings , all bristles at the PWM had dome-shape sockets ( Figure 3F ) , suggesting that BR-Z1 promotes the formation of innervated bristles at the PWM . The br locus encodes three additional isoforms BR-Z2 , BR-Z3 and BR-Z4 , and functional redundancy was observed among these isoforms [46] , [47] . We found that the activity to induce innervated bristles is not limited to BR-Z1 , as overexpression of BR-Z3 also promoted Sens accumulation and innervated bristle formation at the PWM ( Figure S3B , S3C ) . Lastly , we examined the role br plays in promotion of innervated bristle formation when CSN activity is abrogated . To completely inactivate all br isoforms to prevent functional redundancy , the br null allele , brnpr1 , was used . In control clones , Sens expression was elevated in CSN2 RNAi cells ( Figure 3G , 3G′ ) . However , Sens elevation was no longer detected in CSN2 RNAi cells when br was simultaneously inactivated ( Figure 3H , 3H′ ) . These results unequivocally indicate that br is the critical factor required in CSN mutant cells to upregulate Sens expression . In response to ecdysone pulses , BR-Z1 expression is upregulated in wing-disc cells at late third-instar larval stages and peaked at the prepupal stage of 2–6 h APF ( GFP-positive cells in Figure 4A–B′ ) [48] . BR-Z1 expression declined starting around 6–8 h APF and continued declining to almost undetectable level 24–28 h APF ( GFP-positive cells in Figure 4C–F′ ) . Quantification of immunofluorescent intensity following co-staining revealed that the BR-Z1 intensity was 1 . 6-fold of H3 intensity 2–6 h APF , but dramatically dropped to 0 . 07-fold of H3 24–28 h APF ( dashed line in Figure 4G , and Figure S4A–B″ ) , with the strongest reduction occurring between 12–14 h to 16–18 h APF . Strikingly , we found that the CSN initiates BR-Z1 suppression during this time period . From the late third-instar larval stage to 8 h APF , the BR-Z1 levels in CSN4null cells were comparable to neighboring heterozygous +/CSN4null cells , suggesting that CSN4 has no role in regulating BR-Z1 levels at these stages ( Figure 4A–C′ , solid line in 4G ) . However , while BR-Z1 continued to decline at the beginning of the prepupa-to-pupa transition , higher BR-Z1 levels in CSN4null clones were detected . Significant BR-Z1 upregulation in CSN4null cells was first detected 14–16 h APF ( Figure 4D , 4D′ , and arrow and solid line in 4G ) , two hours after the completion of prepupal development . The upregulation in CSN4null cells reached about two-fold of neighboring heterozygous cells 14–18 h APF and three-fold 18–20 APF , and the peak of a 10-fold increase was observed 24–28 h APF ( Figure 4E–F′ , solid line in 4G ) . The enhancement of BR-Z1 expression in CSN4null cells declined after 28 h APF ( solid line in Figure 4G ) . The activation of BR-Z1 expression in CSN mutants at pupal stages prefigures the timing in the upregulation of Sens . At the PWM , Sens was highly expressed in the precursors 6–8 h APF ( Figure 5A ) [4] , declined to lower levels in lineage cells 16–18 h APF ( Figure 5B ) , and became undetectable 20 h APF ( Figure 5C ) . In CSN4null cells , while the Sens levels remained unchanged compared to neighboring control cells 6–8 h APF ( Figure 5D , 5D′ ) , Sens was upregulated at the pupal stage of 16–18 h APF ( Figure 5E , 5E′ ) , and the upregulation was maintained even 20–24 h APF ( Figure 2C , 2C′ ) . Taken together , the CSN downregulates BR-Z1 and Sens levels in a time-dependent manner , detected only after the prepupa-to-pupa transition . To test whether ecdysone receptor activity is also involved in the switch from CSN-resistant to CSN-sensitive BR-Z1 suppression after the prepupa-to-pupa transition , the transcriptional activity of EcR was inactivated by the expression of dominant-negative EcRB1ΔC655F645A ( DN-EcR ) [49] . In DN-EcR expression clones , BR-Z1 expression in late larval wing discs was abolished ( Figure 6A , 6A′ ) , indicating that EcR positively regulates BR-Z1 expression [50] . In contrast , BR-Z1 expression was strongly induced in DN-EcR clones 20–24 h APF ( Figure 6B , 6B′ ) , indicating that transcriptionally active EcR represses BR-Z1 expression after the prepupa-to-pupa transition . To further delineate the temporal regulation of BR-Z1 from activation to repression , DN-EcR was overexpressed by en-GAL4 in the posterior compartment , in combination with temperature-sensitive Tub-GAL80ts . At the permissive temperature throughout larval to pupal stages , en-GAL4-driven DN-EcR expression was blocked by GAL80 , and BR-Z1 remained at low levels in both anterior and posterior compartments ( Figure 6C , 6G ) . At the non-permissive temperature , inactivation of GAL80 allowed expression of DN-EcR in the posterior compartment . We found that expression of DN-EcR from 4 h before puparium formation to 3 h APF maintained BR-Z1 repression in pupal wing discs at 21 h APF ( Figure 6D , 6G ) . However , the DN-EcR expression at 8–12 or 13–21 h APF induced BR-Z1 upregulation in the posterior compartment ( Figure 6E , 6F , 6G ) . Thus , active EcR is required to suppress BR-Z1 expression in pupal wings at and after the prepupa-to-pupa transition . EcR regulates BR-Z1 at the transcriptional level . To test whether the CSN also regulates BR-Z1 through transcriptional regulation , we measured the mRNA level for BR-Z1 in CSN mutants by semi-quantitative RT-PCR . While the BR-Z1 mRNA expression in en-GAL4 control pupal wing discs was undetected , expression of CSN2 RNAi by en-GAL4 induced the BR-Z1 mRNA level ( Figure 7A ) . To test the transcriptional regulation by the CSN , the reporter EcRE-LacZ which contains seven tandem repeats of EcRE was used [51] . In CSN4null and CSN5null clones , the expression levels from EcRE-lacZ were also elevated in comparison to neighboring heterozygous cells in pupal wings ( Figure 7B–C′ ) . The regulation of EcRE-lacZ is specific to the pupal stage , as EcRE-LacZ expression was not elevated in CSN5null clones at the prepupal stage ( Figure 7D , 7D′ ) . Thus , the CSN-downregulated BR-Z1 expression is at the transcriptional level and after the prepupa-to-pupa transition . As a transcriptional regulator , EcRA was localized in nuclei in wing-disc cells ( Figure 7E , 7E′ ) . Examination of protein location of the Myc-tagged CSN2 and CSN4 subunits and the endogenous CSN5 subunit in wing disc cells showed that they primarily localized in nuclei as EcRA ( Figure 7E , 7E″ and Figure S5A–B″ ) . Thus , several CSN subunits and EcRA colocalize in nuclei , consistent with their roles in transcription regulation of BR-Z1 . We then performed co-immunoprecipitation to detect protein-protein interaction between CSN subunits and EcR . The S2 cells were co-transfected with expression plasmids for one of Flag-tagged CSN subunits and Myc-tagged EcRA or EcRB1 . Both EcRA and EcRB1 were specifically detected in the Flag immunoprecipitates of CSN2 , CSN4 , CSN5 and the positive control USP , but not the negative control GFP ( Figure 7F ) . The abilities of various subunits to associate with EcR suggest that the CSN interacts with EcR as protein complexes . The results show that inactivating either the CSN or EcR is sufficient to derepress BR-Z1 expression at the pupal stage ( Figure 3A–B′ , 6B ) . We then examined the effect on BR-Z1 expression when both CSN and EcR were inactivated . In double mutants that expressed DN-EcR in CSN5null mutant clones , BR-Z1 was upregulated to the level comparable to that in single mutants of CSN5null or DN-EcR ( Figure 7G , 7G′ ) . This result supports the notion that the CSN and EcR function as protein complexes to repress BR-Z1 expression . Deneddylation is coupled with neddylation to cycle cullins between two neddylation states for optimal CRL activities [52] . We first addressed whether the deneddylating activity of the CSN is required to repress BR-Z1 and Sens . Elevated BR-Z1 and Sens expressions in CSN5null cells were completely suppressed by the wild-type CSN5 transgene driven by MS1096-GAL4 in wing discs ( Figure 8A , 8A′ 8C , 8C′ ) . However , expression of the deneddylation-defective CSN5D148N mutant [26] failed to decrease BR-Z1 and Sens levels in CSN5null clones ( Figure 8B , 8B′ , 8D , 8D′ ) . This result suggests the Nedd8 conjugation is involved in BR-Z1 and Sens regulations . Indeed , Nedd8AN015 null mutant clones located at the PWM of pupal wings also exhibited BR-Z1 and Sens upregulations ( Figure 8E–E″ ) . We then addressed whether and which cullins are affected in CSN mutants for control of Sens and BR-Z1 suppression . Individual cullin proteins were inactivated by generating clones for the null alleles Cul1EX , Cul3C7 and Cul4JJ11 . Interestingly , these Cul mutants showed upregulations of BR-Z1 and Sens in pupal wing discs of 20–24 h APF ( Figure 8F–H″ ) . Consistent with the pupa-specific regulation , BR-Z1 levels remained unchanged in the Cul1EX , Cul3C7 and Cul4JJ11 clones of late third instar larval wing discs ( Figure S6A–C″ ) . Therefore , our results indicate that cycling multiple cullin components between neddylation and deneddylation is required to completely suppress BR-Z1 and Sens in pupal PWM cells .
The difference between AWM and PWM bristles is that AWM bristles are innervated by sensory neurons to sense mechanical or chemical stimulations . PWM bristles with long and thin hair structures are non-innervated and without socket support , and they are non-functional in sense detection . During development , the transcription factor Sens is expressed dynamically at the wing margin . At the third-instar larval stage , Sens is initially expressed in both anterior and posterior margins . Expressions of proneural proteins Ac and Sc , which are limited to anterior cells , maintain Sens in developing chemosensory bristle cells [4] . At 6–8 h APF , Sens is re-activated in precursors of both anterior mechanosensory and posterior non-innervated bristles for precursor specification . At the pupal stage , however , Sens expression is suppressed in bristle lineage cells , thus preventing neural differentiation at PWM . Timely suppression of Sens expression plays a critical role in disruption of neural differentiation of PWM bristles , as continuing Sens expression allows PWM bristles to differentiate into innervated bristles , even after precursor specification . While sens knockdown in bristle lineage cells prevented transformation of PWM non-innervated to innervated bristles in CSN mutants , it did not transform innervated bristles to non-innervated bristles at AWM ( Figure S7A , S7B ) , indicating that diminishment of Sens levels in the lineage cells is not the sole determinant in non-innervated bristle formation , and additional mechanisms are activated to disrupt the differentiation of neurons and the associated neural cells at the PWM . It is shown that programmed cell death of the underlying neurons and sheath cells is one such mechanism . Suppression of programmed cell death , either by overexpression of anti-apoptotic protein p35 or by blocking Dmyb and Grim activities , induces ectopic neurons associated with PWM bristles [4] , [53] . While overexpression of p35 prevented neuronal death ( Figure S8B ) , we found that the morphology of PWM hairs was not affected and lacked associated sockets ( ) . Thus , apoptosis has an effect only on internal neurons and sheath cells , while other mechanisms are required to prevent socket cell formation and to differentially regulate hair elongation at PWM . Pulses of ecdysone provide temporal information to coordinate various developmental processes in insect development . Activation of ecdysone signaling in late larval and prepupal stages are required for larva-to-prepupa and prepupa-to-pupa transitions , respectively . In contrast to br activation in the larva-to-prepupa transition , br expression was thought to be low even with a large ecdysone pulse in the pupal stage [10] , [19] . Our study indicates that this is indeed due to repression of br by ecdysone signaling , as shown by the expressions of BR-Z1 induced by dominant-negative EcR in pupal wings . The repressive regulation requires the input of the CSN , as BR-Z1 was also upregulated in pupal wing-disc cells of CSN4null and CSN5null mutants . Therefore , our results suggest that by integrating the CSN , ecdysone signaling activity plays a non-canonical role in the prepupa-to-pupa transition , and the br repression is critical to Sens suppression during PWM bristle development . Neddylation is required for activating CRL activity both in vivo and in vitro [52] , [54]–[56] . Deneddylation , while inactivating CRL in vitro , promotes CRL activity in vivo by protecting them from cellular degradation , thus maintaining a physiological pool of CRL for the next round of neddylation [26] , [27] . Our results showed that repression of ecdysone signaling target genes requires cycling of Nedd8 substrates between neddylation and deneddylation states . Similar to CSN mutants , the protein levels of BR-Z1 and Sens were elevated in Nedd8 mutants . Also , CSN suppression of BR-Z1 and Sens depends on the deneddylating activity residing in the CSN5 subunit , as the deneddylation-defective CSN5 mutant failed to rescue the CSN5 phenotype . Thus , the requirement of neddylation and deneddylation is consistent with the involvement of multiple cullin proteins that are the major substrates of Nedd8 conjugation . Transcription regulation of ecdysone responsive genes requires multiple activators and repressors [57]–[61] whose protein activity and stabilities could be in turn regulated by multiple cullin-organized ubiquitin E3 ligases . Alternatively , these cullin ligases might act on the same set of targets , like the regulations of protein stability of Ci by multiple cullin ligases [54] . Our results on the requirement of the CSN holoenzyme in development of PWM bristles suggest different usages of CSN subunits in nuclear receptor signaling . A previous study showed that CSN4 but not CSN5 is required for larval molting [32] . In mammals , single CSN2 and CSN5 subunits interact with nuclear receptors to function as a co-repressor and a co-activator , respectively , in steroid hormone signaling [62]–[64] . Drosophila CSN2 physically interacts with several Drosophila hormone receptors including EcR [62] . In our study , seven out of the eight CSN subunits were shown to be required for Sens suppression and CSN2 , CSN4 and CSN5 were shown to associate with EcR , strongly suggesting that the CSN associates with EcR as a complex . It has been shown that the CSN complex interacts with multiple cullins to protect them from self-ubiquitination and degradation [26] , [65] . Thus , the association between EcR and the CSN holoenzyme might provide a platform for recruiting and maintaining multiple CRLs , composed of cullins and their associated components , in the proximity for efficient down-regulation of EcR-mediated transcription ( Figure S9 ) . Two models are proposed here for the CSN-dependent switch of EcRs from activation to repression of br ( Figure S9 ) . In model A , the switch depends on the induction of specific co-repressors upon the pulse of ecdysone at the prepupa-to-pupa transition . The co-repressor could then replace the co-activator , a step that could be facilitated by the nearby CRLs through the ubiquitination of co-activators for subsequent degradation , for example . In model B , the switch depends on the activation of specific CRLs by ecdysone signaling at the prepupa-to-pupa transition . The activation could be mediated through expression of the specific substrate receptors of CRLs , or phosphorylation of substrates to induce binding to substrate receptors . Once activated , these CRLs could inactivate co-activators to shut down br transcription . It is unlikely that the CSN-mediated pupa-specific BR-Z1 and Sens repression is through mechanisms involving changes in the CSN and EcR protein expression levels during the prepupa-to-pupa transition , as the expression of the catalytic subunit CSN5 and EcRA were expressed at comparable levels in wing discs at late third instar larval and pupal stages ( Figure S10A–C , and S11A–F′ ) . Also , the CSN is active in third-instar larvae for normal wing disc development [37] . Our finding that suppression of EcR target genes in pupae through the CSN and multiple cullins provide insights for a novel negative regulation of steroid hormone signaling in metazoa .
The following fly strains were used in this study . Mutant alleles: CSN5null , CSN4null [32] , Nedd8AN015 , Cul1EX [54] , Cul3C7 [66] , Cul4JJ11 [67] , sc10-1 [68] , brnpr1 [69] , [70] . Reporter line: EcRE-LacZ ( II , BL4516 ) [71] . GAL4 drivers: C96-GAL4 [72] , en-GAL4 [73] , neur-GAL4 [74] and MS1096-GAL4 [75] . UAS lines: UAS-BR-Z1 , UAS-BR-Z3 [76] , UAS-p35 ( P[UAS-p35 . HB]H3 , BL6298 ) [77] , UAS-EcRB1ΔC655F645A [49] , UAS-CSN5 , UAS-CSN5D148N [26] , UAS-myc-CSN2 ( this study ) , UAS-myc-CSN4 ( this study ) , and UAS-3Xflag-sens ( this study ) . The UAS-CSN1bRNAi ( v34727 ) , UAS-CSN2RNAi ( v48044 ) , UAS-CSN3RNAi ( v101516 ) , UAS-CSN6RNAi ( v22308 ) and UAS-CSN7RNAi ( v40691 ) were obtained from Vienna Drosophila RNAi Center ( VDRC ) . The UAS-sensRNAi ( NIG 32120R-2 ) was obtained from NIG-FLY stock center . Clones were generated by FLP/FRT-mediated mitotic recombination [78] . The mutant or MARCM clones in developing tissues were identified by the absence or presence of GFP , respectively . For transient overexpression using GAL80ts , developing animals were grown at 18°C . Prepupae or pupae were then shifted to 37°C for one hour to rapidly inactivate GAL80ts to allow fast GAL4-activated gene expression , and then incubated at 29°C for several hours as indicated . Larvae and prepupae ( 0–10 h APF ) were dissected in 1×PBS for isolating wing discs , which were then fixed in 4% paraformaldehyde for 15 minutes at room temperature ( RT ) . For pupae 12–36 h APF , pupal cases were removed in 1×PBS and the whole pupae were fixed in 4% paraformaldehyde at RT for one hour . Internal pupal membranes on the surface of wing tissues were subsequently removed and the resulting wing discs were fixed again in 4% paraformaldehyde for 15 minutes . The following primary antibodies were used: mouse anti-Ac ( DSHB , 1∶5 ) [79] , mouse anti-ß-galactosidase 40-1a ( DSHB , 1∶500 ) [80] , mouse anti-BR-Z1 Z1 . 3C11 . OA1 ( DSHB , 1∶250 ) [48] , mouse anti-EcRA 15G1a ( DSHB , 1∶100 ) [81] , mouse anti-Elav 9F8A9 ( DSHB , 1∶500 ) [82] , mouse anti-Futsch 22C10 ( DSHB , 1∶500 ) [83] , mouse anti-Hnt 1G9 ( DSHB , 1∶25 ) [84] , mouse anti-Pros MR1A ( DSHB , 1∶100 ) [85] , rabbit anti-c-Myc A-14 ( Santa Cruz , 1∶500 ) , mouse anti-Pol II CTD4H8 ( Santa Cruz , 1∶500 ) , rabbit anti-Histone H3 GTX122148 ( GeneTex , 1∶500 ) , rabbit anti-JAB1 ( CSN5 ) ( Sigma , 1∶250 ) , and guinea pig anti-Sens ( 1∶2000 ) [3] . The anti-JAB1/CSN5 antibody was pre-cleaned by incubation of 10× volume antibody with CSN5null larval brain tissues at 4°C overnight . For assaying anti-BR-Z1 and anti-histone H3 immunostaining intensity , all images were obtained from the Zeiss LSM 510 Meta microscope with the same setting , and analyzed by Image J . For comparing BR-Z1 intensity in CSN4null clones and CSN4null/+ cells , the mean nuclear anti-BR-Z1 intensity per pixel in each wing disc was calculated from twenty randomly selected cells within CSN4null clones or the neighboring heterozygous tissue . For comparing BR-Z1 and histone H3 intensity , the mean intensity per pixel was obtained from the same twenty randomly selected cells in each w1118 wing discs . The mean intensity of CSN5 and Pol II in w1118 wing discs was measured using the same method . S2 cells were maintained in Schneider medium ( Invitrogen ) supplemented with 10% fetal bovine serum . Cells were transiently transfected with UAS-based expression plasmids together with driver pWA-GAL4 [86] . Transfection was carried out using Cellfectin II reagent ( Invitrogen ) . The expression plasmids for CSN2 , 4 , 5 , EcRA , and EcRB1 were constructed by cloning the ORFs into Drosophila gateway vectors pTFW or pTMW ( obtained from Drosophila Genomics Resource Center , DGRC ) . The UAS-3Xflag-sens plasmid was constructed by cloning the 3Xflag-sens DNA into the pUAST vector . The harvested cells were washed twice with ice-cold 1×PBS , and then lysed in mRIPA buffer [50 mM Tris-HCl ( pH 7 . 8 ) , 150 mM NaCl , 5 mM EDTA ( pH 8 . 0 ) , 0 . 5% Triton X-100 , 0 . 5% NP-40] supplemented with complete protease inhibitors ( Roche ) . Lysates were diluted in mRIPA buffer to a final concentration of 5 ug/ul . 20 ul ANTI-FLAG M2 Affinity Gel ( Sigma ) was added to 1 mL lysate and incubated for overnight at 4°C . Associated protein complexes were analyzed by SDS-PAGE followed by western blotting . The primary antibodies used for western blots were mouse anti-Flag M2 ( Sigma , 1∶10000 ) and mouse anti-c-Myc 9E10 ( Santa Cruz , 1∶5000 ) . Secondary antibody was goat anti-mouse HRP used at 1∶5000 . Pupal wing discs were dissected in ice-cold 1×PBS . Total mRNA from thirty to forty pairs of pupal wing discs were extracted by TRIzol RNA Isolation Reagents ( Invitrogen ) and reverse-transcribed to cDNA by MMLV RT ( PROSPEC ) . Expression of BR-Z1 mRNA was detected by the primer set: 5′—TGAAG AGGAG TGGTG ATTGA GCTGC—3′ and 5′—CCATC ACAAG TGCCT CCGGC ATC—3′ . The mRNA for rp49 was used as the internal control . | A critical step in building a functional nervous system is to generate neurons at the appropriate locations . Neural competence is acquired at the precursor stage with the expression of specific transcription factors . One such critical factor is Senseless ( Sens ) , as precursors lacking Sens fail to develop to neurons . Here we describe the critical role of protein complex COP9 signalosome ( CSN ) that regulates Sens expression by integrating temporal and spatial information . This was studied in developing Drosophila wing tissues , in which the anterior wing margin develops neuron-innervated bristles , while the posterior wing margin develops non-innervated bristles . The CSN complex is required for the anterior-posterior difference in spatial patterning of neuron formation , and posterior cells lacking CSN develop innervated bristles like anterior cells . CSN accomplishes this by transforming the temporal hormonal ecdysone signaling from activation to repression of downstream target BR-Z1 . As BR-Z1 itself is a transcription activator , repression of BR-Z1 in turn leads to repression of Sens in posterior wing margin , eventually terminating the neural competence . Repression of BR-Z1 expression requires the interaction between the CSN complex and the ecdysone receptors . Our results suggest a novel CSN-mediated regulation that converts temporal hormone signaling to the patterning of neurons at the right place . | [
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] | 2014 | The COP9 Signalosome Converts Temporal Hormone Signaling to Spatial Restriction on Neural Competence |
We present a network framework for analyzing multi-level regulation in higher eukaryotes based on systematic integration of various high-throughput datasets . The network , namely the integrated regulatory network , consists of three major types of regulation: TF→gene , TF→miRNA and miRNA→gene . We identified the target genes and target miRNAs for a set of TFs based on the ChIP-Seq binding profiles , the predicted targets of miRNAs using annotated 3′UTR sequences and conservation information . Making use of the system-wide RNA-Seq profiles , we classified transcription factors into positive and negative regulators and assigned a sign for each regulatory interaction . Other types of edges such as protein-protein interactions and potential intra-regulations between miRNAs based on the embedding of miRNAs in their host genes were further incorporated . We examined the topological structures of the network , including its hierarchical organization and motif enrichment . We found that transcription factors downstream of the hierarchy distinguish themselves by expressing more uniformly at various tissues , have more interacting partners , and are more likely to be essential . We found an over-representation of notable network motifs , including a FFL in which a miRNA cost-effectively shuts down a transcription factor and its target . We used data of C . elegans from the modENCODE project as a primary model to illustrate our framework , but further verified the results using other two data sets . As more and more genome-wide ChIP-Seq and RNA-Seq data becomes available in the near future , our methods of data integration have various potential applications .
Eukaryotic gene regulation is performed at multiple levels , each distinguished by different spatial and temporal characteristics . The combination and orchestration between regulatory mechanisms in various levels are central to a precise gene expression pattern , which is essential to many critical biological processes [1] , [2] . Transcriptional regulation and post-transcriptional regulation , mediated by regulators including transcription factors ( TFs ) and small non-coding RNAs , such as microRNAs ( miRNAs ) , are two of the most important regulatory mechanisms [3] , [4] . At the transcriptional level , TFs bind to promoters and enhancers to either activate or repress gene transcription [4] . At the post-transcriptional level , miRNAs repress the expression of genes by degrading or inhibiting the translation of their target mRNAs [5] , [6] . In spite of the dramatic differences in their molecular types , TFs and miRNAs share a common “logic” for the control of gene expression [7] . Both of them are trans-acting factors that function through recognizing and binding specific cis-regulatory elements in DNA or RNA . TFs bind to DNA elements often located in or near their target genes , while miRNAs hybridize to RNA elements mostly located in the 3′ untranslated region ( 3′UTR ) of their target mRNAs . TFs and miRNAs tightly coordinate with each other to ensure accurate and precise gene expression . Furthermore , translated proteins form complexes via physical interactions . These complexes can function only if their constituents are properly regulated . Therefore , each TF or miRNA regulates a large number of interacting target genes [8]–[11] and different TFs and miRNAs control one gene in a combinatorial manner [3] , [12] , [13] . This essentially forms an integrated gene regulatory network by connecting TFs and miRNAs with their interacting targets . A deep investigation of this network would help to further understand the “language” of gene expression regulation at multiple levels . Network analysis has proven to be useful in unraveling the complexity of biological regulation [14]–[16] . Different approaches can be employed to gain more insight into the design principles of biological networks . Recently , studies have shown that transcriptional regulation follows a hierarchical organization and regulators at different levels have their own characteristics [17] . In particular , the rearrangement of networks into hierarchy facilitates comparison with other commonplace systems , and provide more intuitive understanding of biological networks [18] . Apart from a top-down approach , one could also study networks via a bottom-up approach by identifying their simple building blocks [19] , [20] . These blocks , referred to as network motifs , are patterns that recur within a network at numbers that are significantly higher than expected at random . Examples such as feedback loops and feed-forward loops in transcriptional regulatory networks are found to be conserved in diverse organisms from bacteria to human [20]–[22] , and experimentally verified to perform distinct functions like pulse generator and response accelerator [23] . Even though numerous efforts have been placed on the network analysis of biological regulation , most of the earlier studies focused on the transcriptional level . More recently , several system-wide studies have attempted to integrate regulation by TFs at the transcriptional level and that by miRNAs at the post-transcriptional level [24]–[26] . Despite the new insights they provided , the datasets were limited by their coverage and were mostly based merely on computational predictions , which have high false positive rate and were potentially biased . To overcome the limitations of previous studies , we have used the genome-wide experimental datasets for TF binding created by the model organism encyclopedia of DNA elements ( modENCODE ) project . The modENCODE project , launched in 2007 , aims to generate a comprehensive annotation of functional elements in the C . elegans and D . melanogaster genomes [27]–[29] . Using recently developed techniques such as ChIP-Seq [30] and RNA-Seq [31] , a large amount of data , including the genomic binding data for more than 20 TFs , expression profiles of all protein-coding genes and miRNAs across the developmental time course , as well as refined annotation of 3′UTRs and their regulatory elements in C . elegans [32] have been generated . Apart from C . elegans and D . melanogaster , similar genome-wide datasets in other higher eukaryotes such as human and mouse are emerging . Together with existing information such as protein-protein interactions and miRNAs target prediction in all these organisms , there is an unprecedented opportunity to examine various levels of eukaryotic regulation . Toward this goal , the proper integration of various datasets plays an essential role . In this study , we propose an integrated network framework for analyzing multi-level regulation in higher eukaryotes , namely the integrated regulatory network . To our knowledge , this is the first attempt to construct system-wide network using experimentally identified TF target genes and miRNAs . More specifically , we identified the target genes and target miRNAs for a set of TFs based on the ChIP-Seq binding profiles . The interactions were then integrated with predicted targets of miRNAs , which are based on annotated 3′UTRs ( the 3′UTRome ) and conservation information . Making use of the system-wide RNA-Seq profiles , we classified the transcription factors into positive and negative regulators and thus assigned a sign for each regulatory interaction . Protein-protein interactions and a novel intra-regulation between miRNAs using the embedding of miRNAs in their host genes were further incorporated . Leveraging the rich data generated by the modENCODE project , we use C . elegans as a primary model to illustrate our formalism and further confirmed our results in human and mouse . As more and more genome-wide ChIP-Seq and RNA-Seq data are generated via the modENCODE and ENCODE project [33] in the near future , the methods of data integration proposed in this work have various potential applications .
At the heart of our study is the construction of an integrated regulatory network . The integrated network consists of three major network components: TF-Gene regulatory network , TF-miRNA regulatory network and miRNA-Gene regulatory network . The TF-Gene and TF-miRNA interactions are extracted from ChIP-Seq binding profiles . Predicted targets of miRNAs are identified by the PicTar and TargetScan algorithm [9] using the 3′UTRome , and the predictions are further refined by conservation information . With the basic network in hand , we color the edges in terms of their signs of regulation via expression data , and incorporate extra edges by protein-protein interactions ( see Figure 1 for a summary and the Materials and Methods for details ) . The basic integrated regulatory network of C . elegans consists of three types of nodes: 393 TFs ( among which 22 have target protein-coding genes and target miRNAs available ) , 5 , 574 non-TF protein-coding genes and 160 miRNAs . There are 22 , 096 TF-gene ( including TF-TF ) interactions , 452 TF-miRNA interactions , and 10 , 069 miRNA-gene interactions ( Figure 2 ) . The number of targets varies dramatically among the 22 TFs , e . g . the number of miRNA targets range from 2 to 73 with a median of 17 . Although the difference in target numbers may arise due to experimental parameters such as the sequencing depth and the data quality , it also reflects the biological functions of transcription factors . For the 22 TFs , the number of target protein coding genes and the number of target miRNAs are positively correlated ( r = 0 . 9 , P<10−8 ) . We compared the number of regulatory miRNAs for TFs with that of non-TFs and found that non-TF mRNAs were on average regulated by 4 . 6 miRNAs , whereas TF mRNAs were regulated by 6 . 3 miRNAs . This suggests that miRNAs are more likely to regulate TFs than non-TFs ( P = 1 . 2E-6 , Wilcoxon Rank Sum test ) , which is consistent with previous reports [25] . To have a systematic overview of the integrated network , we examine the degree distribution of the network . As a result of different types of nodes and edges , there are several kinds of degree distributions ( Figure 3 ) . We examined the number of regulatory TFs for miRNAs as well as for protein-coding genes , and found that both are best fitted by an exponential distribution ( R2 = 0 . 86 , 0 . 84 ) , implying that a single target gene or miRNA is less likely to be regulated by many TFs simultaneously ( Figure 3 , top left and right ) . The number of target genes , and target miRNAs for the 22 TFs , on the other hand , are shown in Table S4 . While it is hard to infer the underlying distribution , the number of target genes varies quite a lot . Of particular interest in the integrated network are the miRNA nodes , as they possess both in-degree ( the number of regulatory TFs ) and out-degree ( the number of their target genes ) . Our analysis indicates that both in-degrees and out-degrees of miRNAs are best fitted by an exponential distribution ( R2 = 0 . 95 , 0 . 81 ) ( Figure 3 , bottom left and right ) , which is distinct from the power law distribution exhibited by many other biological networks . However , the maximum in- and out-degrees of miRNAs are 20 and 200 respectively , and are still much larger than expected by chance [25] . We calculated the correlation between in- and out-degrees for miRNAs and found a weak positive correlation ( r = 0 . 2 , P<0 . 01 ) . This is a mathematical indication of loopy structures in the network . It has been suggested that combinatorial regulation , the tendency of two or more regulators controlling the same target , plays an important role in transcriptional regulation [45]–[49] . Apart from the case of two TFs , the combinatorial effects of a TF-miRNA pair have recently been addressed [24] , [50] , [51] . To explore this combinatorial regulation via the integrated network in C . elegans , we examined the tendency of sharing common protein-coding targets between 22 TFs and 160 miRNAs . Many TF-miRNA pairs show significant target overlap in a hypergeometric test , which are presumably responsible for the same function ( Figure S2 ) . Similarly , we quantified for each possible pair of TFs , the tendency of sharing common protein-coding targets ( Figure S3 ) and common miRNAs ( Figure S4 ) , and found many significant pairs . To better visualize the regulatory interactions in an integrated regulatory network , we built an intuitive hierarchy comprising of TFs and miRNA that would allow a clear mining of underlying regulatory association between various regulators . A conventional hierarchy requires all regulatory interactions to point down in the hierarchical structure; no regulators regulate those above them . This requirement might pose problems in the presence of cycles in the network , which is the case when miRNA are included in the integrated network . To overcome this problem , we used only the transcriptional regulatory interactions to first build a core hierarchy strictly following “chain of command” pointing down as used in previous studies ( see Materials and Methods ) [17] . In C . elegans , this approach results in 3 layers of TFs with 9 at the top , 11 in the middle and 2 TFs in the bottom layer , respectively ( Figure 4A ) . The interactions involving the miRNAs were then added to this core hierarchy to build the integrated hierarchy . The importance of hierarchical analysis is signified by the fact that TFs at different levels are found to have different characteristics . We correlated the hierarchical levels of the 22 TFs with various functional genomics data ( see Table S1 ) , and observed several features that are significantly different between TFs from different levels . First of all , we found that TFs downstream of the hierarchy are more likely to be essential , whereas those at the top are likely to be non-essential ( statistically , this result is not significant due to small sample size ) . More specifically , while 5 out of the 22 TFs are experimentally verified to be essential for the survival of the C . elegans according to RNAi screening [52] , four of them are in the middle or the bottom layers , and only one is in the top layer . Secondly , we found that the TFs in different layers possess different topological properties in the C . elegans protein-protein interaction network . In particular , the average numbers of interaction partners for TFs in the top , middle and bottom layers are 6 , 26 and 95 respectively ( Figure 4B ) . Thirdly , we calculated and compared the tissue specificity of TF at the three layers in 8 different tissues ( see Materials and Methods ) and found that those lower layer TFs are more uniformly expressed in these tissues ( Figure 4C ) . Finally , of particular interest is the number of miRNA regulations targeting the three layers . We found that of the three layers , TFs in the middle layer are more likely to be regulated by miRNAs ( Figure 4D ) . The hierarchical network is constructed to make TFs at higher layers regulating those at lower layers , thus higher layer TFs might also have more target genes and miRNAs . We examined the correlation between other properties of TFs and their corresponding levels , including their expression , conservation information , stage specificities ( see Materials and Methods ) and their target miRNAs across the worm developmental time course . In our analysis , these properties did not show significant differences between the three layers . However , some of them were reported to be significant in the hierarchical network in yeast [53] . While the integrated network we constructed describes the target genes and miRNAs of TFs , the kind of the regulatory interactions are not known . To provide further insights , we examined for each TF , the correlation between the binding signals around the TSS and the corresponding target gene expression ( see Materials and Methods for details ) . As shown in Figure 5 , in C . elegans , many TFs show either a consistent positive or negative correlation from −2 kb to +2 kb of the TSS . We therefore classified the 22 TFs into two classes: positive regulators ( e . g . ALR-1 , CEH-14 ) and negative regulators ( e . g . EGL-5 and EOR-1 ) . With the assignment of positive and negative regulators , an edge in the network pointing from one of the 22 TFs is regarded either as a positive edge or a negative edge , depending on the class of the TF . In addition , we regarded regulatory interactions by miRNAs as negative , due to the very nature of miRNAs [54] . As a result , all of the 32 , 617 regulatory interactions in the integrated regulatory network of C . elegans were assigned with signs . Previous studies suggest that network motifs , a set of recurring patterns originally defined in transcription regulatory networks , are responsible for carrying out specific information-processing functions . Moreover , studies on network motifs have found that motifs with the same geometrical structure but different signs of regulation could have profound differences in terms of functions [23] . Here we categorized several motifs in the C . elegans integrated regulatory network ( Figure 6 ) . A transcriptional auto-regulatory feedback loop is the simplest network motif built out of a transcription factor regulating its own transcription ( Figure 6B , ( i ) and ( ii ) ) . Among the 22 TFs , we identified 6 auto-regulated factors: ELT-3 , PHA-4 , UNC-130 , EGL-5 , LIN-15B and MAB-5 . However , there is no evidence to show that auto-regulation is over-represented in our data set ( P>0 . 1 , permutation test ) , probably due to the small number of TFs . We further divide the auto-regulators into negative auto-regulation ( EGL-5 , LIN-15B and MAB-5 ) if the TF is a repressor and positive auto-regulation ( ELT-3 , PHA-4 and UNC-130 ) if it is an activator [23] . In general , positive regulators ( PAR ) reinforce a signal while negative auto-regulators ( NAR ) stabilize a system . Both of the NAR and PAR have been frequently reported in previous studies [55]–[58] . Particularly , the NAR motif occurs in about half of the repressors in E . coli [59] , and in many eukaryotic repressors [11] . In the integrated regulatory network , there are 452 TF→miRNA regulatory relationships and 81 miRNA→TF regultory relationships . It has been shown that the TF⇔miRNA composite feedback loops ( a TF that regulates a miRNA is itself regulated by that same miRNA ) occur more frequently than expected by chance in C . elegans ( Figure 6A , ( i ) ) . Without taking signs into account , we identified 15 TF⇔miRNA miRNA composite feedback loops ( see Table S2 ) from our integrated network , which is moderately over-represented ( P = 0 . 07 , permutation test ) . We extensively constructed all 3-node sub-graphs ( see Figure S5 and S6 ) in the integrated regulatory network , and compared their occurrence with what would be expected in an ensemble of random integrated networks . The counting of different sub-graphs and network randomization were performed by a sampling tool called FANMOD [60] , [61] ( see Materials and Methods for details ) . Without considering the signs of interactions , we found a set of 5 over-represented 3-node motifs in the integrated network ( Figure 6A ) . Motif A ( iii ) is the traditional transcription factors mediated feed-forward loop ( FFL ) , which is known to be enriched in the transcriptional regulatory networks of organisms like yeast and E . coli [62]–[64] . Motif A ( ii ) is similar to motif A ( iii ) except the target gene is replaced by a miRNA . Motifs A ( v ) and A ( vi ) are novel , and they share a common construction feature in which a miRNA regulates a TF as well as its downstream target . We then repeated the procedures with signs taken into account . Figure 6B demonstrates a list of enriched motifs in the integrated network with the signs taken into consideration . Motif B ( iv ) is the well known coherent type 1 FFL [20] . B ( iii ) , B ( v ) and B ( vii ) share a common design structure: a TF as well as its downstream target ( gene , TF or miRNA ) are simultaneously repressed by a common TF . Interestingly , these motifs are all coherent in the sense the indirect path has the same sign as the direct path . B ( vi ) is a composite motif that consists of a toggle switch formed by a pair of mutually repressing TFs , and both TFs repress a common miRNA . In principle , both enriched and depleted motifs are worth studying , however , no significantly depleted motif was found in our network . The integrated regulatory network we constructed has demonstrated how miRNAs coordinate the transcriptional activities . To systematically explore the coordination of cellular activities by miRNAs , we extended our study to two other levels of miRNA-mediated regulations . First , miRNAs regulate protein complexes by regulating their individual components . Systematically , these could be examined using various genome-wide protein-protein interaction ( PPI ) networks . We studied the regulation in C . elegans using a PPI network downloaded from Worm Interactome Database [65] ( see Materials and Methods for details ) . The network contains 6 , 125 nodes and 177 , 267 edges . From the level of individual proteins , we correlated the degree in the PPI network with the number of regulatory miRNAs . The results indicate that miRNAs tend to regulate hub genes in the PPI network , agreeing with previous observation by Liang et al [66] . In addition , the same pattern is observed in the transcriptional regulation of hub genes . For instance , the genes with degree >20 are on average regulated by 1 . 32 miRNAs , significantly greater than genes with degree ≤20 , which on average have 0 . 95 regulatory miRNAs ( P = 0 . 004 , Wilcoxon Rank Sum test ) . On the other hand , the same set of PPI hubs are regulated by 3 . 40 TFs , significantly higher than the rest , which are regulated by 2 . 03 TFs ( P = 2E-6 , Wilcoxon Rank Sum test ) . Apart from the level of individual proteins , we studied how interacting proteins are collectively regulated by a miRNA by introducing an additional type of edge ( protein-protein interaction ) to the integrated gene regulatory network . We found that , compared to a randomized network with the same degree distribution , interacting proteins in the PPI network are more likely to be regulated by the same miRNAs ( P = 10−7 ) . In other words , we observed another interesting motif with a pair of interacting proteins being regulated by a common miRNA ( Figure 6C ) [67] . Secondly , the embedment of miRNAs in their host genes hinges at a novel intra-regulation between miRNAs . In C . elegans , 60 miRNAs are embedded within the intron of a protein-coding gene ( see Table S3 ) , of which 39 are in the sense orientation ( P = 0 . 007 ) . These miRNAs are likely to be co-transcribed with their host gene [6] , [68] . We examined the regulatory relationship between the miRNAs and their host gene . The regulatory relationships among the 39 miRNA/host-gene pairs form a small miRNA-host network consisting of 5 interactions ( Figure 7 ) . In the network , a directional edge indicates a regulatory relationship from a miRNA to the host gene of another miRNA ( possibly itself ) . As shown in Figure 7 , mir-2 represses the host genes of three other miRNA including mir-233; and the host gene of mir-233 , W03G11 . 4 , is subject to repression by mir-233 itself , mir-2 and mir-87 . So far we have focused on C . elegans using the data from the modENCODE project . As similar data of other species is accumulating , it is worthwhile to apply our data integration approach to various systems like human and mouse . Toward this end , we have gathered system-wide ChIP-Seq profiles of 12 mouse TFs and 13 human TFs , and compiled the integrated regulatory networks for both mouse and human ( see Materials and Methods for details ) . Figure 8A shows the details of these networks . Similar to C . elegans , the transcription factors in human and mouse can be arranged in a hierarchical fashion ( Figure 8B ) . As the number of TFs sampled is too small , it is however not practical to perform correlation analysis similar to ones in C . elegans . To explore the novel intra-regulation between miRNAs , we constructed a miRNA-host network for human miRNAs . Out of the 939 human miRNAs , 588 overlap with a protein-coding gene . Among them , the majority ( 482 , P = 2×10−58 ) is located in the sense strand of the host gene , resulting in 482 miRNA/host-gene pairs . As we did in C . elegans , we identified 1 , 426 regulatory relationships among these miRNA/host-gene pairs , including 8 auto-regulated pairs ( Figure 8C ) . We performed the same motif analysis on the human and mouse integrated regulatory networks ( Figure 9 ) . In fact , the integrated regulatory networks of human and mouse share common motifs with C . elegans . For instance , Motifs 9A ( ii ) and ( v ) are equivalent to Motifs 6A ( vi ) and ( iv ) in C . elegans . In addition , we found another interesting miRNA mediated feed-forward loop in the human integrated regulatory network ( Figure 9A ( i ) ) , which has already been reported in literature [69] . As the number of TFs sampled in these systems is far from complete , one should not expect that the results are entirely representative . Using the recently published human transcription factor physical interaction network and the mouse transcription factor physical interaction network [48] , we found that a single miRNA tends to co-regulate a pair of interacting TFs more frequently than by random ( P = 4×10−20 for human and P = 10−3 for mouse ) . This motif ( Figure 9B ) is shared in C . elegans ( Figure 6C ) . This indicates that miRNAs prefer to coordinately repress physically interacted transcription factors , which might be involved in combinatorial regulation of gene transcription . At the heart of our study is the determination of TF-gene and TF-miRNA interactions from ChIP-Seq profiles . The number of interactions obviously depends on the choice of promoter regions , and the inclusion/exclusion of the so called HOT regions [42] ( see Materials and Methods for details ) . While the results presented are based on the exclusion of HOT regions , and a choice of promoter region defined as 1 kb upstream to 500 bp downstream of the TSS for protein-coding genes or of the start position for the pre-miRNAs , one could include the HOT regions to increase statistical power or shorten the definition of promoter region ( 500 bp upstream to 300 bp downstream ) for higher specificity . Moreover , the number of false positives in the miRNA target prediction can be reduced by increasing the conservation of miRNA binding sites from 3 species ( C . elegans , C . briggsae , and C . remanei ) to 5 species ( including also C . brenneri , C . japonica ) . To test the robustness of our network motif analysis , we explored the influence of these choices and their combinations . We tested all the possibilities , resulting in a total of 8 integrated networks . Our analysis indicates that these integrated networks are similar in their topology and in presence of over-represented network motifs in spite of the difference in the number of interactions ( Table S4 ) . The fact that the number of regulatory interactions depends on the choice of parameters might lead to a possible drawback , namely the assignment change of hierarchical levels in our hierarchy analysis . Even though the precise assignment of layers indeed changes , the signifying characteristics of different layers remain robust . For example , based on 18 larva TFs ( with other 4 embryonic TFs excluded ) , we have constructed another hierarchy using more stringent parameters: promoter regions defined from 500 bp upstream to 300 bp downstream , excluding TF binding peaks overlapped with HOT regions , and using 5-way conservation for miRNA target prediction [42] . In addition , we further filtered the regulatory interactions whose correlation between the TF and the target gene are weak across different developmental stages . The resultant hierarchy consisted of 9 TFs in the top layer , 4 TFs in the bottom layer and 5 TFs in the bottom layer . Although these numbers differed from those in Figure 4 , the overall statistical properties of the two hierarchies are highly consistent . For instance , in the network three essential TFs are in the bottom layer , one in the middle layer , and none in the top layer . Moreover , the TFs in the middle and bottom layers have significantly more physical interactions than those in the top layer ( P = 0 . 002 ) . As done in other studies based on miRNA target prediction , one should take into account the effects of the choice of different prediction methods . While the target genes of miRNAs shown were mainly identified by using the PicTar algorithm [9] , we have also employed the TargetScan algorithm [70] . A comparison of the results between the two algorithms revealed that PicTar identified 99% of seed sites predicted by TargetScan , and conversely , TargetScan identified 89% of seed sites predicted by PicTar , when only the conserved seed sites were considered . It has been demonstrated previously that TargetScan and PicTar are most popular performers for target prediction of miRNAs and generally produce the highest overlap with experimentally determined sites [71] , [72] . Thus , the results based on PicTar algorithm were finally used for determining miRNA target genes . We next examined the sensitivity of the network motif analysis to removal of regulatory interactions . Specifically , we randomly removed 1 , 5 , 10 , 20 , 30% of edges ( TF→gene , TF→miRNA or miRNA→gene interactions ) from the integrated network , and re-performed the network motif analysis . We obtained similar results with the original integrated network . We also examined the effect of excluding one or more of the 22 TFs , namely , removing all the genes and miRNAs targeted by a selected TF . In this case , some of the motifs were not significant , particularly when a TF with large number of target genes/miRNAs was excluded . For these network motifs , a regulatory network containing target information for more TFs would be helpful to gain further confidence on their significance in transcriptional regulation .
In this study , we have presented an integrated network framework for analyzing multi-level regulation in higher eukaryotes , and applied the methods using high-throughput data from C . elegans , human and mouse . Our framework makes use of the ChIP-Seq binding profiles of TFs , RNA-Seq expression profiles , 3′UTRome and protein-protein interactions . Several recent genome-scale studies that have attempted to integrate regulation by TF and miRNAs are limited in terms of their datasets . For instance , the work by Yu et al . [24] on human were mostly based on computational predictions , and while Martinez et al . provided experimental TF→miRNA interactions in C . elegans based on Y1H assays [26] , the regulatory interactions between TF and genes were not incorporated . Neither of these studies considered the signs of the regulatory interactions . Though the number of TFs we used is still relatively small , we believe that our study serves as a first attempt for a comprehensive analysis of multi-levels gene regulation . As more and more data of these types emerge , the methods of integration will play an essential role to decipher the complexity of regulatory network . Our framework can potentially be improved by including reported regulatory interactions from database and literature , by filtering out low confidence interactions , and by including computationally identified regulatory interactions . For example , the existence of TF binding motifs in ChIP-Seq peaks has been examined for improving TF target identification [73] . With the accumulation of the relevant information in C . elegans , we would expect a more comprehensive integrated regulatory network in the future . An interesting observation from our hierarchical analysis in C . elegans network is the fact that TFs at lower levels are more likely to be essential and have more interaction partners in the protein-protein interaction network . This observation is consistent with the work by Yu et al . in yeast [53] . Yu et al . suggested that the middle or bottom layer TFs play the role of “mediators” or “effectors” , and thus require more intensive information exchange with other proteins . These TFs are more likely to be in charge of the fundamental cellular processes , and therefore certain pathways will cease operating upon their deletion , causing a lethal effect . The top layer TFs , on the other hand , act more like “modulators” which coordinating gene expression across different pathways . Even though the inhibition of these TFs affects the precise expression among pathways , most of the pathways remain functional and therefore the organism can survive . Of particular significance is the degree of validity of the design principle in yeast for multi-cellular organisms such as C . elegans . Interestingly enough , TFs at the bottom have lower tissue specificity , i . e . they are expressed in many tissues . This observation is consistent with the fact that the bottom TFs are in charge of the fundamental cellular processes . Our analysis hinges at a close similarity in the hierarchical organization of transcriptional regulatory network in yeast and higher eukaryotes such as C . elegans . The hierarchical layout as shown in Figure 4 suggests another design principle in multi-level genetic regulation , namely miRNAs tend to regulate TFs in the middle of the hierarchy . As observed separately based on transcriptional regulatory networks , protein modification networks and phosphorylation network in Ref . [17] , regulators at the middle level are responsible for the proper organizational effectiveness , and thus they have the highest collaborative propensity and co-regulator partnerships . Our result suggests that , the same principle is also true for different types of regulations in an integrative picture . We have identified several over-represented network motifs in the integrated regulatory network , including the well known transcription factors mediated feed-forward loops . The coherent FFLs share a common design structure , suggesting that both protein-coding genes and miRNAs are regulated by a pair of transcription factors in a similar fashion . Of particular interest are the miRNA-mediated motifs in which miRNA regulatory interactions are employed . For instance , we found 15 composite TF⇔miRNA feedback loops . The same motif was reported to be more frequent than expected by chance in [26] . While feedback loops are rare in pure transcriptional regulatory networks [19] , [20] , the enrichment of composite feedback loops suggests that feedbacks are more likely to involve multiple levels . It has been discussed in Ref . [26] that , in a composite feedback loop , the sign of the transcriptional regulation determines the function of the loop . A loop with a transcriptional repressor works as a bi-stable switch and a loop with a transcriptional activator can function as a steady state or an oscillatory system . Interestingly , among the 15 composite feedback loops we observed , there are 6 transcription factors involved and all of them are activators . It is therefore more reasonable that the composite feedback loops we observed are responsible for ensuring robustness during development [51] or playing a role in periodic processes such as molting in different larval stages . Previous studies [25] and ours have reported that miRNAs tend to regulate transcription factors . As shown in our motifs analysis , instead of targeting individual transcription factors , miRNAs tend to regulate transcription factors as well as their downstream targets . By shutting down a gene together with its transcriptional activator , the motifs could be viewed as an effective strategy to shut down the target gene in a longer time period . A similar motif is observed in the protein-protein interaction network , in which a miRNA tends to target a pair of interacting proteins , presumably comprise a molecular machine . From one point of view , the motif is an effective way to shut down a function . The production of certain useless components is considered wasteful , and they would lead to various promiscuous interactions in the cell . On the other hand , the removal of the unwanted components might increase the response time of the cell when the machine is in need . Therefore , the usage of such motif depends on the production rate of the components . For instance , if a component of the machine requires a longer time to produce than the others , the shut down of every component at the same time might not be very effective . Nevertheless , the two miRNA-mediated motifs described are also found in mouse and human . We have explored a novel mechanism in which miRNAs might regulate one other via their host genes , involving 5 miRNAs and 5 interactions . Though this is a relatively small number compared to the total number of miRNAs in the genome , it is intriguing that the 5 interactions are not separated but connected to form a small network , suggesting that the interactions may have real biological significance . Among the 39 miRNAs that are embedded in the same sense within the intron of a host gene , we found only one case in which the miRNA ( mir-233 ) is being regulated by itself , i . e . mir-233 has a conserved binding site in the 3′UTR of its host gene ( W03G11 . 4 ) . This suggests that the repression of a miRNA on its host is not desirable and thus tends to be eliminated from the genome . A similar but more comprehensive analysis performed using human data points to the same conclusions . It is worthwhile to point out that in this mechanism , the target miRNA might not always be down regulated since miRNAs typically function by degrading the target mRNAs or by inhibiting their translation [6] .
The binding sites of ∼30 C . elegans TFs were determined using ChIP-Seq experiments . The data sets were examined manually to remove experiments with low read mapping rate , small number of calling peaks , or low reproducibility between replicates . After removing these low quality experiments , we finally obtained the binding data sets for 22 TFs . The binding signals for all TFs were normalized against background signals measured using the corresponding input DNA samples . The binding peaks were identified using the PeakSeq method [74] . More detail information about the ChIP-seq assay and data pre-processing has been previously described in [75] . The list of the 22 TFs and their features can be found in Table S1 . Expression levels for all annotated worm transcripts at different developmental stages were quantified using RNA-seq [76] . MicroRNA expression levels at different developmental stages of C . elegans were obtained from small RNA-seq measurements performed by Kato et al . [77] . All these data are available from the modENCODE website at http://www . modencode . org . The C . elegans protein-protein interaction data were downloaded from the Worm Interactome Database [65] . The data contain 178 , 152 interactions that are determined by yeast-two-hybrid experiments , literature curated or by computational analysis . Annotation of worm transcripts was downloaded from WormBase at [78] or from the Ensembl database at http://uswest . ensembl . org/index . html . Annotation of nematode microRNAs was downloaded from the microRNA database miRBASE at http://www . mirbase . org [40] . Assembly version WS180 of C . elegans was used for gene and microRNA annotations as well as for data processing . The TF-TF interaction data set was downloaded from Ravasi et al . [48] . The data set contains 762 and 877 interactions in human and mouse , respectively . Annotation for human and mouse Refseq genes was downloaded from UCSC Genome Browser at http://genome . ucsc . edu/ . Annotation for human and mouse miRNAs was based on miRBase [40] . ChIP-Seq experiments for 12 mouse TFs in embryonic stem cells were performed by Chen et al [41] . These TFs are E2F1 , ESRRB , KLF4 , NANOG , OCT4 , STAT3 , SMAD1 , SOX2 , TCFCP2L1 , ZFX , c-MYC and n-MYC . ChIP-Seq data for 14 human TFs in K562 cell line , E2F4 , E2F6 , FOS , GATA1 , GATA2 , JUN , JUND , MAX , MYC , NFE2 , STAT1 , YY1 and ZNF263 , were generated by the ENCODE project and are available from UCSC Genome Browser . We identified the target protein-coding genes and miRNAs of the 22 TFs based on the ChIP-Seq binding data sets . DNA regions with the binding peaks were potential targets of the transcription factor . We observed that 304 specific DNA regions , about 400 bp in size , were bound by 15 or more factors; we termed these regions the Highly Occupied Target ( HOT ) regions . We found that the binding motif of each individual TF is not highly enriched in these HOT regions , suggesting that the TFs are not directly associated with DNA via specific binding sites . These HOT regions therefore were not regarded as the targets of transcription factors . To identify the list of targets , we obtained the annotations of 27 , 242 worm genes from Ensembl database at http://uswest . ensembl . org/index . html . A gene was considered as the target gene of a TF if the center of at least one binding peak of the TF followed into the promoter region ( 1000 bp upstream and 500 bp downstream of the TSS ) of the gene . Similarly , a miRNA was referred as the target of a TF if at least one peak is found around the start position of the corresponding pre-miRNA ( 1000 bp upstream and 500 bp downstream of the TSS ) . The 1000 bp upstream to 500 bp downstream criteria were determined according to the binding signal distribution of TFs around the TSS . We found that >80% binding signals were restricted to these 1 . 5 kb-DNA regions for most TFs . Other criteria can also be used to obtain stricter ( 500 bp upstream to 300 bp downstream ) or relaxed ( 2000 bp upstream to 500 bp downstream ) target gene sets . PicTar algorithm [9] was applied to a well-defined set of 3′UTRs [32] to identify miRNA target sites . To reduce false predictions , we considered only the miRNA target sites that are conserved across three ( C . elegans , C . briggsae , and C . remanei ) or five ( C . brenneri , C . japonica additionally ) species . The binding sites for all of the 174 annotated miRNAs in miRBase [40] were identified . A gene was considered a target of a miRNA if there was at least one conserved binding site in the 3′UTR of at least one transcript of the gene . More details have been described in [42] . The experimentally identified TF→gene and TF→miRNA interactions were combined with predicted miRNA→gene interactions to form an integrated gene regulatory network . In the network , we only included the genes for which both the TF binding data and miRNA target site prediction were available , namely , the genes used as the input for TF target identification and miRNA target prediction . Enriched motifs were identified by the software FANMOD [60] . Instead of counting the occurrence of a certain motif , the software estimates the occurrence frequency ( Nreal ) via sampling , and the number is compared to the frequencies of an ensemble of 1000 random networks . The set of random networks is generated by FANMOD ( with default parameters ) , in which the edges are rewired while keeping its rough topological statistics constant . Specifically , the null models are generated by a Monte Carlo type algorithm which rewires the original network while keeping the same number of coding gene targets and the number of miRNA targets for a TF node , the number of targets for a miRNA node , and the number of regulatory TFs and miRNAs for a gene node [79] . The occurrence frequencies of a motif in the ensemble follow a Gaussian , and the enrichment of the motif is quantified by a . For the signed integrated regulatory network , the set of random networks is generated by rewiring such that , apart from the number of each type of regulatory interactions in each node is preserved , the number of positive and negative regulations in each node are separately preserved . We therefore divided the DNA region from 2 kb upstream to 2 kb downstream of the TSS of each transcript into 40 small bins , each of 100 bp in size . For each bin , we calculated the average signal of each TF binding profile across all transcripts . Specifically , the number of reads that cover a bin was counted and weighted according to their overlap with the bin . We then calculated for each TF in each bin , the Pearson correlation between the average signal and the expression of the corresponding transcripts . A consistent positive ( negative ) correlation across the bins means that the TF is a positive ( negative ) regulator . We first built a core-hierarchy comprising of only the TFs using a breadth-first search algorithm in a bottom-up fashion in the following way . First , the TFs that were not regulated by any other TF were placed in the top layer . Next , the regulators that were regulated by the top TFs and also regulated other TFs were assigned to the middle layer . Finally , the regulators that did not regulate other TFs formed the bottom layer resulting in 3 layers of TFs . The interactions involving the miRNA were then added to these three layers . The miRNAs regulating the top TFs were placed in the top miRNA layer above the top layer TFs . Note that some of these miRNAs were regulated by lower layer TFs . Of the remaining miRNAs , the ones regulating the middle layer TFs were placed in the middle layer ( between the top and middle layer TFs ) . From the set of remaining miRNAs , the ones that regulate the bottom layer TFs were placed in the lower layer ( between the middle and bottom layer TFs ) . Finally , the remaining miRNAs were placed in the lowest layer; these did not regulate any regulators and only had incoming regulatory edges . Expression levels of all C . elegans genes at 8 different tissues at L2 stage were measured using tiling arrays [42] . The 8 tissues include poA , bone wall muscle , intestine , glr , GABA neurons , excretory cell , coelomocytes and panneural . The tissue specificity score ( TSPS ) for a gene is defined as , where is the ratio of the gene expression level in tissue i to its sum total expression level across all tissues , and = 1/8 for all tissues , is the fractional expression of a gene under a null model assuming uniform expression across tissues . A greater tissue specificity score suggests more specific expression in a single or multiple tissues , whereas a score of zero suggests uniform expression . Apart from tissue specificity , the stage specificity score of a gene throughout its developmental time course is defined in a similar fashion . | The precise control of gene expression lies at the heart of many biological processes . In eukaryotes , the regulation is performed at multiple levels , mediated by different regulators such as transcription factors and miRNAs , each distinguished by different spatial and temporal characteristics . These regulators are further integrated to form a complex regulatory network responsible for the orchestration . The construction and analysis of such networks is essential for understanding the general design principles . Recent advances in high-throughput techniques like ChIP-Seq and RNA-Seq provide an opportunity by offering a huge amount of binding and expression data . We present a general framework to combine these types of data into an integrated network and perform various topological analyses , including its hierarchical organization and motif enrichment . We find that the integrated network possesses an intrinsic hierarchical organization and is enriched in several network motifs that include both transcription factors and miRNAs . We further demonstrate that the framework can be easily applied to other species like human and mouse . As more and more genome-wide ChIP-Seq and RNA-Seq data are going to be generated in the near future , our methods of data integration have various potential applications . | [
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] | 2011 | Construction and Analysis of an Integrated Regulatory Network Derived from High-Throughput Sequencing Data |
A cornerstone of biotechnology is the use of microorganisms for the efficient production of chemicals and the elimination of harmful waste . Pseudomonas putida is an archetype of such microbes due to its metabolic versatility , stress resistance , amenability to genetic modifications , and vast potential for environmental and industrial applications . To address both the elucidation of the metabolic wiring in P . putida and its uses in biocatalysis , in particular for the production of non-growth-related biochemicals , we developed and present here a genome-scale constraint-based model of the metabolism of P . putida KT2440 . Network reconstruction and flux balance analysis ( FBA ) enabled definition of the structure of the metabolic network , identification of knowledge gaps , and pin-pointing of essential metabolic functions , facilitating thereby the refinement of gene annotations . FBA and flux variability analysis were used to analyze the properties , potential , and limits of the model . These analyses allowed identification , under various conditions , of key features of metabolism such as growth yield , resource distribution , network robustness , and gene essentiality . The model was validated with data from continuous cell cultures , high-throughput phenotyping data , 13C-measurement of internal flux distributions , and specifically generated knock-out mutants . Auxotrophy was correctly predicted in 75% of the cases . These systematic analyses revealed that the metabolic network structure is the main factor determining the accuracy of predictions , whereas biomass composition has negligible influence . Finally , we drew on the model to devise metabolic engineering strategies to improve production of polyhydroxyalkanoates , a class of biotechnologically useful compounds whose synthesis is not coupled to cell survival . The solidly validated model yields valuable insights into genotype–phenotype relationships and provides a sound framework to explore this versatile bacterium and to capitalize on its vast biotechnological potential .
Pseudomonas putida is one of the best studied species of the metabolically versatile and ubiquitous genus of the Pseudomonads [1]–[3] . As a species , it exhibits a wide biotechnological potential , with numerous strains ( some of which solvent-tolerant [4] , [5] ) able to efficiently produce a range of bulk and fine chemicals . These features , along with their renowned stress resistance , amenability for genetic manipulation and suitability as a host for heterologous expression , make Pseudomonas putida particularly attractive for biocatalysis . To date , strains of P . putida have been employed to produce phenol , cinnamic acid , cis-cis-muconate , p-hydroxybenzoate , p-cuomarate , and myxochromide [6]–[12] . Furthermore , enzymes from P . putida have been employed in a variety of other biocatalytic processes , including the resolution of d/l-phenylglycinamide into d-phenylglycinamide and l-phenylglycine , production of non-proteinogenic l-amino acids , and biochemical oxidation of methylated heteroaromatic compounds for formation of heteroaromatic monocarboxylic acids [13] . However , most Pseudomonas-based applications are still in infancy largely due to a lack of knowledge of the genotype-phenotype relationships in these bacteria under conditions relevant for industrial and environmental endeavors . In an effort towards the generation of critical knowledge , the genomes of several members of the Pseudomonads have been or are currently being sequenced ( http://www . genomesonline . org , http://www . pseudomonas . com ) , and a series of studies are underway to elucidate specific aspects of their genomic programs , physiology and behavior under various stresses ( e . g . , http://www . psysmo . org , http://www . probactys . org , http://www . kluyvercentre . nl ) . The sequencing of P . putida strain KT2440 , a workhorse of P . putida research worldwide and a microorganism Generally Recognized as Safe ( GRAS certified ) [1] , [14] , provided means to investigate the metabolic potential of the P . putida species , and opened avenues for the development of new biotechnological applications [2] , [14]–[16] . Whole genome analysis revealed , among other features , a wealth of genetic determinants that play a role in biocatalysis , such as those for the hyper-production of polymers ( such as polyhydroxyalkanoates [17] , [18] ) and industrially relevant enzymes , the production of epoxides , substituted catechols , enantiopure alcohols , and heterocyclic compounds 13 , 15 . However , despite the clear breakthrough in our understanding of P . putida through this sequencing effort , the relationship between the genotype and the phenotype cannot be predicted simply from cataloguing and assigning gene functions to the genes found in the genome , and considerable work is still needed before the genome can be translated into a fully functioning metabolic model of value for predicting cell phenotypes [2] , [14] . Constraint-based modeling is currently the only approach that enables the modeling of an organism's metabolic and transport network at genome-scale [19] . A genome-wide constraint-based model consists of a stoichiometric reconstruction of all reactions known to act in the metabolism of the organism , along with an accompanying set of constraints on the fluxes of each reaction in the system [19] , [20] . A major advantage of this approach is that the model does not require knowledge on the kinetics of the reactions . These models define the organism's global metabolic space , network structural properties , and flux distribution potential , and provide a framework with which to navigate through the metabolic wiring of the cell [19]–[21] . Through various analysis techniques , constraint-based models can help predict cellular phenotypes given particular environmental conditions . Flux balance analysis ( FBA ) is one such technique , which relies on the optimization for an objective flux while enforcing mass balance in all modeled reactions to achieve a set of fluxes consistent with a maximal output of the objective function . When a biomass sink is chosen as the objective in FBA , the output can be correlated with growth , and the model fluxes become predictive of growth phenotypes [22] , [23] . Constraint-based analysis techniques , including FBA , have been instrumental in elucidating metabolic features in a variety of organisms [20] , [24] , [25] and , in a few cases thus far , they have been used for concrete biotechnology endeavors [26]–[29] . However , in all previous applications in which a constraint-based approach was used to design the production of a biochemical , the studies addressed only the production of compounds that can be directly coupled to the objective function used in the underlying FBA problem . The major reason for this is that FBA-based methods predict a zero-valued flux for any reaction not directly contributing to the chosen objective . Since the production pathways of most high-added value and bulk compounds operate in parallel to growth-related metabolism , straightforward application of FBA to these biocatalytic processes fails to be a useful predictor of output . Other constraint-based analysis methods , such as Extreme Pathways and Elementary Modes analysis , are capable of analyzing non-growth related pathways in metabolism , but , due to combinatorial explosion inherent to numerical resolution of these methods , they could not be used so far to predict fluxes or phenotypes at genome-scale for guiding biocatalysis efforts [30] . To address both the elucidation of the metabolic wiring in P . putida and the use of P . putida for the production of non-growth-related biochemicals , we developed and present here a genome-scale reconstruction of the metabolic network of Pseudomonas putida KT2440 , the subsequent analysis of its network properties through constraint-based modeling and a thorough assessment of the potential and limits of the model . The reconstruction is based on up-to-date genomic , biochemical and physiological knowledge of the bacterium . The model accounts for the function of 877 reactions that connect 886 metabolites and builds upon a constraint-based modeling framework [19] , [20] . Only 6% of the reactions in the network are non gene-associated . The reconstruction process guided the refinement of the annotation of several genes . The model was validated with continuous culture experiments , substrate utilization assays ( BIOLOG ) [31] , 13C-measurement of internal fluxes [32] , and a specifically generated set of mutant strains . We evaluated the influence of biomass composition and maintenance values on the outcome of flux balance analysis ( FBA ) simulations , and utilized the metabolic reconstruction to predict internal reaction fluxes , to identify different mass-routing possibilities , and to determine necessary gene and reaction sets for growth on minimal medium . Finally , by means of a modified OptKnock approach , we utilized the model to generate hypotheses for possible improvements of the production by P . putida of polyhydroxyalkanoates , a class of compounds whose production consumes resources that would be otherwise used for growth . This reconstruction thus provides a modeling framework for the exploration of the metabolic capabilities of P . putida , which will aid in deciphering the complex genotype-phenotype relationships governing its metabolism and will help to broaden the applicability of P . putida strains for bioremediation and biotechnology .
We reconstructed the metabolism of P . putida at the genome-scale through a process summarized in Figure 1 . The reconstruction process involved: ( 1 ) an initial data collection stage leading to a first pass reconstruction ( iJP815pre1 ) ; ( 2 ) a model building stage in which simulations were performed with iJP815pre1 and reactions were added until the model was able to grow in silico on glucose minimal medium ( iJP815pre2 ) ; and ( 3 ) a model completion stage in which BIOLOG substrate utilization data was used to guide model expansion and in silico viability on varied substrates . The final reconstruction , named iJP815 following an often used convention [33] , consists of 824 intracellular and 62 extracellular metabolites connected by 877 reactions . Eight hundred twenty one ( 94% ) reactions have at least one assigned gene as delineated in the gene-protein-reaction ( GPR ) relationships . GPR relationships are composed of Boolean logic statements that link genes to protein complexes and protein complexes to reactions via combinations of AND and OR operators . An ‘AND’ operator denotes the required presence of two or more genes for a protein to function ( as in the case of multi-protein complexes ) , while an ‘OR’ operator denotes a redundant function that can be catalyzed by any of several genes ( as in the case of isozymes ) . Only 56 reactions , of which nine are non-enzymatic , lack associated genes . The remaining 47 non-gene-associated , enzymatic reactions were added in order to close metabolic network gaps identified during the successive steps of the reconstruction process . Most network gaps ( 27 ) were identified during the second round of the reconstruction and were resolved through detailed literature mining , thereby enabling iJP815 to grow in silico on glucose in minimal medium . The remaining gaps identified in the model completion step ( Figure 1 ) were mostly single missing steps in the pathway for which there is experimental evidence of operation ( e . g . , a compound is consumed but not produced , and no alternative pathways exist ) . It should be noted that for some gaps , there is more than one combination of reactions with which the gap could be closed [34] . In cases where more than one gap closure method was available , the decision of which to use was made based on similarity queries to related bacteria . The iJP815 model includes 289 reactions for which non-zero flux values cannot be obtained under any environmental condition while enforcing the pseudo steady-state assumption ( PSSA ) . We term these reactions “unconditionally blocked” meaning that they are unable to function because not all connections could be made with the information available . Three hundred sixty two metabolites that are only involved in these reactions are classified as “unbalanced metabolites” . Another important subset of model reactions is the “weakly annotated” set , which means that all the genes assigned to these 57 reactions are currently annotated as coding for “putative” or “family” proteins . The relationships between all the subsets are shown in Table 1 and Figures 2 and 3 . The final reconstruction accounts for the function of 815 genes , corresponding to 15% of all genes in the P . putida genome and to 65% ( 1253 ) of those currently assigned to the classes ‘Metabolism’ ( K01100 ) and ‘Membrane Transport’ ( K01310 ) in the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) orthology classification [35] . These figures are consistent with recently published metabolic reconstructions for other prokaryotes ( see Table S1 ) . A high-throughput BIOLOG phenotypic assay was performed on P . putida to validate and extend the model . In this assay , P . putida was tested for its ability to oxidize 95 carbon substrates in minimal medium . Of these 95 substrates , P . putida oxidized 45 . We added 2 other carbon sources to the positive-oxidation group ( l-phenylalanine and l-threonine ) despite a negative BIOLOG result , since these substrates had been previously shown to be growth substrates [16] and since we confirmed these results experimentally ( data not shown ) , giving altogether forty seven compounds utilized in vivo . Forty seven out of the 95 carbon sources tested were accounted for in iJP815pre2 , enabling a comparison of these BIOLOG data with FBA simulations of iJP815 grown on in silico minimal medium with the respective compound as sole carbon source ( see Table 2 and Table S2 ) . The initial working version of the model ( iJP815pre2 ) was able to simulate growth with 14 of the 47 BIOLOG-assayed compounds as sole carbon sources . This version of the reconstruction contained only a few transport reactions , prompting us to identify compounds that could not be utilized in silico simply due to the lack of a transporter . This was achieved by allowing the intracellular pool of each compound of interest to be exchanged with environment in silico , and by evaluating the production of biomass in each case through FBA simulations . This approach increased the number of utilizable substances to 34 but also produced six false-positives ( i . e . , substances that support in silico growth , but which gave a negative phenotype in the BIOLOG assay ) . These included three metabolites involved in central metabolic pathways ( d-glucose 1-phosphate , d-glucose 6-phosphate and glycerol-3-phosphate ) , an intermediate of the l-histidine metabolism pathway ( urocanate ) , an intermediate of branched amino acids biosynthesis ( 2-oxobutanoate ) , and the storage compound glycogen . This analysis suggests that the inability of P . putida to utilize these compounds in vivo is likely due to the lack of appropriate transport machinery . The final P . putida model ( iJP815 ) grew on 39 of the 51 compounds tested in the BIOLOG assay and that concurrently were accounted for in the model . Of these , 33 were true positives ( compounds utilized in vivo and allowing for growth in silico ) . The mode of utilization of the remaining fourteen in vivo oxidized compounds ( i . e . , false negatives ) could not be elucidated . The remaining forty two compounds posed true negatives , eight of which were accounted for in the reconstruction . Ten utilized compounds also lack transport reactions , as nothing is known about their translocation into the cell . Nevertheless , this comparison of in silico growth predictions with BIOLOG substrate utilization data indicates that the core metabolism of P . putida has been properly reconstructed . A note of caution when comparing the BIOLOG assays with growth predictions is that this assay evaluates whether an organism is able to oxidize the tested compound and yield energy from it , which is different from growth . However , as P . putida is able to grow on minimal medium supplemented with these compounds , we considered the assumption to be justified . The reconstruction process systematizes knowledge about the metabolism of an organism , allowing the identification of errors in , and discrepancies between , various sources of data . A major value of a manual model-building effort is the careful revision of the current genome annotation , based on literature evidence encountered during the model building process , BLAST searches , and gap closures . During the reconstruction of the P . putida metabolic network , we discovered a number of genes that appear to have been improperly annotated in biological databases ( Pseudomonas Genome Database , KEGG , NCBI ) . These mis-annotations arose due to a lack of information at the time of the original annotation or because knowledge that was available in the literature had been overlooked in the original annotation . In a number of other cases , the model building process has also generated new hypotheses for gene functions . For instance , our reconstruction process identified an unlikely gap in the l-lysine degradation pathway of P . putida . Extensive literature search and careful reannotation has provided considerable evidence that the genes PP0382 and PP5257 , currently annotated as ‘carbon-hydrogen hydrolase family protein’ and ‘oxidoreductase , FAD binding’ respectively , most probably code for a ‘5-aminopentamidase’ and ‘l-pipecolate oxidase’ , respectively [36] . Another example is the propanoate degradation pathway: In the iJP815pre2 version this pathway was complete except for one enzymatic activity , namely the 2-methylisocitrate dehydratase . Analysis of the enzymes flanking this reaction showed that all of the enzymes are encoded by genes immediately adjacent to the ORF PP2330 . Inspection of this region of the genome revealed that PP2336 is annotated as “aconitate hydratase , putative” , although the flanking genes are responsible for degradation of propanoate . Analysis of PP2330 via BLAST revealed a homology of more than 99% over the whole length of the protein with the 2-methylisocitrate dehydratase from other bacteria , such as other strains of P . putida ( GB-1 , W619 ) , Burkholderia prymatum STM 815 , Burkholderia multivorans ATCC 17616 , Pseudomonas aeruginosa PA7 , and Stenotrophomonas maltophilia R551-3 . Consequently the gene was reannotated to code for this function and the gap in propanoate degradation pathway was thus closed by addition of the corresponding GPR . In other cases , discrepancies exist between various databases , as in the case of PP5029 , which is annotated in KEGG as ‘formiminoglutamase’ but in NCBI as ‘N-formylglutamate deformylase’ . Analysis of network gaps , genomic context and sequence homology provided a strong indication that ‘N-formylglutamate deformylase’ is the correct annotation . In many other cases the reannotation meant changing the substrate specificity of the enzyme ( which corresponds to changing the last part of the EC number ) . These were mainly identified by BLASTing the protein against protein sequences of other microbes and , whenever available , cross-checking the BLAST results against primary research publications . The full list of reannotations suggested by the reconstruction process is shown in Table 3 . After completing the reconstruction , we assessed whether the model was capable of predicting the growth yield of P . putida , a basic property of the modeled organism . In silico growth yield on succinate was calculated by FBA and compared with in vivo growth yield measured in continuous culture [37] . If the in silico yield were lower than the experimental , it would indicate that the network may lack important reactions that influence the efficiency of conversion of carbon source into biomass constituents and/or energy . In fact , the calculated in silico yield ( 0 . 61 gDW⋅gC−1 ) was higher than the experimental yield ( 0 . 47 gDW⋅gC−1 ) , indicating that some of the processes reconstructed in the network might be unrealistically efficient and/or that P . putida may be diverting resources into other processes not accounted for in the model . This greater efficiency of the in silico model versus in vivo growth data is also consistent with recent studies that suggest optimal growth is not necessarily the sole objective ( function ) of biochemical networks [38] , [39] . The in silico growth yield is influenced not only by the structure of the metabolic network , but also by other factors including biomass composition and the growth-associated and non-growth-associated energy maintenance factors ( GAM and NGAM ) , the values of which represent energy costs to the cell of “living” and “growing” , respectively [22] . Therefore , since both the biomass composition and the GAM/NGAM values were taken from the E . coli model [22] , [33] due to a lack of organism-specific experimental information , we evaluated the influence of these factors on the predicted growth yield . First , we analyzed the effects of changes in the ratios of biomass components on the iJP815 growth yield . These analyses ( displayed in the Text S1 , section “Assessment of the influence of the biomass composition the growth yield” ) indicated that varying any single biomass constituent by 20% up or down has a less than 1% effect on the growth yield of P . putida ( Figure S1 ) . These results are consistent with results of a previous study on the sensitivity of growth yield to biomass composition [40] . Although it is still possible that some components of P . putida biomass are not present in E . coli or vice versa , we conclude that the use of E . coli biomass composition in the P . putida model is a justified assumption for the purpose of our application and is probably not a great contributor to the error in our predictions of growth yield . Subsequently , the effects of changes in the GAM on the in silico growth yield were tested ( Figure S2A and S2B ) . It was found that if GAM was of the same order of magnitude as the value used in the E . coli model ( 13 [mmolATP⋅gDW−1 ) , its influence is negligible , as increasing or decreasing it twofold alters the growth yield by merely 5% . A higher GAM value in P . putida than in E . coli could contribute to the discrepancy between the experimental measurements and in silico predictions , but it could not be the only factor unless the E . coli and P . putida values differ more than twofold , which is unlikely . Finally , we assessed the effects of changes in the value of NGAM on in silico growth yield . The NGAM growth dependency is influenced by the rate of carbon source supply , and thus indirectly by the growth rate . If the carbon intake flux is low ( as in the case of the experiments mentioned above , with a dilution rate of 0 . 05 h−1 ) , the fraction of energy utilized for maintenance purposes is high and therefore so is the influence of the NGAM value on growth yield ( Figure S2A ) . Under such low-carbon intake flux conditions , a twofold increase of the NGAM value can decrease the growth yield by about 30% . This indicates that the main cause for the discrepancy between in vivo and in silico growth yields is that the NGAM value is likely to be higher in P . putida than in E . coli . Figure S2A indicates that increasing the NGAM value from 7 . 6 of 12 [mmolATP⋅gDW−1⋅h−1] would reduce the in silico growth yield and lead to a better match with experimental values . Consequently this NGAM value was used in subsequent FBA and Flux Variability Analysis ( FVA ) [41] simulations . For a high influx of carbon source ( Figure S2B ) the influence of NGAM on the growth yield is low and the influence of the NGAM and GAM values on growth yield are comparable . It should be noted that , while FBA predicts the optimal growth yield , few cellular systems operate at full efficiency . Bacteria tend to “waste” or redirect energy if it is abundant [42] , leading to a lower-than-optimal in vivo growth yield . It is also worth mentioning that maintenance values may depend on the carbon source used [43] and on environmental conditions [44]–[46] . Additionally , we computed the growth yields of P . putida on sole sources of three other important elements—Nitrogen ( N ) , Phosphorous ( P ) , and Sulfur ( S ) —and compared these with published experimental data from continuous cultivations [37] , as shown in Table 4 . Since biomass composition can play a role in the efficiency of in silico usage of basic elements , this analysis can aid in assessing how well the biomass equation , which is equivalent to the E . coli biomass reaction , reproduces the true biomass composition of P . putida . The yield on nitrogen differs only by 10% between in silico and in vivo experiments , which suggests that the associated metabolic network for nitrogen metabolism is well characterized in the iJP815 reconstruction . The yields on phosphorous and sulfur , however , differ by more than a factor of two between the in vivo and in silico analyses , suggesting that there may be significant differences between the biomass requirements and the metabolic networks of P . putida and E . coli for these components . The differences in yields , however , may be also caused by the change of the in vivo biomass composition , which decreases the fraction of compounds containing the limited element , when compared to the biomass composition while the bacterium is grown under carbon-limitation . Such changes were observed experimentally in P . putida for nitrogen and phosphate limitations [47] . Thus , the biomass composition of P . putida needs to be determined precisely in the future . However , for the purpose of this work and since the global effect of the biomass composition on the outcome of the simulations is negligible ( as shown above ) , we considered the use of the original biomass equation to be justified . As described above , iJP815 contains 289 unconditionally ( i . e . , not dependent on external sources ) blocked reactions ( that is , reactions unable to function because not all connections are made ) , corresponding to 33% of the metabolic network . In previously published genome-scale metabolic reconstructions , the fraction of blocked reactions varies between 10 and 70 percent [48] . Blocked reactions occur in reconstructions mostly due to knowledge gaps in the metabolic pathways . Accordingly , the blocked-reactions set can be divided into two major groups; ( 1 ) reactions with no connection to the set of non-blocked reactions , and ( 2 ) reactions that are either directly or indirectly connected to the operating core of the P . putida model . The first group of reactions includes members of incomplete pathways that , with increasing knowledge and further model refinement , will gradually become connected to the core . This subset comprises 108 reactions ( 35% of blocked reaction set ) . The second group of reactions comprises also members of incomplete pathways , but many of them belong to pathways that are complete but that lack a transport reaction for the initial or final compound . Examples of pathways lacking a transporter are the degradation of fatty acids and of propanoate . In addition , there could exist compounds whose production is required only in certain environmental conditions , e . g . , under solvent stress , and as such are not included in generic biomass equation . Pathways synthesizing compounds that are not included in the biomass equation but that likely are conditionally required include the synthesis of thiamine , various porphyrins and terpenoids . In this case , reactions involved exclusively in the production of such compounds would be blocked if no alternative outlets exist for those pathways . Allowing a non-zero flux through these reactions would require inclusion into biomass of the conditional biomass constituents , which in turn would require having various biomass equations for various conditions . This level of detail , however , is beyond the scope of our initial metabolic reconstruction and investigation . The high number of blocked reactions in iJP815 clearly indicates that there are still vast knowledge deficits in the model and , thus , in the underlying biochemical and genomic information . Since a genome-scale metabolic model seeks to incorporate all current knowledge of an organism's metabolism , these reactions are integral elements of the metabolic reconstruction and of the modeling scaffold , even if they are not able to directly participate in steady state flux studies . Therefore , the inclusion of these reactions in the model provides a framework to pin-point knowledge gaps , to include novel information as it becomes available and to subsequently study their embedding and function in the metabolic wiring of the cell . The assessment performed as described above by means of high-throughput phenotyping assays , growth experiments and continuous cultivations , has shown that the model is coherent and that it captures the major metabolic features of P . putida . We subsequently used the model to probe the network and to ascertain the distribution of internal fluxes and properties such as network flexibility and redundancy of particular reactions . To this end , we predicted the distribution of reaction fluxes throughout the central pathways of carbon metabolism by flux variability analysis ( FVA ) , and compared the simulations to internal fluxes computed from experimentally obtained 13C data in P . putida [49] , [50] . Genome-scale metabolic networks are , in general , algebraically underdetermined [41] . As a consequence , the optimal growth rate can often be attained through flux distributions different than the single optimal solution predicted by FBA simulations . Therefore we used flux variability analysis ( FVA ) to explore the network , as this method provides the intervals inside which the flux can vary without influencing the value of the growth yield ( if the flux of the reaction cannot vary then the range is limited to a single value ) [41] . The results of the simulations are given in Figure 4 . As isotopic ( 13C ) measurements are not able to distinguish which glucose uptake route is being used by P . putida , all the fluxes in the 13C experiment and in the FVA simulations were computed assuming that glucose is taken up directly into the cell . For the precise description of the network models used in this comparison ( i . e . , FBA/FVA vs . 13C-Flux analysis ) see Text S1 and Text S2 ( sections “Comparison of FVA analyses with 13C flux measurement data” ) . Figure 4 shows that the predictions ( in red ) generally agree well with the measurements ( in green ) throughout the network , as most of the 13C values fall within the FVA intervals , where intervals were predicted , or both values are close to each other ( in absolute values ) , when a single value was predicted . As P . putida lacks phosphofructokinase , glucose can be converted to pyruvate ( the entry metabolite of TCA cycle ) via the pentose phosphate ( PP ) or the Entner-Doudoroff ( ED ) pathways . The ED pathway is energetically more efficient and the 13C measurements indicate that KT2440 uses it preferentially over the PP pathway . Therefore , the FVA yields locally single flux values rather than intervals , which reflects the relative rigidity of this part of the network . In contrast , the energy generating part of the central metabolic network ( the TCA cycle and its vicinity ) exhibits greater flexibility , as illustrated by the broad flux intervals . Firstly , the conversion of phosphoenylpyruvate into pyruvate can proceed either directly or via oxaloacetate , although the bacterium appears to use the direct route ( the 13C-model assumes , in fact , only the direct route; see Text S1 , section “Comparison of FVA analyses with 13C flux measurement data” ) . Secondly , the conversion of malate to oxaloacetate may also occur directly or via pyruvate . The 13C flux measurements indicate that the bacterium uses the indirect route in addition to the direct one although , according to the FVA , the indirect route is energetically less efficient . Interestingly , our model suggests also that the glyoxylate shunt could be used interchangeably with full TCA-cycle without any penalty on growth yield . However , as the glyoxylate shunt is inactivated in many bacterial species via catabolite repression upon glucose growth [51] , it is possible that this alternative is not used in P . putida . Despite the general agreement between in silico predictions and 13C measurements , there still exist a number of discrepancies . For instance , the 13C-experiments suggest that the bacterium utilizes the portion of glycolysis between triose-3-phosphate and d-fructose-6-phosphate in the gluconeogenic direction , which is not energetically optimal and as such is not captured in standard FBA ( or FVA ) simulations . This illustrates one of the possible pitfalls of FBA , which per definition assumes perfect optimality despite the fact that microorganisms might not necessarily allocate their resources towards the optimization function assumed in analysis , and in some cases may not operate optimally at all [52] , [53] . Another group of differences concentrates around the pentose phosphate pathway ( PPP ) , although these are relatively minor and are likely due to differences in the quantities of sugar diverted toward biomass in the 13C model vs . iJP815 . A third group of differences revolves around pyruvate and oxaloacetate , whereby the in vivo conversion of malate to oxaloacetate shuttles through a pyruvate intermediate rather than directly converting between the two . The last area where discrepancies exist between in silico and 13C data is in the TCA cycle , around which the flux is lower in FVA simulations than in the experiment . This suggests that the in silico energetic requirements for growth ( maintenance values ) are still too low when compared to in vivo ones , as the main purpose of the TCA cycle is energy production . To investigate further these differences , we carried out a suboptimal FVA ( Figure 4 , blue values ) , allowing the production of biomass to range between 90 and 100% of its maximum value . In this suboptimal FVA experiment , the 13C-derived fluxes fall between FVA intervals for every flux value in the 13C network . To filter out artifacts , we re-did all FVA computations using the structure of the network used in the 13C-experiment and found no major differences ( see Figure S3 ) . We also assessed the influence of the biomass composition on the distribution of internal fluxes and network structure and found that this was negligible on both accounts ( see Text S2 , section “Evaluation of biomass equation composition on the outcome of FBA/FVA simulations” and Figure S4 ) . The results show that , in principle , the bacterium can use all the alternatives described above and that the penalty on the growth yield is minimal . While this analysis validates the FVA simulation results , the wide breadth of the intervals ( i . e . , the mean ratio of interval width to mean interval value exceeds three ) , suggests that the ( mathematical ) under-determination of central metabolism can be quite high , and indicates that there exist multiple sub-optimal solutions across the network and that is thus difficult to predict exact internal flux and to “pin-point” a particular solution . These results reflect the essence of constraint-based modeling and FBA , which provide only a space of possible flux distributions and not exact values . Therefore , deductions from results of FBA simulations have to be made with great care . This underscores the notion that constraint based modeling should be seen more as navigation framework to probe and explore networks rather than as an exact predictive tool of cellular metabolism . Assessment of network models through comparison of in silico growth-phenotypes with the growth of knock-out strains is a powerful way to validate predictions . This has been done in a number of studies for which knock-out mutant libraries were available [59] , [60] . As there is currently no mutant library for P . putida , we tested gene knock-out predictions with a set of P . putida auxotrophic mutant strains created in our laboratory that are incapable of growth on minimal medium with acetate as the sole carbon source . First we compared whether the corresponding in silico mutants followed the same behavior ( lack of growth on minimal medium with acetate , where zero biomass flux during FBA corresponded to a no-growth phenotype ) . This comparison was performed only for strains whose knocked-out gene is included in iJP815 . Thirty-eight out of the 51 strains tested did not grow in silico ( Table S4 ) . Of the remaining 13 false positives ( i . e . , those growing in silico but not in vivo ) , four ( PP1470 , PP1471 , PP4679 , and PP4680 ) are mutated in genes considered non-essential in silico due to “weakly annotated” gene putatively encoding redundant isozymes . In the case of PP5185 ( coding for N-acetylglutamate synthase ) , its essentiality is removed by PP1346 ( coding for bifunctional ornithine acetyltransferase/N-acetylglutamate synthase protein ) , which is not only an isozyme of PP5185 ( the N-acetylglutamate synthase function ) but which also catalyses a reaction ( ornithine acetyltransferase ) that produces N-acetyl-l-glutamate ( the product of N-acetylglutamate synthase ) and thus renders the activity of PP5185 redundant . It appears either that this is a mis-annotation or that the enzyme is utilized only under different conditions . In addition , PP0897 ( fumC ) seems to have two paralogues ( PP0944 , PP1755 ) coding for isoenzymes of fumarate hydratase , but since the mutant in PP0897 does not grow auxotrophically , they are either non functional or mis-annotated . The enzyme complex that is composed of proteins expressed from the genes knocked-out in the two false positives PP4188 and PP 4189 catalyzes the decarboxylation of α-ketoglutarate to succinyl-CoA in the TCA cycle , concurrently producing succinyl-CoA for anabolic purposes . In the model , this functionality is not needed as this part of the TCA cycle can be circumvented by the glyoxylate shunt , whereas succinyl-CoA can be produced by reverse operation of succinate-CoA ligase . Restricting this reaction to be irreversible renders both genes essential . This altogether suggests that either the succinate-CoA ligase is irreversible or the glyoxylate shunt is inactive . The latter solution is , however , impossible , due to the essentiality of the glyoxylate shunt upon growth on acetate . The false positive PP4782 is involved in thiamine biosynthesis . This cofactor is not included in the biomass , which is why the gene is not in silico essential . This suggests thus that the in-silico P . putida biomass reaction should be enriched with this cofactor . The remaining false positives ( PP1768 , PP4909 , PP5155 ) are involved in the serine biosynthesis pathway . We found experimentally that mutants in these genes can grow on acetate if the medium also contains l-serine . These genes can be rendered in silico essential by setting glycine hydroxymethyltransferase to operate only unidirectionally from l-serine to glycine . The operation of this enzyme , however , is required for growth of the bacterium on glycine , which is possible; though very slow ( results not shown ) . One of these genes ( PP5155 ) has also a weakly annotated isozyme ( PP2335 ) . We found out as well that several of the mutants ( PP1612 , PP4188-9 , PP4191-4 ) grow in silico on glucose , which we confirmed experimentally ( results not shown ) . Altogether , these experimental results assisted us in improving the accuracy of the model . Albeit limited to a relatively small mutant set , this analysis shows that while constraint-based models are not always able to predict exact flux values , they are very useful in the identification of essential reactions and , through the GPRs , the genes responsible for their catalysis . This enables identification of vulnerable points in the metabolic network . To illustrate the utility of a genome-scale model for metabolic engineering , we used iJP815 to predict possible improvements to an industrially relevant process; namely , the production of polyhydroxyalkanoates ( PHAs ) from non-alkanoic substrates for biomedical purposes [61]–[63] . As the production of PHAs uses resources that would be otherwise funneled towards growth , increasing in silico PHA production would decrease the growth . Consequently , in classic optimization-based approaches ( e . g . , FBA ) , no PHA production would be predicted while optimizing for growth yield . The aim was thus to increase the available pool of the main precursor of PHAs—Acetyl Coenzyme A ( AcCoA ) . This approach was based on the observation that inactivation of isocitrate lyase ( ICL ) enhances the production of PHAs in P . putida due to increased availability of AcCoA that is not consumed by ICL [64] . We therefore searched for other possible intervention points ( mutations ) in the metabolic network that could lead to the accumulation of AcCoA . This analysis was performed through application of a modified OptKnock approach [28] , which allowed for parallel prediction of mutations and carbon source ( s ) that together provide the highest production of the compound of interest . Two main methods were employed to model a cellular pooling of AcCoA . The first was the maximization of AcCoA production by pyruvate dehydrogenase ( PDH ) . In the second , an auxiliary reaction was introduced that consumed AcCoA ( concurrently producing CoA , to avoid cofactor cycling artifacts ) and that would represent the pooling of AcCoA ( Figure 6A and 6B , insets ) . It is noteworthy that the value of ‘AcCoA production’ predicted by the first method includes AcCoA that is then consumed in other reactions ( some of which will lead towards biomass production for instance ) , whereas the value of ‘AcCoA pooling’ predicted by the second method includes only AcCoA that is taken completely out of the system , and therefore made available for PHA production but unusable for growth or other purposes . Therefore , only with the first method ( AcCoA production ) can AcCoA fluxes and growth rates be compared directly with the wild-type AcCoA flux and growth rate , as the second method ( AcCoA pooling ) will display lower values for AcCoA fluxes and growth rates but will avoid ‘double counting’ AcCoA flux that is shuttled towards growth , and therefore is not available for PHA production ( see plots in Figure 6A and 6B ) . To create the in silico mutants , we allowed the OptKnock procedure to block a maximum of two reactions , which corresponds , experimentally , to the creation of a double mutant . To avoid lethal in silico strains , the minimal growth yield was limited to a value ranging between 0 . 83 and 6 . 67 gDW⋅molC−1 , corresponding to about 5 and 40 percent of maximum growth yield , respectively . Six mutational strategies suggested by this approach are presented in Table 5 . The first three were generated by the AcCoA production method , and the last three were generated by the AcCoA pooling method . The results provide a range of options for possibly increasing AcCoA production , some of which constrain growth more than others ( see Figure 6A and 6B ) . One promising hypothesis ( strategy 2 ) generated by the AcCoA production method predicted that a double-mutant devoid of 6-phosphogluconolactonase ( pgl/PP1023 ) and periplasmatic glucose dehydrogenase ( gcd/PP1444 ) , would produce 29% more AcCoA than the wild type growing on glucose as a carbon source ( Figure 6A ) . As we are currently still in the process of generating this mutant , we were not yet able to test the prediction . Another promising hypothesis ( strategy 1 ) included knocking-out triose phosphate isomerase ( tpiA/PP4715 ) . As the mutant for tpiA was generated in this work , we tested whether it is able to grow on the predicted carbon source ( d-fructose ) , but the observed growth was very weak ( only very small colonies grew on agar plates after three days ) . This suggests that growth might be too inhibited by this strategy for it to be of great use . One strategy suggested by the AcCoA pooling method ( strategy 4 ) called for knocking out 2-methylcitrate dehydratase ( prpD/PP2338 ) and citrate synthase ( gltA/PP4194 ) , and supplying P . putida with valine . Using this strategy , AcCoA pooling could theoretically reach 21 . 9 mmol⋅gDW−1⋅h−1 , but at a severe expense in bacterial growth ( Figure 6B ) . The other strategies suggested by the AcCoA pooling method highlight a somewhat linear tradeoff between growth and AcCoA pooling , which could be investigated experimentally to determine how much growth disruption is acceptable in a bioengineered production strain of P . putida ( Figure 6B ) . These strategies illustrate the possible approaches to optimizing production of a non-growth associated compound , and highlight the need for further experimental work to assess the performance of this approach .
A primary value of genome-scale metabolic models is their ability to provide a holistic view of metabolism allowing , for instance , for quantitative investigation of dependencies between species existing far apart in the metabolic network [20] . Once experimentally validated , these models can be used to characterize metabolic resource allocation , to generate experimentally testable predictions of cell phenotype , to elucidate metabolic network evolution scenarios , and to design experiments that most effectively reveal genotype-phenotype relationships . Furthermore , owing to their genome-wide scale , these models enable systematic assessment of how perturbations in the metabolic network affect the organism as a whole , such as in determining lethality of mutations or predicting the effects of nutrient limitations . Since these multiple and intertwined relationships are not immediately obvious without genome-scale analysis , they would not be found during investigation of small , isolated circuits or genes as is typical in a traditional reductionist approach [65] , [66] . We present here a genome-scale reconstruction and constraint-based model of the P . putida strain KT2440 , accounting for 815 genes whose products correspond to 877 reactions and connect 886 metabolites . The manually curated reconstruction was based on the most up-to-date annotation of the bacterium , the content of various biological databases , primary research publications and specifically designed functional genomics experiments . New or refined annotations for many genes were suggested during the reconstruction process . The model was validated with a series of experimental sets , including continuous culture data , BIOLOG substrate utilization assays , 13C flux measurements and a set of specifically-generated mutant strains . FBA and FVA were used to ascertain the distribution of resources in KT2440 , to systematically assess gene and reaction essentiality and to gauge the robustness of the metabolic network . Hence , this work represents one of the most thorough sets of analyses thus far performed for an organism by means of constraint-based modeling , providing thereby a solid genome-scale framework for the exploration of the metabolism of this fascinating and versatile bacterium . However , since this modeling endeavor relies upon a number of approximations , the limits , potential and applicability of the analysis must be clearly identified and defined . We address these points below . Altogether , our results and analyses show that the model accurately captures a substantial fraction of the metabolic functions of P . putida KT2440 . Therefore , the model was used to generate hypotheses on constraining and redirecting fluxes towards the improvement of production of polyhydroxyalkanoates , which are precursors for industrially and medically important bioplastics . This is , to our knowledge , the first reported application of constraint-based modeling to direct and improve the yield of a compound of which the production is not directly coupled to the growth of the organism . This opens up novel areas of application for the constraint-based approach . Our approach , based on the OptKnock algorithm , allows for both prediction of mutants with desirable properties and identification of conditions that support the expression of these properties . Notwithstanding the generally good agreement between experimental results and simulations of our model , several of the discrepancies encountered reflect pitfalls inherent to constraint-based modeling that go beyond the scope of our study: Firstly , the high number of blocked reactions and the mismatches with the BIOLOG data show that there are still many areas of the metabolism that require thorough exploration . The genes encoding transport-related are particularly relevant , as for most of them , neither the translocated compound nor the mechanism of translocation is known . Furthermore , it should be highlighted that the genome still has 1635 genes annotated as “hypothetical” or “conserved hypothetical” , more than 800 genes annotated as putative , and over 800 for which the functional annotation gives no information beyond the protein family name . It is thus likely that a fraction of the hypothetical and non-specifically annotated genes in the current P . putida annotation are responsible for unknown metabolic or transport processes , or that some might code for proteins that add redundancy to known pathways . This observation is common to all genomes sequenced so far and illustrates a major hurdle in the model building process ( and hence , its usefulness ) that can be overcome only through extensive studies in functional genomics . Secondly , although we carefully constrained the in silico flux space through FBA and FVA and obtained distribution spaces roughly consistent with those experimentally determined via 13C- flux analysis , these approaches are inherently limited as they assume growth as a sole metabolic objective and ignore any effects not explicitly represented in a constraint-based metabolic model . It has been shown that FBA using objective functions other than growth can improve predictive accuracy under certain conditions [53] . Kinetic limitations also may play a very important role in determining the extent to which a particular reaction or pathway is used . Teusink et al . [52] showed that in the case of L . plantarum these factors may lead to false predictions . Thirdly , the reconstruction includes causal relationships between genes and reactions via gene-protein-relationships ( GPRs ) but it lacks explicit information regarding gene regulation . The regulation of gene expression causes that there are many genes in the cell that are expressed only under certain growth conditions . Therefore , the in silico flux space is generally larger than the true in vivo flux space of the metabolic network . This , in turn , may influence the robustness of the metabolic network and the essentiality of some reactions and genes . The lack of regulatory information and of the genetic interactions involved is likely to be one of the causes for faulty predictions of the viability of mutant strains . Adding this information will be an important step in the further development and improvement of the accuracy of the reconstruction . Fourthly , although our analyses indicated that growth yield is relatively insensitive to changes in biomass composition , these analyses also suggest that factors other than the structure of the metabolic network play an important role in defining the relationship between the growth yield and environmental conditions . The prediction of the exact growth yield requires the precise measurement of maintenance values , which may vary substantially from one condition to the other [44]–[46] . As the maintenance accounts for 10–30% of the total carbon source provided in unstressed conditions , this may set a limit to the accuracy of the growth yield predictions . To enhance the usefulness and predictiveness of the model , several avenues could be followed in the future . Firstly , additional constraints can be overlaid on the network to reduce the space of possibilities and increase the accuracy of predictions . In addition to specific knowledge of particular enzymatic or transport processes , such constraints are best based on high-throughput experimental evidence such as transcriptomic and proteomic data , which are instrumental in expanding genotype-phenotype relationships in the context of genome-scale metabolic models [67] . Microarray experiments have guided the discovery of metabolic regulons , and usage of microarray and proteomic data to constrain metabolic models has improved model accuracy for other systems [23] . Secondly , P . putida provides a good opportunity for incorporating kinetic information into a genome-scale model as there are various kinetic models available and under development for small circuits in P . putida [68]–[71] . Incorporating data from these models into the genome-scale reconstruction would provide insights into the relationships of isolated metabolic subsystems within the global metabolism . This synthesis would also improve the flux predictions of the global model , particularly in areas where current FBA-based predictions methods fail due to their inherent limitations . Experimental validation of a genome-scale model is an iterative process that is performed continuously as a model is refined and improved through novel information and validation rounds . In this work , we have globally validated iJP815 as well as specific parts thereof by using both up-to-date publicly available data and data generated in our lab , but there will be always parts of the model that include blocked reactions and pathways that will require further , specific validation . As more knowledge becomes available from the joint efforts of the large P . putida community ( e . g . , http://www . psysmo . org ) , focus will be put on these low-knowledge areas for future experimental endeavors . We anticipate that this model will be of valuable assistance to those efforts . The metabolic reconstruction , the subsequent mathematical computation and the experimental validation reported here provide a sound framework to explore the metabolic capabilities of this versatile bacterium , thereby yielding valuable insights into the genotype-phenotype relationships governing its metabolism and contributing to our ability to exploit the biotechnological potential of pseudomonads . By providing the means to examine all aspects of metabolism , an iterative modeling process can generate logical hypotheses and identify conditions ( such as regulatory events or conditional expression of cellular functions ) that would reconcile disagreements between experimental observations and simulation results . Through a detailed in silico analysis of polyhydroxyalkanoate production , we show how central metabolic precursors of a compound of interest not directly coupled to the organism's growth function might be increased via modification of global flux patterns . Furthermore , as the species Pseudomonas putida encompasses strains with a wide range of metabolic features and numerous isolates with unique phenotypes , the reconstruction presented provides a basic scaffold upon which future models of other P . putida strains can be built with the addition or subtraction of strain-specific metabolic pathways . Due to its applicability across the numerous P . putida strains iJP815 provides a sound basis for many future studies towards the elucidation of habitat-specific features , bioremediation applications and metabolic engineering strategies with members of this ubiquitous , metabolically versatile and fascinating genus .
The P . putida model we present was built using a constraint-based ( CB ) approach . A constraint-based model consists of a genome wide stoichiometric reconstruction of metabolism and a set of constraints on the fluxes of reactions in the system [19] , [20] , [24] . The reconstruction represents stoichiometry of the set of all reactions known to act in metabolism of the organism , which can be determined in large part from genomic data since most cellular reactions are catalyzed by enzymes . Thus the model does not require any knowledge regarding the kinetics of the reactions , and the requisite thermodynamic knowledge is limited to the directionality of reactions . In addition to the reactions , the model includes a set of genes tied via Boolean logic to reactions that their protein products catalyze , which allows for accurate discrimination of the effects of genetic perturbations such as knockouts [33] , [72] . These Boolean rules together form the gene-protein-reaction relationships ( GPRs ) of the metabolic reconstruction [33] . The second part of the CB-model , namely the constraints , constitutes a set of rules that narrow down the interval within which the flux of particular reaction must lie . These constraints rest upon physico-biological knowledge . One of them , the information regarding reaction directionality , has already been mentioned above . Another constraint that is widely applied in biological systems is the Pseudo-Steady-State Assumption ( PSSA ) [73] , which states that a concentration of a chemical compound stays constant over the simulated time frame . The reactants to which this constraint is applied are usually called internal compounds , and in biological models correspond to the chemical substances located inside the cell or its compartments . Remaining substances , external compounds , correspond to species that can be taken up or secreted and thus exchanged with the environment . Other types of constraints are top and bottom limits that correspond to catalytic capabilities of the enzymes . More detailed description of constraint based modeling approach can be found in [74] and the Text S1 , section “Constraint based models—mathematical explanation” . OptKnock is an approach for identification of mutations that selectively increase production of a certain compound of interest , assuming that the mutant would optimize for the same quantity as the wild type ( e . g . , growth yield ) [28] . OptKnock points out reactions ( and genes , through GPR logic ) that must be blocked in order to maximize a linear combination of target fluxes ( outer objective ) while simultaneously maximizing for the cell's assumed objective ( growth yield; inner objective ) . OptKnock poses a bi-level optimization approach that is solved via Mixed-Integer Linear Programming ( MILP ) . Further details can be found in Text S1 , section “OptKnock – mathematical formulation” and [28] . The minimal growing set was identified using a Mixed Integer Linear Programming ( MILP ) approach , by modifying original FBA LP problem . For every non-blocked and non-essential reaction a binary variable was added that reflects the activity of the reaction . When the binary variable takes value of 1 the corresponding reaction is virtually unlimited ( or limited by rules of original LP problem ) . When the variable is set to 0 the corresponding reaction is blocked ( non-zero flux is impossible ) . This was achieved by adding a following set of equations to the original LP problem:for reversible reactions , andfor irreversible reactions . In order to assure that growth was not overly restricted , a minimal flux value was established for the biomass reaction . We set the lower limit on biomass flux to 0 . 05 when the supply of carbon source was 60 mmolC·gDW−1h−1 , which corresponds to growth yield of 0 . 07 gDW·gC−1 , 16 times lower than the wild type . The objective of the problem was set to minimize the sum of all binary variables yi: This method searches for a minimal set that is able to sustain growth greater than or equal to to the minimal growth requirement . The main sources of information regarding the composition of the metabolic network of Pseudomonas putida KT2440 were various biological databases . Most of the information came from the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) [35] , [81] and Pseudomonas Genome Database ( PGD ) [82] . Information regarding P . putida contained in these two databases is mainly based on the published genome annotation of the bacterium [14] , so there is a large overlap between them . Additionally , substantial information was taken from the BRENDA database , which catalogs reaction and enzyme information [83] . This all was augmented with knowledge coming directly from primary research publications ( see Text S3 ) . The reconstruction process was performed in an iterative manner , i . e . , by adding or removing reactions from the model in between rounds of model testing . First , reaction information for P . putida was collected from KEGG and PGD . Reactions supported by sufficient evidence and with specific enough functional annotations were incorporated into the model . For every accepted reaction its reversibility was assessed basing on assignments in KEGG pathways as well as information from BRENDA database . For reactions with inconsistent assignments a decision about reversibility was made basing on analysis of the reaction as well as its reversibility in other organisms . Hereby , a first version of the metabolic model was created ( iJP815pre1 ) . The next step involved assessing whether the reconstructed metabolic network is able to produce energy from glucose . This was achieved by running FBA with ATP production set as the objective function . Subsequently , the ability of the model to grow in silico on glucose was tested . Successful in silico growth indicates that every chemical compound belonging to the biomass equation can be synthesized from present sources , using the reactions contained in the model . Since the exact cellular composition of P . putida is not known , the composition of E . coli biomass was used as an approximation . This test was performed by running FBA with production of each biomass constituent set as the objective . If a compound could not be synthesized , the gaps in the pathway leading to it were identified manually and a search was performed for reactions that could fill the gaps . If this approach was unsuccessful , gaps were filled with reactions from the E . coli model . This yielded the second version of the reconstruction ( iJP815pre2 ) . The third round of reconstruction consisted of two sub-steps . First , the compounds for which transport proteins exist were identified and appropriate reactions added . Second , the results of BIOLOG carbon-source utilization experiments were compared with in silico simulations for growth on those compounds . It was assumed that the ability to grow in silico on the particular compound as the sole carbon source approximates the in vivo utilization . For those compounds that did not show in silico growth , a literature search was performed in order to identify possible pathways of utilization . The results of this search , in the form of reactions and GPRs , were added to the model . The outcome was the final version of the model ( iJP815 ) . Growth yields on sources of basic elements ( C , N , P , S ) were compared with experimental values obtained by Duetz et al . [37] . The yields of the model were computed using FBA , by setting the growth rate to the value of the dilution rate used in experiments and subsequently minimizing for consumption of source of respective element ( succinate , ammonia , phosphate and sulfate ) . The model was created and maintained using ToBiN ( Toolbox for Biochemical Networks , http://www . lifewizz . com ) . The optimizations ( FBA , FVA , OptKnock ) were computed by free , open source , solvers from the COIN-OR family ( COmputational INfrastructure for Operations Research , http://www . coin-or . org ) or by the lp_solve ver . 5 . 5 ( http://lpsolve . sourceforge . net/5 . 5/ ) software package . All computations were performed on a Personal Computer with a Intel Core 2 2 . 40 GHz CPU and 2GB of RAM . | The pseudomonads include a diverse set of bacteria whose metabolic versatility and genetic plasticity have enabled their survival in a broad range of environments . Many members of this family are able to either degrade toxic compounds or to efficiently produce high value compounds and are therefore of interest for both bioremediation and bulk chemical production . To better understand the growth and metabolism of these bacteria , we developed a large-scale mathematical model of the metabolism of Pseudomonas putida , a representative of the industrially relevant pseudomonads . The model was initially expanded and validated with substrate utilization data and carbon-tracking data . Next , the model was used to identify key features of metabolism such as growth yield , internal distribution of resources , and network robustness . We then used the model to predict novel strategies for the production of precursors for bioplastics of medical and industrial relevance . Such an integrated computational and experimental approach can be used to study its metabolism and to explore the potential of other industrially and environmentally important microorganisms . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"biotechnology/biocatalysis",
"biotechnology/applied",
"microbiology",
"computational",
"biology/metabolic",
"networks",
"biotechnology/bioengineering",
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"biology"
] | 2008 | Genome-Scale Reconstruction and Analysis of the Pseudomonas
putida KT2440 Metabolic Network Facilitates Applications in
Biotechnology |
Protein arginine methyltransferase 4 ( PRMT4 ) –dependent methylation of arginine residues in histones and other chromatin-associated proteins plays an important role in the regulation of gene expression . However , the exact mechanism of how PRMT4 activates transcription remains elusive . Here , we identify the chromatin remodeller Mi2α as a novel interaction partner of PRMT4 . PRMT4 binds Mi2α and its close relative Mi2β , but not the other components of the repressive Mi2-containing NuRD complex . In the search for the biological role of this interaction , we find that PRMT4 and Mi2α/β interact with the transcription factor c-Myb and cooperatively coactivate c-Myb target gene expression in haematopoietic cell lines . This coactivation requires the methyltransferase and ATPase activity of PRMT4 and Mi2 , respectively . Chromatin immunoprecipitation analysis shows that c-Myb target genes are direct transcriptional targets of PRMT4 and Mi2 . Knockdown of PRMT4 or Mi2α/β in haematopoietic cells of the erythroid lineage results in diminished transcriptional induction of c-Myb target genes , attenuated cell growth and survival , and deregulated differentiation resembling the effects caused by c-Myb depletion . These findings reveal an important and so far unknown connection between PRMT4 and the chromatin remodeller Mi2 in c-Myb signalling .
Protein arginine methyltransferases ( PRMTs ) constitute a family of nine members ( PRMT1-9 ) in mammals , which are characterised by a conserved catalytic domain [1] , [2] . They post-translationally mono- and dimethylate arginine residues in proteins using S-adenosylmethionine ( SAM ) as methyl group donor . Dimethylation can be either asymmetric or symmetric [3] . PRMTs regulate a plethora of cellular functions , including signal transduction , ribosome biogenesis , RNA processing , nucleo-cytoplasmic transport and chromatin-dependent processes , such as DNA repair , imprinting and transcriptional regulation , for which they usually require their catalytic activity . In agreement with their chromatin-related functions , a subgroup of PRMTs methylates histones as well as other chromatin-associated proteins and in this way contributes either to activation or repression of gene expression [4] . PRMT4 , also named CARM1 ( coactivator associated arginine methyltransferase 1 ) , was the first member linked to transcriptional activation through asymmetric dimethylation of histone H3 at arginine 17 ( H3R17me2a ) [5]–[7] . Together with other coactivators , such as PRMT1 and the histone acetyltransferase ( HAT ) CBP/p300 , PRMT4 is recruited to specific target genes through interaction with transcription factors , for example p53 , NF-κB and nuclear hormone receptors such as the estrogen receptor ( ER ) [8]–[11] . The hierarchy of sequential coactivator recruitment in ER signalling has been studied in detail revealing that PRMT1-mediated dimethylation of histone H4 at arginine 3 ( H4R3me2a ) occurs as an early event following hormone treatment and is a prerequisite for promoter hyperacetylation [12] , [13] . Subsequent histone acetylation by CBP/p300 facilitates promoter recognition by PRMT4 and methylation of H3R17 [14] . These various histone modifications at promoter-proximal nucleosomes of the target genes coincide with transcriptional activation . PRMT1- and CBP/p300-mediated histone modifications are required for the subsequent recruitment and enhanced activity of coactivators explaining their direct support of active transcription [12] , [14] , [15] . Furthermore , histone acetylation is read by Bromo domain-containing proteins , such as the TAFII250 subunit of the TFIID complex , linking this modification directly to pre-initiation complex formation [16] . In the case of PRMT4 and H3R17 methylation , the mechanistic contribution is less clear , although the general relevance of PRMT4 and its catalytic activity in ER-dependent gene activation and embryonic development have been demonstrated in knockout and knockin mice [17] , [18] . The recent identification of the Tudor domain-containing protein TDRD3 and the transcription elongation-associated PAF1 complex as readers of methylated H3R17 in the context of ER signalling provides a first hint to how this modification might directly promote transcriptional activation [19] , [20] . Besides histone arginine methylation , modification of non-histone proteins plays a similarly important role for the transcriptional function of PRMT4 . PRMT4 methylates HATs , such as CBP/p300 and SRC-3 , thereby influencing their half-life and capability to interact with other proteins and thus modulating their coactivating function [21]–[24] . Similar to the majority of chromatin modifiers and transcriptional coregulators , PRMT4 seems to exert its functions not as an individual protein , but in close association with interaction partners or within multi-protein complexes . For example , PRMT4 was found in a complex of at least ten proteins , called the nucleosomal methylation coactivator complex ( NUMAC ) , which includes components of the SWI/SNF remodeller complex and coactivates ER-dependent transcription in breast cancer cells [25] . As part of NUMAC PRMT4 acquires the ability to methylate nucleosomal histone H3 , whereas recombinant PRMT4 preferentially methylates free H3 . Such protein associations are likely to explain how PRMT4 contributes to cell type-specific functions and to cell lineage specification despite its ubiquitious expression pattern [5] . In the early embryo , PRMT4 regulates the development of the inner cell mass and activates expression of pluripotency markers [26] , [27] , whereas in differentiating skeletal muscle cells PRMT4 is required for the late myogenic transcription programme [28] . Deregulated expression of PRMT4 in certain tissues leads to aberrant transcription and is linked to tumorigenesis , such as high-grade breast tumors [29] . Further knowledge on interaction partners of PRMT4 would be necessary to understand its cell type-specific functions and its contribution to pathogenesis . In an attempt to identify such novel interaction partners of PRMT4 using a biochemical approach , we discovered here the ATP-dependent chromatin remodellers Mi2α and Mi2β , also called CHD3 and CHD4 ( chromodomain-helicase-DNA binding protein 3 and 4 ) respectively , as such candidates . We found that PRMT4 coactivates c-Myb-dependent gene expression together with Mi2α as well as Mi2β in a cooperative manner . PRMT4 and Mi2 simultaneously occupy c-Myb target gene promoters in a c-Myb-dependent fashion and are regulators of cell survival and differentiation of the haematopoietic lineage resembling the function of c-Myb .
To explore the molecular function of PRMT4 in gene regulation we aimed to identify novel interaction partners of the enzyme . We performed gel filtration analysis of protein extracts from several cell lines and detected PRMT4 by Western Blot analysis . In HEK293 and Molt-4 cells endogenous PRMT4 protein formed higher molecular weight complexes than expected from its monomeric or dimeric molecular weight ( monomeric MW of PRMT4 = 65 kDa ) and peaked in elution fractions of approximately 500 kDa ( Figure 1A ) . Similar results were obtained for overexpressed PRMT4 in HEK293 cells ( data not shown ) . In MCF-7 cells PRMT4 did not peak in the 500 kDa fraction , but significantly eluted with proteins of 100 kDa molecular weight ( Figure 1A ) . These results indicate that PRMT4 stably associates with other proteins in higher molecular weight complexes in a cell type-dependent manner . In order to purify endogenous PRMT4 with associated proteins we next performed cation and anion exchange chromatography of HEK293 extracts ( Figure 1B ) . The presence of PRMT4 was detected by Western Blot analysis and by methyltransferase assay towards histone H3 in each chromatographic fraction ( data not shown ) . PRMT4 did not bind the cation exchanger phosphocellulose , but eluted from the following anion exchangers DEAE and MonoQ at defined salt concentrations ( Figure 1B ) . Thereby , PRMT4 was separated for example from the PRMT1 enzyme , which eluted from the DEAE column at a higher salt concentration ( data not shown ) . To confirm that the high molecular weight complexes of PRMT4 remained stably associated during the ion exchange chromatography we performed gel filtration analysis after each chromatographic step and detected the presence of PRMT4 by Western Blot analysis , as exemplarily shown for the elution fractions of the MonoQ column ( Figure 1C ) . Using the PRMT4-containing MonoQ fractions we performed affinity purification of the endogenous PRMT4 protein by immunoprecipitation ( IP ) . Both anti-PRMT4 and control IPs were analysed by SDS-PAGE and silver staining ( Figure 1D ) . Silver-stained protein bands specifically detected in the anti-PRMT4 samples were excised and protein identity was determined by mass spectrometry analysis . Among other proteins ( not shown ) , we identified PRMT4 itself and the ATP-dependent chromatin remodeller Mi2α , also called CHD3 ( chromodomain-helicase-DNA binding protein 3 ) ( Figure 1D ) . Components of the NUMAC complex were not identified [25] . This result suggests that Mi2α is a putative interaction partner of PRMT4 . We next analysed the putative interaction between PRMT4 and Mi2α by performing co-immunoprecipitations ( co-IP ) followed by Western Blot analysis . Immunoprecipitation ( IP ) of endogenous PRMT4 from HEK293 cells copurified overexpressed Flag-tagged Mi2α ( Figure 2A ) and reciprocally immunoprecipitates of Flag-Mi2α revealed the presence of endogenous PRMT4 ( Figure 2B ) . IP of Mi2α from the PRMT4-enriched MonoQ fractions using a newly generated anti-Mi2α serum revealed also an interaction between PRMT4 and Mi2α on the endogenous level and supported the mass spectrometrical result ( Figure S1 ) . To address the question of whether other PRMTs , such as PRMT1 , PRMT3 and PRMT6 , are also able to interact with Mi2α we performed GST-pulldown assays . Flag-Mi2α preferentially bound to the GST-fusion of PRMT4 and to a lower extent to GST-PRMT1 , but not to the other tested PRMT members ( Figure 2C , Figure S2 ) . These results confirm the specificity of this novel interaction and identify PRMT4 as the predominant PRMT to interact with Mi2α . Given that Mi2α has a close relative , Mi2β/CHD4 , we asked whether Mi2β could interact as well with PRMT4 . Co-IP analysis revealed an interaction between HA-tagged Mi2β and PRMT4 ( Figure 2D ) . Both Mi2 proteins harbour several conserved functional domains [30] , including two PHD fingers , which possess individual histone-binding activities enabling bivalent recognition of two histone H3 tails within nucleosomes [31] , [32] , two Chromo domains that bind DNA [33] and a SNF2-type ATPase domain . To map the interaction domain of PRMT4 in Mi2 we expressed and radiolabeled Mi2α deletion mutants in an IVT system and performed pulldown experiments with GST-PRMT4 . The deletion constructs contained either the N-terminus , the two PHD domains , the two Chromo domains , the helicase domain or the C-terminus ( Figure S3 ) . This assay revealed that PRMT4 interacts with the N-terminal region and the Chromo domains of Mi2α ( Figure S3 ) . Together , these results show that PRMT4 interacts with both Mi2 proteins and narrow down , as exemplified for Mi2α , the interaction surface of PRMT4 in Mi2 . Mi2α and Mi2β have been reported to be part of the NuRD ( nucleosome remodelling and deacetylation ) complex and accordingly to function in transcriptional repression , as NuRD provides a physical link between ATP-dependent chromatin remodelling and HDAC ( histone deacetylase ) activity [34]–[36] . Therefore we investigated whether PRMT4 associates with other subunits of the NuRD complex . In co-IP assays we confirmed the interaction of Mi2α with the NuRD components MBD3 or HDAC1 ( Figure S4 ) . However , specific interactions with MBD3 or HDAC1 were not detected in the PRMT4 immunopreciptates . These results indicate that PRMT4 selectively binds both Mi2 proteins , but no other components of the NuRD complex suggesting that the PRMT4-Mi2 interaction might not be linked to NuRD-mediated transcriptional repression . Apart from their repressive function within the NuRD complex , both Mi2 proteins are also involved in transcriptional activation . For example , human Mi2β is required for T cell development and activation of the CD4 gene [37] . The Drosophila orthologue of Mi2β/CHD4 is localised to actively transcribed regions of polytene chromosomes [38] . Furthermore , Mi2α coactivates c-Myb-mediated transcription independently of its helicase activity [39] . The proto-oncogenic transcription factor c-Myb plays a central role in the proliferation and differentiation of different haematopoitic lineages , in particular of erythrocytes and thymocytes [40] , [41] , and similarly PRMT4 and Mi2 knockout studies revealed severe defects in early T-cell development [37] , [42] . This led us to investigate whether PRMT4 as well as both Mi2 proteins are able to interact with c-Myb . HEK293 cells were transfected with untagged PRMT4 and HA-tagged c-Myb and co-IP assays were performed with antibodies against PRMT4 , HA or IgG control . We detected PRMT4 in HA-c-Myb-immunoprecipitates and reciprocally HA-tagged c-Myb in PRMT4-immunoprecipitates ( Figure 3A ) . Both proteins also interacted endogenously in the T lymphocyte cell line Jurkat ( Figure 3B ) . Moreover , pulldown assays using GST-PRMTs and bacterially expressed His-tagged c-Myb revealed a preferential and direct interaction between c-Myb and GST-PRMT4 , whereas GST-PRMT1 exhibited a weak interaction and GST-PRMT6 no interaction with c-Myb ( Figure 3C ) . Furthermore , we showed that Flag-tagged Mi2α and HA-tagged c-Myb coimmunoprecipitate ( Figure 3D , Figure S5 ) , as previously reported [39] . Additionally , using the same approach we uncovered that also Mi2β was able to interact with c-Myb ( Figure 3D ) . Using protein extracts from Jurkat cells , which reveal high expression levels of PRMT4 , c-Myb and Mi2 ( data not shown ) , we validated that Mi2α interacts with c-Myb and PRMT4 also endogenously ( Figure 3E ) . These results identify PRMT4 and both Mi2 proteins as novel interaction partners of the c-Myb transcription factor . To address whether PRMT4 together with Mi2α and Mi2β regulates the transcriptional activity of c-Myb , we employed the chicken myelomonocytic cell line HD11 that does not endogenously express c-Myb , but is competent to induce endogenous c-Myb target genes , such as Mim-1 and Lysozyme , upon overexpression of c-Myb [43] , [44] . Mim-1 is one of the best-characterised Myb target genes and its transcription is strongly upregulated by c-Myb designating the gene an excellent model to study the influence of transcriptional coregulators [45] . We transfected HD11 cells with c-Myb alone or in combination with PRMT4 and Mi2 , which both did not affect the expression levels of c-Myb itself ( Figure S6 ) , and measured the levels of Mim-1 and Lysozyme transcripts by reverse transcription-quantitative PCR ( RT-qPCR ) . We found that increasing amounts of PRMT4 enhanced the transcript levels of Mim-1 and Lysozyme in a c-Myb-dependent and concentration-dependent manner ( Figure 4A , Figure S7 ) . In contrast , overexpression of PRMT1 and PRMT6 did not augment the transcriptional activity of c-Myb , as exemplified for the Mim-1 and Lysozyme gene ( Figure 4B , Figure S8 ) , rather PRMT6 repressed the c-Myb-mediated activation in line with its corepressor function [46] , [47] . Noticeably , coexpression of PRMT4 and Mi2α further enhanced the transcriptional activity of c-Myb ( Figure 4A , Figure S7 ) . The same result was obtained for coexpression of PRMT4 and Mi2β ( Figure 4C ) suggesting that PRMT4 cooperates with Mi2α and Mi2β in coactivating c-Myb target gene transcription . This effect required the catalytic activity of PRMT4 , as overexpression of a methyltransferase-deficient mutant of PRMT4 ( VLD ) resulted in the loss of coactivation of c-Myb ( Figure 4D , Figure S9 ) . Similarly , overexpression of an ATPase-deficient mutant of Mi2α ( KA ) led to a reduced coactivation in case of Mim-1 and to a loss of coactivation in case of Lysozyme gene expression ( Figure 4D , Figure S9 ) . Moreover , for both target genes the cooperativity between PRMT4 and Mi2 was impaired upon transfection of both catalytic mutants . Together , these data indicate that PRMT4 is a novel coactivator of the c-Myb transcription factor and synergises with both Mi2 proteins in a methyltransferase- and helicase-dependent manner to coregulate c-Myb activity . To analyse whether the effect of PRMT4 and Mi2 on Mim-1 gene activation correlates with their concomitant recruitment to the Mim-1 regulatory regions , in which c-Myb binding sites have been identified [43]–[45] , [48] , we performed chromatin immunoprecipitation ( ChIP ) . We used HD11 cells stably expressing a doxycycline-inducible c-Myb construct ( HD11-C3 ) . In response to doxycycline , the levels of c-Myb protein ( Figure 5A ) and consistently of Mim-1 transcript ( Figure 5B ) were increased . ChIP analysis revealed that upon doxycycline treatment c-Myb binds the promoter and to a lower extent the enhancer of the Mim-1 gene , whereas an upstream control region was not occupied by c-Myb ( Figure 5C , 5D ) . Recruitment of both PRMT4 and Mi2 was detected at the Mim-1 promoter as well as enhancer in a c-Myb dependent manner , but not at the upstream control region ( Figure 5E , 5F ) . Therefore the recruitment of the two coactivators did not reflect the binding preference of c-Myb for the promoter . The occurrence of H3R17 methylation ( H3R17me2a ) exclusively correlated with the binding of PRMT4 at the promoter of the Mim-1 gene ( Figure 5G ) , suggesting that histone H3 is the substrate of PRMT4 preferentially at the promoter but not at the enhancer . Concomitantly with transcriptional induction of Mim-1 , a reduction of histone H3 occupancy was detectable at the promoter ( Figure 5H ) . When this decrease in total H3 levels was taken into account for the calculation of H3R17 methylation , its promoter-specific increase was even enhanced ( Figure 5I ) . These findings indicate that the c-Myb-dependent Mim-1 gene is a direct target of PRMT4 and Mi2 and that the two coactivators are concomitantly recruited with c-Myb . We next investigated whether PRMT4 and Mi2 were also relevant transcriptional coactivators of c-Myb in mammalian cells . Given that c-Myb is a key regulator of haematopoiesis , we decided to use the CML-derived erythroleukemic cell line K562 , in which numerous c-Myb target genes have been identified [49] , and performed siRNA-mediated depletion of c-Myb , PRMT4 , Mi2α or Mi2β . For each knockdown condition , we then analysed the mRNA levels of published c-Myb targets . Depletion of c-Myb , as documented by Western Blot analysis ( Figure 6A ) , led to a decrease in transcript levels of Cdc7 , c-Myc , Gata3 and CycB1 ( Figure 6B ) as previously reported [49] , [50] . Noticeably , the transcript levels of these genes were reduced to the same extent after PRMT4 depletion ( Figure 6A , 6B ) . These results indicate that an overlapping set of target genes is regulated by c-Myb and PRMT4 in human haematopoietic cells . Next we asked whether expression of the same c-Myb target genes is influenced by Mi2α and Mi2β . K562 cells efficiently depleted of either Mi2α or Mi2β subsequent to siRNA transfection ( Figure 6C ) exhibited in most cases reduced transcript levels of the above identified PRMT4-regulated c-Myb targets ( Figure 6D ) . The mRNA levels of Cdc7 , c-Myc and Gata3 were downregulated , most strongly upon Mi2β depletion , whereas knockdown of Mi2α showed a weaker ( Cdc7 , Gata3 ) or no effect ( c-Myc , Figure 6D ) . In contrast , transcript levels of CycB1 were up-regulated upon Mi2 depletion , again most strongly upon Mi2β depletion , suggesting that both proteins exert a repressive function in this case . Comparison of the mRNA levels of both Mi2 in wild type K562 cells revealed that Mi2β is predominantly expressed ( data not shown ) , which might explain its stronger effects on gene expression in these cells . Together , these results reveal that PRMT4 and Mi2 influence an overlapping set of c-Myb target genes in human haematopoietic cells . All tested c-Myb targets were activated by PRMT4 , whereas Mi2 operated in a gene-specific manner either as an activator or repressor . In case of Cdc7 , c-Myc and Gata3 genes , PRMT4 as well as Mi2 enhanced transcription corroborating our findings on a synergism of the two coregulators in c-Myb signalling . In order to study whether PRMT4 and Mi2 proteins bind the regulatory regions of these c-Myb target genes , we performed ChIP analysis in K562 cells . We found that the promoter regions of Cdc7 , c-Myc and CycB1 genes , that contain c-Myb binding sites , were enriched in the immunoprecipitates of c-Myb , PRMT4 as well as Mi2 compared to the IgG control ( Figure 6E–6G ) . In contrast , control regions of the three genes and the β-Tubulin gene promoter , which are free of c-Myb binding sites , were not bound by c-Myb or the two coregulators ( Figure 6E–6G ) . These results indicate that PRMT4 and Mi2 are concomitantly recruited together with c-Myb to target gene promoters and are directly involved in coactivating a subset of c-Myb-dependent genes in human haematopoietic cells . The c-Myb target genes coregulated by PRMT4 and Mi2 in K562 cells fulfil well-established functions in cell cycle and proliferation control . Given that c-Myb is essential for the self-renewal and proliferative capacity of haematopoietic progenitor cells and suppresses differentiation [49] , we next explored the biological significance of PRMT4 and Mi2 for the regulation of c-Myb activity in haematopoiesis . For this purpose we used K562 cells as a model for haematopoietic cell proliferation and differentiation . First we intended to clarify the role of these novel coactivators in c-Myb-dependent proliferation . In agreement with earlier reports showing that inactivation of c-Myb in K562 cells leads to G2/M arrest [51] , we found an increased cell number in the G2/M phase upon siRNA-mediated knockdown of c-Myb compared to control siRNA-transfected K562 cells , as analysed by propidium iodide ( PI ) FACS ( Figure 7A , Figure S10 ) . Furthermore , the number of cells in G1 phase was reduced , while the number of apoptotic cells ( sub-G1 peak ) was increased in c-Myb-depleted cells , corroborating the pro-proliferative and anti-apoptotic capacity of c-Myb in these cells . Next , we investigated whether depletion of PRMT4 and Mi2α/Mi2β , respectively , affects cell cycle distribution of K562 cells . Similarly , knockdown of the coactivators resulted in a G2/M arrest and in an increased apoptotic rate ( Figure 7A , Figure S10 ) . Depletion of Mi2β had a stronger effect on the cell cycle distribution than depletion of the other two coregulators or of c-Myb itself revealing that Mi2β might be implicated in additional c-Myb-independent pro-proliferative functions . To investigate the c-Myb-dependence of the PRMT4 as well as Mi2 function in cell cycle regulation , we depleted the three coactivators in U2OS cells , which clearly expressed lower levels of c-Myb compared to K562 cells , and monitored their cell cycle profile by PI-FACS ( Figure S11 ) . We found that PRMT4- or Mi2-depleted U2OS cells revealed no effect on apoptosis or at most a slightly decreased number of apoptotic cells in contrast to the enhanced apoptosis in K562 cells . However , depletion of the coactivators resulted in a decreased number of U2OS cells in G1 phase and in a G2/M arrest similar to our findings in PRMT4- and Mi2-depleted K562 cells suggesting that the apoptotic effects of PRMT4 and Mi2 might be c-Myb-dependent . These data show that PRMT4 and both Mi2 proteins regulate the cell cycle of K562 cells similar to c-Myb and might be functionally relevant coactivators of c-Myb with respect to its apoptotic function in haematopoietic cells . Transformed cell lines are able to proliferate and form colonies in semi-solid medium . We then asked whether depletion of c-Myb or the coactivators influences this property of K562 cells . Depletion of either c-Myb , PRMT4 , Mi2α or Mi2β resulted in reduced numbers of colonies in the methylcellulose colony formation assay compared to control transfected cells ( Figure 7B , 7C ) . Again in this assay , the effect of Mi2β depletion exceeded the effect of c-Myb knockdown hinting at an additional c-Myb-independent role for Mi2β . These results independently confirm a pro-proliferative function of PRMT4 and Mi2 resembling the proliferation-promoting effect of c-Myb . Finally , we aimed to investigate the potential function of the novel coactivators in the differentiation-suppressive activity of c-Myb . Expression levels of c-Myb are elevated in haematopoietic progenitors of different lineages including the myeloid lineage and decrease during differentiation into the various sub-lineages to allow cell cycle exit and terminal differentiation [49] . K562 cells maintain characteristics of multipotent haematopoietic progenitors and are able to differentiate along the erythroid lineage when treated with hemin [52] . Overexpression of c-Myb has been shown to specifically restrain the chemically induced differentiation of K562 along the erythroid lineage [53] . Therefore , we investigated whether depletion of c-Myb or its coregulators could enhance the hemin-induced differentiation of K562 cells . In the absence of hemin treatment , depletion of c-Myb as well as Mi2β enhanced the spontaneous differentiation into erythrocytes compared to the other siRNA-transfected cells , as quantified by staining of benzidine-positive cells ( Figure 7D ) . Furthermore , the hemin-induced erythroid differentiation was enhanced upon depletion of c-Myb , PRMT4 and Mi2β . A weak increase was also obtained upon depletion of Mi2α ( Figure 7D ) . Together with our findings that PRMT4 and Mi2 interact with c-Myb and coactivate c-Myb-dependent gene expression , these results suggest that PRMT4 and Mi2 are relevant coregulators of c-Myb and might contribute to its differentiation-blocking activity in human erythropoiesis .
In this study we searched for novel chromatin-associated interaction partners of the arginine methyltransferase PRMT4 to extend our understanding of its physiological and pathophysiological roles in transcriptional regulation . We found that PRMT4 forms stable complexes with other proteins as observed by gel filtration analysis , which revealed a cell type-specific elution profile of PRMT4 . In HEK293 and Molt-4 extracts , PRMT4 is part of protein complexes that peak around 500 kDa , whereas MCF7 extracts predominantly showed PRMT4 in fractions of 100 kDa in size . Interestingly , the PRMT4-containing NUMAC complex was isolated from hormone-treated MCF7 cells using epitope-tagged PRMT4 [25] indicating that PRMT4 forms dynamic associations not only in a cell type- but also stimulus-dependent manner . In search for novel interaction partners of PRMT4 we combined ion exchange chromatography of HEK293 protein extracts with endogenous co-IP of PRMT4 and mass spectrometrical analysis . The silver-stain analysis of the separated PRMT4 immunoprecipitates revealed that copurifying protein bands did not occur in stoichiometric ratio with PRMT4 . Thus , the purification does not contain a predominant PRMT4-containing complex , rather PRMT4 temporarily and independently interacts with several proteins and these multiple interactions cause the molecular weight shift of PRMT4 in the gel filtration analysis . Components of the NUMAC complex were not identified in the mass spectrometrical analysis , which conforms to the fact that the PRMT4-containing complexes in HEK293 cells are around 500 kD in size , whereas the NUMAC complex possesses a size of approximately 1 . 5 MDa [25] . This finding additionally underlines that PRMT4 associates with other proteins in a cell type- and stimulus-dependent manner . The mass spectrometry identified Mi2α as a novel interaction partner of PRMT4 and subsequently we found that also its close homologue Mi2β is able to bind PRMT4 . Both Mi2 proteins belong to the CHD family of chromatin remodellers containing a tandem Chromo domain and the SNF2-like ATPase domain as signature motifs [30] . They have predominantly been described in the literature as transcriptional repressors , since they are subunits of the NuRD repressor complex [34]–[36] . Several transcription factors , for example NF-κB and KRAB , have been shown to recruit the repressive activity of NuRD to target genes [54] , [55] . Nevertheless , the two Mi2 proteins seem to have distinct function , since KRAB is specifically corepressed by NuRD containing Mi2α and not by Mi2β . We excluded the possibility that the PRMT4-Mi2 interaction takes place within the NuRD complex , since PRMT4 did not interact with two other subunits of NuRD and additionally the molecular weight of the PRMT4 complex separated via gel filtration was smaller than the expected size ( ∼1 MDa ) of the NuRD complex [36] . PRMT4 acts as a coactivator of several transcription factors , particularly well-studied for ER , and requires for this function its methyltransferase activity , which marks active gene promoters by H3R17 methylation [7] , [18] . We therefore envisaged a potential role of the PRMT4-Mi2 interaction in transcriptional activation , to which Mi2 proteins have also been functionally linked . It was found that Mi2β associates with essential transcription factors of lymphocyte development , such as Ikaros and HEB . Mi2β promotes CD4 gene expression together with HEB and the HAT p300 in developing thymocytes [37] , whereas Mi2β and Ikaros antagonise each other's silencing activity and thereby allow transcriptional activation of lymphocyte-specific genes [56] , [57] . Mi2α was reported to coactivate transcription mediated by the transcription factor c-Myb [39] . Given that PRMT4 , Mi2β and c-Myb consistently revealed defects in the development of specific haematopoietic lineages in knockout models [37] , [42] , [49] , we focussed on the potential functional link of the PRMT4-Mi2 interaction in the context of c-Myb signalling . We found that PRMT4 and c-Myb bind each other and additionally c-Myb associates with both Mi2 proteins . When we studied the impact of PRMT4 and Mi2 in HD11 macrophage cells , a well-established model for c-Myb function , we detected that PRMT4 and Mi2 are recruited to the endogenous target gene Mim-1 in c-Myb-dependent manner and cooperate in activation of c-Myb-mediated transcription . A recent report showed that among the C/EBP transcription factors , which are also known to activate Mim-1 , C/EBPβ is antagonised by PRMT4-mediated arginine methylation [58] . However , in our experimental system the coactivating function of PRMT4 is predominant for the Mim-1 gene regulation . Interestingly , recruitment of PRMT4 and Mi2 was detected at the Mim-1 promoter as well as enhancer in a c-Myb-dependent manner , whereas c-Myb preferentially bound to the promoter . Therefore , the recruitment of the two coactivators did not reflect the binding preference of c-Myb for the promoter , which might be due to c-Myb associations that differ in their composition at the enhancer and the promoter and lead to the masking of the antibody epitope within the c-Myb protein specifically when bound to the enhancer . Furthermore , other factors than c-Myb , with preferential binding to the enhancer , might promote binding of PRMT4 and Mi2 to the enhancer . Another interpretation of this observation is that after 30 hours of doxycycline treatment the chromatin structure has changed . Nucleosome repositioning , which has been described in the regulatory regions of the mim-1 gene [59] , might lead to a better access of c-Myb to its binding sites selectively in the promoter region and therefore stronger recruitment of c-Myb to the promoter compared to the enhancer . Although PRMT4 and Mi2 recruitment to the promoter and enhancer depends on c-Myb , directly or indirectly , the coactivators do not necessarily have to follow c-Myb in their binding strength . For both Mi2 proteins , the isolated N-terminus was shown to activate transcription in reporter gene assays [39] , [60] , which is interesting , as we mapped the N-terminus together with the Chromo domains in Mi2 to be responsible for the interaction with PRMT4 . Given that the tandem Chromo domains were shown to stimulate the ATPase function of Mi2 [61] , association of PRMT4 through these domains might influence the remodelling activity . Furthermore , we show here that PRMT4 and Mi2 need their catalytic activity for coactivation of c-Myb , which is contrary to recent findings that Mi2α coactivates c-Myb in a helicase-independent fashion and that solely the repressive function of Mi2α requires the helicase activity [39] . Interestingly , the Mim-1 gene undergoes intensive nucleosome remodelling at its enhancer region upon transcriptional activation in HD11 [59] . Whether nucleosome remodelling of c-Myb target genes is mediated by Mi2 will be subject of future studies . Our findings open up additional interesting questions e . g . of how PRMT4 and Mi2 mechanistically cooperate and whether Mi2 is a substrate of PRMT4 . c-Myb is predominantly expressed in immature haematopoietic cells and is involved in the regulation of proliferation and differentiation of stem cells and progenitor cells of the bone marrow , but also of colon and adult brain [49] . Several c-Myb target genes have been identified in the mammalian system in the past , the majority of which are activated by c-Myb and hint at its cell type-specific functions , i . e . cell cycle progression , differentiation and survival [49] , [50] . Therefore we investigated whether PRMT4 and Mi2 are also relevant coactivators of c-Myb in the mammalian haematopoietic system . In these studies we used the CML-derived erythroleukemia cell line K562 , since c-Myb has been shown to be functionally relevant in these cells [51] . We found that PRMT4 and Mi2 directly activated c-Myb target gene transcription . Depletion of PRMT4 or Mi2 resulted in deregulation of cell proliferation , apoptosis and erythrocyte differentiation resembling the effects caused by c-Myb depletion . These functional correlations are consistent with the physical interaction of the three proteins and their cooperation in gene expression and suggest for the first time a connection between PRMT4 and the Mi2α/β remodeller in c-Myb signalling and c-Myb-dependent erythropoiesis . Elevated c-Myb levels due to overexpression or inappropriate activation by structural alterations of the protein sequence lead to a block in differentiation and contribute to the onset of certain human leukaemias , in particular AML , CML and T-ALL [49] . Recently , PRMT4 was identified in an shRNA screen among the group of genes required for disease maintenance in an AML mouse model , in which c-Myb is a critical driver of oncogenesis [62] . These findings together with our observations turn PRMT4 as well as Mi2 into attractive targets for cancer research and therapy in the future .
HEK293 , HeLa , Molt-4 , MCF-7 and U2OS cells were maintained in Dulbeccos Modified Eagle's Medium ( Lonza ) , while K562 and Jurkat cells were cultured in RPMI 1640 ( PAA ) . Growth medium was supplemented with 10% fetal calf serum ( FCS , Invitrogen ) and 1% Penicillin/Streptomycin ( Lonza ) . HD11 and HD11-C3 ( stably expressing a doxycycline-inducible chicken c-Myb ) cells were cultured in Iscove's Medium ( Biochrom AG ) supplemented with 8% FCS and 2% chicken serum ( Sigma ) . For induction of c-Myb expression , HD11-C3 cells were treated with 1 µg/ml doxycycline ( Sigma ) for 30 hours . Transient transfections of HEK293 and HeLa cells with plasmids were performed following a standard CaPO4 protocol . HD11 cells were transiently transfected with plasmids using Fugene HD reagent ( Roche ) . For siRNA transfection of K562 cells , 5×106 cells were electroporated in 300 µl growth medium together with 6 µg siRNA ( Dharmacon ) at 220 V and 950 µF ( Electroporator BioRad ) using 4 mm cuvettes and subsequently cultured in growth medium . U2OS cells ( 1 . 6×105 cells per 6-well ) were transfected with 20 nM siRNA using Lipofectamin RNAiMax ( Invitrogen ) . Short interfering RNA ( siRNA ) oligonucleotide duplexes were obtained from Dharmacon or Eurogentec . The siRNA sequences , the plasmids and antibodies used are listed in the Text S1 . For whole-cell extracts , cells were lysed in IPH buffer ( 50 mM Tris at pH 8 , 150 mM NaCl , 5 mM EDTA , 0 . 5% NP40 , 1 mM DTT ) . To digest the cellular DNA , lysates were subjected to 62 . 5 units Benzonase ( Invitrogen ) per mg protein with addition of 7 mM MgCl2 for 40 min at 4°C . For co-immunoprecipitation ( co-IP ) assay , samples of 0 . 5–1 mg protein were adjusted to the same volume and to 10 µg/ml ethidium bromide . The IP procedure was performed according to [63] . Gel filtration chromatography for determination of the native size of PRMT4 and ion exchange chromatography for purification of PRMT4 with associated proteins followed by IP and mass spectrometry are described in the Text S1 . GST- and His-tagged proteins were purified from E . coli BL21 according to standard protocols . In vitro transcription and translation ( IVT ) of Mi2α deletion constructs in the presence of 35S-labelled methionine was performed with TnT T7 Coupled Reticulocyte Lysate System ( Promega ) according to the manufacturer's protocol . Between 0 . 1–10 µl of each IVT product and 1 µg GST-/His-tagged proteins were used per pulldown reaction . GST-pulldown experiments were performed as previously described [63] . The reactions were finally separated by SDS-PAGE and proteins were detected either by Western Blot analysis or autoradiography . Total RNA was isolated using PeqGold total RNA Kit ( PeqLab ) . First strand cDNA was synthesised from 0 . 5 µg of RNA by incubation with oligodT17 primer and 100 units M-MLV reverse transcriptase ( Invitrogen ) as described by the manufacturer . For chromatin immunoprecipitation ( ChIP ) analysis , a 145 cm2 dish of HD11 cells or 1×107 K562 cells were used per IP . The protocol was carried out according to [12] except that chromatin was fragmented by sonication 50×3 sec and 5 sec pause on ice at 30% amplitude ( Branson Sonifier W-250-D ) . cDNA and eluted chromatin were subjected to qPCR analysis in triplicates with gene-specific primers listed in the Text S1 . Quantitative PCR was performed using Absolute qPCR SYBR Green Mix ( Thermo Scientific ) and the Mx3000P real-time detection system ( Agilent ) . Each qPCR reaction was performed in triplicates from the same experiment ( technical replicates ) and the standard deviation ( indicated by error bars ) was calculated accordingly . The presented data sets are representative of at least 3 independent experiments ( biological replicates ) . For RT-qPCR , each mRNA expression was normalised to GAPDH mRNA expression . ChIP-qPCR results were expressed as % input . For quantification of the cell cycle distribution , 1×106 K562 cells were harvested 3 days after siRNA transfection , washed in PBS and fixed in ice-cold ethanol for 30 min . Cells were washed again in PBS and DNA was stained with 54 µM propidium iodide ( PI ) in the presence of 38 mM sodium citrate and 10 µg DNase-free RNase A ( Applichem ) in the dark for 30 min at 37°C . Samples were then analysed in a Flow Cytometer FACS Calibur using ModfitLT Mac3 Software and for sub-G1 using CellQuest-Pro software ( BD Biosciences ) . Reproducible and representative data sets are shown . Three days after transfection of K562 cells with siRNA , 800 cells/100 µl growth medium were seeded in duplicates in 300 µl Methocult M2334 ( Stem Cell Biotechnologies ) supplemented with 20% RPMI and 1% Pen/Strep in 24-well plates . After 2 days of incubation , colonies were stained with 50 µl 1 mg/ml INT ( Iodonitrotetrazolium chloride ) for 5 days . Pictures of each well were captured with a binocular microscope ( Leica MZ 125 ) using the Leica DC300 camera . Colonies larger than 0 . 1 mm were counted . Three independent experiments were analysed for quantification . Three days after transfection with siRNA , K562 cells were seeded in triplicates at a density of 4×105 cells/ml and were treated for 3 days with 30 µM hemin [64] . Subsequently , 700 µl of the suspension cells were pelleted and washed twice with 0 . 9% NaCl solution . Then cells were resuspended in 100 µl 0 . 9% NaCl and 50 µl TMB solution ( 10 mg 3 , 3′ , 5 , 5′-Tetramethyle-benzidine-dihydrochloride , 1 . 2 ml acetic acid , 8 . 8 ml ddH2O , 2% H2O2 ) . After 30 min incubation , 200 µl 0 . 9% NaCl was added . The number of benzidine-positive cells was estimated by counting triplicates of 300 cells under a microscope using the Neubauer counting chamber . | Our manuscript deals with the Protein arginine methyltransferase 4 ( PRMT4 ) , which modifies arginine residues in histones and other chromatin-associated proteins and plays an important role in the regulation of gene expression . We addressed the question of how the transcriptional function of PRMT4 might contribute to cell lineage specification despite its ubiquitious expression pattern and how this could explain its involvement in tumorigenesis . As protein associations are likely to provide an answer to this question , we attempted to identify novel interaction partners of PRMT4 using a biochemical approach . By this means , we found that PRMT4 binds Mi2α and its close relative Mi2β . In the search for the biological role of this interaction , we found that PRMT4 and Mi2α/β interact with the transcription factor c-Myb and cooperatively coactivate c-Myb target gene expression in haematopoietic cell lines . Depletion of PRMT4 or Mi2α/β in human erythroleukemia cells resulted in deregulated cell proliferation and differentiation resembling the effects caused by c-Myb depletion . Our findings unravel an important and so far unknown connection between PRMT4 and the chromatin remodeller Mi2 in c-Myb signalling and gene activation and identify both coregulators as attractive targets for leukaemia research and therapy in the future . | [
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] | 2013 | PRMT4 Is a Novel Coactivator of c-Myb-Dependent Transcription in Haematopoietic Cell Lines |
Genomic imprinting causes the expression of an allele depending on its parental origin . In plants , most imprinted genes have been identified in Arabidopsis endosperm , a transient structure consumed by the embryo during seed formation . We identified imprinted genes in rice seed where both the endosperm and embryo are present at seed maturity . RNA was extracted from embryos and endosperm of seeds obtained from reciprocal crosses between two subspecies Nipponbare ( Japonica rice ) and 93-11 ( Indica rice ) . Sequenced reads from cDNA libraries were aligned to their respective parental genomes using single-nucleotide polymorphisms ( SNPs ) . Reads across SNPs enabled derivation of parental expression bias ratios . A continuum of parental expression bias states was observed . Statistical analyses indicated 262 candidate imprinted loci in the endosperm and three in the embryo ( 168 genic and 97 non-genic ) . Fifty-six of the 67 loci investigated were confirmed to be imprinted in the seed . Imprinted loci are not clustered in the rice genome as found in mammals . All of these imprinted loci were expressed in the endosperm , and one of these was also imprinted in the embryo , confirming that in both rice and Arabidopsis imprinted expression is primarily confined to the endosperm . Some rice imprinted genes were also expressed in vegetative tissues , indicating that they have additional roles in plant growth . Comparison of candidate imprinted genes found in rice with imprinted candidate loci obtained from genome-wide surveys of imprinted genes in Arabidopsis to date shows a low degree of conservation , suggesting that imprinting has evolved independently in eudicots and monocots .
Maternal and paternal alleles , inherited in plants and animals after fertilization , are usually equivalently expressed during the developmental cycle . In plants and in placental animals , a subset of genes is preferentially transcribed depending on the gender of the parent from which the gene originates and they are defined as imprinted genes [1] . Mono-allelic expression and also preferential expression of parental alleles has been observed for imprinted genes . A combination of epigenetic processes including DNA and histone methylation and demethylation are involved in repressing expression from one parental allele and enabling expression from the other in plants and animals [2]–[16] . In animals , many hundreds of imprinted genes have been identified that are thought to be involved in various functions including regulation of nutrient transfer from the foetal placenta to the embryo , in embryo growth and in adult brain development [17]–[22] . In flowering plants , disruption of imprinting alters seed development [23]–[25] . The majority of plant imprinted genes have been found in seeds of the model plant Arabidopsis where they manifest their biased expression in the endosperm [1]–[3] . The endosperm is a terminal seed tissue with a nutritive function that forms following double fertilization in the ovule of the flower [1]–[3] , [26] . In plants , the endosperm is triploid as nuclei contain a 2 maternal∶1 paternal genome complement resulting from the fusion of the diploid maternal central cell nucleus with a sperm cell . The other product of double fertilization is a diploid zygote that develops into an embryo , the progenitor of the seedling . Embryo nuclei contain a 1 maternal∶1 paternal genome complement ( Figure 1A ) . Until recently , only 21 imprinted genes had been identified in flowering plants from studies in eudicot Arabidopsis and in monocot cereals rice and maize [2] . With the exception of one maternally expressed maize embryo gene , Mee1 [27] , the remainder were imprinted in the endosperm . Transcriptomic surveys of Arabidopsis seeds have significantly expanded the set of imprinted genes in eudicot endosperm to over 170 candidates but additional embryo imprinted genes have not been identified [28 , Wolff et al . 2011 unpublished] . In plants , nutrient allocation is considered to be the driving force for the evolution of imprinting in the endosperm according to the parental conflict theory [29] . This theory predicts that excess dosage of paternal alleles promotes larger seeds while an excess of maternal alleles produces small seeds . These predictions have held true in interploidy crosses that alter maternal and paternal gene dosage in Arabidopsis [23] . Some of the Arabidopsis imprinted genes have roles controlling endosperm cell proliferation and growth [14] , [30] , [31] but for most of the newly identified genes their roles in endosperm formation and nutrient allocation are unknown [28] . The recently identified Arabidopsis imprinted genes have a diverse range of putative functions suggesting they have the potential to influence conflict between maternal and paternal genomes at a range of molecular regulatory levels [28] . Dilkes and Comai [32] have suggested imprinting may play a wider role in plants by regulating dosage sensitive gene expression . The significance of the parental conflict theory in self-fertilizing plants like Arabidopsis has also been questioned and it has been proposed that imprinting may serve to promote hybridity in outcross situations [33] , [34] . Different seed development profiles are observed in plants . The endosperm is transient and consumed by the embryo during seed development in Arabidopsis . By contrast , in rice and maize , the endosperm is persistent and contributes to the bulk of the mature seed acting as a nutrient source for the embryo at germination . Comparisons of imprinted genes found in such developmentally distinct seed types of different evolutionary origin , should provide further insight to the conservation , role and importance of imprinted genes in seed development . Prior to his study , only seven locus-specific imprinted genes had been reported in rice and maize [35]–[42] . The two known rice imprinted genes , OsFIE1 and OsMADS87 , were found using comparative sequence similarity with the maize imprinted gene FIE 1 and the Arabidopsis imprinted gene PHERES1 , respectively [41] , [42] . We have conducted a transcriptomic analysis of imprinted genes in the embryo and endosperm of rice , an important food crop , and one in which both of these tissues persist in the mature seed . We report the identification of 262 candidate loci with parentally biased expression in the endosperm and the experimental verification of 56 of these loci . Imprinting in rice , like Arabidopsis , primarily occurs in the endosperm and is rare in the embryo . Only one gene was found to be maternally expressed , albeit transiently in the embryo , and it was also maternally expressed in the endosperm . A comparison of rice candidate imprinted loci with the reported set of candidate Arabidopsis imprinted genes indicates that these two species share very few imprinted genes in common , suggesting that imprinting is likely to have evolved independently in monocots and eudicots . Interestingly , a small number of candidate imprinted genes that share high sequence homology in rice and Arabidopsis are associated with epigenetic regulation , including DNA methylation , histone modification and small RNA pathways . The identified rice loci provide a comprehensive platform to further explore imprinted gene function and imprinting mechanisms in cereal seed development .
We examined imprinting in endosperm and embryos of seeds derived from reciprocal crosses between Nipponbare ( Nip ) , a Japonica rice and 93-11 , a Chinese Indica rice subspecies . The genomic sequences for these two subspecies are publically available and reciprocal crosses result in viable progeny ( Figure S1 ) . Cytological analysis of seed growth in selfed parents and in reciprocal crosses indicated that they followed previously described seed developmental profiles ( Figure 1B–1D ) [43] . Endosperm was nuclear in seeds 2 days after fertilization ( DAF ) , became cellular at 3 DAF and embryo morphogenesis was complete at 10 DAF . Some variation in the timing of rice seed development was observed in the intervening stages ( Figure S1A ) . Unlike Arabidopsis , rice seeds are large . They attain maximum length soon after fertilization . The embryo and endosperm are found in distinct compartments in the rice seed and they can be manually isolated as pure fractions since their boundaries are readily separable and washing of the embryo is sufficient to remove all traces of endosperm ( Figure S1B ) . Endosperm for transcriptomic analyses was harvested at 5 DAF ( Figure 1C ) when seeds had nearly attained their maximum length; maternal nucellar tissue was absent and milky endosperm could readily be extracted from the top of the seed . Embryos for transcriptomic analysis were harvested at 6 DAF when the first to third leaf primordia were developing . Embryos of the same size and morphological stage were collected for RNA isolation to minimize stage variation ( Figure S1B ) . We conducted a genome-wide survey of gene expression in the harvested endosperm and embryo tissues using the Illumina high-throughput short read sequencing platform to identify loci that were exclusively or preferentially expressed from maternal and paternal genomes . In the endosperm , a total of 4 . 1 million reads could be aligned uniquely to both genomes at publicly reported SNP loci ( SNP reads ) , covering a total of 116 , 291 SNPs with a median read count of 35 reads per SNP location ( Table S1 ) . In the embryo , read mapping resulted in 9 . 9 million reads across 168 , 250 SNPs and a median read count at SNP loci of 59 ( Table S1 ) . SNP reads were required to align to a unique position in both genomes covering exactly one publicly reported SNP . Throughout we considered a genomic feature expressed if it showed at least 10 uniquely aligned reads and progressed a feature for analysis if it contained at least 10 SNP reads . The use of SNP reads allows the allelic assignment of expressed regions to their parental genome of origin and the characterization of any allelic bias of expression of that region . Using this approach we were able to analyse allelic expression of approximately 50% and 70% of annotated and expressed genes across the endosperm and embryo transcriptome respectively ( Table 1 ) . Of those that we were not able to analyse approximately 20% were not annotated as containing SNPs and the remainder displayed insufficient read density across SNP loci in these datasets . We analysed allelic expression bias of both overlapping physical windows ( 1 kb width every 0 . 5 kb ) tiled across the genome and publicly annotated features relevant to the transcriptome including cDNAs , introns and intergenic regions ( Table S2 ) . This redundant strategy allowed us to maximize exploration of imprinted transcripts from annotated and unannotated regions of the genome . Physical windows were considered for analysis if they included at least 10 SNP reads in each cross . The relative allelic proportions of SNP reads within windows were observed to globally agree with the expected 1∶1 maternal to paternal allelic contributions expected in the embryo transcriptome ( Figure 1A , Figure S2A and S2B ) . Similarly , across the endosperm transcriptome allelic expression was observed to closely approximate the expected 2∶1 maternal to paternal ratio ( Figure 1A , Figure S2C and S2D ) . This evident discrimination in global patterns of the endosperm and embryo transcriptomes suggested that the contamination of maternal tissues in both endosperm and embryo was neglible and specific parental contributions could be determined in this dataset . Evidence for imprinting was determined through statistical analysis of observed to expected allelic contributions to expression and loci were considered putatively imprinted at P≤0 . 05 after a Pearson chi-square test . Analysis of the embryo identified 56 windows with evidence of imprinting ( 55 maternal , 1 paternal ) whereas in the endosperm , 1 , 220 windows showed evidence of imprinting ( 24% maternal , 76% paternal; Table S2 ) . Allelic analysis of expression also allowed the identification of subspecies bias of expression , for example more than 2 , 500 windows showed increased expression of the 93-11 allele in endosperm ( Table S2 ) . These subspecies expression biases are potentially of interest for the characterisation of the genetic regulation of expression in these subspecies , however these biases were not further investigated in this study . Imprinted physical windows showed no evidence of physical clustering throughout the genome . The median distance between significant windows was 1 . 1 Mb and only 4 . 5% ( 58 ) of all window candidates were within 10 kb of another putatively imprinted window . We observed that the majority ( 97% ) of all significant windows mapped either to within annotated gene features or within several kilobases upstream or downstream of annotated genes . The 3% of remaining window candidate loci ( 41 ) could be classified as intergenic . They were located several kilobases away from annotated transcription , and 64% ( 26 ) of these involved significance at a single window or region of 1 kb; with the remainder involving two or at most three consecutive windows . Given the strong overlap of candidate imprinted loci with annotated features , we decided to focus our analysis on annotated features for imprinting status , however , all physical window candidate loci were investigated for overlap with imprinted candidates detected by the annotated feature analysis . SNP reads were mapped to annotated gene features including cDNAs , introns and intergenic regions ( Table S2 ) . This mapping utilised all SNP reads analysed in the physical window analysis where reads were allocated to cDNA and intron regions as appropriate when they overlapped annotated features , and intergenic regions when they did not overlap annotated features . In addition , we considered all annotated transcript isoforms for all genes , analysing each transcript isoform independently for imprinting status . This reallocation accounted for all SNP reads analysed in the physical window analysis . Statistical analysis of the expression of annotated features identified 262 candidate loci with evidence of a parental expression bias including 177 maternally expressed and 85 paternally expressed candidate loci ( Table 1 , Tables S2 and S3 ) . The majority of these candidate loci ( 165 ) coincided with annotated cDNAs , whereas the remaining loci ( 97 ) were localized to intergenic regions ( 75 ) or introns ( 22 ) ( Table 1 , Tables S2 and S3 ) . These intergenic loci might represent non-coding RNAs as identified in mammals [17] , [18] or various types of mis-annotated transcription . A search of miRBase ( http://www . mirbase . org/ ) with the sequences of these intergenic loci did not detect any matches to known miRNAs . Most of the biased transcription annotated as intronic are possibly unannotated alternative splicing events as suggested by their close proximity to annotated transcripts and the observation that the flanking exon sequences show similar modes of parental bias . In contrast to the endosperm , only three maternally expressed cDNAs were identified in the embryo transcriptome , despite the extra sequencing depth ( Table 1 , Tables S2 and S3 ) . To explore whether the candidate imprinted endosperm genes are also imprinted in the embryo , expression of all 165 endosperm cDNAs showing a parental bias were examined in the embryo transcriptome ( Table S3 ) . We found that 64 cDNAs ( 39% ) were detected in the embryo transcriptome with less than 10 SNP reads in at least one cross; thus suggesting that these genes are minimally expressed in the embryo and their expression was too low to confidently examine their imprinting status in the embryo in this dataset . Another 85 cDNAs showed no evidence of parentally biased expression including 15 which appeared to show subspecies specific expression . Only one , cDNA Os08g08960 displayed statistically significant evidence of maternally biased expression in both endosperm and embryo transcriptomes ( Table S3 ) . A comparison of the two analytical strategies for both embryo and endosperm transcriptomes revealed that the majority of putatively imprinted physical windows overlapped with candidates suggested through the annotated features analysis . There was , however , a portion of the windows analysis ( 36% ) not represented in the annotated features analysis , and a much smaller subset of the annotated feature candidates ( 7% ) not captured in the physical windows analysis . In all but three cases , these analysis-specific loci overlapped annotated gene features in the rice genome . On closer inspection it was observed that some biased windows lost evidence of imprinting when summarised into larger features such as cDNA . This could suggest the presence of noise over smaller numbers of SNP loci that could be eliminated by analysis of a larger region with more SNP evidence . It could also suggest that in some cases differentially biased expression of windows within one gene may correspond to parent of origin-specific alternative splicing as found in animals [44] . As mentioned previously , our analysis of annotated features considered all annotated alternate splice variants . We extended this in a modification of the physical windows analysis to search for evidence of unannotated alternative splicing differentiated by parent of origin in the endosperm and embryo transcriptomes . This analysis identified nine candidates in the endosperm and none in the embryo that showed evidence of a novel phenomenon ( Table 1 and Table S3 ) . Multiple parentally biased transcripts of differing sequence and genic structure appeared to be arising from the same locus in the nine identified endosperm candidates as a result of alternative splicing and we termed this parent of origin-specific alternative splicing . In summary , the collective in silico transcriptome analysis of the embryo and endosperm tissues of hybrid F1 seeds derived from reciprocal crosses identified 93 maternally and 72 paternally biased genes , in addition to 97 parentally biased non-genic transcripts in endosperm . The analysis also identified nine putative cases of parent of origin-specific alternate splicing . By contrast , parentally biased gene expression was restricted to only three maternally expressed genes in the embryo ( Table 1 ) . This tissue-specific profile of imprinting was also reflected in analysis of physical windows tiled across the genome which identified 20-fold more putatively imprinted loci in the endosperm relative to the embryo . The majority of putative imprinted loci identified through the window analysis were co-located with annotated transcripts either as cDNA or as non-genic regions associated with a transcript . A small subset of unannotated intergenic loci identified in both the analysis of physical windows and annotated features may be non-coding RNAs , but they do not match any currently known non-coding RNA sequences . Since the profiling utilized poly A+ RNA they are unlikely to be small RNAs . These putative intergenic transcripts will require further characterization to confirm their transcription and explore their function . Most of the candidate rice imprinted genes and intergenic loci identified are not organized in physically co-localized clusters in the rice genome of the type found in mammals [45] . There are six pairs of candidate imprinted genes located close to each other that we term micro-clusters ( Cluster 1: Os01g12860-Os01g12890; Cluster 2: Os02g29140-Os02g29150; Cluster 3: Os03g27450-Os03g27460; Cluster 4: Os05g05780-Os05g05790; Cluster 5: Os06g33640-Os06g33690; Cluster 6: Os12g32150-Os12g32170 ) . Only one of these appears to contain a duplicated gene ( Cluster 5 ) . Whether these micro-clusters are regulated in a similar manner to mammalian clusters via an imprinting control region remains to be determined . We found no significant evidence for enrichment of transposons or repeats around the candidate imprinted rice genes relative to non-imprinted genes ( based on the annotation in http://rice . plantbiology . msu . edu/; Tables S4 and S5 ) . However , given the frequent distribution of repeats around many rice genes , and the currently less advanced annotation of transposons and repeat-like elements in the rice genome , we cannot rule out the possibility that some of these elements play a role in influencing imprinting in the rice transcriptome . The three putative embryo imprinted gene candidates were further examined for maternal allele-specific gene expression in embryos by RT-PCR using gene specific primers and sequencing across SNPs ( Tables S2 and S3 ) . Imprinting was not confirmed in any of these statistically predicted candidates . We then tested four additional embryo candidates that were originally statistically excluded from analysis because they showed parentally-biased expression in one cross , but had low SNP read coverage in the other cross ( Table S3 ) . One of these , Os10g05750 was confirmed to be maternally expressed in both the embryo and endosperm ( Figure 1E ) . Os10g05750 is a homolog of olive Ole e 1 which encodes an allergenic protein thought to control pollen tube emergence and guidance [46] . The transcript abundance of this gene is in the mid-range of imprinted transcripts detected in the endosperm ( Table S3 ) , thus it is unlikely that it results from a major endosperm contaminant . Analysis of Os10g05750 expression in the embryo and endosperm at 6 , 8 and 10 DAF showed persistence of expression of the maternal allele in the endosperm . However , biallelic expression of Os10g05750 was observed in embryos of the Nip×93-11 cross at 8 DAF and some paternal expression was observed at 10 DAF in the reciprocal cross ( Figure 1E ) . Maternal expression of the Os10g05750 gene in the embryo during seed development is therefore transient . The remainder of our analyses focused on candidate imprinted loci identified in the endosperm . Parent-specific gene expression biases observed in endosperm formed a continuum similar to that found for the transcriptomic analysis of imprinting in the mouse brain [18] ( Table S6 ) . For experimental confirmation , we primarily selected loci with 90% or greater expression bias from one parental allele ( bias of 0 . 9 and above in Tables S3 and S6 ) . Within the 165 cDNAs with parental expression bias in the endosperm , 121 imprinted genes comprising 62 maternally expressed and 59 paternally expressed gene candidates met the selection criteria of strong parental expression bias ( Table S6 ) . From this subset , we randomly selected 60 putatively imprinted endosperm loci for validation by RT-PCR sequencing across SNPs . These loci included 54 from the set of 165 expressed genes including OsFIE1 ( Os08g04290 ) , a maternally expressed rice endosperm gene that we had previously identified [41] , and six other loci showing putative imprinting within introns and intergenic regions . In sharp contrast to the single imprinted gene identified in the embryo , we confirmed a total of 22 predicted maternally expressed genes and 27 predicted paternally expressed genes in the endosperm ( Table 2 and Figure S3 ) . We also confirmed maternal expression of an intergenic region between two non-imprinted genes Os01g69110 and Os01g69120 where there is currently no annotated ORF ( Table 2 and Figure S4 ) . Os04g20774 produced transcripts with preferentially maternal reads from introns and paternal reads from coding regions and this was confirmed using exon and intron primers ( Table 2; Figure S3 ) . Maternally expressed transcription within the intron of another putatively imprinted gene , Os07g42390 . 1 was also confirmed ( Table 2 and Figure S3 ) . Collectively , these results provide support that the methods utilized appear robust for the identification of parent of origin-specific gene expression . We selected Os01g70060 , one of the nine candidate loci showing evidence of novel alternative splicing patterns to closely examine the types of maternal and paternal transcript isoforms generated from this gene using 3′-end race and RT-PCR . Figure 2 and Figure S5 show the range of transcript isoforms produced from Os01g70060 , which encodes a putative DUF-domain containing protein . Correctly spliced paternally biased transcripts were detected with nine different polyadenylation sites within the small region downstream of the stop codon of this gene . In addition , a range of truncated , maternally biased polyadenylated transcripts were also formed . They contained the 250 bp exon and they terminated at various positions in the adjoining large intron of the gene . A total of 18 different termination sites were identified located in the 5′ end of the 2 . 4 kb intron . In some cases , additional alternative splicing occurred within this intron ( Figure 2 ) . There is a stop codon just inside the large intron , thus the truncated maternally biased transcripts containing the 5′ portion of the gene are likely to produce a small protein . These data provide a clear example of parent of origin-biased transcript isoforms arising from the same plant gene . The other eight candidate loci remain to be examined as does the mechanism giving rise to this phenomenon . We examined whether transcripts from the imprinted loci were seed-specific in expression or if they were also expressed in other parts of the rice plant by RT-PCR . Maternally and paternally expressed loci were clearly not all silenced in other rice tissues , nor were all maternally expressed genes restricted to the endosperm of rice . Expression analyses conducted on 46 confirmed imprinted endosperm loci ( Figure S6 ) indicated that most paternally expressed endosperm loci were expressed in various tissues in the rice plant ( 17/21 ) , while approximately half of the examined maternal loci ( 13/25 ) were specifically expressed in the endosperm at 5 DAF ( Figure S6 ) . These genes may have functions in the development of a number of plant tissues . The imprinted genes in the endosperm were originally identified using endosperm harvested from seeds at 5 DAF . We also examined the imprinting status of 43 confirmed genes at various stages of endosperm development ( 3 . 5 , 6 , 8 , and 10 DAF ) to assess whether imprinting was persistent during seed development . In 39 examined cases biased expression was evident at 3 . 5 or 5 DAF and persisted in the endosperm until 10 DAF ( Figure S3 ) . In four cases ( Os06g33640 , Os09g03500 , Os12g32170 , and Os12g40520 ) imprinting was lost in later stages of endosperm development as expression became biallelic in at least one cross ( Figure S3 ) . We also examined the expression of these imprinted genes in isolated rice sperm cells by microarray which revealed that 37 of the maternally expressed endosperm genes examined are also expressed in sperm cells . This suggests that the paternal allele might be silenced after fertilization ( Table 2 and Table S3 , and Materials and Methods ) . We noted that four genes ( Os02g55560 , Os04g39560 , Os04g42250 and Os06g40490 ) were biallelically expressed or incompletely imprinted in the endosperm at 3 . 5 DAF and became uniparentally expressed at later stages of endosperm development in both crosses ( Figure 1F and Figure S3 ) . Figure 1F summarizes the data obtained for these genes showing that two become maternally expressed and two become paternally expressed ( RT-PCR sequencing traces supporting these data are provided in Figure S3 ) . We confirmed the stage specific imprinting of Os02g55560 by allele-specific RT-PCR ( Figure 1G ) . We also subcloned and sequenced RT-PCR products from the endosperm RNAs of both crosses for Os02g55560 , obtaining 21 maternal and 11 paternal SNPs ( 21m: 11p ) in the Nip×93-11 cross and 19m: 20p paternal SNPs in the 93-11×Nip cross at 3 . 5 DAF . By contrast , maternal SNPs were almost exclusively detected in 5 DAF endosperm from the Nip×93-11 cross ( 38m: 0p ) and the 93-11×Nip cross ( 45m: 2p ) . Of the two genes Os02g55560 and 0s04g39560 that become maternally biased in the endosperm at later stages of seed development , 0s04g39560 ( Figure 1F ) is also expressed in sperm cells ( Table 2 and ) , thus paternal message carry over may be a possibility at 3 . 5DAF [47] . Alternatively , the paternal allele may be silenced after fertilization . In the case of the two genes that become paternally expressed in endosperm , Os06g40490 is expressed in sperm and Os04g42250 is not ( Figure 1F and Table 2 and Table S3 ) . If the latter gene is epigenetically silenced in sperm it may be activated upon fertilization or during early endosperm development . Collectively these data indicate that there are dynamic changes in imprinted gene expression during rice endosperm development . These may involve selective silencing or activation of parental alleles after fertilization during endosperm development for some genes . Database comparisons of the 165 candidate imprinted rice genic endosperm loci revealed that 52 ( ∼30% ) were of unknown function . The remainder were enriched in a range of putative regulatory processes including DNA and RNA binding and signal transduction , and cellular component organization ( Figure S7 and Table 2 and Table S3 ) . For example , several rice parentally biased endosperm genes are homologous to those regulating mRNA levels in other species , including CCR4-NOT [48] , Pumilio [49] and LEUNIG [50] . Chromatin remodelling genes , including OsFIE1 ( Os08g04290 ) [41] the homolog of the maize imprinted Polycomb gene Zmfie1 [38] , a putative H3 K9 methyltransferase gene ( Os03g20430 ) and an SSXT type gene homologous to a component of the SWI/SNF complex [51] were also identified . Comparisons of our 165 candidate rice imprinted loci with the known cereal imprinted loci in maize and rice [35]–[42] revealed that only the Polycomb-group genes Zmfie1 and OsFIE1 ( Os08g04290 ) were in common , with both showing endosperm specific expression [38] , [41] . OsMADS87 ( Os03g38610 ) a previously reported rice imprinted homolog of Arabidopsis PHERES 1 [42] showed no evidence of imprinting in our dataset because very low expression of OsMADS87 was observed in our hybrid endosperm with a total of only 6 reads from both reciprocal crosses . The 165 putative rice imprinted genes found here were also compared with two recent datasets of candidate Arabidopsis imprinted genes identified by seed transcriptome sequencing [28 , Wolff et al . 2011 unpublished] and a third dataset of Arabidopsis genes predicted to be imprinted by genome methylation analysis [52] . We found a total of only 27 rice candidate imprinted genes with significant homology to the three candidate Arabidopsis imprinted gene datasets ( Table S7 ) . Some of the imprinted genes with high sequence homology between rice and Arabidopsis include C3HC4 ring finger genes , YUCCA10 potentially involved in auxin biosynthesis , rice AUXIN RESPONSE FACTOR 18 , a MYB gene and protein kinases ( Table S7 ) . Nine of the genes with high homology are implicated in epigenetic regulation , potentially involved in small RNA , chromatin remodelling , and DNA methylation functions ( Table 3 ) . Examples of the latter include ARGONAUTE ( AGO ) and DsRNA BINDING ( DRB ) protein genes potentially associated with the small RNA pathway [53] . H3K9 methyltransferase genes ( SUVH ) [54] and PICKLE-like genes [55] are potential chromatin remodelling factors , and a paternally biased rice homolog ( Os04g22240 ) of imprinted Arabidopsis VARIANT IN METHYLATION 5 ( VIM5 ) [28] was also detected ( Table 3 ) . Not all 27 candidate imprinted genes with high homology between rice and Arabidopsis have been experimentally verified . However , from current data , it is interesting to note that parental expression biases appear to be retained in 20 cases ( Table 3 and Table S7 ) .
Our approach to identify imprinted genes in reciprocal crosses between Nipponbare ( Nip ) and 93-11 Indica rice relied on the use of SNP reads to enable allelic assignment of expressed sequences to their genome of origin and the characterization of any allelic bias in expression . Using this approach we have been able to analyse allelic expression of approximately 50% and 70% of annotated and expressed genes across the endosperm and embryo transcriptome , respectively . This resulted in the identification of 262 candidate loci in the endosperm and only three in the embryo . Given that 50% of the endosperm expressed genes and 30% of the embryo expressed genes either lacked SNPs or were expressed at a level too low to be characterized , we estimate that there might be at least an additional 260 loci in the endosperm , bringing the total closer to 520 candidate imprinted loci in the rice endosperm; and potentially additional loci in the embryo if imprinting is more prevalent in earlier stages of embryo development than those examined here . By contrast , there are 750 candidate genes estimated to be imprinted in Arabidopsis [28] . Subsequent validation of our in silico data led to the confirmation of only one gene in the embryo . This Ole e 1 homologue [46] was found to be maternally expressed in the embryo , and it was also maternally expressed in the endosperm , although imprinting of this gene was lost during later embryo development . It may be that we have missed other imprinted embryo genes because our analyses were conducted using embryos at 6 DAF . Nonetheless , we conclude that imprinting in the embryo of rice occurs at low frequency and is transient . This is supported by the observation that imprinted genes have not been found in rice seedlings [56] . By contrast , 262 loci with evidence of a parental expression bias were found in the endosperm , 177 maternally expressed and 85 paternally expressed . The majority , 165 , coincided with annotated cDNAs and 97 with non-genic loci . The non-genic loci localized to introns in 22 instances , and in 75 cases to intergenic regions . Verification of the imprinting status of 92% ( 55/60 ) of candidate imprinted loci examined provided us with confidence that the method is robust , and importantly confirms the notion that in rice seed , like Arabidopsis , the majority of the imprinted genes are expressed in endosperm . Evidence that biased transcripts arise from intron and intergenic loci was also obtained amongst the 60 examined candidates . While the intergenic regions may be unannotated regions , it is tempting to speculate that intergenic regions encode non-coding RNAs and further characterization of this class is required . While most of the genes examined remain persistently imprinted during rice endosperm development , we have observed dynamic changes in imprinted gene expression , including gain or loss of imprinting . Similar cases are also found in mouse , where the imprinting status of some genes depends on the stage of development [18] . Similarly , our analyses indicate that the establishment of imprinting for some rice genes may not be predetermined in gametes , and reprogramming of epigenetic marks may be effected soon after fertilization . We found that parentally biased transcript isoforms can arise from a gene via alternative splicing and differential transcript termination . The observed transcripts are polyadenylated . Nine such putative candidate loci were identified and one investigated in detail . A similar phenomenon has also been documented at a mouse locus where the maternal allele produces functional full-length transcripts whereas the paternal transcripts are alternatively spliced in a novel manner and truncated . The production of paternally truncated transcripts was found to depend on the status of DNA methylation within the maternal allele [44] . In the case examined here in detail in rice , full-length paternally biased transcripts were formed whereas various maternally derived transcripts were truncated and the mechanisms are unknown . In summary , we have observed that in rice seed , imprinting primarily occurs in the endosperm from both genic and non-genic regions and we have observed novel features pertaining to imprinted gene expression during endosperm development after fertilization . Rice and Arabidopsis are developmentally distinct seed types . In rice and other cereals , the endosperm persists in the mature seed and is utilized by the embryo during germination . During cereal seed development , significant translocation of photosynthetic products from the vegetative parts of the plant occurs during endosperm differentiation and grain filling . By contrast , in Arabidopsis , the endosperm is transient and consumed by the developing embryo , which stores reserves for germination . Nevertheless , in both types of seeds , imprinting is predominant in the endosperm . The identified imprinted genes are not physically clustered in the genomes of these monocot and eudicot plants as found in animals , but a few paired micro-clusters are found in rice and Arabidopsis [Wolff et al . 2011 unpublished] . The candidate imprinted genes identified in rice and Arabidopsis have diverse functions which suggest that they may be involved in controlling endosperm and seed development at different regulatory levels [28] . However , only 27 of the identified 165 candidate rice imprinted genes display significant sequence homology with the candidate Arabidopsis imprinted genes found by transcriptome analyses [28 , Wolff et al . 2011 unpublished] and predicted by methylation analyses in the Arabidopsis genome [52] . This contrasts with the situation observed in animals where conservation of imprinted genes and their expression modes are more frequently observed [57] . In Arabidopsis , imprinting appears to be a consequence of global epigenetic reprogramming during the events of sexual reproduction [52] , [58] . A combination of the RETINOBLASTOMA pathway and DEMETER ( DME ) activity in the central cell results in a genome-wide hypomethylation of central cell DNA and the activation of maternally expressed alleles [9] , [52] , [58] . CG hypomethylation results from DME activity which removes methylcytosines from DNA and accompanying down-regulation of METHYLTRANSFERASE 1 ( MET1 ) and VIM5 activity . The latter functions to recruit DNA methyltransferases to hemi-methylated DNA [59] , [28] The non-expressed parental allele can be silenced by DNA methylation , histone K27 tri-methylation mediated by the Polycomb group ( PcG ) complex , or both [7]–[14] . Genome hypomethylation has also been found in rice endosperm [60] , however , a DME homologue is not evident in rice [60] . The observed hypomethylation and imprinting in rice endosperm may be due to the activity of other members of this family or by other currently unknown mechanisms [60] . Our analyses indicate that VIM5 homologues are paternally expressed in both rice and Arabidopsis endosperm ( Table 3 ) . Imprinting of the putative rice VIM5 remains to be confirmed , however , this observation suggests that some mechanisms regulating imprinting may be common in the endosperm of both species . Analyses of Arabidopsis imprinted gene expression in mutant backgrounds has revealed that imprinting in a subset of maternally expressed genes is not controlled by DME , FIE ( a member of the PcG complex ) or MET1 suggesting additional unknown mechanisms for allele silencing and activation [28] . It has been hypothesized , but not yet proven , that these maternal transcripts may be transferred or deposited into the endosperm from surrounding maternal seed tissues . Some of the rice candidate imprinted genes have homology to this set of putative maternally deposited Arabidopsis genes ( Table 3 and Table S7 ) . The low level of homology in candidate imprinted genes in rice and Arabidopsis suggests imprinted genes are likely to have evolved independently after the divergence of monocots and eudicots . It is possible that the development of these distinct seed types may require different sets of imprinted genes , which may contribute to reproductive isolation [34] . Testing the roles of the identified parentally biased genes in both the regulation of imprinting and in rice endosperm development will provide further insights into the molecular basis for and functional significance of imprinting in plants .
The Japonica rice subspecies Nipponbare ( Nip ) and the Chinese Indica subspecies 93-11 were grown in the glasshouse at 26°C to 28°C under natural light supplemented with artificial light to achieve a 16 hour photoperiod . Emasculation of Nip florets was carried out by cutting mature florets in half to remove anthers and this was conducted early morning before anther dehiscence and exertion . A hot water treatment was used for early morning emasculation of 93-11 and the panicles were immersed in water at 43°C for 8 minutes . Reciprocal crosses between the emasculated subspecies were carried out after emasculation . Fertilization in rice occurs one hour after pollination . Nip×93-11 indicates Nip female pollinated with 93-11 , and the reciprocal cross 93-11×Nip indicates 93-11 female crossed with Nip pollen . Seed development in reciprocal crosses and selfed subspecies parents was monitored cytologically and compared to previously described stages of seed development by Itoh et al . [43] . These data are presented in the text and Figure S1 . Endosperm samples from reciprocal crosses for transcriptome sequencing were harvested at 5 days after fertilization ( DAF ) by cutting the tip of the seed at the opposite end to that containing the embryo and gently removing a small portion of endosperm . Endosperm from 50 seeds for each cross was pooled for RNA extraction . Embryos for transcriptome sequencing were manually extracted at 6 DAF to minimize contamination with endosperm as separation from the endosperm compartment is established; they were washed three times in RNAlater and then size selected to obtain comparable developmental stages for RNA extraction . 600 embryos were harvested from each cross . RNA was isolated using the QIAGEN RNeasy Plant mini kit with DNase I treatment according to the manufacturer's protocol . 40 µg of total RNA from endosperm for each reciprocal cross , and 30 µg of embryo RNA for each reciprocal cross were sent to AGRF ( Australian Genome Research Facility http://www . agrf . org . au ) , where libraries were constructed for each sample and sequenced using the Illumina platform ( see below ) . Additional material was subsequently harvested utilizing the above procedures for verification of imprinted genes and for developmental analyses . Endosperm samples for developmental analysis of gene expression were harvested at 3 . 5 , 5 , 6 , 8 and 10 DAF , and embryos at 6 , 8 and 10 DAF . Other rice tissues harvested for gene expression analyses included husk of developing seed , anther , mature ovule , root , flag leaf , stem and endosperm from both subspecies . The generation of sequence libraries and sequence data was completed in accordance with recommended Illumina GAII protocols ( http://www . illumina . com ) by AGRF ( Australian Genome Research Facility ) . Briefly , total RNA samples were tested for quality using the Agilent BioAnalyzer . Poly-A containing RNAs were purified and reverse transcribed to cDNAs using random primers . Double-stranded cDNAs were synthesized and these fragments were end-repaired and extended through ligation with a single ‘A’ base and then Illumina adaptor sequences . After PCR amplification , the sequencing libraries were size-selected on an agarose gel and purified for sequencing . The endosperm sequencing libraries were sequenced to a length of 36 base pairs , while the embryo library was sequenced to a length of 75 base pairs . Raw sequence data were generated by the Illumina analysis pipeline ( version 1 . 3 ) . For endosperm , one sequencing library was generated for each reciprocal cross . For embryos , one library was also constructed for each reciprocal cross . Sequence data was pre-processed to trim off any 3′ adaptor sequence present and exclude sequences that were less than 18 bases in length or >50% repetitive in nature . A non-redundant sequence dataset consisting of unique sequences and associated total read counts for each sample was used in subsequent analyses ( Table S1 ) . SOAP [61] was used to align the reads to the assembled Japonica ( Nip ) and 93-11 genomes [62] , [63] . Reads were filtered to those that aligned perfectly to a single location in one genome , and to the other with exactly a single mismatch corresponding to a SNP reported in the public SNP repository ( http://rice . plantbiology . msu . edu/cgi-bin/gbrowse/rice/ ) ( Table S1 ) . For direct comparability to the endosperm , only the first 36 bases of 75 base embryo reads were analysed . This maintained a similar risk of reads being excluded from consideration due to the presence of more than one SNP in the read . Even with this conservative approach , the embryo data were observed to have greater sequencing depth and SNP coverage than the endosperm data ( Table 1 and Table S1 ) which is indicative of the greater yield of sequences now possible in rapidly emerging sequencing chemistries . On the basis of SNPs detected , reads were assigned to either the maternal or paternal genome in each cross . Read counts were normalised between crosses for discrepancies in total read count by conversion to reads per million and multiplication by a fixed constant to return values to integer counts similar in scale to the observed data . Two approaches were used to complete genome-wide scans of putatively maternal or paternal reads . One approach used overlapping windows of 1 kb ( every 0 . 5 kb ) to summarise read counts across the whole genome , and the other summarised reads into annotated genic features including cDNAs , introns and intergenic regions . In both approaches only those features showing a total normalized read count of at least 10 reads for both crosses were considered ( Table S2 ) . Parental expression bias ( B ) of window and genic features were calculated from totals of normalised read counts across each feature . For example , maternal bias was calculated as shown below . Where Bmat is maternal expression bias , Rmat and Rpat are the total normalized read count across the feature allocated to the maternal genome and paternal genome respectively , and Re is the expected read count as calculated from proportioning the sum of Rmat and Rpat into the expected maternal∶paternal contributions ( 2∶1 in the endosperm; 1∶1 in the embryo ) ( Figure S2 ) . Statistical significance ( −log10P ) was assessed using a Pearson chi-square test ( degrees of freedom = 1 ) . Deviation from expected contributions was considered statistically significant at −log10P≥1 . 3 ( P≥0 . 05 ) . Putatively imprinted features were identified as those that showed concordant statistically significant bias in both crosses associated with the gender and not the subspecies of the parent . Subspecies dependent features were identified as those that showed significant bias concordant between crosses and associated with the subspecies of the parent and not gender . Where evidence from both crosses was combined , single bias and −log10P values were calculated as the minimum ( least significant ) of the two crosses . Genome-wide scans for involvement of alternate splicing were completed in two ways . Firstly , the genic feature analysis considered all annotated alternate transcript isoforms . Secondly , a modification of the sliding window analysis allowed us to investigate the dataset for the involvement of novel , unannotated alternate splicing in transcribed regions . In both cases , the data was screened for transcripts that contained genic features or parts of genic features displaying a mixture of imprinting status . All candidates proposed by these methods were closely inspected with regard to read distribution relative to reported annotations . In cases where annotated alternate transcripts shared identical support ( read counts ) , these were collapsed and compound annotated ( Table S3 ) . Similarly , in cases where intergenic regions were implicated and it was not possible to unambiguously determine functional association with a single neighbouring gene , these findings were collapsed and compound annotated ( Table S3 ) . Putatively biased genic features in both embryo and endosperm were investigated for proximity to transposons and repeats with the rice genome and differential GC content ( Tables S4 and S5 ) . Transposon and repeat annotation were obtained from a public rice genome browser ( http://rice . plantbiology . msu . edu/cgi-bin/gbrowse/rice/ ) . At the time of download this annotation contained predictions of 264 , 562 repeats and transposons including MITE-like ( 43 . 7% ) , transposon ( 22 . 2% ) , retrotransposon LINE-like ( 0 . 1% ) , retrotransposon SINE-like ( 2 . 7% ) , retrotransposon unclassified ( 22 . 1% ) and unclassified repetitive elements ( 5 . 4% ) . The analysis investigated both proximity and density of transposons and repeats to imprinted cDNAs relative to similar measures for all annotated cDNAs in the genome . Genic features were annotated with gene function ontology terms ( GO terms ) and GO enrichment analysis was performed using a hypergeometric test for enrichment of terms relative to expected frequencies . Expected frequencies were calculated from the annotation of all transcripts found to contain at least 10 reads in the dataset . Identified imprinted rice candidates were investigated for sequence homology to previously reported candidates from maize [35]–[40] and Arabidopsis [28] , [52 , Wolff et al . 2011 unpublished] using blast [64] . Candidates from maize and Arabidopsis were aligned to the rice genome ( Osa1 , Release 6 . 1 ) and high-scoring ( E-value<1e-10 ) blasts hits were ordered by increasing E-value . If any of the rice candidates were identified amongst the blast hits its highest ( most significant ) rank was recorded . Similarly , candidates from this study in rice were aligned to the maize ( B73 RefGen_v2 ) and Arabidopsis genome ( TAIR9 ) . Consequently , the rank of 1 means that this is the best match reported in the target genome . Subsequent rank numbers can be interpreted as a measure of the number of genes ( rank - 1 ) with better sequence homology in the target genome than the match reported . This approach not only identifies sequence homology but indicates the specificity of the homology relative to other possible sequence matches throughout the genome which is particularly relevant for comparisons in large gene families . Primers for RT-PCR sequencing and gene expression were designed to be flanking at least one SNP and , if possible , at least one intron . About 0 . 5 µg total RNA from various tissues was used for cDNA synthesis using an Invitrogen Superscript III Reverse Transcriptase kit . The 20 ul PCR mix contained 2 ul of 10× buffer , 1 . 3 ul of MgCl2 ( 25 mM ) , 1 ul of dNTPs ( 5 mM ) , 0 . 5 ul of each primer ( 20 uM ) and 1 unit Taq DNA polymerase ( Perkin Elmer ) . The Thermal cycler programme includes an initial incubation of 3 minutes at 95°C , followed by 27–35 cycles , depending on the experimentally determined level of gene expression , where each cycle consisted of 20 s at 95°C , 20 s at 55°C and 1 min at 72°C , followed by 2 min at 72°C and 1 min at 25°C . PCR products were visualised on a 1 . 5% agarose gel with ethidium bromide . RT-PCR products generated from F1 endosperm and embryo were submitted for direct sequencing . RT-PCR products for Os02g55560 were subcloned using pGEM-T Easy Vector System ( Promega ) and then sequenced . Several imprinted genes were also confirmed by allelic RT-PCR . For a map of the gene and locations of primers utilized see Figure 2 and Figure S5 . A GeneRacer Kit ( Invitrogen ) was initially used to examine the 3′ end of Os01g70060 transcripts from the endosperm of reciprocal crosses between Nip and 93-11 . After reverse transcription using the GeneRacer Oligo dT Primer according to the manufacturer recommended protocol , primers from exon 3 ( 3eRace ) , intron 3 ( 3iRace ) and exon 8 ( 8eRace ) were used with GeneRacer 3′ Primer to PCR-amplify the RT products . These RT-PCR products were directly sequenced or subcloned and sequenced . Additional pairs of primers were designed from intron 3 or exons to detect detailed information regarding the parent of origin of the transcripts . These primers include a pair from intron 3 ( 3iF and 3iR ) , a pair from exon 2 and exon 3 ( 2eF and 3eR ) , and a pair from exon 3 and exon 8 ( 3eF and 8eR ) . Primers 3eF and 3iR-1 were used to detect transcripts proceeding from exon 3 into the adjacent intron 3 . Note that 3eF overlaps with 3eRace . 3iRace is complementary to 3iR-1 . For additional information refer to text . Sperm cells were isolated from mature anthers of field-grown rice ( Oryza sativa subspecies Japonica , cultivar ‘Katy’ ) . A centrifugation-based separation method was used for isolating sperm cells from mature pollen grains [65] . Total RNA was purified using the RNeasy plant mini kit according to manufacturer's instructions ( Qiagen , http://www . qiagen . com/ ) . For sperm cell RNA , we could not accurately determine concentration because of limited material . All accumulated isolated sperm RNA and 100 ng total RNA of seedlings and mature pollen were used for probe preparation for each of the three biological/replicates performed . Amplified sperm RNAs and pollen RNAs were used to test specificity of sperm isolation with qPCR . An 82-fold difference for Ory s 1 ( a sperm specific gene ) [66] transcripts and 15 fold difference for GCS1 ( HAP2 ) ( a specific sperm gene ) [67] homolog transcripts between pollen and sperm cells confirmed the specificity of sperm cells . Since the amount of starting total RNA was low ( in the range of 10–100 ng per sample for sperm ) , the Affymetrix GeneChip Two-Cycle cDNA Synthesis Kit ( Affymetrix , Santa Clara , CA , USA , http://www . affymetrix . com/ ) was used for target preparation with signal amplification . A mixture containing 15 µg of fragmented cRNA was hybridized to the Affymetrix 57K Rice Genome GeneChip at 45°C for 16 h . Subsequent washing and staining steps were performed on a GeneChip Fluidics Station 450 and the chips were scanned on a GeneChip Scanner 3000 . The microarray data generated from all chips met quality control criteria set by Affymetrix . Materials are archived on NCBI Gene Expression Omnibus ( GEO ) as a data set within experimental series GSE17002 . For determination of sperm expression , data was background subtracted , where background was considered to be 5% of signal and normalised using the dChip MBEI method [68] . Expression levels were averaged for the three replicates . We used the dChip protocol to determine the presence or absence calls in sperm cells ( http://biosun1 . harvard . edu/complab/dchip/ ) . In this approach , P≤0 . 05 was declared present ( P ) , marginal ( M ) was defined as 0 . 05>P≤0 . 065 and p>0 . 065 was considered absent ( A ) . Expression status of imprinted genes in sperm can be seen in Table S3 . | The expression of maternal or paternal alleles in either a preferentially or exclusively uniparental manner , termed imprinting , is prevalent in the transient endosperm of seeds in the model plant Arabidopsis . Cereals form seeds where both the embryo and endosperm are present at seed maturity . They are an important world food source . To date , very few imprinted genes have been identified in cereal seeds . How parental gene expression biases contribute to rice seed development has not yet been studied in detail . The deep resolution of transcript sequencing platforms was used to identify loci expressed in a parentally biased manner in the embryo and endosperm of Indica and Japonica rice at a genome-wide level . We identified 262 candidate imprinted loci expressed in the endosperm , experimentally verified 56 of these , and found novel features pertaining to their expression . Only one gene was found to be imprinted in the rice embryo . Imprinting in Arabidopsis and rice seeds is confined primarily to the endosperm , but the identified loci do not share extensive sequence conservation . Imprinting thus appears to have evolved independently in these plant species . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"agriculture",
"biology"
] | 2011 | A Genome-Wide Survey of Imprinted Genes in Rice Seeds Reveals Imprinting Primarily Occurs in the Endosperm |
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